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Volume 9
Issue 22
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Appl. Sci., Volume 9, Issue 22 (November-2 2019) – 255 articles

Cover Story (view full-size image): This paper reports a one-step method to fabricate a novel sodium alginate-polyacrylamide (Alg–PAM) composite aerogel, which exhibits high affinity and selectivity towards Pb2+. The prepared Alg–PAM is a macroscopic adsorbent that can be easily applied for solid–liquid separation. This adsorbent can be regenerated by simple acid washing, and its adsorption performance remains stable after repeated use. Given its use of low-cost and green raw materials, simple preparation process, and high efficiency removal performance, Alg–PAM has broad application potential in treating heavy metal ions in wastewater. View this paper.
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31 pages, 12565 KiB  
Article
An Octahedric Regression Model of Energy Efficiency on Residential Buildings
by Francisco J. Navarro-Gonzalez and Yolanda Villacampa
Appl. Sci. 2019, 9(22), 4978; https://doi.org/10.3390/app9224978 - 19 Nov 2019
Cited by 9 | Viewed by 2744
Abstract
System modeling is a main task in several research fields. The development of numerical models is of crucial importance at the present because of its wide use in the applications of the generically named machine learning technology, including different kinds of neural networks, [...] Read more.
System modeling is a main task in several research fields. The development of numerical models is of crucial importance at the present because of its wide use in the applications of the generically named machine learning technology, including different kinds of neural networks, random field models, and kernel-based methodologies. However, some problems involving the reliability of their predictions are common to their use in the real world. Octahedric regression is a kernel averaged methodology developed by the authors that tries to simplify the entire process from raw data acquisition to model generation. A discussion about the treatment and prevention of overfitting is presented and, as a result, models are obtained that allow for the measurement of this effect. In this paper, this methodology is applied to the problem of estimating the energetic needs of different buildings according to their principal characteristics, a problem that has importance in architecture and civil and environmental engineering due to increasing concerns about energetic efficiency and ecological footprint. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
►▼ Show Figures

Figure 1

Figure 1
<p>Relationship between the independent variables for the cooling load problem. (<b>a</b>) Surface vs. Compactness; (<b>b</b>) Wall vs. Compactness; (<b>c</b>) Roof vs. Compactness; (<b>d</b>) Height vs. Compactness; (<b>e</b>) Orientation vs. Compactness; (<b>f</b>) Glazing area vs. Compactness; (<b>g</b>) Glazing distribution vs. Compactness; (<b>h</b>) Wall vs. Surface; (<b>i</b>) Roof vs. Surface; (<b>j</b>) Height vs. Surface; (<b>k</b>) Orientation vs. Surface; (<b>l</b>) Glazing area vs. Surface; (<b>m</b>) Glazing distribution vs. Surface; (<b>n</b>) Roof vs. Wall; (<b>o</b>) Height vs. Wall; (<b>p</b>) Orientation vs. Wall; (<b>q</b>) Glazing area vs. wall; (<b>r</b>) Glazing distribution vs. Wall; (<b>s</b>) Height vs. Roof; (<b>t</b>) Orientation vs. Roof; (<b>u</b>) Glazing area vs. Roof; (<b>v</b>) Glazing distribution vs. Roof; (<b>w</b>) Orientation vs. Height; (<b>x</b>) Glazing area vs. Height; (<b>y</b>) Glazing distribution vs. Height; (<b>z</b>) Glazing area vs. Orientation; (<b>aa</b>) Glazing distribution vs. Orientation and (<b>bb</b>) Glazing distribution vs. Glazing area.</p> Full article ">Figure 1 Cont.
<p>Relationship between the independent variables for the cooling load problem. (<b>a</b>) Surface vs. Compactness; (<b>b</b>) Wall vs. Compactness; (<b>c</b>) Roof vs. Compactness; (<b>d</b>) Height vs. Compactness; (<b>e</b>) Orientation vs. Compactness; (<b>f</b>) Glazing area vs. Compactness; (<b>g</b>) Glazing distribution vs. Compactness; (<b>h</b>) Wall vs. Surface; (<b>i</b>) Roof vs. Surface; (<b>j</b>) Height vs. Surface; (<b>k</b>) Orientation vs. Surface; (<b>l</b>) Glazing area vs. Surface; (<b>m</b>) Glazing distribution vs. Surface; (<b>n</b>) Roof vs. Wall; (<b>o</b>) Height vs. Wall; (<b>p</b>) Orientation vs. Wall; (<b>q</b>) Glazing area vs. wall; (<b>r</b>) Glazing distribution vs. Wall; (<b>s</b>) Height vs. Roof; (<b>t</b>) Orientation vs. Roof; (<b>u</b>) Glazing area vs. Roof; (<b>v</b>) Glazing distribution vs. Roof; (<b>w</b>) Orientation vs. Height; (<b>x</b>) Glazing area vs. Height; (<b>y</b>) Glazing distribution vs. Height; (<b>z</b>) Glazing area vs. Orientation; (<b>aa</b>) Glazing distribution vs. Orientation and (<b>bb</b>) Glazing distribution vs. Glazing area.</p> Full article ">Figure 1 Cont.
<p>Relationship between the independent variables for the cooling load problem. (<b>a</b>) Surface vs. Compactness; (<b>b</b>) Wall vs. Compactness; (<b>c</b>) Roof vs. Compactness; (<b>d</b>) Height vs. Compactness; (<b>e</b>) Orientation vs. Compactness; (<b>f</b>) Glazing area vs. Compactness; (<b>g</b>) Glazing distribution vs. Compactness; (<b>h</b>) Wall vs. Surface; (<b>i</b>) Roof vs. Surface; (<b>j</b>) Height vs. Surface; (<b>k</b>) Orientation vs. Surface; (<b>l</b>) Glazing area vs. Surface; (<b>m</b>) Glazing distribution vs. Surface; (<b>n</b>) Roof vs. Wall; (<b>o</b>) Height vs. Wall; (<b>p</b>) Orientation vs. Wall; (<b>q</b>) Glazing area vs. wall; (<b>r</b>) Glazing distribution vs. Wall; (<b>s</b>) Height vs. Roof; (<b>t</b>) Orientation vs. Roof; (<b>u</b>) Glazing area vs. Roof; (<b>v</b>) Glazing distribution vs. Roof; (<b>w</b>) Orientation vs. Height; (<b>x</b>) Glazing area vs. Height; (<b>y</b>) Glazing distribution vs. Height; (<b>z</b>) Glazing area vs. Orientation; (<b>aa</b>) Glazing distribution vs. Orientation and (<b>bb</b>) Glazing distribution vs. Glazing area.</p> Full article ">Figure 1 Cont.
<p>Relationship between the independent variables for the cooling load problem. (<b>a</b>) Surface vs. Compactness; (<b>b</b>) Wall vs. Compactness; (<b>c</b>) Roof vs. Compactness; (<b>d</b>) Height vs. Compactness; (<b>e</b>) Orientation vs. Compactness; (<b>f</b>) Glazing area vs. Compactness; (<b>g</b>) Glazing distribution vs. Compactness; (<b>h</b>) Wall vs. Surface; (<b>i</b>) Roof vs. Surface; (<b>j</b>) Height vs. Surface; (<b>k</b>) Orientation vs. Surface; (<b>l</b>) Glazing area vs. Surface; (<b>m</b>) Glazing distribution vs. Surface; (<b>n</b>) Roof vs. Wall; (<b>o</b>) Height vs. Wall; (<b>p</b>) Orientation vs. Wall; (<b>q</b>) Glazing area vs. wall; (<b>r</b>) Glazing distribution vs. Wall; (<b>s</b>) Height vs. Roof; (<b>t</b>) Orientation vs. Roof; (<b>u</b>) Glazing area vs. Roof; (<b>v</b>) Glazing distribution vs. Roof; (<b>w</b>) Orientation vs. Height; (<b>x</b>) Glazing area vs. Height; (<b>y</b>) Glazing distribution vs. Height; (<b>z</b>) Glazing area vs. Orientation; (<b>aa</b>) Glazing distribution vs. Orientation and (<b>bb</b>) Glazing distribution vs. Glazing area.</p> Full article ">Figure 2
<p>Heating load values depending on (<b>a</b>) relative compactness; (<b>b</b>) surface area; (<b>c</b>) wall area; (<b>d</b>) roof area; (<b>e</b>) overall height; (<b>f</b>) orientation; (<b>g</b>) glazing area; (<b>h</b>) glazing area distribution.</p> Full article ">Figure 2 Cont.
<p>Heating load values depending on (<b>a</b>) relative compactness; (<b>b</b>) surface area; (<b>c</b>) wall area; (<b>d</b>) roof area; (<b>e</b>) overall height; (<b>f</b>) orientation; (<b>g</b>) glazing area; (<b>h</b>) glazing area distribution.</p> Full article ">Figure 3
<p>Results for the heating load model depending on the h-parameter. The values of the <span class="html-italic">ω</span> parameter are shown in the legend. Continuous lines represent full models, while discontinuous corresponds to restricted or partial models: (<b>a</b>) MAPE; (<b>b</b>) R2.</p> Full article ">Figure 4
<p>Results of the heating load problem for the full, partial, and mixed models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> </mrow> </semantics></math>. (<b>a</b>) Mean absolute percentage error (MAPE) depending on <span class="html-italic">h</span>; (<b>b</b>) Mean absolute error (MAE) depending on <span class="html-italic">h</span>.</p> Full article ">Figure 5
<p>Results of the heating load problem for the full and restricted models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>. (<b>a</b>) Estimated vs experimental values; (<b>b</b>) Estimated vs experimental values (sorted).</p> Full article ">Figure 6
<p>Errors over independent variables. (<b>a</b>) Relative compactness; (<b>b</b>) Surface area; (<b>c</b>) Wall area; (<b>d</b>) Roof area; (<b>e</b>) Overall height; (<b>f</b>) Orientation; (<b>g</b>) Glazing area; (<b>h</b>) Glazing area distribution.</p> Full article ">Figure 7
<p>Regression error characteristic (REC) curve for the heating load full restricted and null (Mean) models with <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Results of the heating load 1_floor problem for the full, restricted and mixed models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> </mrow> </semantics></math>. (<b>a</b>) MAPE depending on <span class="html-italic">h</span>; (<b>b</b>) MAE depending <span class="html-italic">h</span>.</p> Full article ">Figure 9
<p>Results of the Heating load 1_floor problem for the full and restricted models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>. (<b>a</b>) Estimated vs experimental values; (<b>b</b>) REC curve for the full, restricted and null (mean) models.</p> Full article ">Figure 10
<p>Results of the Heating load 2_floors problem for the full, restricted and mixed models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> </mrow> </semantics></math>. (<b>a</b>) MAPE depending on <span class="html-italic">h</span>; (<b>b</b>) MAE depending on <span class="html-italic">h</span>.</p> Full article ">Figure 11
<p>Results of the Heating load 2_floors problem for the full and restricted models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>. (<b>a</b>) Estimated vs experimental values; (<b>b</b>) REC curve for the full, restricted, and null (mean) models.</p> Full article ">Figure 12
<p>Results of the Heating load (reduced variable set) problem for the full, restricted and mixed models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> </mrow> </semantics></math>. (<b>a</b>) MAPE depending on <span class="html-italic">h</span>; (<b>b</b>) MAE depending on <span class="html-italic">h</span>.</p> Full article ">Figure 13
<p>Results of the heating load (reduced variable set) problem for the full and restricted models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.04</mn> </mrow> </semantics></math>. (<b>a</b>) Estimated vs experimental values; (<b>b</b>) REC curve for the full, restricted, and null (mean) models.</p> Full article ">Figure 14
<p>Results of the Cooling load problem for the complete and reduced variable dataset models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> </mrow> </semantics></math>. (<b>a</b>) MAPE depending on <span class="html-italic">h</span> for the model with 8 variables; (<b>b</b>) MAE depending on <span class="html-italic">h</span> for the model with 8 variables; (<b>c</b>) MAPE depending on <span class="html-italic">h</span> for the model with 5 variables; (<b>d</b>) MAE depending on <span class="html-italic">h</span> for the model with 5 variables.</p> Full article ">Figure 15
<p>Results of the cooling load problem (<b>a</b>) Estimated vs experimental values for the full and restricted models for the eight-variable dataset with parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>; (<b>b</b>) REC curve for the full, restricted and null (mean) models for the full and restricted models for the eight-variable dataset with parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>; (<b>c</b>) Estimated vs experimental values for the model with five variables and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>; (<b>d</b>) REC curve for the model with five variables and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>.</p> Full article ">Figure 15 Cont.
<p>Results of the cooling load problem (<b>a</b>) Estimated vs experimental values for the full and restricted models for the eight-variable dataset with parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>; (<b>b</b>) REC curve for the full, restricted and null (mean) models for the full and restricted models for the eight-variable dataset with parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>; (<b>c</b>) Estimated vs experimental values for the model with five variables and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>; (<b>d</b>) REC curve for the model with five variables and <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.033</mn> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p>Errors over independent variables for the cooling problem including all the variables. (<b>a</b>) Relative compactness; (<b>b</b>) Surface area; (<b>c</b>) Wall area; (<b>d</b>) Roof area; (<b>e</b>) Overall height; (<b>f</b>) Orientation; (<b>g</b>) Glazing area; (<b>h</b>) Glazing area distribution.</p> Full article ">Figure 16 Cont.
<p>Errors over independent variables for the cooling problem including all the variables. (<b>a</b>) Relative compactness; (<b>b</b>) Surface area; (<b>c</b>) Wall area; (<b>d</b>) Roof area; (<b>e</b>) Overall height; (<b>f</b>) Orientation; (<b>g</b>) Glazing area; (<b>h</b>) Glazing area distribution.</p> Full article ">Figure 17
<p>Results of the cooling load problem for the complete and reduced variable dataset models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> </mrow> </semantics></math>. (<b>a</b>) MAPE depending on <span class="html-italic">h</span> for the model one-floor model with four variables; (<b>b</b>) MAE depending on <span class="html-italic">h</span> for the one-floor model; (<b>c</b>) MAPE depending on <span class="html-italic">h</span> for the two-floors model with five variables; (<b>d</b>) MAE depending on <span class="html-italic">h</span> for the two-floors model.</p> Full article ">Figure 17 Cont.
<p>Results of the cooling load problem for the complete and reduced variable dataset models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> </mrow> </semantics></math>. (<b>a</b>) MAPE depending on <span class="html-italic">h</span> for the model one-floor model with four variables; (<b>b</b>) MAE depending on <span class="html-italic">h</span> for the one-floor model; (<b>c</b>) MAPE depending on <span class="html-italic">h</span> for the two-floors model with five variables; (<b>d</b>) MAE depending on <span class="html-italic">h</span> for the two-floors model.</p> Full article ">Figure 18
<p>Results of the cooling load reduced problem for the full and restricted models with the parameter selection <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mi>ω</mi> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>. (<b>a</b>) Estimated vs experimental values for 1_floor model with five variables; (<b>b</b>) REC curve for the full, restricted and null (mean) 1_floor model; (<b>c</b>) Estimated vs experimental values for 2_floors model; (<b>d</b>) REC curve for the full, restricted, and null (mean) 2_floors model.</p> Full article ">
15 pages, 4854 KiB  
Article
Effect of Boron and Oxygen on the Structure and Properties of Protective Decorative Cr–Al–Ti–N Coatings Deposited by Closed Field Unbalanced Magnetron Sputtering (CFUBMS)
by Ph. V. Kiryukhantsev-Korneev, Zh. S. Amankeldina, A. N. Sheveyko, S. Vorotilo and E. A. Levashov
Appl. Sci. 2019, 9(22), 4977; https://doi.org/10.3390/app9224977 - 19 Nov 2019
Cited by 3 | Viewed by 3067
Abstract
Boron and oxygen-doped Cr–Al–Ti–N coatings were deposited by closed field unbalanced magnetron sputtering (CFUBMS) of TiB target manufactured by self-propagating high-temperature synthesis, and Ti, Cr, and Al targets. To evaluate the influence of doping elements, as-deposited coatings were studied by glow discharge optical [...] Read more.
Boron and oxygen-doped Cr–Al–Ti–N coatings were deposited by closed field unbalanced magnetron sputtering (CFUBMS) of TiB target manufactured by self-propagating high-temperature synthesis, and Ti, Cr, and Al targets. To evaluate the influence of doping elements, as-deposited coatings were studied by glow discharge optical emission spectroscopy (GDOES), SEM, XRD, and optical profilometry. Mechanical properties were measured by nanoindentation and tribological, abrasive and electrochemical testing. The introduction of boron suppresses columnar growth and leads to structural refinement and a decrease of coating’s surface roughness. The addition of 2.3 at.% boron results in the highest mechanical properties: hardness H = 15 GPa, stable friction coefficient f = 0.65, and specific wear Vw = 7.5 × 10−6 mm3N−1m−1. To make the coating more visually appealing, oxygen was introduced in the chamber near the end of the deposition cycle. Upper Cr–Al–Ti–B–O–N layers were studied in terms of their composition and coloration, and the developed two-layer decorative coatings were deposited on cast metallic art pieces. Full article
(This article belongs to the Special Issue Microstructural and Mechanical Properties of Metallic Materials)
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Figure 1

Figure 1
<p>Profiles of elements’ distribution across the thickness of coating with 10 at.% B.</p> Full article ">Figure 2
<p>SEM images of fractured Cr–Al–Ti–B–N coatings: (<b>а</b>) 0 at.% B, (<b>b</b>) 0.4 at.% B, (<b>c</b>) 2 at.% B, (<b>d</b>) 2.3 at.% B, (<b>e</b>) 7.3 at.% B, (<b>f</b>) 10 at.% В.</p> Full article ">Figure 3
<p>XRD patterns for the Cr–Al–Ti–B–N coatings.</p> Full article ">Figure 4
<p>The mechanical (<b>a</b>) and elastic-plastic (<b>b</b>) properties of coatings as a function of boron content.</p> Full article ">Figure 5
<p>The plot of friction coefficients versus the wear distance for Cr–Al–Ti–B–N coatings.</p> Full article ">Figure 6
<p>3D profiles of wear tracks for the coatings with varied boron content: (<b>a</b>)—0 at.%; (<b>b</b>)—2 at.%; (<b>c</b>)—2.3 at.%; (<b>d</b>)—10 at.%.</p> Full article ">Figure 7
<p>Specific wear of Cr–Al–Ti–B–N coatings.</p> Full article ">Figure 8
<p>2D (<b>a</b>,<b>d</b>) and 3D (<b>b</b>,<b>c</b>) profiles of worn zones for coatings with 0 (<b>a</b>,<b>b</b>) and 2.3 (<b>c</b>,<b>d</b>) at.% of boron after the abrasive test.</p> Full article ">Figure 9
<p>Mass loss after the abrasive test.</p> Full article ">Figure 10
<p>The plot of current densities versus the free corrosion potential.</p> Full article ">Figure 11
<p>Decorative Cr–Al–Ti–B–O–N coatings on SS substrates. Specimens denotation (D1-D9) corresponds with the compositions in <a href="#applsci-09-04977-t003" class="html-table">Table 3</a>.</p> Full article ">Figure 12
<p>Uncoated brass pendants (<b>a</b>); pendants with Cr–Al–Ti–B–N coating deposited in P4 mode (<b>b</b>); pendants with Cr–Al–Ti–B–O–N coatings deposited in D5 and D3 modes (<b>c</b>).</p> Full article ">
18 pages, 877 KiB  
Article
Bayesian Proxy Modelling for Estimating Black Carbon Concentrations using White-Box and Black-Box Models
by Martha A. Zaidan, Darren Wraith, Brandon E. Boor and Tareq Hussein
Appl. Sci. 2019, 9(22), 4976; https://doi.org/10.3390/app9224976 - 19 Nov 2019
Cited by 21 | Viewed by 4474
Abstract
Black carbon (BC) is an important component of particulate matter (PM) in urban environments. BC is typically emitted from gas and diesel engines, coal-fired power plants, and other sources that burn fossil fuel. In contrast to PM, BC measurements are not always available [...] Read more.
Black carbon (BC) is an important component of particulate matter (PM) in urban environments. BC is typically emitted from gas and diesel engines, coal-fired power plants, and other sources that burn fossil fuel. In contrast to PM, BC measurements are not always available on a large scale due to the operational cost and complexity of the instrumentation. Therefore, it is advantageous to develop a mathematical model for estimating the quantity of BC in the air, termed a BC proxy, to enable widening of spatial air pollution mapping. This article presents the development of BC proxies based on a Bayesian framework using measurements of PM concentrations and size distributions from 10 to 10,000 nm from a recent mobile air pollution study across several areas of Jordan. Bayesian methods using informative priors can naturally prevent over-fitting in the modelling process and the methods generate a confidence interval around the prediction, thus the estimated BC concentration can be directly quantified and assessed. In particular, two types of models are developed based on their transparency and interpretability, referred to as white-box and black-box models. The proposed methods are tested on extensive data sets obtained from the measurement campaign in Jordan. In this study, black-box models perform slightly better due to their model complexity. Nevertheless, the results demonstrate that the performance of both models does not differ significantly. In practice, white-box models are relatively more convenient to be deployed, the methods are well understood by scientists, and the models can be used to better understand key relationships. Full article
(This article belongs to the Special Issue Air Quality Prediction Based on Machine Learning Algorithms)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Scatter plots between black carbon (BC) mass concentrations and size-fractionated particulate matter (PM) mass/number concentrations measured throughout Jordan. Pearson (r<math display="inline"><semantics> <msub> <mrow/> <mi>p</mi> </msub> </semantics></math>) and Spearman (r<math display="inline"><semantics> <msub> <mrow/> <mi>s</mi> </msub> </semantics></math>) correlation coefficients and mutual information (MI) values are shown on the top of each subplot. The red data points represent the used features for the BC proxies’ inputs whereas the blue data points indicate the remaining unused features.</p> Full article ">Figure 2
<p>The estimated posterior distributions. The left-hand side is the marginal posterior distribution and the right-hand side is the sampling chain of each model parameter. The multi-process sampling is done in parallel and both demonstrate similar results.</p> Full article ">Figure 3
<p>The procedure of the modelling processes for the white-box (WB) and black-box (BB) proxies.</p> Full article ">Figure 4
<p>Time-series data of BC mass concentrations in the Amman city center. The blue dot is the measured BC, whereas the red dashed line and the light green region are the measured BC and its <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>σ</mi> </mrow> </semantics></math> uncertainty.Two numerical solutions</p> Full article ">Figure 5
<p>Regression plots between measured and estimated BC mass concentrations and error histogram between measured and estimated BC mass concentrations. (<b>a</b>) Regression plot.(<b>b</b>) Error histogram.</p> Full article ">Figure 6
<p>Bar chart of the percentage of measured BC data points within three levels of the confidence interval (<math display="inline"><semantics> <mi>σ</mi> </semantics></math>) of the estimated BC.</p> Full article ">
15 pages, 1843 KiB  
Article
Evolution of Residual Stress Based on Curvature Coupling in Multi-Roll Levelling
by Guodong Yi, Ye Liang, Chao Wang and Jinghua Xu
Appl. Sci. 2019, 9(22), 4975; https://doi.org/10.3390/app9224975 - 19 Nov 2019
Cited by 10 | Viewed by 6915
Abstract
Residual stress is the main cause of flatness defects in sheet metal and the basic method to improve the shape quality of the sheet is to reduce and eliminate the residual stress by multi-roll levelling. The curvature coupling between repeated sheet bendings in [...] Read more.
Residual stress is the main cause of flatness defects in sheet metal and the basic method to improve the shape quality of the sheet is to reduce and eliminate the residual stress by multi-roll levelling. The curvature coupling between repeated sheet bendings in multi-roll levelling greatly affects the accuracy of the analysis of the residual stress evolution, which is rarely considered in current research. Aiming to address this problem, a method for eliminating residual stress by multi-roll levelling based on curvature coupling is discussed in this article. An evaluation criterion and an analysis model are proposed to investigate the evolution of the residual stress in multi-roll levelling considering the curvature coupling between bendings. The effects of the intermesh of the work rolls and the plastic deformation of the sheet on the residual stress are also discussed. The results show that multi-roll levelling will cause rolling residual stress while reducing the initial residual stress of the sheet and the larger plastic deformation caused by the intermesh of the work rolls at the entry is beneficial for the complete elimination of the initial residual stress, but the rolling residual stress will increase at the same time. Therefore, the total residual stress of the sheet after levelling depends on the appropriate levelling parameters. Full article
(This article belongs to the Section Mechanical Engineering)
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<p>Variation of the residual stress in multi-roll levelling.</p> Full article ">Figure 2
<p>Distribution of the residual stresses before and after levelling.</p> Full article ">Figure 3
<p>Evolution of residual stress in the first bending. (<b>a</b>) A sheet without initial residual stress and (<b>b</b>) a sheet with initial residual stress.</p> Full article ">Figure 4
<p>Evolution of residual stress in the second bending.</p> Full article ">Figure 5
<p>Evolution process of the residual stress in multi-roll levelling.</p> Full article ">Figure 6
<p>Distribution of the residual stress after multi-roll levelling.</p> Full article ">Figure 7
<p>Influence of the intermesh of the work rolls at the entry on the maximum plastic deformation ratio.</p> Full article ">Figure 8
<p>Variation of the residual stress with the intermesh of the work rolls at the entry.</p> Full article ">
20 pages, 615 KiB  
Article
Multi-Objective Optimization of Massive MIMO 5G Wireless Networks towards Power Consumption, Uplink and Downlink Exposure
by Michel Matalatala, Margot Deruyck, Sergei Shikhantsov, Emmeric Tanghe, David Plets, Sotirios Goudos, Kostas E. Psannis, Luc Martens and Wout Joseph
Appl. Sci. 2019, 9(22), 4974; https://doi.org/10.3390/app9224974 - 19 Nov 2019
Cited by 23 | Viewed by 5771
Abstract
The rapid development of the number of wireless broadband devices requires that the induced uplink exposure be addressed during the design of the future wireless networks, in addition to the downlink exposure due to the transmission of the base stations. In this paper, [...] Read more.
The rapid development of the number of wireless broadband devices requires that the induced uplink exposure be addressed during the design of the future wireless networks, in addition to the downlink exposure due to the transmission of the base stations. In this paper, the positions and power levels of massive MIMO-LTE (Multiple Input Multiple Output-Long Term Evolution) base stations are optimized towards low power consumption, low downlink and uplink electromagnetic exposure and maximal user coverage. A suburban area in Ghent, Belgium has been considered. The results show that the higher the number of BS antenna elements, the fewer number of BSs the massive MIMO network requires. This leads to a decrease of the downlink exposure (−12% for the electric field and −32% for the downlink dose) and an increase of the uplink exposure (+70% for the uplink dose), whereas both downlink and uplink exposure increase with the number of simultaneous served users (+174% for the electric field and +22% for the uplink SAR). The optimal massive MIMO network presenting the better trade-off between the power consumption, the total dose and the user coverage has been obtained with 37 64-antenna BSs. Moreover, the level of the downlink electromagnetic exposure (electric field) of the massive MIMO network is 5 times lower than the 4G reference scenario. Full article
(This article belongs to the Special Issue 5G Networks: Optimization, Machine Learning And Blockchain Technologies)
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<p>Multi-cell massive Multiple Input Multiple Output (MIMO) network system model.</p> Full article ">Figure 2
<p>Selected area in Ghent, Belgium and the possible location of the base stations.</p> Full article ">Figure 3
<p>Optimization algorithm implemented in the capacity-based network deployment, designing optimized networks towards power consumption, downlink (DL) and uplink (UL) exposure.</p> Full article ">Figure 4
<p>Number of simultaneously served users on a hourly basis in Ghent, Belgium.</p> Full article ">Figure 5
<p>Impact of the number of users on the DL and UL exposure (scenario 1, tri-objective optimization).</p> Full article ">Figure 6
<p>Impact of the number of users on the DL and UL doses (scenario 1, bi-objective optimization).</p> Full article ">Figure 7
<p>Impact of the number of users on the number of base stations (BSs) deployed.</p> Full article ">Figure 8
<p>Impact of the number of antenna elements on the DL and UL exposure (scenario 2, tri-objective optimization).</p> Full article ">Figure 9
<p>Impact of the number of antenna elements on the DL and UL exposure (scenario 2, bi-objective optimization).</p> Full article ">Figure 10
<p>Pareto front for the bi-objective optimization (224 users and various BS antenna elements: 16, 32, 64, 128).</p> Full article ">Figure 11
<p>Comparision of the cumulative distribution function (CDF) of the downlink exposure (scenario 2): 4G vs. 5G (<math display="inline"><semantics> <mrow> <mi>E</mi> <mn>4</mn> <mi>G</mi> </mrow> </semantics></math> is the DL electric field due to a 4G BS, while <math display="inline"><semantics> <mrow> <mi>E</mi> <mn>5</mn> <mi>G</mi> </mrow> </semantics></math> refers to the DL electric field due to a 5G BS).</p> Full article ">
18 pages, 3332 KiB  
Review
Hybrid Micro-Grids Exploiting Renewables Sources, Battery Energy Storages, and Bi-Directional Converters
by Sergio Saponara, Roberto Saletti and Lucian Mihet-Popa
Appl. Sci. 2019, 9(22), 4973; https://doi.org/10.3390/app9224973 - 19 Nov 2019
Cited by 25 | Viewed by 6187
Abstract
This paper analyzes trends in renewable-energy-sources (RES), power converters, and control strategies, as well as battery energy storage and the relevant issues in battery charging and monitoring, with reference to a new and improved energy grid. An alternative micro-grid architecture that overcomes the [...] Read more.
This paper analyzes trends in renewable-energy-sources (RES), power converters, and control strategies, as well as battery energy storage and the relevant issues in battery charging and monitoring, with reference to a new and improved energy grid. An alternative micro-grid architecture that overcomes the lack of flexibility of the classic energy grid is then described. By mixing DC and AC sources, the hybrid micro-grid proposes an alternative architecture where the use of bi-directional electric vehicle chargers creates a micro-grid that directly interconnects all the partner nodes with bi-directional energy flows. The micro-grid nodes are the main grid, the RES and the energy storage systems, both, on-board the vehicle and inside the micro-grid structure. This model is further sustained by the new products emerging in the market, since new solar inverters are appearing, where a local energy storage for the RES is available. Therefore, the power flow from/towards the RES becomes bi-directional with improved flexibility and efficiency. Full article
(This article belongs to the Special Issue DC & Hybrid Micro-Grids)
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<p>Classic local grid where RES and EV charger nodes are only connected to the main grid and with unidirectional energy flows. RES: renewable-energy-sources; EV: full-electric vehicles; PV: photovoltaic.</p> Full article ">Figure 2
<p>A comparison between the shares of RES in electricity, heat and transport for 2017 and 2023. RES: Renewable Energy Sources.</p> Full article ">Figure 3
<p>Power electronic converters used in micro-grids.</p> Full article ">Figure 4
<p>Main functions performed by a Battery Management System (BMS).</p> Full article ">Figure 5
<p>Hierarchical architecture of a BMS matching the battery structure consisting of four paralleled strings of 6 series connected modules each. The Pack Management Unit (PMU) is the master BMS, connected to the lower layer slave BMSs (MMUs) [<a href="#B49-applsci-09-04973" class="html-bibr">49</a>].</p> Full article ">Figure 6
<p>New hybrid micro-grid with full connectivity among RES node (DC), EV node (DC) and main grid node (AC) and relevant bi-directional flows. EV: Electric Vehicle.</p> Full article ">Figure 7
<p>Circuit schematic of the bi-directional converter (1 AC grid port and 1 DC EV battery bidirectional ports; 1 DC unidirectional solar power port).</p> Full article ">Figure 8
<p>Bi-directional DC/DC converter: (<b>A</b>) half-bridge at the primary stage; (<b>B</b>) dual-active bridge topology.</p> Full article ">
19 pages, 5104 KiB  
Article
StreamflowVL: A Virtual Fieldwork Laboratory that Supports Traditional Hydraulics Engineering Learning
by Domenica Mirauda, Nicola Capece and Ugo Erra
Appl. Sci. 2019, 9(22), 4972; https://doi.org/10.3390/app9224972 - 19 Nov 2019
Cited by 14 | Viewed by 3797
Abstract
This paper describes an innovative virtual laboratory for students of Hydraulic Engineering at an Italian university that shows water discharge measurement techniques applied in open-channel flows. Such new technology, which supports traditional practical classes, has the potential to increase students’ motivation and improve [...] Read more.
This paper describes an innovative virtual laboratory for students of Hydraulic Engineering at an Italian university that shows water discharge measurement techniques applied in open-channel flows. Such new technology, which supports traditional practical classes, has the potential to increase students’ motivation and improve their skills, as well as simultaneously reducing the costs, time, and possible dangers that continuous field experiments would involve. Thanks to this immersive and interactive experience that is carried out indoors, students learn to move around a fluvial environment, as well as work more safely and with reduced risks of accidents. Besides, the virtual lab can boost learners’ interest by combining education with pleasure and making knowledge more fun. Collaboration with a group of students enrolled in the Master’s degree course of the Civil and Environmental Engineering program at Basilicata University at the early stages of developing the educational tool led to improvements in its performance and features. Also, a preliminary testing procedure carried out on a student sample, verified the achievement of the students’ learning objectives in terms of knowledge and skills. Such analysis indicated that students took more active role in the teaching/learning process and they showed greater interest in the topic dealt with through the new technology compared to the involvement of students observed during traditional lessons in previous years. The architecture and operational modes of the virtual laboratory as well as the results of the preliminary analysis are discussed. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Traditional practical lesson in the field: (<b>a</b>) selection and demarcation of the cross-section; (<b>b</b>) measurement of width with graduated tape; (<b>c</b>) detection of different verticals for the velocity acquisition, and (<b>d</b>) placing of the current meter at the desired location.</p> Full article ">Figure 2
<p>3D virtual reality (VR) scene of a fluvial reach.</p> Full article ">Figure 3
<p>Different steps for the computation of the water discharge through StreamflowVL: (<b>a</b>) demarcation of the site; (<b>b</b>) estimation of width; (<b>c</b>) identification of measurement verticals; (<b>d</b>) acquisition of depth; velocity visualisation with (<b>e</b>) the current meter and (<b>f</b>) the Acoustic Doppler Velocimeter.</p> Full article ">Figure 4
<p>3D VR scene of the fluvial reach (<b>a</b>) without and (<b>b</b>) with the light refraction and the water transparency in depth.</p> Full article ">Figure 5
<p>Examples of (<b>a</b>) the sounding rod and (<b>b</b>) the Acoustic Doppler Velocimeter highlighted in yellow at the user’s touch.</p> Full article ">Figure 6
<p>(<b>a</b>) Misplaced post and (<b>b</b>) correctly placed post.</p> Full article ">Figure 7
<p>(<b>a</b>) An abandoned instrument in the water and (<b>b</b>) a warning message.</p> Full article ">Figure 8
<p>Handheld keypad with (<b>a</b>) flat buttons and (<b>b</b>) lifted buttons.</p> Full article ">
20 pages, 3647 KiB  
Article
Characterization of Biofilm Extracts from Two Marine Bacteria
by Delphine Passerini, Florian Fécamp, Laetitia Marchand, Laetitia Kolypczuk, Sandrine Bonnetot, Corinne Sinquin, Véronique Verrez-Bagnis, Dominique Hervio-Heath, Sylvia Colliec-Jouault and Christine Delbarre-Ladrat
Appl. Sci. 2019, 9(22), 4971; https://doi.org/10.3390/app9224971 - 19 Nov 2019
Cited by 6 | Viewed by 3659
Abstract
In the marine environment, biofilm formation is an important lifestyle for microorganisms. A biofilm is comprised of cells embedded in an extracellular matrix that holds them close together and keeps the biofilm attached to the colonized surface. This predominant lifestyle and its main [...] Read more.
In the marine environment, biofilm formation is an important lifestyle for microorganisms. A biofilm is comprised of cells embedded in an extracellular matrix that holds them close together and keeps the biofilm attached to the colonized surface. This predominant lifestyle and its main regulation pathway, namely quorum-sensing (QS), have been shown to induce specific bioactive metabolites. In this study, we investigated the biofilm formation by two marine bacteria belonging to the Vibrio species to discover potentially innovative bioactive compounds. We proposed a protocol to isolate biofilm extracts, to analyze their biochemical composition, and to compare them to planktonic cell extracts. Cells were grown attached to a plastic surface; extracts were prepared in water, NaOH, or in ethyl acetate and analyzed. Extracellular matrix components featured carbohydrates, proteins, lipids, and low amount of DNA. Carbohydrates appeared to be the main constituent of biofilm but also of the planktonic cell supernatant. Moreover, antimicrobial and QS-signaling activities were evidenced in extracts. Full article
(This article belongs to the Special Issue Polysaccharides from Marine Environments)
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<p>Extraction scheme. TOT is the total extract obtained with ethyl acetate treatment; Sn refers to the broth supernatant; A contains the substances bound to the cell surface that were extracted using NaOH; C refers to the extraction using ethyl acetate on cell pellets. The extract names are followed by B for biofilm cells and P for planktonic cells.</p> Full article ">Figure 2
<p>16S rDNA sequence-based phylogenetic analysis of the MS969 strain.</p> Full article ">Figure 3
<p>Evaluation of the biofilm formation by <span class="html-italic">V. diabolicus</span> and <span class="html-italic">Vibrio</span> sp. MS969. (<b>a</b>) Effect of the surface type at 20 and 30 °C in PBS; incubation was performed for 24 h. (<b>b</b>) Effect of the addition of glucose; biofilm was measured after 24 h and 48 h at 20 °C. (<b>c</b>) Kinetics of biofilm formation at 20 °C in plastic tubes; cells were suspended in PBS or in Zobell. The OD of fixed crystal violet was normalized by dividing it per OD 600 nm.</p> Full article ">Figure 3 Cont.
<p>Evaluation of the biofilm formation by <span class="html-italic">V. diabolicus</span> and <span class="html-italic">Vibrio</span> sp. MS969. (<b>a</b>) Effect of the surface type at 20 and 30 °C in PBS; incubation was performed for 24 h. (<b>b</b>) Effect of the addition of glucose; biofilm was measured after 24 h and 48 h at 20 °C. (<b>c</b>) Kinetics of biofilm formation at 20 °C in plastic tubes; cells were suspended in PBS or in Zobell. The OD of fixed crystal violet was normalized by dividing it per OD 600 nm.</p> Full article ">Figure 4
<p>Recovery yield of each extract from 15 Petri dishes. (<b>a</b>) Amount of recovered cells. (<b>b</b>) Yield (mg) recovered after freeze drying or evaporation calculated for the whole biofilm or planktonic sample.</p> Full article ">Figure 5
<p>Composition analysis of supernatants (Sn) and NaOH extracts (A) from attached cells (B) or planktonic cells (P). Supernatants recovered directly using biofilm sample centrifugation were labeled “Sn” and those resulting from NaOH extraction were labeled “A”.</p> Full article ">Figure 6
<p>Electrophoretic analysis of extracts. Gels were stained with Stains All. (<b>a</b>) PAGE of <span class="html-italic">V. diabolicus</span> extracts and MS969 strain extracts. 1: O’GeneRuler 1 kb Plus DNA Ladder, 2: Prestained Protein MW Marker (Thermo Scientific, 26612), 3: SnB, 4: AB, 5: SnP, and 6: AP. (<b>b</b>) Agarose gel of <span class="html-italic">V. diabolicus</span> (3: SnB, 4: AB, 5: SnP, 6: AP) and MS969 extracts (3′: SnB, 4′: AB, 5′: SnP, 6′: AP). A: GY785 EPS and B: HE800 EPS were used as standards [<a href="#B67-applsci-09-04971" class="html-bibr">67</a>,<a href="#B68-applsci-09-04971" class="html-bibr">68</a>].</p> Full article ">Figure 7
<p>Osidic composition (% <span class="html-italic">w</span>/<span class="html-italic">w</span>) of <span class="html-italic">V. diabolicus</span> and MS969 extracts. GalNAc: N-Acetylgalactosamine, GlcNAc: N-Acetylglucosamine, GalA: Galacturonic acid, GlcA: Glucuronic acid, Glc: Glucose, Gal: Galactose, Man: Mannose, Fuc: Fucose, Rha: Rhamnose. Added inserts zoom on extracts containing low level of sugars. The osidic composition of EPS produced in the presence of 30 g/L glucose is indicated for both strains [<a href="#B54-applsci-09-04971" class="html-bibr">54</a>].</p> Full article ">Figure 8
<p>RI (refractive index) and UV profiles on the HPSEC column of <span class="html-italic">V. diabolicus</span> and MS969 extracts.</p> Full article ">Figure 9
<p>Mobility assays on media containing agar at two concentrations. Z: Zobell medium; Glc: 30 g/L glucose.</p> Full article ">Figure 10
<p>Antimicrobial activities of extracts against 13 indicator strains. 0: no activity; 1: medium activity; 2: high activity.</p> Full article ">Figure 11
<p>Effect of extracts on <span class="html-italic">V. harveyi</span> growth and luminescence. Results were registered at maximum luminescence (12 h growth). Vh: <span class="html-italic">V. harveyi</span> without the addition of the extract (reference). Sm: Streptomycin at 50 µg/mL. Blue stars: the significant effect on luminescence at <span class="html-italic">p</span>-value &lt; 0.01. Red stars: the significant effect on growth (OD 600 nm) at <span class="html-italic">p</span>-value &lt; 0.01. Assays were performed four times and were statistically compared to 92 assays with <span class="html-italic">V. harveyi</span> alone using Student’s test.</p> Full article ">
14 pages, 2755 KiB  
Article
A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
by Hongtang Gao, Zhongyu Wang, Yinbao Cheng, Yaru Li, Shuanghua Sun and Zhendong Shang
Appl. Sci. 2019, 9(22), 4970; https://doi.org/10.3390/app9224970 - 19 Nov 2019
Cited by 3 | Viewed by 2621
Abstract
End-plate surface distance is important for length value dissemination in the field of metrology. For the measurement of distance of two surfaces, the positioning method is the key for realizing high precision. A practical method with nanometer positioning precision is introduced in consideration [...] Read more.
End-plate surface distance is important for length value dissemination in the field of metrology. For the measurement of distance of two surfaces, the positioning method is the key for realizing high precision. A practical method with nanometer positioning precision is introduced in consideration of the complexity of positioning laser sources of the traditional methods and new methods. The surface positioning is realized by the combination of laser interference and white light interference. In order to verify the method, a 0.1 mm height step is made, and an experiment system based on the method is established. The principle and the basic theory of the method are analyzed, and the measures to enhance the repeatability from optical and mechanical factors and signal processing methods are presented. The experimental result shows that the surface positioning repeatability is in the order of 10 nm. The measurement uncertainty evaluation shows that the standard uncertainty is 21 nm for a 0.1 mm step. It is concluded that the method is suitable to be applied to the length measurement standard of the lab. Full article
(This article belongs to the Special Issue Manufacturing Metrology)
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<p>Block diagram of the system.</p> Full article ">Figure 2
<p>The interference pattern of white light and RGB light interference components. (<b>a</b>) White light interference pattern; (<b>b</b>) red light interference pattern; (<b>c</b>) green light interference pattern; (<b>d</b>) blue light interference pattern.</p> Full article ">Figure 3
<p>Positioning signal of 0.1 mm step surface.</p> Full article ">Figure 4
<p>Actual white light positioning signal for surface.</p> Full article ">Figure 5
<p>Theoretical white light interference signal.</p> Full article ">Figure 6
<p>The layout of positioning unit.</p> Full article ">Figure 7
<p>Positioning signal at moving speed of 30 μm/s.</p> Full article ">Figure 8
<p>Positioning signal at moving speed of 50 μm/s.</p> Full article ">Figure 9
<p>Positioning signal at moving speed of 80 μm/s.</p> Full article ">Figure 10
<p>Signal difference in different driving modes. AIC is automatic interference comparator.</p> Full article ">Figure 11
<p>The semiconductor laser wavelength stability monitoring.</p> Full article ">Figure 12
<p>The signal of the combination of laser interference and white light interference.</p> Full article ">Figure 13
<p>Block diagram of signal processing system.</p> Full article ">Figure 14
<p>The shaping of white light interference signal.</p> Full article ">Figure 15
<p>Positioning signal of first surface.</p> Full article ">Figure 16
<p>Positioning signal of second surface.</p> Full article ">Figure 17
<p>Bidirectional measurement results and the convergence of average result.</p> Full article ">
16 pages, 8288 KiB  
Article
Substructuring of a Petrol Engine: Dynamic Characterization and Experimental Validation
by Enrico Armentani, Venanzio Giannella, Roberto Citarella, Antonio Parente and Mauro Pirelli
Appl. Sci. 2019, 9(22), 4969; https://doi.org/10.3390/app9224969 - 19 Nov 2019
Cited by 9 | Viewed by 3570
Abstract
In this work, the vibration behavior of a 4-cylinder, 4-stroke, petrol engine was simulated by leveraging on the Finite Element Method (FEM). A reduced modelling strategy based on the component mode synthesis (CMS) was adopted to reduce the size of the full FEM [...] Read more.
In this work, the vibration behavior of a 4-cylinder, 4-stroke, petrol engine was simulated by leveraging on the Finite Element Method (FEM). A reduced modelling strategy based on the component mode synthesis (CMS) was adopted to reduce the size of the full FEM model of the engine. Frequency response function (FRF) analyses were used to identify the resonant frequencies and corresponding modes of the different FEM models, and the obtained results were compared with experimental data to get the model validation. Subsequently, modal-based frequency forced response analyses were performed to consider the loads acting during the real operating conditions of the engine. Finally, the impact on vibrations at the mounts, produced by an additional bracket connecting the engine block and gearbox, was also investigated. Both the full and reduced FEM model demonstrated and reproduced with high accuracy the vibration response at the engine mounts, providing a satisfactory agreement with the vibrations measured experimentally. The reduced modelling strategy required significantly shorter runtimes, which decreased from 24 h for the full FEM model to nearly 2 h for the reduced model. Full article
(This article belongs to the Special Issue Aeroacustic and Vibroacoustic Advancement in Aerospace and Automotive Systems)
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<p>FEM model of the engine with all its sub-components.</p> Full article ">Figure 2
<p>Full FEM model with details of the relevant points for the FRF analyses for load cases of (<b>a</b>) vertical bending and (<b>b</b>) lateral bending.</p> Full article ">Figure 3
<p>Close up of the crankcase–gearbox bracket under analysis.</p> Full article ">Figure 4
<p>Acceleration/force ratio for the full FEM model for the vertical bending load case with and without the crankcase–gearbox bracket (shown in <a href="#applsci-09-04969-f003" class="html-fig">Figure 3</a>) at: (<b>a</b>) engine mount; (<b>b</b>) gear mount.</p> Full article ">Figure 4 Cont.
<p>Acceleration/force ratio for the full FEM model for the vertical bending load case with and without the crankcase–gearbox bracket (shown in <a href="#applsci-09-04969-f003" class="html-fig">Figure 3</a>) at: (<b>a</b>) engine mount; (<b>b</b>) gear mount.</p> Full article ">Figure 5
<p>Acceleration/force ratio for the full FEM model for the lateral bending load case with and without the crankcase–gearbox bracket (shown in <a href="#applsci-09-04969-f003" class="html-fig">Figure 3</a>) at: (<b>a</b>) engine mount; (<b>b</b>) gear mount.</p> Full article ">Figure 6
<p>Numerical/experimental comparison on the acceleration/force ratio measured at the engine mount for the vertical bending load case: (<b>a</b>) with and (<b>b</b>) without the crankcase–gearbox bracket shown in <a href="#applsci-09-04969-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 7
<p>Displacement magnitude calculated at the (<b>a</b>) engine and (<b>b</b>) gear mounts considering the realistic operating loading condition.</p> Full article ">Figure 8
<p>Experimental/numerical comparison in terms of displacements at the gearbox mount (<b>a</b>) with and (<b>b</b>) without consideration of the bracket highlighted in <a href="#applsci-09-04969-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 9
<p>Experimental/numerical comparison in terms of displacements at the engine mount (<b>a</b>) with and (<b>b</b>) without consideration of the bracket (highlighted in <a href="#applsci-09-04969-f003" class="html-fig">Figure 3</a>).</p> Full article ">Figure 10
<p>Experimental setup for modal analyses with highlights of the supporting structure.</p> Full article ">Figure 11
<p>Evidence of the limitations given by the modelling strategy of the contacts, with reference to the relative motion between gear mount and gearbox at 210 Hz.</p> Full article ">Figure 12
<p>(<b>a</b>) Engine model built up with PLOTEL elements; (<b>b</b>) highlight of RBE3 elements.</p> Full article ">Figure 12 Cont.
<p>(<b>a</b>) Engine model built up with PLOTEL elements; (<b>b</b>) highlight of RBE3 elements.</p> Full article ">Figure 13
<p>Reduced model of the engine with all sub-components modelled by FEM.</p> Full article ">Figure 14
<p>Relevant points (yellow dots) on the (<b>a</b>) gear mount; (<b>b</b>) alternator and (<b>c</b>) intake manifold considered in the FRF analyses.</p> Full article ">Figure 15
<p>Comparisons of the acceleration/force ratio in the three directions for the full and reduced models at: (<b>a</b>–<b>c</b>) gear mount; (<b>d</b>–<b>f</b>) alternator; (<b>g</b>–<b>i</b>) intake manifold.</p> Full article ">Figure 15 Cont.
<p>Comparisons of the acceleration/force ratio in the three directions for the full and reduced models at: (<b>a</b>–<b>c</b>) gear mount; (<b>d</b>–<b>f</b>) alternator; (<b>g</b>–<b>i</b>) intake manifold.</p> Full article ">Figure 16
<p>Comparisons of the modal shapes for (<b>a</b>,<b>c</b>,<b>e</b>) the full FEM and (<b>b</b>,<b>d</b>,<b>f</b>) the reduced FEM model for: (<b>a</b>,<b>b</b>) gear mount, (<b>c</b>,<b>d</b>) alternator, (<b>e</b>,<b>f</b>) intake manifold.</p> Full article ">
16 pages, 1511 KiB  
Article
An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
by Dengyong Zhang, Shanshan Wang, Feng Li, Jin Wang, Arun Kumar Sangaiah, Victor S. Sheng and Xiangling Ding
Appl. Sci. 2019, 9(22), 4968; https://doi.org/10.3390/app9224968 - 18 Nov 2019
Cited by 46 | Viewed by 5158
Abstract
Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy [...] Read more.
Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods. Full article
(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )
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<p>Wavelet decomposition.</p> Full article ">Figure 2
<p>The framework of the proposed approach.</p> Full article ">Figure 3
<p>The results of de-noising 114 records in the MIT-BIH database: (<b>a</b>) Green is the signal de-noised by wavelet transform (including burrs), and red is the sub-band segmented by the minimum of maximum and the maximum of minimum. (<b>b</b>) The comparison between the signal after wavelet de-noising and the signal after wavelet de-noising and smoothing. (<b>c</b>) The comparison between the original signal and the signal after wavelet de-noising and smoothing. (<b>d</b>) The signal after de-noising and the resulting noise.</p> Full article ">Figure 4
<p>The de-noising results of adding 20 dB WGN to No. 119 record of the MIT-BIH database: (<b>a</b>) The original signal. (<b>b</b>) The signal after adding 20 dB WGN. (<b>c</b>–<b>f</b>) De-noised signals using wavelet soft threshold, EMD, EMD soft threshold, proposed method.</p> Full article ">Figure 5
<p>The de-noising results of signal added 50 <math display="inline"><semantics> <mrow> <mi>HZ</mi> </mrow> </semantics></math> power line interference to the 106 record of the MIT-BIH database: (<b>a</b>) The original signal. (<b>b</b>) The signal after adding power line interference. (<b>c</b>–<b>f</b>) De-noised signals using wavelet soft threshold, EMD, EMD soft threshold, proposed method.</p> Full article ">Figure 6
<p>The de-noising results of adding 20 dB WGN to No. 113 record of the MIT-BIH database: (<b>a</b>) The original signal. (<b>b</b>) The signal after adding 20 dB WGN. (<b>c</b>–<b>f</b>) De-noised signals using wavelet soft threshold, EMD, EMD soft threshold, proposed method.</p> Full article ">Figure 7
<p>The de-noising results after adding EMG noise to No. 115 record of the MIT-BIH database: (<b>a</b>) The original signal. (<b>b</b>) The signal after adding EMG noise. (<b>c</b>–<b>f</b>) De-noised signals using wavelet soft threshold, EMD, EMD soft threshold, proposed method.</p> Full article ">Figure 8
<p>The de-noising results after adding 15 dB WGN to the synthetic ECG: (<b>a</b>) The original signal. (<b>b</b>) The signal after adding WGN. (<b>c</b>–<b>f</b>) De-noised signals using wavelet soft threshold, EMD, EMD soft threshold, proposed method.</p> Full article ">Figure 9
<p>WT and proposed method output SNR improvement versus different input SNRs for the first 4096 samples of the MIT-BIH record number 100.</p> Full article ">Figure 10
<p>Comparison of the PRD(%) obtained by using different de-noising methods.</p> Full article ">
18 pages, 31820 KiB  
Article
IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
by Menghua Xia, Wenjun Yan, Yi Huang, Yi Guo, Guohui Zhou and Yuanyuan Wang
Appl. Sci. 2019, 9(22), 4967; https://doi.org/10.3390/app9224967 - 18 Nov 2019
Cited by 11 | Viewed by 9024
Abstract
Reliable detection of the media-adventitia border (MAB) and the lumen-intima border (LIB) in intravascular ultrasound (IVUS) images remains a challenging task that is of high clinical interest. In this paper, we propose a superpixel-wise fuzzy clustering technique modified by edges, followed by level [...] Read more.
Reliable detection of the media-adventitia border (MAB) and the lumen-intima border (LIB) in intravascular ultrasound (IVUS) images remains a challenging task that is of high clinical interest. In this paper, we propose a superpixel-wise fuzzy clustering technique modified by edges, followed by level set evolution (SFCME-LSE), for automatic border extraction in 40 MHz IVUS images. The contributions are three-fold. First, the usage of superpixels suppresses the influence of speckle noise in ultrasound images on the clustering results. Second, we propose a region of interest (ROI) assignment scheme to prevent the segmentation from being distracted by pathological structures and artifacts. Finally, the contour is converged towards the target boundary through LSE with an appropriately improved edge indicator. Quantitative evaluations on two IVUS datasets by the Jaccard measure (JM), the percentage of area difference (PAD), and the Hausdorff distance (HD) demonstrate the effectiveness of the proposed SFCME-LSE method. SFCME-LSE achieves the minimal HD of 1.20 ± 0.66 mm and 1.18 ± 0.70 mm for the MAB and LIB, respectively, among several state-of-the-art methods on a publicly available dataset. Full article
(This article belongs to the Special Issue Image Processing Techniques for Biomedical Applications)
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Figure 1

Figure 1
<p>The example intravascular ultrasound (IVUS) image (<b>left</b>), and the same image with intravascular structures and artifacts labeled (<b>right</b>).</p> Full article ">Figure 2
<p>The flowchart of the proposed segmentation algorithm.</p> Full article ">Figure 3
<p>Filters for detecting edges. We use a bank of 2D Morlet wavelet kernels distributed in 36 orientations at 10° intervals.</p> Full article ">Figure 4
<p>The definition of the region <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> outside <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>o</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mrow> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> is the thickness of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>o</mi> <mi>u</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>An example of the assignment flow on the regions of interest (ROIs), (<b>a</b>) media-adventitia border (MAB) initialization, (<b>b</b>–<b>g</b>) assignment of ROIs in B_1 and update of MAB, (<b>h</b>) B_2, (<b>i</b>) assignment of ROIs in B_2, (<b>j</b>) lumen-intima border (LIB) extraction.</p> Full article ">Figure 6
<p>(<b>a</b>) The cumulative distributions of the percentage of area differences (PADs) in the three experiments. (<b>b</b>) The performance of the superpixel-wise fuzzy clustering technique modified by edges, followed by level set evolution (SFCME-LSE) method on images with different signal-to-noise ratio (SNR).</p> Full article ">Figure 7
<p>Bland–Altman plots (the first column) and the linear regression results (in the second column) for the comparison of areas inside the border detected by the proposed method and the ground truth.</p> Full article ">Figure 8
<p>Examples of the segmentation results on Dataset I (the first three rows) and Dataset II (the last two rows). The green dashed line, cyan dashed line, magenta solid line, and red solid line correspond to the manually annotated lumen-intima border (LIB), the manually annotated media-adventitia border (MAB), the auto-detected LIB, and the auto-detected MAB, respectively.</p> Full article ">Figure 9
<p>Comparison of the clustering results from SFCME (the second column) and pixel-wise fuzzy c-means with spatial information (the third column).</p> Full article ">Figure 10
<p>Plots of the indicators and attributions of ROIs when <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>a</mi> <mi>d</mi> <mi>v</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> <mo>≤</mo> <msub> <mi>T</mi> <mi>θ</mi> </msub> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mrow> <mi>a</mi> <mi>d</mi> <mi>v</mi> <mi>e</mi> <mi>n</mi> </mrow> </msub> <mo>&gt;</mo> <msub> <mi>T</mi> <mi>θ</mi> </msub> </mrow> </semantics></math> (<b>right</b>).</p> Full article ">Figure 11
<p>The analysis of the inaccuracy in the extraction of the MAB (①) and LIB (②).</p> Full article ">
27 pages, 6744 KiB  
Article
UniChain: A Design of Blockchain-Based System for Electronic Academic Records Access and Permissions Management
by Eman-Yasser Daraghmi, Yousef-Awwad Daraghmi and Shyan-Ming Yuan
Appl. Sci. 2019, 9(22), 4966; https://doi.org/10.3390/app9224966 - 18 Nov 2019
Cited by 43 | Viewed by 7585
Abstract
Although blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing academic records, is a new mission for recent research to balance the needs for increasing data privacy and the regular interaction among students and universities. In [...] Read more.
Although blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing academic records, is a new mission for recent research to balance the needs for increasing data privacy and the regular interaction among students and universities. In this paper, a design for a blockchain-based system, namely UniChain, for managing Electronic Academic Records (EARs) is proposed. UniChain is designed to improve the current management systems as it provides interoperable, secure, and effective access to EARs by students, universities, and other third parties, while keeping the students’ privacy. UniChain employs timed-based smart contracts for governing transactions and controlling access to EARs. It adopts advanced encryption techniques for providing further security. This work proposes a new incentive mechanism that leverages the degree of universities regarding their efforts on maintaining academic records and creating new blocks. Extensive experiments were conducted to evaluate the UniChain performance, and the results indicate the efficiency of the proposal in handling a large dataset at low latency. Full article
(This article belongs to the Special Issue Blockchain for Smart Cities)
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Figure 1
<p>Software Components of the UniChain System.</p> Full article ">Figure 2
<p>The structure of EAR—Example 1.</p> Full article ">Figure 3
<p>The structure of EAR—Example 2.</p> Full article ">Figure 4
<p>The structure of EAR—Example 3.</p> Full article ">Figure 5
<p>The proposed smart contracts.</p> Full article ">Figure 6
<p>Generating/validating/appending a new block.</p> Full article ">Figure 7
<p>Average response time (s) vs. submitted queries.</p> Full article ">Figure 8
<p>Average number of messages vs. submitted queries.</p> Full article ">Figure 9
<p>Average response time (s) vs. academic records.</p> Full article ">Figure 10
<p>Average number of messages vs. academic records.</p> Full article ">Figure 11
<p>Throughput vs. Submitted queries.</p> Full article ">Figure 12
<p>Throughput vs. Academic Records.</p> Full article ">Figure A1
<p>The ERD of the university registration office [<a href="#B31-applsci-09-04966" class="html-bibr">31</a>].</p> Full article ">Figure A2
<p>The ERD of student score [<a href="#B32-applsci-09-04966" class="html-bibr">32</a>].</p> Full article ">Figure A3
<p>The ERD of examination scheduling [<a href="#B33-applsci-09-04966" class="html-bibr">33</a>].</p> Full article ">
42 pages, 5949 KiB  
Article
A Generalized Model of Complex Allometry I: Formal Setup, Identification Procedures and Applications to Non-Destructive Estimation of Plant Biomass Units
by Héctor Echavarria-Heras, Cecilia Leal-Ramirez, Enrique Villa-Diharce and Juan Ramón Castro-Rodríguez
Appl. Sci. 2019, 9(22), 4965; https://doi.org/10.3390/app9224965 - 18 Nov 2019
Cited by 4 | Viewed by 3226
Abstract
(1) Background: We previously demonstrated that customary regression protocols for curvature in geometrical space all derive from a generalized model of complex allometry combining scaling parameters expressing as continuous functions of covariate. Results highlighted the relevance of addressing suitable complexity in enhancing the [...] Read more.
(1) Background: We previously demonstrated that customary regression protocols for curvature in geometrical space all derive from a generalized model of complex allometry combining scaling parameters expressing as continuous functions of covariate. Results highlighted the relevance of addressing suitable complexity in enhancing the accuracy of allometric surrogates of plant biomass units. Nevertheless, examination was circumscribed to particular characterizations of the generalized model. Here we address the general identification problem. (2) Methods: We first suggest a log-scales protocol composing a mixture of linear models weighted by exponential powers. Alternatively, adopting an operating regime-based modeling slant we offer mixture regression or Takagi–Sugeno–Kang arrangements. This last approach allows polyphasic identification in direct scales. A derived index measures the extent on what complexity in arithmetic space drives curvature in arithmetical space. (3) Results: Fits on real and simulated data produced proxies of outstanding reproducibility strength indistinctly of data scales. (4) Conclusions: Presented analytical constructs are expected to grant efficient allometric projection of plant biomass units and also for the general settings of allometric examination. A traditional perspective deems log-transformation and allometry inseparable. Recent views assert that this leads to biased results. The present examination suggests this controversy can be resolved by addressing adequately the complexity of geometrical space protocols. Full article
(This article belongs to the Special Issue Biomass Research and Applications)
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Figure 1
<p>Huxley’s model of simple allometry fitted on multiple-parameter complex allometry (MCA) data simulated based on a lognormal distribution Panel (<b>a</b>) displays spread of <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mrow> <mi>k</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mi>ϵ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> replicates around the reference curve <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>e</mi> <mi>x</mi> <mi mathvariant="normal">p</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi>P</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> <mo>)</mo> </mrow> <mi>exp</mi> <mrow> <mo>(</mo> <mrow> <msub> <mi>Q</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mi>l</mi> <mi>n</mi> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> compared to the fitted Huxley’s mean response. Panel (<b>b</b>) shows the plot of curvature index <math display="inline"><semantics> <mrow> <mi>κ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>.</mo> </mrow> </semantics></math> We can ascertain that deviations from linearity in geometrical space can be expected for this data.</p> Full article ">Figure 2
<p>Fit of MCA on simulated data <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>u</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mi>k</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mi>ϵ</mi> <mo>)</mo> </mrow> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Regression Equation (46) was fitted to simulated <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi mathvariant="bold-italic">u</mi> <mrow> <mi mathvariant="bold-italic">i</mi> <mo>,</mo> </mrow> </msub> <msub> <mi mathvariant="bold-italic">v</mi> <mrow> <mi mathvariant="bold-italic">k</mi> <mrow> <mo>(</mo> <mi mathvariant="bold-italic">i</mi> <mo>)</mo> </mrow> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> data pairs. Panel (<b>a</b>) shows dispersion of <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">v</mi> <mrow> <mi mathvariant="bold-italic">k</mi> <mrow> <mo>(</mo> <mi mathvariant="bold-italic">i</mi> <mo>)</mo> </mrow> </mrow> </msub> </mrow> </semantics></math> about the mean response function <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>.</mo> </mrow> </semantics></math> This plot also shows the traditional analysis method of allometry’s (TAMA) fitted mean response <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. We can ascertain notorious bias by this last approach. Panel (<b>b</b>) shows corresponding retransformation results. Compared to significant reproducibility of mean response <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> TAMA’s counterpart <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> entails significant bias.</p> Full article ">Figure 3
<p>Comparison of <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> fits on lognormally distributed data. Panel (<b>a</b>) comparison of mean response functions fitted by means of a <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and a <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> proxies. Panel (<b>b</b>) compares related retransformed forms <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> </mrow> </semantics></math> Panel (<b>c</b>) compares <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> <mo> </mo> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">H</mi> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> fitted by the regression model of Equation (33) in direct arithmetical scales. Panel (<b>d</b>) is a comparison of <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>P</mi> <mi>Q</mi> <mn>2</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>, the directly acquired projection function. This can be obtained by replacing the fitted parameter vector <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>Q</mi> </mrow> </semantics></math> into addressed MCA form in arithmetical space. We can be aware of reliable projections even without a CF.By acquiring non lognormally distributed replicates <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mrow> <mi>k</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>β</mi> <mi>exp</mi> <mrow> <mo>(</mo> <mrow> <mi>α</mi> <mi>l</mi> <mi>n</mi> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>ξ</mi> <mi>k</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> according to procedure of Equation (44), we examined error structure effects on performance of addressed proxies. <a href="#applsci-09-04965-f004" class="html-fig">Figure 4</a> displays plots corresponding to retransformed forms. <a href="#applsci-09-04965-t002" class="html-table">Table 2</a> presents related model performance statistics. Results reveal that reliability of the MCA and TSK approaches does not depend in error structure for the present assay.</p> Full article ">Figure 4
<p>Comparison of <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> MCA fits the simulated non-lognormally distributed data. Panel (<b>a</b>) comparison of mean response functions <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>2</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>u</mi> <mo>|</mo> <mi>v</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>u</mi> <mo>|</mo> <mi>v</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Panel (<b>b</b>) compares related retransformed forms <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> </mrow> </semantics></math> Panel (<b>c</b>) compares <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mrow> <mi mathvariant="sans-serif">H</mi> <mi>TSK</mi> </mrow> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> acquired from regression model of Equation (34) fitted in direct arithmetical scales. Panel (<b>d</b>) is a comparison of <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>P</mi> <mi>Q</mi> <mn>2</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>, the directly acquired projection function. This is obtained by replacing fitted parameter vector <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>Q</mi> </mrow> </semantics></math> into addressed MCA form in arithmetical space. We can be aware of dependable projections even without a correction factor (CF).</p> Full article ">Figure 5
<p>MCA proxies fitted in geometrical space for the Ramirez-Ramirez et al. [<a href="#B6-applsci-09-04965" class="html-bibr">6</a>] data. Panel (<b>a</b>) dispersion of response <math display="inline"><semantics> <mi>v</mi> </semantics></math> about mean functions <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> <mo>.</mo> </mrow> </semantics></math> Panel (<b>b</b>) corresponding spreading about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>B</mi> <mi>L</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> <mo>.</mo> </mrow> </semantics></math> Panel (<b>c</b>) shows deviations of <math display="inline"><semantics> <mi>v</mi> </semantics></math> values around polynomial mean response <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>π</mi> <mn>3</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Panel (<b>d</b>) shows scattering about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>M</mi> <mi>L</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. The plots suggest consistency of MCA fits in all cases.</p> Full article ">Figure 6
<p>Fit of the <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mi>TSK</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> proxy fitted for the Ramirez-Ramirez et al. [<a href="#B6-applsci-09-04965" class="html-bibr">6</a>] data. Panel (<b>a</b>) shows firing strengths <math display="inline"><semantics> <mrow> <msup> <mi>ϑ</mi> <mn>1</mn> </msup> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mi>ϑ</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for the biphasic characterization <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> of <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mi>TSK</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> intersecting at break point. Panel (<b>b</b>) exhibits dispersion of <math display="inline"><semantics> <mi>v</mi> </semantics></math> about fitted TSK mean response, vertical segment signposts estimated break point. Panel (<b>c</b>) shows residual dispersion about the zero line. Panel (<b>d</b>) displays normal QQ (quantile-quantile) plot.</p> Full article ">Figure 7
<p>Plots of retransformed mean response functions identified for the Ramirez-Ramirez et al. [<a href="#B6-applsci-09-04965" class="html-bibr">6</a>] data. Panel (<b>a</b>) presents dispersion of observed values response values about mean response function <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>1</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Panel (<b>b</b>) displays corresponding spread about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>B</mi> <mi>L</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Panel (<b>c</b>) shows this around <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>π</mi> <mn>3</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and panel (<b>d</b>) associates to named scattering about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>M</mi> <mi>L</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> panel (<b>a</b>), broken line <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mstyle mathvariant="bold" mathsize="normal"> <mi>B</mi> <mi>L</mi> </mstyle> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (panel (<b>b</b>)), polynomial <math display="inline"><semantics> <mrow> <msub> <mi>π</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (panel (<b>c</b>)) and mixture lines <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mi>ML</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (panel (<b>d</b>)).</p> Full article ">Figure 7 Cont.
<p>Plots of retransformed mean response functions identified for the Ramirez-Ramirez et al. [<a href="#B6-applsci-09-04965" class="html-bibr">6</a>] data. Panel (<b>a</b>) presents dispersion of observed values response values about mean response function <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>1</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Panel (<b>b</b>) displays corresponding spread about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>B</mi> <mi>L</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>. Panel (<b>c</b>) shows this around <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>π</mi> <mn>3</mn> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and panel (<b>d</b>) associates to named scattering about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">Ω</mi> <mi>M</mi> <mi>L</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> panel (<b>a</b>), broken line <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mstyle mathvariant="bold" mathsize="normal"> <mi>B</mi> <mi>L</mi> </mstyle> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (panel (<b>b</b>)), polynomial <math display="inline"><semantics> <mrow> <msub> <mi>π</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (panel (<b>c</b>)) and mixture lines <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">Ω</mi> <mrow> <mi>ML</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (panel (<b>d</b>)).</p> Full article ">Figure 8
<p>Retransformed biphasic <math display="inline"><semantics> <mrow> <msub> <mi>Ω</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (panel (<b>a</b>)), direct scales fitted biphasic <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">H</mi> <mrow> <mi>TSK</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (panel (<b>b</b>)), Huxley’s model of simple allometry (panel (<b>c</b>)) and curvature index <math display="inline"><semantics> <mrow> <mi>κ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (panel (<b>d</b>)). Deviations of fitted Huxley’s power function relative to <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="sans-serif">H</mi> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> in panel c) explain deviations of <math display="inline"><semantics> <mrow> <mi>κ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> from line <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> shown in panel (<b>d</b>).</p> Full article ">Figure 9
<p>MCA protocols fitted in geometrical space for the <span class="html-italic">Zostera marina</span> data set. Panel (<b>a</b>) dispersion of response <math display="inline"><semantics> <mi>v</mi> </semantics></math> about mean functions <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> <mo>.</mo> </mrow> </semantics></math> The break point signposted by vertical segment is determined by the interpolation lines criterion presented in <a href="#app1-applsci-09-04965" class="html-app">Appendix A</a>. Panel (<b>b</b>) shows corresponding spreading about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>Ω</mi> <mi>B</mi> <mi>L</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> <mo>.</mo> </mrow> </semantics></math> Panel (<b>c</b>) shows deviations of <math display="inline"><semantics> <mi>v</mi> </semantics></math> values around polynomial mean response <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>π</mi> <mn>5</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Panel (<b>d</b>) is for <math display="inline"><semantics> <mrow> <mi>v</mi> </mrow> </semantics></math> spread about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>M</mi> <mi>L</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Plots suggest consistency of MCA fits in all cases.</p> Full article ">Figure 10
<p>Fit of the <math display="inline"><semantics> <mrow> <msub> <mi>Ω</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> proxy on the eelgrass data. Panel (<b>a</b>) shows firing strengths <math display="inline"><semantics> <mrow> <msup> <mi>ϑ</mi> <mn>1</mn> </msup> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mi>ϑ</mi> <mn>2</mn> </msup> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for the biphasic characterization of <math display="inline"><semantics> <mrow> <msub> <mi>Ω</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. Panel (<b>b</b>) exhibits dispersion of <math display="inline"><semantics> <mi>v</mi> </semantics></math> about fitted TSK mean response <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>Ω</mi> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> vertical segment signposts estimated break point. Panel (<b>c</b>) shows residual dispersion about the zero line. Panel (<b>d</b>) displays normal QQ plot.</p> Full article ">Figure 11
<p>Plots of retransformed MCA protocols fitted on logtransformmed eelgrass data. Panel (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>2</mn> <mi>B</mi> <mi>I</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> function obtained by retransformation of interpolation lines <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>2</mn> <mi>B</mi> <mi>I</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>v</mi> <mo>|</mo> <mi>u</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> (<a href="#applsci-09-04965-f009" class="html-fig">Figure 9</a>a) Panel (<b>b</b>) spread about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>Ω</mi> <mi>B</mi> <mi>L</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Panel (<b>c</b>) presents dispersion around mean function <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>π</mi> <mn>5</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> produced by the <math display="inline"><semantics> <mrow> <msub> <mi>π</mi> <mn>5</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> polynomial. Panel (<b>d</b>) displays spread about mean funcion <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>Ω</mi> <mi>M</mi> <mi>L</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> deriving from retransformation of the mixture lines proxy <math display="inline"><semantics> <mrow> <msub> <mi>Ω</mi> <mrow> <mi>M</mi> <mi>L</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. These plots suggest remarkable consistency of the retransformed MCA output.</p> Full article ">Figure 12
<p>Plots of retransformed <math display="inline"><semantics> <mrow> <msub> <mi>Ω</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and direct <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> proxies identified on eelgrass data. Panel (<b>a</b>) shows spread about <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>Ω</mi> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Panel (<b>b</b>) depicts dispersion about fitted biphasic form <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo> </mo> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Panel (<b>c</b>) compares <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>T</mi> <mi>S</mi> <mi>K</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mrow> <mi>q</mi> <mo>=</mo> <mn>2</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, to the power function by Huxley’s model of simple allometry fitted on direct scales. Panel (<b>d</b>) displays the plot of curvature index <math display="inline"><semantics> <mrow> <mi>κ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. This plot exhibits curvature effects more pronounced for the smaller leaves.</p> Full article ">Figure A1
<p>Huxley’s model of simple allometry fitted by DNLR on simulated lognormally distributed data. Panel (<b>a</b>) displays spread of <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mrow> <mi>k</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mi>ϵ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> replicates about the reference curve <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mi>p</mi> <mn>0</mn> </msub> <mrow> <mo> </mo> <mi>exp</mi> </mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>q</mi> <mn>0</mn> </msub> <mi>l</mi> <mi>n</mi> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> <mo>.</mo> </mrow> </semantics></math> Panel (<b>b</b>) compares reference curve and mean response <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>β</mi> <msup> <mi>x</mi> <mi>α</mi> </msup> </mrow> </semantics></math> fitted in direct arithmetical space by means of non-linear regression.</p> Full article ">Figure A2
<p>Panel (<b>a</b>) displays spread of <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>k</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mi>ϵ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> replicates about the mean response line <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>p</mi> <mn>0</mn> </msub> <mo>+</mo> <msub> <mi>q</mi> <mn>0</mn> </msub> <mi>u</mi> <mo>.</mo> <mo> </mo> </mrow> </semantics></math> Panel (<b>b</b>) comparison of retransformed mean response <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and fitted power function <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>β</mi> <msup> <mi>x</mi> <mi>α</mi> </msup> </mrow> </semantics></math> resulting from a DNLR fit on simulated data <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mrow> <mi>k</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mi>ϵ</mi> <mo>)</mo> </mrow> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. Panel (<b>a</b>) reveals no curvature effects induced by a log transformation. Panel (<b>b</b>) suggest reliable agreement between observed and retransforming mean response <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> No biased results can be attributed to using a logtransformation procedure.</p> Full article ">Figure A3
<p>Huxley’s model fitted by DNLR on simulated non–normally distributed data. Panel (<b>a</b>) spread of <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mrow> <mi>k</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> values about reference curve <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mi>p</mi> <mn>0</mn> </msub> <mrow> <mo> </mo> <mi>exp</mi> </mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>q</mi> <mn>0</mn> </msub> <mi>l</mi> <mi>n</mi> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> Panel (<b>b</b>) comparison of reference curve and mean response <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mi>β</mi> <msup> <mi>x</mi> <mi>α</mi> </msup> </mrow> </semantics></math> fitted by a DNLR protocol.</p> Full article ">Figure A4
<p>TAMA fit on simulated non-lognormally distributed replicates. Panel (<b>a</b>) displays spread of <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>k</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mi>ϵ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> replicates about the mean response line <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>p</mi> <mn>0</mn> </msub> <mo>+</mo> <msub> <mi>q</mi> <mn>0</mn> </msub> <mi>u</mi> </mrow> </semantics></math>. Panel (<b>b</b>) comparison of mean response <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi>λ</mi> <mn>0</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mi>y</mi> <mo>|</mo> <mi>x</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and power function <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>=</mo> <mover accent="true"> <mi>β</mi> <mo>^</mo> </mover> <msubsup> <mi>x</mi> <mi>i</mi> <mover accent="true"> <mi>α</mi> <mo>^</mo> </mover> </msubsup> </mrow> </semantics></math> fitted in direct arithmetical space by means of non-linear regression to simulated data <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>y</mi> <mrow> <mi>k</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </msub> <mrow> <mo>(</mo> <mi>ξ</mi> <mo>)</mo> </mrow> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>. It can be inferred that regardless of an error structure a log transformation step in the TAMA approach dos not induce curvature in geometrical space.</p> Full article ">Figure A5
<p>Direct scales fit of the TSK fuzzy model to simulated data based on Huxley’s formula of simple allometry. Panel (<b>a</b>) shows the spread about the <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">E</mi> <mrow> <mi>HTSK</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>y</mi> <mo>|</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> mean response fitted on log normally distributed replicates. Panel (<b>b</b>) corresponds to the fit performed on non-lognormally distributed data. We can be aware of oustanding interpolation capabilities by the TSK approach. Panel (<b>c</b>) and (<b>d</b>) present variations of the <math display="inline"><semantics> <mrow> <mi>κ</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> index. No curvature in geometrical space could be expected for this data.</p> Full article ">
15 pages, 2428 KiB  
Article
A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
by Wei Yue, Xianhe Guan and Liyuan Wang
Appl. Sci. 2019, 9(22), 4964; https://doi.org/10.3390/app9224964 - 18 Nov 2019
Cited by 27 | Viewed by 3909
Abstract
In this paper, the important topic of cooperative searches for multi-dynamic targets in unknown sea areas by unmanned aerial vehicles (UAVs) is studied based on a reinforcement learning (RL) algorithm. A novel multi-UAV sea area search map is established, in which models of [...] Read more.
In this paper, the important topic of cooperative searches for multi-dynamic targets in unknown sea areas by unmanned aerial vehicles (UAVs) is studied based on a reinforcement learning (RL) algorithm. A novel multi-UAV sea area search map is established, in which models of the environment, UAV dynamics, target dynamics, and sensor detection are involved. Then, the search map is updated and extended using the concept of the territory awareness information map. Finally, according to the search efficiency function, a reward and punishment function is designed, and an RL method is used to generate a multi-UAV cooperative search path online. The simulation results show that the proposed algorithm could effectively perform the search task in the sea area with no prior information. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs))
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Figure 1
<p>Airborne sensor detection model.</p> Full article ">Figure 2
<p>Reinforcement learning (RL) search initial stage.</p> Full article ">Figure 3
<p>Sea area information learning search.</p> Full article ">Figure 4
<p>Unmanned aerial vehicle (UAV) search result under proposed method.</p> Full article ">Figure 5
<p>Random search result.</p> Full article ">Figure 6
<p>Traversing search result.</p> Full article ">Figure 7
<p>The initial stage of the v-shaped formation search.</p> Full article ">Figure 8
<p>RL search of the v-shaped formation.</p> Full article ">Figure 9
<p>Search result under RL.</p> Full article ">Figure 10
<p>Profile of <span class="html-italic">T</span> variation.</p> Full article ">Figure 11
<p>Relation between <span class="html-italic">P</span> and <span class="html-italic">M</span>.</p> Full article ">
11 pages, 3294 KiB  
Article
Cover the Violence: A Novel Deep-Learning-Based Approach Towards Violence-Detection in Movies
by Samee Ullah Khan, Ijaz Ul Haq, Seungmin Rho, Sung Wook Baik and Mi Young Lee
Appl. Sci. 2019, 9(22), 4963; https://doi.org/10.3390/app9224963 - 18 Nov 2019
Cited by 86 | Viewed by 8506
Abstract
Movies have become one of the major sources of entertainment in the current era, which are based on diverse ideas. Action movies have received the most attention in last few years, which contain violent scenes, because it is one of the undesirable features [...] Read more.
Movies have become one of the major sources of entertainment in the current era, which are based on diverse ideas. Action movies have received the most attention in last few years, which contain violent scenes, because it is one of the undesirable features for some individuals that is used to create charm and fantasy. However, these violent scenes have had a negative impact on kids, and they are not comfortable even for mature age people. The best way to stop under aged people from watching violent scenes in movies is to eliminate these scenes. In this paper, we proposed a violence detection scheme for movies that is comprised of three steps. First, the entire movie is segmented into shots, and then a representative frame from each shot is selected based on the level of saliency. Next, these selected frames are passed from a light-weight deep learning model, which is fine-tuned using a transfer learning approach to classify violence and non-violence shots in a movie. Finally, all the non-violence scenes are merged in a sequence to generate a violence-free movie that can be watched by children and as well violence paranoid people. The proposed model is evaluated on three violence benchmark datasets, and it is experimentally proved that the proposed scheme provides a fast and accurate detection of violent scenes in movies compared to the state-of-the-art methods. Full article
(This article belongs to the Special Issue Multimodal Deep Learning Methods for Video Analytics)
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<p>Proposed framework for violent scene detection in movies.</p> Full article ">Figure 2
<p>Sample frames from movie ‘Undisputed II’ along with their saliency maps. The frames with red bounding boxes are discarded while the frame in blue is selected as a key-frame with a maximum saliency score of 0.7261.</p> Full article ">Figure 3
<p>Visualization of different kinds convolution operations: (<b>a</b>) standard convolution; (<b>b</b>) depth-wise convolution; (<b>c</b>) point-wise convolution.</p> Full article ">Figure 4
<p>Feature maps of violence and non-violence scenes.</p> Full article ">Figure 5
<p>Sample frames from the datasets used for evaluation. In each row, the first three samples are from the violent class and the last three samples are from the non-violent class. (<b>a</b>) Violence in Movies, (<b>b</b>) Hockey Fights, and (<b>c</b>) Violence Scenes Detection datasets. The frames in red and blue are detected as violent and non-violent by the proposed method, respectively.</p> Full article ">Figure 6
<p>Confusion matrices of the (<b>a</b>) Violence in Movies, (<b>b</b>) Hockey Fight, (<b>c</b>) Violence Scenes Detection datasets, and the (<b>d</b>) combined dataset.</p> Full article ">Figure 7
<p>Performance evolution of the proposed model.</p> Full article ">
24 pages, 3874 KiB  
Article
Extracting Production Rules for Cerebrovascular Examination Dataset through Mining of Non-Anomalous Association Rules
by Chao Ou-Yang, Chandrawati Putri Wulandari, Mohammad Iqbal, Han-Cheng Wang and Chiehfeng Chen
Appl. Sci. 2019, 9(22), 4962; https://doi.org/10.3390/app9224962 - 18 Nov 2019
Cited by 2 | Viewed by 2821
Abstract
Today, patients generate a massive amount of health records through electronic health records (EHRs). Extracting usable knowledge of patients’ pathological conditions or diagnoses is essential for the reasoning process in rule-based systems to support the process of clinical decision making. Association rule mining [...] Read more.
Today, patients generate a massive amount of health records through electronic health records (EHRs). Extracting usable knowledge of patients’ pathological conditions or diagnoses is essential for the reasoning process in rule-based systems to support the process of clinical decision making. Association rule mining is capable of discovering hidden interesting knowledge and relations among attributes in datasets, including medical datasets, yet is more likely to produce many anomalous rules (i.e., subsumption and circular redundancy) depends on the predefined threshold, which lead to logical errors and affects the reasoning process of rule-based systems. Therefore, the challenge is to develop a method to extract concise rule bases and improve the coverage of non-anomalous rule bases, i.e., one that not only reduces anomalous rules but also finds the most comprehensive rules from the dataset. In this study, we generated non-anomalous association rules (NAARs) from a cerebrovascular examination dataset through several steps: obtaining a frequent closed itemset, generating association rule bases, subsumption checking, and circularity checking, to fit production rules (PRs) in rule-based systems. Toward the end, the rule inferencing part was performed by PROLOG to obtain possible conclusions toward a specific query given by a user. The experiment shows that compared with the traditional method, the proposed method eliminated a significant number of anomalous rules while improving computational time. Full article
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<p>Case Illustration.</p> Full article ">Figure 2
<p>Illustration of the proposed method for rule-based systems on a cerebrovascular examination dataset.</p> Full article ">Figure 3
<p>The performance comparison of level of anomalies (<b>left</b>) and number of final rules (<b>right</b>).</p> Full article ">Figure 4
<p>The performance comparison of processing time.</p> Full article ">Figure 5
<p>Screenshot of command window MATLAB after subsumption checking.</p> Full article ">Figure 6
<p>Screenshot of command window MATLAB after circularity checking.</p> Full article ">Figure 7
<p>Example of rule inference through PROLOG.</p> Full article ">
15 pages, 1181 KiB  
Communication
Quantification of Trans-Resveratrol-Loaded Solid Lipid Nanoparticles by a Validated Reverse-Phase HPLC Photodiode Array
by Roberta B. Rigon, Naiara Fachinetti, Patrícia Severino, Alessandra Durazzo, Massimo Lucarini, Atanas G. Atanasov, Soukaina El Mamouni, Marlus Chorilli, Antonello Santini and Eliana B. Souto
Appl. Sci. 2019, 9(22), 4961; https://doi.org/10.3390/app9224961 - 18 Nov 2019
Cited by 21 | Viewed by 3843
Abstract
A new method based on reverse-phase HPLC combined with photodiode array (PDA) was developed to quantify the release of trans-resveratrol (tRES) from solid lipid nanoparticles (SLN). The mobile phase was composed of 75:0:25 (V/V) water/methanol/acetonitrile at 0–3.5 min, 32.5:30.0:37.5 (V/V) water/methanol/acetonitrile at [...] Read more.
A new method based on reverse-phase HPLC combined with photodiode array (PDA) was developed to quantify the release of trans-resveratrol (tRES) from solid lipid nanoparticles (SLN). The mobile phase was composed of 75:0:25 (V/V) water/methanol/acetonitrile at 0–3.5 min, 32.5:30.0:37.5 (V/V) water/methanol/acetonitrile at 3.6–5.8 min, and 75:0:25 (V/V) water/methanol/acetonitrile at 5.9–10 min. The flow rate was set at 1.0 mL/min, and tRES was detected at the wavelength of 306.6 nm. A concentration range of 1–100 µg/mL was used to obtain the linear calibration curve. SLN were produced by ultrasound technique to load 0.1% (wt/wt) of tRES, and the in vitro release of the drug was run in modified Franz diffusion cells. The mean recovery of tRES was found to be 96.84 ± 0.32%. The intra-assay and inter-assay coefficients of variation were less than 5%. The proposed method was applied to in vitro permeability studies, and the Weibull model was found to be the one that best fits the tRES release, which is characterized by a simultaneous lipid chain relaxation and erosion during drug release. Full article
(This article belongs to the Section Chemical and Molecular Sciences)
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<p>Conversion of <span class="html-italic">trans</span>- to <span class="html-italic">cis</span>-resveratrol after exposure to ultraviolet radiation.</p> Full article ">Figure 2
<p>Retention times of <span class="html-italic">trans</span>- (7.194 min) and <span class="html-italic">cis</span>-resveratrol (7.847 min) in reverse-phase (RP)-HPLC chromatogram of standard solution of (50 μg/mL) after 1 h of exposition to UV light (<b>a</b>); UV absorption spectrum of <span class="html-italic">trans</span>-resveratrol (<b>b</b>) and <span class="html-italic">cis</span>-resveratrol (<b>c</b>) recorded in the range of 280–350 nm from the diode array.</p> Full article ">Figure 3
<p>Retention time recorded in the chromatogram of the (<b>a</b>) filtrated drug-free SLN formulation (F1) (<b>b</b>) and in the chromatogram of <span class="html-italic">trans</span>-resveratrol (tRES) standard solution (50.0 μg/mL).</p> Full article ">Figure 4
<p>Trans-resveratrol (tRES) release profile from SLN after 24 h of analysis (n = 6). F1.RES comprised 5.0% SA, 3.5% P407, 0.18% MP, 0.02% PP, and 0.1% tRES, and F2.RES comprised 5.0% SA, 1.2% SPC, 3.5% P407, 0.18% MP, 0.02% PP, and 0.1% tRES.</p> Full article ">
17 pages, 8738 KiB  
Article
A Granularity-Based Intelligent Tutoring System for Zooarchaeology
by Laia Subirats, Leopoldo Pérez, Cristo Hernández, Santiago Fort and Gomez-Monivas Sacha
Appl. Sci. 2019, 9(22), 4960; https://doi.org/10.3390/app9224960 - 18 Nov 2019
Cited by 3 | Viewed by 3318
Abstract
This paper presents a tutoring system which uses three different granularities for helping students to classify animals from bone fragments in zooarchaeology. The 3406 bone remains, which have 64 attributes, were obtained from the excavation of the Middle Palaeolithic site of El Salt [...] Read more.
This paper presents a tutoring system which uses three different granularities for helping students to classify animals from bone fragments in zooarchaeology. The 3406 bone remains, which have 64 attributes, were obtained from the excavation of the Middle Palaeolithic site of El Salt (Alicante, Spain). The coarse granularity performs a five-class prediction, the medium a twelve-class prediction, and the fine a fifteen-class prediction. In the coarse granularity, the results show that the first 10 most relevant attributes for classification are width, bone, thickness, length, bone fragment, anatomical group, long bone circumference, X, Y, and Z. Based on those results, a user-friendly interface of the tutor has been built in order to train archaeology students to classify new remains using the coarse granularity. A pilot has been performed in the 2019 excavation season in Abric del Pastor (Alicante, Spain), where the automatic tutoring system was used by students to classify 51 new remains. The pilot experience demonstrated the usefulness of the tutoring system both for students when facing their first classification activities and also for seniors since the tutoring system gives them valuable clues for helping in difficult classification problems. Full article
(This article belongs to the Special Issue Smart Learning)
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<p>Geographical location of “El Salt”, site overview and current excavation surface.</p> Full article ">Figure 2
<p>Taphonomic damage and bone remains recovered examples: manganese (<b>A</b>), concreteness (<b>B</b>), root-marks (<b>C</b>), bone flake (<b>D</b>), burned bone (<b>E</b>), erosion (<b>F</b>), bones, teeth and deer antler (<b>G</b>), bones and wild goat teeth (<b>H</b>), horse tooth (<b>I</b>), phalanges and lynx maxillary (<b>J</b>), and rabbit bones (<b>K</b>).</p> Full article ">Figure 3
<p>Bioestratinomic damage examples. There are anthropogenic marks: slicing and scraping marks (<b>A</b>–<b>D</b>), Predator non-anthropogenic marks: digestion (<b>E</b>,<b>F</b>), punctures (<b>G</b>), and scores (<b>H</b>).</p> Full article ">Figure 4
<p>Confusion matrix of the coarse granularity.</p> Full article ">Figure 5
<p>Confusion matrix of the medium granularity.</p> Full article ">Figure 6
<p>Confusion matrix of the fine granularity.</p> Full article ">Figure 7
<p>Tutor interface to introduce the bone fragments.</p> Full article ">Figure 8
<p>Tutor prediction (<b>a</b>) and student’s/tutor prediction table (<b>b</b>).</p> Full article ">
20 pages, 20854 KiB  
Article
Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm
by Hesheng Tang, Xueyuan Guo, Liyu Xie and Songtao Xue
Appl. Sci. 2019, 9(22), 4959; https://doi.org/10.3390/app9224959 - 18 Nov 2019
Cited by 5 | Viewed by 2742
Abstract
The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is [...] Read more.
The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run. Full article
(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)
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<p>The flowchart of the algorithm.</p> Full article ">Figure 2
<p>n-DOF structure model.</p> Full article ">Figure 3
<p>Evolution of the scale reduction factor for the eight estimation parameters: (<b>a</b>) Case 1: 0% noise + Full Outputs; (<b>b</b>) Case 2: 10% noise + Full Outputs; (<b>c</b>) Case 3: 10% noise + Partial Outputs.</p> Full article ">Figure 4
<p>Generated samples for the three estimation parameters (<span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>5</sub>, <span class="html-italic">ζ</span><sub>1</sub>): (<b>a</b>) Case 1: 0% noise + Full Outputs; (<b>b</b>) Case 2: 10% noise + Full Outputs; (<b>c</b>) Case 3: 10% noise + Partial Outputs.</p> Full article ">Figure 4 Cont.
<p>Generated samples for the three estimation parameters (<span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>5</sub>, <span class="html-italic">ζ</span><sub>1</sub>): (<b>a</b>) Case 1: 0% noise + Full Outputs; (<b>b</b>) Case 2: 10% noise + Full Outputs; (<b>c</b>) Case 3: 10% noise + Partial Outputs.</p> Full article ">Figure 5
<p>Marginal posterior probability distributions of the three estimation parameters (<span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>5</sub>, <span class="html-italic">ζ</span><sub>1</sub>): (<b>a</b>) Case 1: 0% noise + Full Outputs; (<b>b</b>) Case 2: 10% noise + Full Outputs; (<b>c</b>) Case 3: 10% noise + Partial Outputs.</p> Full article ">Figure 6
<p>Evolution of the scale reduction factor for the estimation parameters.</p> Full article ">Figure 7
<p>Generated samples for the six estimation parameters (<span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>6</sub>, <span class="html-italic">k</span><sub>9</sub>, <span class="html-italic">m</span><sub>1</sub>, <span class="html-italic">m</span><sub>6</sub>, <span class="html-italic">ζ</span><sub>1</sub>).</p> Full article ">Figure 8
<p>Marginal posterior probability distributions of the six estimation parameters (<span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>6</sub>, <span class="html-italic">k</span><sub>9</sub>, <span class="html-italic">m</span><sub>1</sub>, <span class="html-italic">m</span><sub>6</sub>, <span class="html-italic">ζ</span><sub>1</sub>).</p> Full article ">Figure 9
<p>Schematic diagram and the test model of the 5-DOF structure system.</p> Full article ">Figure 10
<p>Evolution of the scale reduction factor for the seven estimation parameters.</p> Full article ">Figure 11
<p>Generated samples for the six estimation parameters (<span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>2</sub>, <span class="html-italic">k</span><sub>3</sub>, <span class="html-italic">k</span><sub>4</sub>, <span class="html-italic">k</span><sub>5</sub>, <span class="html-italic">ζ</span><sub>1</sub>).</p> Full article ">Figure 12
<p>Marginal posterior probability distributions of the six estimation parameters (<span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>2</sub>, <span class="html-italic">k</span><sub>3</sub>, <span class="html-italic">k</span><sub>4</sub>, <span class="html-italic">k</span><sub>5</sub>, <span class="html-italic">ζ</span><sub>1</sub>).</p> Full article ">Figure 13
<p>Experimental and simulation acceleration time histories in the linear structural system: (<b>a</b>) At the 3rd floor; (<b>b</b>) At the 5th floor.</p> Full article ">Figure 14
<p>Experimental and simulation acceleration time histories in the linear structural system: (<b>a</b>) At the 3rd floor; (<b>b</b>) At the 5th floor.</p> Full article ">Figure 15
<p>The test model and a simplified schematic of the system.</p> Full article ">Figure 16
<p>Evolution of the scale reduction factor for the 11 estimation parameters.</p> Full article ">Figure 17
<p>Generated samples for the six estimation parameters (<span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>3</sub>, <span class="html-italic">k</span><sub>5</sub>, <span class="html-italic">ζ</span><sub>1</sub>, <span class="html-italic">ω</span><sub>p</sub>, <span class="html-italic">ζ</span><sub>c</sub>).</p> Full article ">Figure 18
<p>Marginal posterior probability distributions of the six estimation parameters (<span class="html-italic">k</span><sub>1</sub>, <span class="html-italic">k</span><sub>3</sub>, <span class="html-italic">k</span><sub>5</sub>, <span class="html-italic">ζ</span><sub>1</sub>, <span class="html-italic">ω</span><sub>p</sub>, <span class="html-italic">ζ</span><sub>c</sub>).</p> Full article ">Figure 19
<p>Experimental and simulation acceleration time histories in the nonlinear structural system: (<b>a</b>) At the 3rd floor; (<b>b</b>) At the 5th floor.</p> Full article ">Figure 20
<p>Prediction uncertainty ranges of acceleration histories in the nonlinear structural system: (<b>a</b>) At the 3rd floor; (<b>b</b>) At the 5th floor.</p> Full article ">Figure 20 Cont.
<p>Prediction uncertainty ranges of acceleration histories in the nonlinear structural system: (<b>a</b>) At the 3rd floor; (<b>b</b>) At the 5th floor.</p> Full article ">
12 pages, 644 KiB  
Article
Saturation Based Nonlinear FOPD Motion Control Algorithm Design for Autonomous Underwater Vehicle
by Lichuan Zhang, Lu Liu, Shuo Zhang and Sheng Cao
Appl. Sci. 2019, 9(22), 4958; https://doi.org/10.3390/app9224958 - 18 Nov 2019
Cited by 8 | Viewed by 2456
Abstract
The application of Autonomous Underwater Vehicle (AUV) is expanding rapidly, which drives the urgent need of its autonomy improvement. Motion control system is one of the keys to improve the control and decision-making ability of AUVs. In this paper, a saturation based nonlinear [...] Read more.
The application of Autonomous Underwater Vehicle (AUV) is expanding rapidly, which drives the urgent need of its autonomy improvement. Motion control system is one of the keys to improve the control and decision-making ability of AUVs. In this paper, a saturation based nonlinear fractional-order PD (FOPD) controller is proposed for AUV motion control. The proposed controller is can achieve better dynamic performance as well as robustness compared with traditional PID type controller. It also has the advantages of simple structure, easy adjustment and easy implementation. The stability of the AUV motion control system with the proposed controller is analyzed through Lyapunov method. Moreover, the controlled performance can also be adjusted to satisfy different control requirements. The outperformed dynamic control performance of AUV yaw and depth systems with the proposed controller is shown by the set-point regulation and trajectory tracking simulation examples. Full article
(This article belongs to the Special Issue Optimization of Motion Planning and Control for Automatic Machines, Robots and Multibody Systems)
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<p>Saturation function.</p> Full article ">Figure 2
<p>Set-point control performance comparison.</p> Full article ">Figure 3
<p>Set-point control performance.</p> Full article ">Figure 4
<p>Control performance indicators trends.</p> Full article ">Figure 5
<p>Trajectory tracking performance of AUV yaw system.</p> Full article ">Figure 6
<p>Trajectory tracking performance of AUV depth system.</p> Full article ">
17 pages, 6724 KiB  
Article
Influencing Factors of Motion Responses for Large-Diameter Tripod Bucket Foundation
by Xianqing Liu, Puyang Zhang, Mingjie Zhao, Hongyan Ding and Conghuan Le
Appl. Sci. 2019, 9(22), 4957; https://doi.org/10.3390/app9224957 - 18 Nov 2019
Cited by 13 | Viewed by 2974
Abstract
Large-diameter multi-bucket foundation is well suited for offshore wind turbines at deeper water than 20 m. Air floating transportation is one of the key technologies for the cost-effective development of bucket foundation. To predict the dynamic behavior of large-diameter tripod bucket foundation (LDTBF) [...] Read more.
Large-diameter multi-bucket foundation is well suited for offshore wind turbines at deeper water than 20 m. Air floating transportation is one of the key technologies for the cost-effective development of bucket foundation. To predict the dynamic behavior of large-diameter tripod bucket foundation (LDTBF) supported by an air cushion and a water plug inside every bucket in waves, three 1/25-scale physical model tests with different bucket spacing were conducted in waves; detailed prototype foundation models were established using a hydrodynamic software MOSES with a draft of 4.0 m, 4.5 m, and 5.0 m and with a water depth of 10.0 m, 11.25 m, and 12.5 m. The numerical and experimental results are consistent for heaving motion, while exhibiting favorable agreement for pitching motion. The results show that the resonant periods for heaving motion increased with increasing draft and water depth. The maximum amplitude for heaving motion first decreased and then increased with the increase of water depth and spacing between the buckets. The maximum amplitude for pitching motion first decreased and then increased with increasing water depth but decreased with increasing spacing between the buckets. The wider the spacing between the bucket foundations, the larger the heave response amplitude operators (RAOs). Simply improving the pitch RAOs by increasing the spacing between bucket foundations is limited and negatively affects motion performance during the transportation of LDTBF. Full article
(This article belongs to the Special Issue High-Efficiency Conversion in Renewable Energy, Challenge, or Reflection)
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<p>Offshore wind turbines (OWTs) with different types of bucket foundations: (<b>a</b>) Monobucket foundation, (<b>b</b>) suction bucket jacket (SBJ), (<b>c</b>) composite bucket foundation (CBF) with multi compartments.</p> Full article ">Figure 2
<p>Transportation of bucket foundation: (<b>a</b>) Monobucket with seven rooms as air cushions, (<b>b</b>) self-floating towing, (<b>c</b>) multibucket foundation, (<b>d</b>) air floating towing, (<b>e</b>) one-step transportation and installation technology.</p> Full article ">Figure 3
<p>Schematic diagram of the floating state for a single bucket foundation.</p> Full article ">Figure 4
<p>Prototypes and physical models of large-diameter multi-bucket foundation (LDMBF): (<b>a</b>) Prototype 1 with a distance of 1.5 D between one bucket and another bucket, (<b>b</b>) Prototype 2 with a distance of 2.0 D between one bucket and another bucket, (<b>c</b>) Prototype 3 with a distance of 2.5 D between one bucket and another bucket, (<b>d</b>) Laboratory model 1 for Prototype 1, (<b>e</b>) Laboratory model 2 for Prototype 2, (<b>f</b>) Laboratory model 3 for Prototype 3.</p> Full article ">Figure 5
<p>Composition of LDTBF.</p> Full article ">Figure 6
<p>Sketch of the experiment setup for multibucket foundation.</p> Full article ">Figure 7
<p>Model of a single bucket foundation.</p> Full article ">Figure 8
<p>Hydrodynamic models of LDTBF in MOSES: (<b>a</b>) Simulated model 1 for Prototype 1, (<b>b</b>) Simulated model 2 for Prototype 2, and (<b>c</b>) Simulated model 3 for Prototype 3.</p> Full article ">Figure 9
<p>Experimental and simulated RAOs for Model 1, Model 2, and Model 3: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">Figure 10
<p>Experimental RAOs for Model 1 with different drafts: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">Figure 11
<p>Experimental RAOs for Model 2 with different drafts: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">Figure 12
<p>Experimental RAOs for Model 3 with different drafts: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">Figure 13
<p>Experimental RAOs for Model 1 with different water depths: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">Figure 14
<p>Experimental RAOs for Model 2 with different water depths: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">Figure 15
<p>Experimental RAOs for Model 1 with different water depths: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">Figure 16
<p>Experimental RAOs at a 4.0 m draft with different bucket spacings: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">Figure 17
<p>Experimental RAOs at a 4.5 m draft with different bucket spacings: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">Figure 18
<p>Experimental RAOs at a 5.0 m draft with different bucket spacings: (<b>a</b>) heave RAO and (<b>b</b>) pitch RAO.</p> Full article ">
19 pages, 3540 KiB  
Article
Security Analysis of Discrete-Modulated Continuous-Variable Quantum Key Distribution over Seawater Channel
by Xinchao Ruan, Hang Zhang, Wei Zhao, Xiaoxue Wang, Xuan Li and Ying Guo
Appl. Sci. 2019, 9(22), 4956; https://doi.org/10.3390/app9224956 - 18 Nov 2019
Cited by 15 | Viewed by 3204
Abstract
We investigate the optical absorption and scattering properties of four different kinds of seawater as the quantum channel. The models of discrete-modulated continuous-variable quantum key distribution (CV-QKD) in free-space seawater channel are briefly described, and the performance of the four-state protocol and the [...] Read more.
We investigate the optical absorption and scattering properties of four different kinds of seawater as the quantum channel. The models of discrete-modulated continuous-variable quantum key distribution (CV-QKD) in free-space seawater channel are briefly described, and the performance of the four-state protocol and the eight-state protocol in asymptotic and finite-size cases is analyzed in detail. Simulation results illustrate that the more complex is the seawater composition, the worse is the performance of the protocol. For different types of seawater channels, we can improve the performance of the protocol by selecting different optimal modulation variances and controlling the extra noise on the channel. Besides, we can find that the performance of the eight-state protocol is better than that of the four-state protocol, and there is little difference between homodyne detection and heterodyne detection. Although the secret key rate of the protocol that we propose is still relatively low and the maximum transmission distance is only a few hundred meters, the research on CV-QKD over the seawater channel is of great significance, which provides a new idea for the construction of global secure communication network. Full article
(This article belongs to the Special Issue Quantum Communications and Quantum Networks)
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<p>(Color online.) The attenuation coefficients of the four different types of seawater as functions of wavelength of the signal light. The blue dashed line, the green dashed line, and the red solid line represent the change curves of the absorption coefficient <math display="inline"> <semantics> <mrow> <mi>a</mi> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>, the scattering coefficient <math display="inline"> <semantics> <mrow> <mi>b</mi> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>, and the total attenuation coefficient <math display="inline"> <semantics> <mrow> <mi>c</mi> <mo>(</mo> <mi>λ</mi> <mo>)</mo> </mrow> </semantics> </math>, respectively.</p> Full article ">Figure 2
<p>(Color online.) Light intensity distribution at different transmission distances of the four-state protocol over a pure sea water channel: (<b>a</b>–<b>c</b>) the three-dimensional views of the intensity distribution of the signal light at the position of initial, 8 m, and 20 m of the seawater channel, respectively; and (<b>d</b>–<b>f</b>) are their corresponding plane views.</p> Full article ">Figure 2 Cont.
<p>(Color online.) Light intensity distribution at different transmission distances of the four-state protocol over a pure sea water channel: (<b>a</b>–<b>c</b>) the three-dimensional views of the intensity distribution of the signal light at the position of initial, 8 m, and 20 m of the seawater channel, respectively; and (<b>d</b>–<b>f</b>) are their corresponding plane views.</p> Full article ">Figure 3
<p>Three link models of end-to-end underwater CV-QKD system.</p> Full article ">Figure 4
<p>The entanglement-based scheme of the discrete-modulated underwater CV-QKD. Alice randomly prepares one of the <span class="html-italic">N</span> states and sends it to Bob through the untrusted seawater channel. Bob detects the received model to derive a sequence of bits shared with Alice by using a homodyne detector or a heterodyne detector. The seawater channel is assumed to be a linear channel.</p> Full article ">Figure 5
<p>(Color online.) Secret key rate of the four-state protocol for realistic reconciliation efficiency of <math display="inline"> <semantics> <mrow> <mn>90</mn> <mo>%</mo> </mrow> </semantics> </math> and a quantum efficiency of Bob’s detection equal to <math display="inline"> <semantics> <mrow> <mn>0.6</mn> </mrow> </semantics> </math> with thermal noise <math display="inline"> <semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mspace width="3.33333pt"/> <mo>=</mo> <mspace width="3.33333pt"/> <mn>0.01</mn> </mrow> </semantics> </math>. <math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mi>A</mi> </msub> <mspace width="3.33333pt"/> <mo>=</mo> <mspace width="3.33333pt"/> <mn>0.6</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math> (in shot-noise unit).</p> Full article ">Figure 6
<p>(Color online.) Secret key rate of the eight-state protocol for realistic reconciliation efficiency of <math display="inline"> <semantics> <mrow> <mn>90</mn> <mo>%</mo> </mrow> </semantics> </math> and a quantum efficiency of Bob’s detection equal to <math display="inline"> <semantics> <mrow> <mn>0.6</mn> </mrow> </semantics> </math> with thermal noise <math display="inline"> <semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>. <math display="inline"> <semantics> <mrow> <msub> <mi>V</mi> <mi>A</mi> </msub> <mo>=</mo> <mspace width="3.33333pt"/> <mn>0.6</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math> (in shot-noise unit).</p> Full article ">Figure 7
<p>(Color online.) The maximum tolerable excess noise at each distance for four-state protocol. <math display="inline"> <semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.9</mn> <mo>,</mo> <msub> <mi>V</mi> <mi>A</mi> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mi>η</mi> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 8
<p>(Color online.) The maximum tolerable excess noise at each distance for eight-state protocol. <math display="inline"> <semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.9</mn> <mo>,</mo> <msub> <mi>V</mi> <mi>A</mi> </msub> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <mi>η</mi> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 9
<p>(Color online.) The compressed variation trend of <math display="inline"> <semantics> <msub> <mi>V</mi> <mi>A</mi> </msub> </semantics> </math> optimal interval of the four-state protocol as the transmission distance extends. <math display="inline"> <semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.9</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.005</mn> <mo>,</mo> <mi>η</mi> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 10
<p>(Color online.) The compressed variation trend of <math display="inline"> <semantics> <msub> <mi>V</mi> <mi>A</mi> </msub> </semantics> </math> optimal interval of the eight-state protocol as the transmission distance extends. <math display="inline"> <semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.9</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.005</mn> <mo>,</mo> <mi>η</mi> <mo>=</mo> <mn>0.6</mn> <mo>,</mo> <msub> <mi>v</mi> <mrow> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 11
<p>(Color online.) The finite-size and the asymptotic secret key rate of the four-state underwater CV-QKD protocol. From left to right in every sub-figure, curves of different colors correspond, respectively, to block lengths of <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>8</mn> </msup> </semantics> </math>, <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>10</mn> </msup> </semantics> </math>, <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>12</mn> </msup> </semantics> </math>, and <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>14</mn> </msup> </semantics> </math>, and the asymptotic curves.</p> Full article ">Figure 11 Cont.
<p>(Color online.) The finite-size and the asymptotic secret key rate of the four-state underwater CV-QKD protocol. From left to right in every sub-figure, curves of different colors correspond, respectively, to block lengths of <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>8</mn> </msup> </semantics> </math>, <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>10</mn> </msup> </semantics> </math>, <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>12</mn> </msup> </semantics> </math>, and <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>14</mn> </msup> </semantics> </math>, and the asymptotic curves.</p> Full article ">Figure 12
<p>(Color online.) The finite-size and the asymptotic secret key rate of the eight-state underwater CV-QKD protocol. From left to right in every sub-figure, curves of different colors correspond, respectively, to block lengths of <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>8</mn> </msup> </semantics> </math>, <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>10</mn> </msup> </semantics> </math>, <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>12</mn> </msup> </semantics> </math>, and <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>14</mn> </msup> </semantics> </math>, and the asymptotic curves.</p> Full article ">
14 pages, 4391 KiB  
Article
Measurement Enhancement on Two-Dimensional Temperature Distribution of Methane-Air Premixed Flame Using SMART Algorithm in CT-TDLAS
by Min-Gyu Jeon, Deog-Hee Doh and Yoshihiro Deguchi
Appl. Sci. 2019, 9(22), 4955; https://doi.org/10.3390/app9224955 - 18 Nov 2019
Cited by 10 | Viewed by 3417
Abstract
In this study, the temperature distribution of the Methane-Air premixed flame was measured. In order to enhance the measurement accuracy of the CT-TDLAS (Computed tomography-tunable diode laser absorption spectroscopy), the SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm has been adopted. Further, the SLOS [...] Read more.
In this study, the temperature distribution of the Methane-Air premixed flame was measured. In order to enhance the measurement accuracy of the CT-TDLAS (Computed tomography-tunable diode laser absorption spectroscopy), the SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm has been adopted. Further, the SLOS (summation of line of sight) and the CSLOS (corrective summation of line of sight) methods have been adopted to increase measurement accuracies. It has been verified that the relative error for the temperatures measured by the thermocouples and calculated by the CT-TDLAS was about 10%. Full article
(This article belongs to the Special Issue Selected Papers from the ICMR 2019)
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Figure 1

Figure 1
<p>Relative intensity of theoretical H<sub>2</sub>O absorption spectra (1388 nm~1388.5 nm).</p> Full article ">Figure 2
<p>Theoretical temperature dependence H<sub>2</sub>O absorption spectra.</p> Full article ">Figure 3
<p>Calculation grids for tomographic reconstructions.</p> Full article ">Figure 4
<p>Calculation process of temperature and concentration at all grids in CT-TDLAS.</p> Full article ">Figure 5
<p>Experimental apparatus for 2D temperature measurement in flame burner using CT-TDLAS.</p> Full article ">Figure 6
<p>16-paths CT-TDLAS measurement cell.</p> Full article ">Figure 7
<p>Comparisons between the temperatures measured by TDLAS and thermocouple.</p> Full article ">Figure 8
<p>MSE (mean square error) comparison for initial temperature (Case 3).</p> Full article ">Figure 9
<p>The spatial resolution test of temperature distribution (Case 3).</p> Full article ">Figure 10
<p>Temperature distribution measured by thermocouple.</p> Full article ">Figure 11
<p>Total MSE variations for the iteration number in case of adopting SMART algorithm.</p> Full article ">Figure 12
<p>Temperature and concentration distributions reconstructed by SMART algorithm.</p> Full article ">Figure 13
<p>Comparisons of temperature distribution at Y = 0mm, −7 mm and −14 mm calculated by the SMART algorithm of CT-TDLAS and measured by the thermocouple.</p> Full article ">
18 pages, 5407 KiB  
Article
Using Unmanned Aerial Vehicle Remote Sensing and a Monitoring Information System to Enhance the Management of Unauthorized Structures
by Yuanrong He, Weiwei Ma, Zelong Ma, Wenjie Fu, Chihcheng Chen, Cheng-Fu Yang and Zhen Liu
Appl. Sci. 2019, 9(22), 4954; https://doi.org/10.3390/app9224954 - 18 Nov 2019
Cited by 9 | Viewed by 4459
Abstract
In this research, we investigated using unmanned aerial vehicle (UAV) photographic technology to prevent the further expansion of unauthorized construction and thereby reduce postdisaster losses. First, UAV dynamic aerial photography was used to obtain dynamic digital surface model (DSM) data and elevation changes [...] Read more.
In this research, we investigated using unmanned aerial vehicle (UAV) photographic technology to prevent the further expansion of unauthorized construction and thereby reduce postdisaster losses. First, UAV dynamic aerial photography was used to obtain dynamic digital surface model (DSM) data and elevation changes of 2–8 m as the initial sieve target. Then, two periods of dynamic orthophoto images were superimposed for human–computer interaction interpretation, so we could quickly distinguish buildings undergoing expansion, new construction, or demolition. At the same time, mobile geographic information system (GIS) software was used to survey the field, and the information gathered was developed to support unauthorized construction detection. Finally, aerial images, interpretation results, and ground survey information were integrated and released on WebGIS to build a regulatory platform that can achieve accurate management and effectively prevent violations. Full article
(This article belongs to the Special Issue Intelligent System Innovation)
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<p>The built detection and management platform. DSM: digital surface model; DOM: digital orthophoto map</p> Full article ">Figure 2
<p>Automatic extraction flow chart of building elevation change detection.</p> Full article ">Figure 3
<p>Identification of (<b>a</b>) unauthorized construction and (<b>b</b>) undiminished unauthorized structures.</p> Full article ">Figure 4
<p>Identification of building height: (<b>a</b>) change distribution map and (<b>b</b>) building height variation reclassification.</p> Full article ">Figure 5
<p>(<b>a</b>) Patches elevation query and (<b>b</b>) patches elevation drawing.</p> Full article ">Figure 6
<p>Example of artificial visual interpretation results.</p> Full article ">Figure 7
<p>Building measurement based on 3D model.</p> Full article ">Figure 8
<p>Functional modules of designed system.</p> Full article ">Figure 9
<p>Hierarchical relationship of the OpenGeo Suite series software.</p> Full article ">Figure 10
<p>Layer modification of (<b>a</b>) the vector layer group and (<b>b</b>) the label layer.</p> Full article ">Figure 11
<p>Image release.</p> Full article ">Figure 12
<p>Vector layer release.</p> Full article ">Figure 13
<p>The physical structure of the system.</p> Full article ">
25 pages, 10458 KiB  
Article
The Effect of Targeted Field Investigation on the Reliability of Earth-Retaining Structures in Active State
by Panagiotis Christodoulou, Lysandros Pantelidis and Elias Gravanis
Appl. Sci. 2019, 9(22), 4953; https://doi.org/10.3390/app9224953 - 18 Nov 2019
Cited by 4 | Viewed by 2744
Abstract
This paper introduces the concept of targeted field investigation on the reliability of earth-retaining structures in an active state, which is implemented in a random finite element method (RFEM) framework. The open source RFEM software REARTH2D was used and modified suitably in order [...] Read more.
This paper introduces the concept of targeted field investigation on the reliability of earth-retaining structures in an active state, which is implemented in a random finite element method (RFEM) framework. The open source RFEM software REARTH2D was used and modified suitably in order to accommodate the purposes of the present research. Soil properties are modeled as random fields, and measurements are modeled by sampling from different points of the field domain. Failure is considered to have occurred when the “actual” resultant earth pressure force on the retaining wall (calculated using the friction angle random field) is greater than the respective “predicted” force (calculated using an homogenous friction angle field characterized by the mean of the values sampled from the respective random field). Two sampling strategies are investigated, namely, sampling from a single point and sampling from a domain, through an extensive parametric analysis. As shown, the statistical uncertainty related to soil properties may be significant and can only be minimized by performing targeted field investigation. Among the main findings is that the optimal sampling location in the active state is immediately adjacent to the wall face. In addition, it is advisable that the entire wall height be considered in sampling. Finally, it was observed that the benefit from a targeted field investigation is much greater as compared to the benefit gained using characteristic values in a Load and Resistance Factor Design framework. Full article
(This article belongs to the Section Civil Engineering)
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Figure 1
<p>Active earth failure of the “reference” wall. Graphical representation of a random field of <math display="inline"><semantics> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </semantics></math> (this is a typical random finite element method (RFEM) realization); light areas correspond to lower friction angles and vice versa. For the soil shown, <math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mi>H</mi> </mrow> <mo>=</mo> <mn>8.3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>O</mi> <msub> <mi>V</mi> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </msub> </mrow> </semantics></math> = 0.3.</p> Full article ">Figure 2
<p>Graphical representation of different sampling scenarios: Scenarios A and B refer to a single sampling point (each located at depth <span class="html-italic">d<sub>p</sub></span>), whilst Scenarios C and D refer to sampling domains (each having length <span class="html-italic">d<sub>d</sub></span>).</p> Full article ">Figure 3
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for various <math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> values for the case of sliding (figure (<b>a</b>,<b>c</b>,<b>e</b>)) and overturning wall (figure (<b>b</b>,<b>d</b>,<b>f</b>)).</p> Full article ">Figure 3 Cont.
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for various <math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> values for the case of sliding (figure (<b>a</b>,<b>c</b>,<b>e</b>)) and overturning wall (figure (<b>b</b>,<b>d</b>,<b>f</b>)).</p> Full article ">Figure 4
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for various <math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> values and <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (smooth wall) for the case of (<b>a</b>) sliding and (<b>b</b>) overturning wall.</p> Full article ">Figure 5
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for various <math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> values and <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (perfectly rough wall) for the case of (<b>a</b>) sliding and (<b>b</b>) overturning wall; please compare with <a href="#applsci-09-04953-f004" class="html-fig">Figure 4</a> (perfectly smooth wall).</p> Full article ">Figure 6
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for different wall heights, <span class="html-italic">H</span>, and <math display="inline"><semantics> <mi>θ</mi> </semantics></math> = 20 m for the case of (<b>a</b>) sliding and (<b>b</b>) overturning wall.</p> Full article ">Figure 7
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example relationships by considering different values of COV of <math display="inline"><semantics> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </semantics></math> and γ; figure (<b>a</b>,<b>b</b>) refer to COV of <math display="inline"><semantics> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </semantics></math>, whilst figure (<b>c</b>,<b>d</b>) refer to COV of γ for the sliding and overturning moments, respectively.</p> Full article ">Figure 7 Cont.
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example relationships by considering different values of COV of <math display="inline"><semantics> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </semantics></math> and γ; figure (<b>a</b>,<b>b</b>) refer to COV of <math display="inline"><semantics> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </semantics></math>, whilst figure (<b>c</b>,<b>d</b>) refer to COV of γ for the sliding and overturning moments, respectively.</p> Full article ">Figure 8
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example relationships for (<b>a</b>) sliding and (<b>b</b>) overturning wall considering different <math display="inline"><semantics> <mrow> <msub> <mi>μ</mi> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </msub> </mrow> </semantics></math> values.</p> Full article ">Figure 9
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for different <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>S</mi> </mrow> </semantics></math> values for the case of (<b>a</b>) sliding and (<b>b</b>) overturning wall.</p> Full article ">Figure 10
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for various <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> values for (<b>a</b>) sliding and (<b>b</b>) overturning wall considering anisotropic soil (to be compared with <a href="#applsci-09-04953-f003" class="html-fig">Figure 3</a>c and Figure <a href="#applsci-09-04953-f003" class="html-fig">3</a>d respectively).</p> Full article ">Figure 11
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>d</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example relationships for different values of scaled correlation length <math display="inline"><semantics> <mrow> <mrow> <mrow> <mo stretchy="false">(</mo> <mi>θ</mi> </mrow> <mo>/</mo> <mi>H</mi> </mrow> <mo stretchy="false">)</mo> </mrow> </semantics></math> and lateral distance from the wall face (<math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math>). Figure (<b>a</b>,<b>c</b>,<b>e</b>) refer to the case of sliding wall whilst figure (<b>b</b>,<b>d</b>,<b>f</b>) to the case of overturning wall.</p> Full article ">Figure 12
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>d</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example relationships for the case of (<b>a</b>) sliding and (<b>b</b>) overturning wall by considering different scaled <math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> values.</p> Full article ">Figure 13
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>d</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for <math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> = 8.3 and <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (rough and smooth wall) for the case of (<b>a</b>) sliding and (<b>b</b>) overturning wall.</p> Full article ">Figure 14
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>d</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for different wall heights <math display="inline"><semantics> <mi>H</mi> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>20</mn> <mtext> </mtext> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> for the case of (<b>a</b>) sliding and (<b>b</b>) overturning wall.</p> Full article ">Figure 15
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>d</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example relationships by considering different values of COV of <math display="inline"><semantics> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </semantics></math> (figure (<b>a</b>,<b>b</b>)) and γ (figure (<b>c</b>,<b>d</b>)); figure (<b>a</b>,<b>c</b>) refer to the case of sliding wall, whilst figure (<b>b</b>,<b>d</b>) refer to the case of overturning wall.</p> Full article ">Figure 16
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>d</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for different <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>S</mi> </mrow> </semantics></math> values for the case of (<b>a</b>) sliding and (<b>b</b>) overturning wall.</p> Full article ">Figure 17
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>d</mi> <mi>d</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> example curves for the case of (<b>a</b>) sliding and (<b>b</b>) overturning wall considering anisotropic soil (<math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>θ</mi> <mi>h</mi> </msub> </mrow> <mo>/</mo> <mrow> <mi>H</mi> <mo>=</mo> <mn>20.8</mn> </mrow> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi>θ</mi> <mi>v</mi> </msub> </mrow> <mo>/</mo> <mrow> <mi>H</mi> <mo>=</mo> <mn>2.08</mn> </mrow> </mrow> </mrow> </semantics></math>) and isotropic soil (<math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mrow> <mi>H</mi> <mo>=</mo> <mn>2.08</mn> </mrow> </mrow> </mrow> </semantics></math>).</p> Full article ">Figure 18
<p>Graphical representation of the random field of <math display="inline"><semantics> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </semantics></math> of Example #2 (<math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> = 4.2; see <a href="#applsci-09-04953-t001" class="html-table">Table 1</a>). Light areas correspond to lower friction angles and vice versa.</p> Full article ">Figure 19
<p>Graphical representation of the random field of <math display="inline"><semantics> <msup> <mi>ϕ</mi> <mo>′</mo> </msup> </semantics></math> of Example #3 (<math display="inline"><semantics> <mrow> <mrow> <mi>θ</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> = 0.42; see <a href="#applsci-09-04953-t001" class="html-table">Table 1</a>). Light areas correspond to lower friction angles and vice versa.</p> Full article ">Figure 20
<p>Example #1: <span class="html-italic">F<sub>predicted</sub></span>/<span class="html-italic">F<sub>“actual”</sub></span> and <span class="html-italic">M<sub>predicted</sub></span>/<span class="html-italic">M<sub>“actual”</sub></span> vs. <span class="html-italic">x</span>/<span class="html-italic">H</span> curves for various <span class="html-italic">d<sub>d</sub></span>/<span class="html-italic">H</span> values and for both the sliding and overturning failure case (see also <a href="#applsci-09-04953-t001" class="html-table">Table 1</a> and <a href="#applsci-09-04953-f001" class="html-fig">Figure 1</a>).</p> Full article ">Figure 21
<p>Example #2: <span class="html-italic">F<sub>predicted</sub></span>/<span class="html-italic">F<sub>“actual”</sub></span> and <span class="html-italic">M<sub>predicted</sub></span>/<span class="html-italic">M<sub>“actual”</sub></span> vs. <span class="html-italic">x</span>/<span class="html-italic">H</span> curves for various <span class="html-italic">d<sub>d</sub></span>/<span class="html-italic">H</span> values and for both the sliding and overturning failure case (see also <a href="#applsci-09-04953-t001" class="html-table">Table 1</a> and <a href="#applsci-09-04953-f018" class="html-fig">Figure 18</a>).</p> Full article ">Figure 22
<p>Example #3: <span class="html-italic">F<sub>predicted</sub></span>/<span class="html-italic">F<sub>“actual”</sub></span> and <span class="html-italic">M<sub>predicted</sub></span>/<span class="html-italic">M<sub>“actual”</sub></span> vs. <span class="html-italic">x</span>/<span class="html-italic">H</span> curves for various <span class="html-italic">d<sub>d</sub></span>/<span class="html-italic">H</span> values and for both the sliding and overturning failure case (see also <a href="#applsci-09-04953-t001" class="html-table">Table 1</a> and <a href="#applsci-09-04953-f019" class="html-fig">Figure 19</a>).</p> Full article ">Figure 23
<p><span class="html-italic">F<sub>predicted</sub></span>/<span class="html-italic">F<sub>“actual”</sub></span> vs. <span class="html-italic">x</span>/<span class="html-italic">H</span> curves using both mean and characteristic values (dashed and solid lines respectively) for <span class="html-italic">FS</span> = 1 and 1.3. Figure referring to the case of a sliding wall and to two sampling domain cases (<span class="html-italic">d<sub>d</sub></span>/<span class="html-italic">H</span> = 1 (figure (<b>a</b>)) and to <span class="html-italic">d<sub>d</sub></span>/<span class="html-italic">H</span> = 0.25 (figure (<b>b</b>))). The reference wall was used. Soil characteristics as shown in <a href="#applsci-09-04953-t001" class="html-table">Table 1</a> (Example #3).</p> Full article ">Figure A1
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus number of realizations charts for different (<b>a</b>) correlation length values (case of isotropic field), (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> values (case of anisotropic field), (<b>c</b>) COV value of <math display="inline"><semantics> <mi>γ</mi> </semantics></math> (isotropic field), (<b>d</b>) COV value of <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> (isotropic field), and (<b>e</b>) wall heights; figures referring to sampling from <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi mathvariant="normal">d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> <mo>=</mo> <mn>0.667</mn> </mrow> </semantics></math>.</p> Full article ">Figure A1 Cont.
<p><math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>f</mi> </msub> </mrow> </semantics></math> versus number of realizations charts for different (<b>a</b>) correlation length values (case of isotropic field), (<b>b</b>) <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> </mrow> </semantics></math> values (case of anisotropic field), (<b>c</b>) COV value of <math display="inline"><semantics> <mi>γ</mi> </semantics></math> (isotropic field), (<b>d</b>) COV value of <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> (isotropic field), and (<b>e</b>) wall heights; figures referring to sampling from <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="normal">x</mi> <mo>/</mo> <mi>H</mi> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mrow> <msub> <mi mathvariant="normal">d</mi> <mi>p</mi> </msub> </mrow> <mo>/</mo> <mi>H</mi> </mrow> <mo>=</mo> <mn>0.667</mn> </mrow> </semantics></math>.</p> Full article ">Figure A2
<p>Effect of element size on the failure probability.</p> Full article ">
14 pages, 8443 KiB  
Article
Construction of Silver Quantum Dot Immobilized Zn-MOF-8 Composite for Electrochemical Sensing of 2,4-Dinitrotoluene
by Sushma Rani, Bharti Sharma, Shivani Kapoor, Rajesh Malhotra, Rajender S. Varma and Neeraj Dilbaghi
Appl. Sci. 2019, 9(22), 4952; https://doi.org/10.3390/app9224952 - 18 Nov 2019
Cited by 20 | Viewed by 4651
Abstract
In the present study, we report a highly effective electrochemical sensor for detecting 2,4-dinitrotoluene (2,4-DNT). The amperometric determination of 2,4-DNT was carried out using a gold electrode modified with zinc–metal organic framework-8 and silver quantum dot (Zn-MOF-8@AgQDs) composite. The synthesized nanomaterials were characterized [...] Read more.
In the present study, we report a highly effective electrochemical sensor for detecting 2,4-dinitrotoluene (2,4-DNT). The amperometric determination of 2,4-DNT was carried out using a gold electrode modified with zinc–metal organic framework-8 and silver quantum dot (Zn-MOF-8@AgQDs) composite. The synthesized nanomaterials were characterized by using transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR) and X-ray powder diffraction (XRD). The synthesized nanocomposite proved to be efficient in electro-catalysis thereby reducing the 2,4-DNT. The unique combination present in Zn-MOF-8@AgQDs composite offered an excellent conductivity and large surface area enabling the fabrication of a highly sensitive (−0.238 µA µM−1 cm−2), selective, rapid and stable 2,4-DNT sensor. The dynamic linear range and limit of detection (LOD) was about 0.0002 µM to 0.9 µM and 0.041 µM, respectively. A 2,4-DNT reduction was also observed during the linear sweep voltammetry (LSV) experiments with reduction peaks at −0.49 V and −0.68 V. This is an unprecedented report with metal organic framework (MOF) composite for sensing 2,4-DNT. In addition, the presence of other species such as thiourea, urea, ammonia, glucose, and ascorbic acid displayed no interference in the modified electrode suggesting its practicability in various environmental applications. Full article
(This article belongs to the Special Issue Recent Developments in Electrochemical Sensors and Biosensors for Applications to Agri-Food, Environmental, and Biomedical Analysis)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>FTIR spectra of (<b>a</b>) Zn–MOF-8, (<b>b</b>) Zn–MOF-8@AgQDs (<b>c</b>) Powder- XRD of Zn–MOF-8 and (<b>d</b>) Zn–MOF-8@AgQDs.</p> Full article ">Figure 2
<p>TGA curves of (<b>a</b>) Zn–MOF-8 and, (<b>b</b>) Zn–MOF-8@Ag.</p> Full article ">Figure 3
<p>TEM image of (<b>a</b>) Ag QDs particles, (<b>b</b>) Zn–MOF-8, (<b>c</b>) Zn–MOF-8@AgQDs.</p> Full article ">Figure 4
<p>EDX analysis of (<b>a</b>) Zn–MOF-8, (<b>b</b>) Zn–MOF-8@AgQDs.</p> Full article ">Figure 5
<p>(<b>a</b>) Nyquist plot of (a) bare and (b) Zn–MOF-8@Ag/AuE in PBS (7.4). (<b>b</b>) CV of bare and modified in PBS containing 0.25 mM of 2,4-DNT at scan rate of 0.005 mV/s. (<b>c</b>) CV sweep curve for the Zn–MOF-8@Ag modified AuE at scan rate range (5 to 500 mV/s) in 0.1 M PBS containing 0.25 mM 2-4-DNT, and (<b>d</b>) Plot between cathodic peak current and square root of scan rate.</p> Full article ">Figure 6
<p>(<b>a</b>) CV sweep curve of the Zn–MOF-8@AgQDs modified gold electrode with subsequent addition of 2-4-DNT and plot between current vs. 2-4-DNT concentrations and, (<b>b</b>) typical amperometric response of Zn–MOF-8@Ag modified Au electrode by successive addition of 2-4-DNT (0.10 mM to 10 mM) in 0.1 M phosphate buffer saline (PBS) at constant potential of 0.05 V, (<b>c</b>) display of the linear relationship between constant current and 2-4-DNT concentration, (<b>d</b>) plot of 1/current vs. 1/concentration represents a linear relationship at constant current in different concentrations of 2-4-DNT.</p> Full article ">Figure 7
<p>The amperometric anti-interference ability of a Zn–MOF-8@AgQDs modified Au electrode by addition of 10 mM 2,4-DNT and 80 mM of each interfering reagent (hydrazine, thiourea, glucose, ascorbic acid, ethanol, urea, ammonium, KI) followed by addition of 10 mM 2,4-DNT in PBS at 0.05 V applied potential.</p> Full article ">Figure 8
<p>(<b>a</b>) LSV of modified Au electrode potential is swept linearly with respect to time and, (<b>b</b>) LSV of modified Au electrode in 0.1 M PBS (pH 7.4) in the presence of DNT and absence of DNT at the slow scan rate (50 mV/s).</p> Full article ">Figure 9
<p>Reaction mechanism involved in the reduction of the aromatic (Ar) nitro group (1) reduction of nitro group into nitroso derivative, (2) conversion of nitroso group into hydroxylamine and (3) finally the conversion of hydroxylamine into aromatic amine.</p> Full article ">Figure 10
<p>Schematic representation of the synthesis of Zn–MOF-8, its composite form, and its fabrication on a gold electrode; CV response depicted for reduction of 2-4-DNT.</p> Full article ">
11 pages, 4875 KiB  
Article
Temperature Analysis of Obstacle Lighting Lamp Working under Various Ambient Conditions: Theoretical and Practical Experiments
by Daria Wotzka, Andrzej Błachowicz and Patryk Weisser
Appl. Sci. 2019, 9(22), 4951; https://doi.org/10.3390/app9224951 - 17 Nov 2019
Viewed by 2190
Abstract
The article presents the results of experimental and theoretical works aimed at determining the distribution of heat emitted by an obstacle lighting lamp. These kind of lamps are commonly applied as a warning for air traffic vehicles. There is a need for lighting [...] Read more.
The article presents the results of experimental and theoretical works aimed at determining the distribution of heat emitted by an obstacle lighting lamp. These kind of lamps are commonly applied as a warning for air traffic vehicles. There is a need for lighting devices with various intensities, whose application depends on the location and operating conditions. The overall aim of the author’s work is to develop a computer model that would enable us to conduct research aimed at determining the optimal parameters of lamp operation without the need to build many physical models. Measurements of heat emitted by a currently manufactured lamp were made, and based on these, a numerical model of the lamp operating under laboratory conditions was developed. The considered lamp has two heat sources, one of which is light-emitting diodes (LEDs), while the other heat source consists of stabilizers and other elements of the lamp power supply system. After positive experimental verification of the numerical model, theoretical analyses of heat emission under various meteorological conditions were carried out, while the values of ambient temperature and airflow velocity were changed; then, the influence of these parameters on the temperature distribution on the surface of the lamp was determined. Full article
(This article belongs to the Section Applied Industrial Technologies)
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Figure 1

Figure 1
<p>A CAD drawing of the computational domain depicting the rectangular tunnel with the lamp and the lamps enlarged view (to the right).</p> Full article ">Figure 2
<p>CAD drawing depicting the applied materials: (<b>a</b>) parts made of aluminum, (<b>b</b>) parts made of acrylic plastic, and the critical boundaries: (<b>c</b>) heat sources, (<b>d</b>) thermal insulation.</p> Full article ">Figure 2 Cont.
<p>CAD drawing depicting the applied materials: (<b>a</b>) parts made of aluminum, (<b>b</b>) parts made of acrylic plastic, and the critical boundaries: (<b>c</b>) heat sources, (<b>d</b>) thermal insulation.</p> Full article ">Figure 3
<p>Examples of images gathered from thermal imaging camera: (<b>a</b>) made after 6 min of operation; (<b>b</b>) made after 7 h of operation of the lamp.</p> Full article ">Figure 4
<p>(<b>a</b>) Example image from simulation result depicting temperature distribution at the surface of the lamp in the steady state; (<b>b</b>) location on the edge of the lamp and at point P2 in the computational domain.</p> Full article ">Figure 5
<p>Temperature values measured (in blue) and calculated using the numerical model (in red) along the edge of the lamp on its surface established after 8 h of operation.</p> Full article ">Figure 6
<p>Results of measurements (in red) and simulation using the numerical model (in blue) gathered in the transient state during the heating process.</p> Full article ">Figure 7
<p>Example simulation results depicting temperature distribution at the surface of the lamp in the steady state for <span class="html-italic">T</span><sub>amb</sub> = 44 °C and wind speed equal to (<b>a</b>) <span class="html-italic">v</span> = 0.3 m/s; (<b>b</b>) <span class="html-italic">v</span> = 3 m/s.</p> Full article ">Figure 8
<p>Results of simulations performed using the numerical model depicting the calculated temperature values in dependence from wind speed (log scale) and ambient temperature (linear scale) values at location point P2 (see <a href="#applsci-09-04951-f004" class="html-fig">Figure 4</a>b).</p> Full article ">Figure 9
<p>Results of simulations performed using the numerical model depicting the calculated temperature values in dependence from wind speed (log scale) along the lamp edge (see <a href="#applsci-09-04951-f004" class="html-fig">Figure 4</a>b) by ambient temperature <span class="html-italic">T</span><sub>amb</sub> = 24 °C.</p> Full article ">
18 pages, 5842 KiB  
Article
Evaluation of the Indoor Air Quality in Governmental Oversight Supermarkets (Co-Ops) in Kuwait
by Azel Almutairi, Abdullah Alsanad and Heba Alhelailah
Appl. Sci. 2019, 9(22), 4950; https://doi.org/10.3390/app9224950 - 17 Nov 2019
Cited by 6 | Viewed by 3115
Abstract
Examining the indoor air environment of public venues, especially populated supermarkets such as Co-Ops in Kuwait, is crucial to ensure that these venues are safe from indoor environmental deficits such as sick building syndrome (SBS). The aim of this study was to characterize [...] Read more.
Examining the indoor air environment of public venues, especially populated supermarkets such as Co-Ops in Kuwait, is crucial to ensure that these venues are safe from indoor environmental deficits such as sick building syndrome (SBS). The aim of this study was to characterize the quality of the indoor air environment of the Co-Ops supermarkets in Kuwait based on investigation of CO2, CO, NO2, H2S, TVOCs, and NMHC. On-site measurements were conducted to evaluate these parameters in three locations at the selected Co-Ops, and the perceived air quality (PAQ) was determined to quantify the air’s pollutants as perceived by humans. Moreover, the indoor air quality index (AQI) was constructed for the selected locations, and the ANOVA test was used to analyze the association between the observed concentrations among these environmental parameters. At least in one spot at each Co-Op, the tested environmental parameters exceeded the threshold limit set by the environmental agencies. The PAQ for Co-Op1, 2, and 3 are 1.25, 1.00, and 0.75 respectively. CO2 was significantly found in an association with CO, H2S, and TVOCs, and its indoor-outdoor concentrations were significantly correlated with R2 values ranges from 0.40 to 0.86 depending on the tested location. Full article
(This article belongs to the Special Issue New Challenges for Indoor Air Quality)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The geographical locations of the Co-Ops under study.</p> Full article ">Figure 2
<p>CO<sub>2</sub> concentration for all Co-Ops, spot S1, S2, and S3: (<b>a</b>) Morning; (<b>b</b>) Noon; (<b>c</b>) Evening.</p> Full article ">Figure 3
<p>CO<sub>2</sub> concentration for Co-Op 1, S1.</p> Full article ">Figure 4
<p>CO concentration for all the Co-Ops, spot S1, S2, and S3 for: (<b>a</b>) morning, (<b>b</b>) noon, and (<b>c</b>) evening.</p> Full article ">Figure 4 Cont.
<p>CO concentration for all the Co-Ops, spot S1, S2, and S3 for: (<b>a</b>) morning, (<b>b</b>) noon, and (<b>c</b>) evening.</p> Full article ">Figure 5
<p>The H<sub>2</sub>S concentration for Co-Ops, spot S1, S2, and S3 for the morning, noon, and evening periods.</p> Full article ">Figure 6
<p>The volatile organic compounds concentration: (<b>a</b>) TVOCs in spot S3; (<b>b</b>) benzene in spot S1; (<b>c</b>) styrene in spot S2.</p> Full article ">Figure 6 Cont.
<p>The volatile organic compounds concentration: (<b>a</b>) TVOCs in spot S3; (<b>b</b>) benzene in spot S1; (<b>c</b>) styrene in spot S2.</p> Full article ">Figure 7
<p>NO<sub>2</sub> concentrations in S1 for several time periods.</p> Full article ">Figure 8
<p>NMHC concentrations for S1 for different time periods.</p> Full article ">Figure 9
<p>Indoor AQI for CO<sub>2</sub>, CO, VOC, and NO<sub>2</sub> for 27 sampling points. (The sampling name format is as follows: the first number indicates the Co-Op number, S is the spot location, M = morning, N = noon, and E = evening; for example, 2S3M = Co-Op 2, Spot 3, morning).</p> Full article ">Figure 10
<p>Correlation of PAQ to the average CO<sub>2</sub> concentration (<b>a</b>) and the average H<sub>2</sub>S concentration (<b>b</b>).</p> Full article ">Figure 11
<p>The outdoor-indoor regression of CO<sub>2</sub> for Co-Op 1: (<b>a</b>) morning; (<b>b</b>) noon; (<b>c</b>) evening.</p> Full article ">Figure 12
<p>The outdoor-indoor regression of CO<sub>2</sub> for Co-Op 2: (<b>a</b>) morning; (<b>b</b>) noon; (<b>c</b>) evening.</p> Full article ">Figure 13
<p>The outdoor-indoor regression of CO<sub>2</sub> for Co-Op 3: (<b>a</b>) morning; (<b>b</b>) noon; (<b>c</b>) evening.</p> Full article ">
16 pages, 8202 KiB  
Article
Physics-Based Vehicle Simulation Using PD Servo
by Daeun Kang, Jinuk Jeong, Seung-wook Ko, Taesoo Kwon and Yejin Kim
Appl. Sci. 2019, 9(22), 4949; https://doi.org/10.3390/app9224949 - 17 Nov 2019
Viewed by 4757
Abstract
In this paper, we introduce a novel system for physics-based vehicle simulation from input trajectory. The proposed system approximates the physical movements of a real vehicle using a proportional derivative (PD) servo which estimates proper torques for wheels and controls a vehicle’s acceleration [...] Read more.
In this paper, we introduce a novel system for physics-based vehicle simulation from input trajectory. The proposed system approximates the physical movements of a real vehicle using a proportional derivative (PD) servo which estimates proper torques for wheels and controls a vehicle’s acceleration based on the conditions of the given trajectory. To avoid expensive simulation calculation, the input trajectory is segmented and compared to the optimized trajectories stored in a path library. Based on the similarity of the curve shape between the input and simulated trajectories, an iterative search method is introduced to generate a physically derivable trajectory for convincing simulation results. For an interaction with other objects in the virtual environment, the surface of the vehicle is subdivided into several parts and deformed individually from external forces. As demonstrated in the experimental results, the proposed system can create diverse traffic scenes with multiple vehicles in a fully automated way. Full article
(This article belongs to the Special Issue Control and Soft Computing)
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Figure 1

Figure 1
<p>Vehicle model: deformable front cover with rotational and translational axes used for wheel movements.</p> Full article ">Figure 2
<p>Overview of vehicle simulation on input trajectory.</p> Full article ">Figure 3
<p>Steering vehicle toward target point.</p> Full article ">Figure 4
<p>Path simulated by a proportional derivative (PD) servo on hypothetical and input trajectories.</p> Full article ">Figure 5
<p>Overview of generation of optimized trajectory using an iterative search method.</p> Full article ">Figure 6
<p>Segmentation of input trajectories (blue) with local extremes and inflection points (red).</p> Full article ">Figure 7
<p>Trajectory optimization process based on similarity of curve shapes between input and simulated segments.</p> Full article ">Figure 8
<p>Trajectory optimization with multi-stages: At each iteration, current segments (green) are newly optimized based on previously optimized segments (yellow), where their shape can be modified depending on difference of vehicle conditions (direction and velocity) at the end points between the input and simulated trajectories.</p> Full article ">Figure 9
<p>Comparison between input (blue) and simulated segments (green) searched in a path library.</p> Full article ">Figure 10
<p>Comparison between translated (gray) and deformed bumper (red).</p> Full article ">Figure 11
<p>Comparison between deformed bumper without translated wheels (<b>left</b>) and with translated wheels (<b>right</b>).</p> Full article ">Figure 12
<p>Vehicle crash scene using a weighted skinning method.</p> Full article ">Figure 13
<p>Traffic scene created with background and multiple vehicle models.</p> Full article ">Figure 14
<p>Traffic scene with two vehicles passing each other.</p> Full article ">Figure 15
<p>Various crash scenes.</p> Full article ">Figure 16
<p>Input trajectory estimated from GPS data extracted from dashboard camera.</p> Full article ">
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