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Volume 11
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Appl. Sci., Volume 11, Issue 22 (November-2 2021) – 574 articles

Cover Story (view full-size image): As populations become concentrated in cities, traffic congestion increases, and urban air mobility (UAM) is being considered to face this problem. Accordingly, many institutions and companies around the world are developing UAM vehicles, building infrastructure, and researching flight operating systems. In this study, three holding area concepts have been designed that can control air traffic flows and avoid bad weather conditions when UAM vehicles are operating. The holding area concepts and the turning procedure of this study can be used as guidelines when designing UAM corridors or UAM flight routes. View this paper
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22 pages, 14647 KiB  
Article
Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning
by Dariusz Mrozek, Rafał Gȯrny, Anna Wachowicz and Bożena Małysiak-Mrozek
Appl. Sci. 2021, 11(22), 11078; https://doi.org/10.3390/app112211078 - 22 Nov 2021
Cited by 12 | Viewed by 3382
Abstract
One of the causes of mortality in bees is varroosis, a bee disease caused by the Varroa destructor mite. Varroa destructor mites may occur suddenly in beehives, spread across them, and impair bee colonies, which finally die. Edge IoT (Internet of Things) devices [...] Read more.
One of the causes of mortality in bees is varroosis, a bee disease caused by the Varroa destructor mite. Varroa destructor mites may occur suddenly in beehives, spread across them, and impair bee colonies, which finally die. Edge IoT (Internet of Things) devices capable of processing video streams in real-time, such as the one we propose, may allow for the monitoring of beehives for the presence of Varroa destructor. Additionally, centralization of monitoring in the Cloud data center enables the prevention of the spread of this disease and reduces bee mortality through monitoring entire apiaries. Although there are various IoT or non-IoT systems for bee-related issues, such comprehensive and technically advanced solutions for beekeeping and Varroa detection barely exist or perform mite detection after sending the data to the data center. The latter, in turn, increases communication and storage needs, which we try to limit in our approach. In the paper, we show an innovative Edge-based IoT solution for Varroa destructor detection. The solution relies on Tensor Processing Unit (TPU) acceleration for machine learning-based models pre-trained in the hybrid Cloud environment for bee identification and Varroa destructor infection detection. Our experiments were performed in order to investigate the effectiveness and the time performance of both steps, and the study of the impact of the image resolution on the quality of detection and classification processes prove that we can effectively detect the presence of varroosis in beehives in real-time with the use of Edge artificial intelligence invoked for the analysis of video streams. Full article
(This article belongs to the Special Issue Cloud Computing, Big Data, and Internet of Things Technologies in Healthcare and Industry (Industry 4.0))
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Overview of the IoT device used for monitoring a beehive.</p> Full article ">Figure 2
<p>A software organisation for the <span class="html-italic">Varroa destructor</span> detection performed on the Edge IoT device.</p> Full article ">Figure 3
<p>The architecture of the Edge- and Cloud-based experimental environment for monitoring bees and detection of varroosis.</p> Full article ">Figure 4
<p>General image analysis employing CNNs with reinforced learning for bee identification and varroosis detection.</p> Full article ">Figure 5
<p>A sample picture extracted from the video data stream with bees marked with rectangles (frames) used for training the model of bee detection.</p> Full article ">Figure 6
<p>Images no. 7 (<b>a</b>) and no. 10 (<b>b</b>).</p> Full article ">Figure 7
<p>Time performance computing steps for bee identification and <span class="html-italic">Varroa destructor</span> detection.</p> Full article ">Figure 8
<p>Time performance for bee identification.</p> Full article ">Figure 9
<p>Time performance for the <span class="html-italic">V. destructor</span> detection.</p> Full article ">Figure 10
<p>Sensitivity achieved for the bee identification process for different camera resolutions.</p> Full article ">Figure 11
<p>F1 score obtained for the bee identification process for different camera resolutions.</p> Full article ">Figure 12
<p>Average processing time of a single bee picture for different resolutions.</p> Full article ">Figure A1
<p>Structure of the convolutional neural network (CNN) used for bee identification.</p> Full article ">Figure A2
<p>Structure of the convolutional neural network (CNN) used for <span class="html-italic">Varroa destructor</span> detection.</p> Full article ">
17 pages, 1864 KiB  
Article
A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
by David Suescum-Morales, Lorenzo Salas-Morera, José Ramón Jiménez and Laura García-Hernández
Appl. Sci. 2021, 11(22), 11077; https://doi.org/10.3390/app112211077 - 22 Nov 2021
Cited by 17 | Viewed by 2865
Abstract
Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it difficult to predict the compressive strength of concrete, which is an obstacle to the incorporation [...] Read more.
Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it difficult to predict the compressive strength of concrete, which is an obstacle to the incorporation of RCA in concrete production. The compressive strength of recycled aggregate concrete is closely related to the dosage of its constituents. This article proposes a novel artificial neural network (ANN) model to predict the 28-day compressive strength of recycled aggregate concrete. The ANN used in this work has 11 neurons in the input layer: the mass of cement, fly ash, water, superplasticizer, fine natural aggregate, coarse natural or recycled aggregate, and their properties, such as: sand fineness modulus of sand, water absorption capacity, saturated surface dry density of the coarse aggregate mix and the maximum particle size. Two training methods were used for the ANN combining 15 and 20 hidden layers: Levenberg–Marquardt (LM) and Bayesian Regularization (BR). A database with 177 mixes selected from 15 studies incorporating RCA were selected, with the aim of having an underlying set of data heterogeneous enough to demonstrate the efficiency of the proposed approach, even when data are heterogeneous and noisy, which is the main finding of this work. Full article
(This article belongs to the Special Issue Human-Computer Interaction for Industrial Applications)
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<p>ANN architecture followed in the tests.</p> Full article ">Figure 2
<p>Correlations between target and output data for the four cases tested. (<b>a</b>): LM-15; (<b>b</b>): LM-20; (<b>c</b>) BR-15; (<b>d</b>): BR-20.</p> Full article ">Figure 2 Cont.
<p>Correlations between target and output data for the four cases tested. (<b>a</b>): LM-15; (<b>b</b>): LM-20; (<b>c</b>) BR-15; (<b>d</b>): BR-20.</p> Full article ">Figure 3
<p>Distribution of errors for the training methods tested. (<b>a</b>): LM-15; (<b>b</b>): LM-20; (<b>c</b>) BR-15; (<b>d</b>): BR-20.</p> Full article ">
24 pages, 1131 KiB  
Article
How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders
by Xabier Cid Vidal, Lorena Dieste Maroñas and Álvaro Dosil Suárez
Appl. Sci. 2021, 11(22), 11076; https://doi.org/10.3390/app112211076 - 22 Nov 2021
Cited by 5 | Viewed by 2691
Abstract
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects [...] Read more.
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects such as the Large Hadron Collider (LHC). The main advantage provided by ML is a reduction in the time and effort required for the measurements carried out by experiments, and improvements in the performance. With this work we aim to encourage scientists working with particle colliders to use ML and to try the different alternatives that are available, focusing on the separation of signal and background. We assess some of the most-used libraries in the field, such as Toolkit for Multivariate Data Analysis with ROOT, and also newer and more sophisticated options such as PyTorch and Keras. We also assess the suitability of some of the most common algorithms for signal-background discrimination, such as Boosted Decision Trees, and propose the use of others, namely Neural Networks. We compare the overall performance of different algorithms and libraries in simulated LHC data and produce some guidelines to help analysts deal with different situations. Examples include the use of low or high-level features from particle detectors or the amount of statistics that are available for training the algorithms. Our main conclusion is that the algorithms and libraries used more frequently at LHC collaborations might not always be those that provide the best results for the classification of signal candidates, and fully connected Neural Networks trained with Keras can improve the performance scores in most of the cases we formulate. Full article
(This article belongs to the Special Issue Machine Learning and Physics)
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<p>Usage of ML at LHCb across the years. We show the number of papers published every year (denoted as “Total number” and corresponding to the right axis), as well as the fraction of them reporting the use of TMVA, Sklearn, Keras, PyTorch and Generic NNs (in the left axis). The latter category corresponds to papers mentioning the use of NNs but never referencing any of the aforementioned libraries.</p> Full article ">Figure 2
<p>Usage of ML at ATLAS across the years. We show the number of papers published every year (denoted as “Total number” and corresponding to the right axis), as well as the fraction of them reporting the use of TMVA, Sklearn, Keras, PyTorch and Generic NNs (in the left axis). The latter category corresponds to papers mentioning the use of NNs but never referencing any of the aforementioned libraries.</p> Full article ">Figure 3
<p>Usage of ML at CMS across the years. We show the number of papers published every year (denoted as “Total number” and corresponding to the right axis), as well as the fraction of them reporting the use of TMVA, Sklearn, Keras, PyTorch and Generic NNs (in the left axis). The latter category corresponds to papers mentioning the use of NNs but never referencing any of the aforementioned libraries.</p> Full article ">Figure 4
<p>Characterization of the signal and background and definition of several variables to discriminate between them. For the <math display="inline"> <semantics> <mrow> <mi>μ</mi> <mi>μ</mi> </mrow> </semantics> </math> background, the usual candidates are formed by muons from different <span class="html-italic">B</span> mesons that are incorrectly matched together.</p> Full article ">Figure 5
<p>ROC Curve for the <math display="inline"> <semantics> <mrow> <mi>B</mi> <mo>→</mo> <msup> <mi>μ</mi> <mo>+</mo> </msup> <msup> <mi>μ</mi> <mo>−</mo> </msup> </mrow> </semantics> </math> decay, corresponding to the high-level features and high stats option for training.</p> Full article ">Figure 6
<p>ROC Curve for the <math display="inline"> <semantics> <mrow> <mi>B</mi> <mo>→</mo> <msup> <mi>μ</mi> <mo>+</mo> </msup> <msup> <mi>μ</mi> <mo>−</mo> </msup> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>B</mi> <mo>→</mo> <msup> <mi>π</mi> <mo>+</mo> </msup> <msup> <mi>π</mi> <mo>−</mo> </msup> </mrow> </semantics> </math> decays and the four different options for training. The options are those listed in <a href="#sec3-applsci-11-11076" class="html-sec">Section 3</a>.</p> Full article ">Figure 7
<p>ROC Curve for the <math display="inline"> <semantics> <mrow> <mi>B</mi> <mo>→</mo> <mn>3</mn> <mi>π</mi> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>B</mi> <mo>→</mo> <mn>4</mn> <mi>π</mi> </mrow> </semantics> </math> decays and the four different options for training. The options are those listed in <a href="#sec3-applsci-11-11076" class="html-sec">Section 3</a>.</p> Full article ">
21 pages, 1693 KiB  
Review
Magnetite-Silica Core/Shell Nanostructures: From Surface Functionalization towards Biomedical Applications—A Review
by Angela Spoială, Cornelia-Ioana Ilie, Luminița Narcisa Crăciun, Denisa Ficai, Anton Ficai and Ecaterina Andronescu
Appl. Sci. 2021, 11(22), 11075; https://doi.org/10.3390/app112211075 - 22 Nov 2021
Cited by 21 | Viewed by 4627
Abstract
The interconnection of nanotechnology and medicine could lead to improved materials, offering a better quality of life and new opportunities for biomedical applications, moving from research to clinical applications. Magnetite nanoparticles are interesting magnetic nanomaterials because of the property-depending methods chosen for their [...] Read more.
The interconnection of nanotechnology and medicine could lead to improved materials, offering a better quality of life and new opportunities for biomedical applications, moving from research to clinical applications. Magnetite nanoparticles are interesting magnetic nanomaterials because of the property-depending methods chosen for their synthesis. Magnetite nanoparticles can be coated with various materials, resulting in “core/shell” magnetic structures with tunable properties. To synthesize promising materials with promising implications for biomedical applications, the researchers functionalized magnetite nanoparticles with silica and, thanks to the presence of silanol groups, the functionality, biocompatibility, and hydrophilicity were improved. This review highlights the most important synthesis methods for silica-coated with magnetite nanoparticles. From the presented methods, the most used was the Stöber method; there are also other syntheses presented in the review, such as co-precipitation, sol-gel, thermal decomposition, and the hydrothermal method. The second part of the review presents the main applications of magnetite-silica core/shell nanostructures. Magnetite-silica core/shell nanostructures have promising biomedical applications in magnetic resonance imaging (MRI) as a contrast agent, hyperthermia, drug delivery systems, and selective cancer therapy but also in developing magnetic micro devices. Full article
(This article belongs to the Special Issue Synthesis and Characterization of Nanoparticles Volume II: Organic Aspects)
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Figure 1
<p>Nanoparticle’s synthesis methods.</p> Full article ">Figure 2
<p>Surface stabilization protocols in developing porous versus non-porous core@shell magnetic nanostructures.</p> Full article ">Figure 3
<p>Graphical representation of a surface functionalization model.</p> Full article ">Figure 4
<p>Graphical representation of biomedical applications.</p> Full article ">
24 pages, 5115 KiB  
Article
Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
by Hyerin Kim and Jungmin So
Appl. Sci. 2021, 11(22), 11074; https://doi.org/10.3390/app112211074 - 22 Nov 2021
Cited by 5 | Viewed by 3093
Abstract
With the density of wireless networks increasing rapidly, one of the major goals in next-generation wireless LANs (Local Area Networks) is to support a very dense network with a large number of closely deployed APs (Access Points) and crowded users. However, the CSMA [...] Read more.
With the density of wireless networks increasing rapidly, one of the major goals in next-generation wireless LANs (Local Area Networks) is to support a very dense network with a large number of closely deployed APs (Access Points) and crowded users. However, the CSMA (Carrier-Sense Multiple Access)-based medium access control of current wireless network systems suffers from significantly degraded performance when the network becomes dense. Recent WLAN (Wireless Local Area Networks) standards include measures for increasing spatial reuse such as BSS (Basic Service Set) coloring, but the schemes based on BSS coloring such as OBSS/PD (Overlapping BSS/Preamble Detection) have limitations in improving spatial reuse. In this paper, we propose a spatial reuse method for uplink which can utilize BSS color and proximity information to improve the efficiency of carrier sensing and thus spatial reuse. Specifically, through the BSS color and the proximity information, a node receiving a preamble can figure out how far the receiver of the ongoing traffic is located. This information is used to determine whether the node should aggressively start transmitting or defer its transmission to protect the ongoing transmission. Simulation results show that the proposed method outperforms existing methods in terms of throughput and fairness. Full article
(This article belongs to the Special Issue Next-Generation Wireless Network Protocol Design)
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<p>CST selection with transmit power control in the OBSS/PD method.</p> Full article ">Figure 2
<p>A hidden terminal scenario. Node B is hidden to node A, and the packet at AP<sub>1</sub> is lost when both node A and B transmit together.</p> Full article ">Figure 3
<p>An exposed terminal scenario. Node A is exposed to node B, so node B unnecessarily defers its packet even both transmissions can take place in parallel.</p> Full article ">Figure 4
<p>A scenario where node B transmits in parallel with node A according to PSR, but node B’s packet is lost due to low SNR at AP<sub>2</sub>.</p> Full article ">Figure 5
<p>A scenario illustrating how a node determines concurrent transmission in the proposed method.</p> Full article ">Figure 6
<p>A flow diagram illustrating the operation of the proposed method.</p> Full article ">Figure 7
<p>The default simulation environment. One-hundred APs are placed in a 100 m × 100 m area, and 100 user stations are randomly deployed inside the area.</p> Full article ">Figure 8
<p>Performance of methods varying number of nodes. (<b>a</b>) Total throughput; (<b>b</b>) Bottom 50% throughput; (<b>c</b>) Bottom 25% throughput; (<b>d</b>) Fairness; (<b>e</b>) Non-starvation Ratio; (<b>f</b>) Delivery Ratio.</p> Full article ">Figure 8 Cont.
<p>Performance of methods varying number of nodes. (<b>a</b>) Total throughput; (<b>b</b>) Bottom 50% throughput; (<b>c</b>) Bottom 25% throughput; (<b>d</b>) Fairness; (<b>e</b>) Non-starvation Ratio; (<b>f</b>) Delivery Ratio.</p> Full article ">Figure 9
<p>Sorted node throughput. Panel (<b>a</b>) is the full graph whereas panel (<b>b</b>) is a partial graph where the y-axis is from 0 to 3 Mbps.</p> Full article ">Figure 10
<p>Performance of methods varying number of APs. (<b>a</b>) Total throughput; (<b>b</b>) Bottom 50% throughput; (<b>c</b>) Bottom 25% throughput; (<b>d</b>) Fairness; (<b>e</b>) Non-starvation Ratio; (<b>f</b>) Delivery Ratio.</p> Full article ">Figure 11
<p>Performance of methods varying area size. (<b>a</b>) Total throughput; (<b>b</b>) Bottom 50% throughput; (<b>c</b>) Bottom 25% throughput; (<b>d</b>) Fairness; (<b>e</b>) Non-starvation Ratio; (<b>f</b>) Delivery Ratio.</p> Full article ">Figure 11 Cont.
<p>Performance of methods varying area size. (<b>a</b>) Total throughput; (<b>b</b>) Bottom 50% throughput; (<b>c</b>) Bottom 25% throughput; (<b>d</b>) Fairness; (<b>e</b>) Non-starvation Ratio; (<b>f</b>) Delivery Ratio.</p> Full article ">Figure 12
<p>Performance of methods varying margin. (<b>a</b>) Total throughput; (<b>b</b>) Bottom 50% throughput; (<b>c</b>) Bottom 25% throughput; (<b>d</b>) Fairness; (<b>e</b>) Non-starvation Ratio; (<b>f</b>) Delivery Ratio.</p> Full article ">Figure 12 Cont.
<p>Performance of methods varying margin. (<b>a</b>) Total throughput; (<b>b</b>) Bottom 50% throughput; (<b>c</b>) Bottom 25% throughput; (<b>d</b>) Fairness; (<b>e</b>) Non-starvation Ratio; (<b>f</b>) Delivery Ratio.</p> Full article ">
15 pages, 1196 KiB  
Article
Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices
by Jisu Kwon and Daejin Park
Appl. Sci. 2021, 11(22), 11073; https://doi.org/10.3390/app112211073 - 22 Nov 2021
Cited by 15 | Viewed by 4421
Abstract
On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing. Although most edge devices have limited resources, time and energy costs are important when running TinyML applications. In this paper, we [...] Read more.
On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing. Although most edge devices have limited resources, time and energy costs are important when running TinyML applications. In this paper, we propose a structure in which the part that preprocesses externally input data in the TinyML application is distributed to the hardware. These processes are performed using software in the microcontroller unit of an edge device. Furthermore, resistor–transistor logic, which perform not only windowing using the Hann function, but also acquire audio raw data, is added to the inter-integrated circuit sound module that collects audio data in the voice-recognition application. As a result of the experiment, the windowing function was excluded from the TinyML application of the embedded board. When the length of the hardware-implemented Hann window is 80 and the quantization degree is 2−5, the exclusion causes a decrease in the execution time of the front-end function and energy consumption by 8.06% and 3.27%, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Services, Applications and Education)
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<p>Proposed architecture overview for a custom hardware preprocessor coupled with a machine learning (ML) application.</p> Full article ">Figure 2
<p>Overview of proposed TinyML application structure and design flow using machine learning (ML) framework at host. (<b>a</b>) hardware and Software partitioning at divided preprocessing stage (<b>b</b>) host machine (<b>c</b>) edge device.</p> Full article ">Figure 3
<p>Audio waveform and spectrogram comparison according to words. (<b>a</b>) “yes” word waveform and spectrogram (<b>b</b>) “no” word waveform and spectrogram.</p> Full article ">Figure 4
<p>Overview of the audio data preprocessing to generate a spectrogram.</p> Full article ">Figure 5
<p>Proposed architecture of the custom I<math display="inline"> <semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics> </math>S module. (<b>a</b>) I<math display="inline"> <semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics> </math>S data path (<b>b</b>) synthesized schematics of I<math display="inline"> <semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics> </math>S implementation.</p> Full article ">Figure 6
<p>Function proportions of the TinyML application on the MCU edge device.</p> Full article ">Figure 7
<p>Synthesis result cell area comparison according to Hann window coefficient length and quantize precision degree. (<b>a</b>) Design Compiler synthesis result according to coefficient length and quantization degreeu (<b>b</b>) FPGA synthesis result with the smallest cell area (<b>c</b>) FPGA synthesis result with the largest cell area.</p> Full article ">
14 pages, 6104 KiB  
Article
Local Modal Frequency Improvement with Optimal Stiffener by Constraints Transformation Method
by Shenyan Chen, Ziqi Dai, Wenjing Shi, Yanjie Liu and Jianhongyu Li
Appl. Sci. 2021, 11(22), 11072; https://doi.org/10.3390/app112211072 - 22 Nov 2021
Viewed by 1521
Abstract
Local modal vibration could adversely affect the dynamical environment, which should be considered in the structural design. For the mode switching phenomena, the traditional structural optimization method for problems with specific order of modal frequency constraints could not be directly applied to solve [...] Read more.
Local modal vibration could adversely affect the dynamical environment, which should be considered in the structural design. For the mode switching phenomena, the traditional structural optimization method for problems with specific order of modal frequency constraints could not be directly applied to solve problems with local frequency constraints. In the present work, a novel approximation technique without mode tracking is proposed. According to the structural character, three reasonable assumptions, unchanged mass matrix, accordant modal shape, and reversible stiffness matrix, have been used to transform the optimization problem with local frequency constraints into a problem with nodal displacement constraints in the local area. The static load case is created with the modal shape equilibrium forces, then the displacement constrained optimization is relatively easily solved to obtain the optimal design, which satisfies the local frequency constraints as well. A numerical example is used to verify the feasibility of the proposed approximation method. Then, the method is further applied in a satellite structure optimization problem. Full article
(This article belongs to the Special Issue Structural Vibration: Analysis, Control, Experiment, and Applications II)
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<p>Optimization flowchart.</p> Full article ">Figure 2
<p>FE model of the plate.</p> Full article ">Figure 3
<p>The first four order mode shape of the initial model.</p> Full article ">Figure 4
<p>FE model of the stiffened plate.</p> Full article ">Figure 5
<p>The first four order mode shape of the model with the reinforced stiffener.</p> Full article ">Figure 6
<p>The displacements result of the static analysis (Max dis = 4.12 m).</p> Full article ">Figure 7
<p>Objective iteration curve.</p> Full article ">Figure 8
<p>Local mode of the optimized model (local frequency = 60.77).</p> Full article ">Figure 9
<p>Static nodal displacement of the optimized model.</p> Full article ">Figure 10
<p>The local modal shape of the satellite.</p> Full article ">Figure 11
<p>The arrangement of the stiffener.</p> Full article ">Figure 12
<p>Displacement results of the satellite.</p> Full article ">Figure 13
<p>The local modal shape of the optimal design (59.677 Hz).</p> Full article ">
12 pages, 25194 KiB  
Article
Thermal Performance of Cryogenic Micro-Pin Fin Coolers with Two-Phase Liquid Nitrogen Flows
by Kyoung Joon Kim, Hyeon Ho Yang, Wooheon Noh, Bongtae Han and Avram Bar-Cohen
Appl. Sci. 2021, 11(22), 11071; https://doi.org/10.3390/app112211071 - 22 Nov 2021
Cited by 1 | Viewed by 2268
Abstract
This study experimentally explores the thermofluidic performance of a cryogenic micro-pin fin cooler with two-phase liquid nitrogen flows. The liquid nitrogen cooling system is introduced to investigate the performance of the micro-pin cooler in a cryogenic condition. The result reveals that the nominal [...] Read more.
This study experimentally explores the thermofluidic performance of a cryogenic micro-pin fin cooler with two-phase liquid nitrogen flows. The liquid nitrogen cooling system is introduced to investigate the performance of the micro-pin cooler in a cryogenic condition. The result reveals that the nominal value of the base heat transfer coefficients of the micro-pin fin cooler with liquid nitrogen flows, 240 kW/m2-K at a mass flow rate of 2.23 g/s, is an order of magnitude greater than that with FC-72 flows. The result also demonstrates that the base heat transfer coefficient of the micro-pin fin cooler is nearly three times greater than that of the micro-gap cooler, not containing any fins. This study shows the feasibility of the cryogenic micro-pin fin cooler for thermally controlling very high heat density devices such as high-power laser diode bars, of which the heat density can reach 2000 kW/m2. Full article
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<p>Structure of the copper micro-pin fin array.</p> Full article ">Figure 2
<p>Cross-section of the cryogenic micro-pin fin cooler.</p> Full article ">Figure 3
<p>Schematic of a test rig to investigate the performance of cryogenic microcoolers with LN<sub>2</sub> flows.</p> Full article ">Figure 4
<p>(<b>a</b>) Micro-pin fin cooler manifold; (<b>b</b>) Assembled LN<sub>2</sub> flow loop apparatus.</p> Full article ">Figure 5
<p>(<b>a</b>) Base heat transfer coefficient and (<b>b</b>) pressure drop of the cryogenic micro-pin fin cooler as a function of exit quality.</p> Full article ">Figure 6
<p>(<b>a</b>) Base heat transfer coefficient and (<b>b</b>) pressure drop of the cryogenic micro-gap cooler as a function of exit quality.</p> Full article ">Figure 7
<p>Base heat transfer coefficient of the cryogenic micro-pin fin and micro-gap coolers as a function of exit quality for various mass fluxes of LN<sub>2</sub> and FC-72.</p> Full article ">Figure 8
<p>Pressure drop of the cryogenic micro-pin fin and micro-gap coolers as a function of exit quality for various mass fluxes of LN<sub>2</sub> and FC-72.</p> Full article ">
19 pages, 14211 KiB  
Article
Methodology of Multicriterial Optimization of Geometric Features of an Orthopedic Implant
by Małgorzata Muzalewska
Appl. Sci. 2021, 11(22), 11070; https://doi.org/10.3390/app112211070 - 22 Nov 2021
Cited by 2 | Viewed by 1764
Abstract
The main purpose of the article is to describe the methodology used for multi-criteria optimization of the geometric features of the orthopedic implant used for the reconstruction of the anterior cruciate ligament located in the knee joint. The methodology includes: 1. Method of [...] Read more.
The main purpose of the article is to describe the methodology used for multi-criteria optimization of the geometric features of the orthopedic implant used for the reconstruction of the anterior cruciate ligament located in the knee joint. The methodology includes: 1. Method of development of the bones of the knee joint model; 2. Method of multi-criteria optimization of the geometric features of the orthopedic implant using an artificial immune system, the objective function and the Pareto front; 3. Expert evaluation method based on forms. The work confirmed that the assumed thesis, a multi-criteria optimization using an artificial immune system, which is a specially defined objective function, and the Pareto method, which allows to determine the geometrical features of the implant, will lead to fulfill optimal blood perfusion and sufficient strength properties of the implant simultaneously. We conclude that the described methodology allowed to achieve the optimal geometrical features of the orthopedic implant used for reconstruction of the anterior cruciate ligament located in the knee joint. Full article
(This article belongs to the Special Issue Smart Manufacturing and Materials)
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<p>Geometrical features of orthopedic implant.</p> Full article ">Figure 2
<p>The method of modeling the femur model.</p> Full article ">Figure 3
<p>Models of the femur and tibia.</p> Full article ">Figure 4
<p>General algorithm of the immune system used.</p> Full article ">Figure 5
<p>Through holes in implant concepts.</p> Full article ">Figure 6
<p>Designation of parameters controlling the form and dimensions of through holes.</p> Full article ">Figure 7
<p>An exemplary individual is saved with four items.</p> Full article ">Figure 8
<p>The process of generating geometrical features of orthopedic implants.</p> Full article ">Figure 9
<p>Defining a finite element mesh.</p> Full article ">Figure 10
<p>Fixation and force application definition.</p> Full article ">Figure 11
<p>Preparation of the model of the fluid volume in the implant.</p> Full article ">Figure 12
<p>Determination of fluid inlet and outlet.</p> Full article ">Figure 13
<p>Optimization using the Pareto function.</p> Full article ">Figure 14
<p>Optimal geometrical characteristics of the implant selected by the immune algorithm using the objective function.</p> Full article ">Figure 15
<p>Pareto-optimal individuals with Pareto front; k1—calculated value of the strength properties criterion; k2—calculated value of the blood perfusion criterion.</p> Full article ">Figure 16
<p>Geometric features of Pareto-optimal individuals.</p> Full article ">Figure 17
<p>Comparison of geometric features obtained with both methods for the FPI and FC implants.</p> Full article ">Figure 18
<p>Applied force and implant fixation.</p> Full article ">Figure 19
<p>Stress distribution in the implant.</p> Full article ">Figure 20
<p>Distribution of deformation in the implant.</p> Full article ">Figure 21
<p>Define a restraint site and apply forces to the femur.</p> Full article ">Figure 22
<p>Stresses in the cortical bone tissue.</p> Full article ">Figure 23
<p>Stress in spongy tissue and implant (scale change).</p> Full article ">
8 pages, 43018 KiB  
Article
Mandibular Reconstruction with Bridging Customized Plate after Ablative Surgery for ONJ: A Multi-Centric Case Series
by Salvatore Battaglia, Francesco Ricotta, Salvatore Crimi, Rosalia Mineo, Fabio Michelon, Achille Tarsitano, Claudio Marchetti and Alberto Bianchi
Appl. Sci. 2021, 11(22), 11069; https://doi.org/10.3390/app112211069 - 22 Nov 2021
Cited by 2 | Viewed by 2100
Abstract
Purpose: Computer-aided methods for mandibular reconstruction have improved both functional and morphological results in patients who underwent segmental mandibular resection. The purpose of this study is to evaluate the overlaying of virtual planning in terms of measures of the Computer Assisted Design/Computer Assisted [...] Read more.
Purpose: Computer-aided methods for mandibular reconstruction have improved both functional and morphological results in patients who underwent segmental mandibular resection. The purpose of this study is to evaluate the overlaying of virtual planning in terms of measures of the Computer Assisted Design/Computer Assisted Manufacturing CAD/CAM plate for mandibular reconstruction in patients who are ineligible for the insertion of reconstructing the titanium plate supported by fibular free flap, due to their poor health status, or in the presence of specific contraindications to autologous bone flap harvest. Materials and methods: The retrospective study performed analyzed the results of nine patients. The patients were treated at the Maxillofacial Surgery Unit of Policlinico S. Orsola of Bologna, Italy, and Policlinico San Marco, Catania, Italy, from April 2016 to June 2021. Superimposition between planning and post operative Computed Tomography CT scan was performed to assess the accuracy. Results: All reconstructive procedures were carried out successfully. No microsurgery-related complications occurred. In two cases, we had plate misplacement, and in one case, plate exposure that led to plate removal. The average accuracy of the series assessed after CT superimposition, as previously described, was 0.95 mm. Conclusions: Considering that microvascular bone transfer is a high-risk procedure in BRONJ patients, we can conclude that the positioning of a customized bridging mandibular prosthesis (CBMP), whether or not it is associated with a microvascular soft tissue transfer, is a safe technique in terms of surgical outcome and feasibility. Full article
(This article belongs to the Special Issue Bioengineering Tools Applied to Medical and Surgical Sciences)
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<p>3D model of the patient.</p> Full article ">Figure 2
<p>Virtual resection planning.</p> Full article ">Figure 3
<p>Virtual reconstruction. A grid is created in the body portion of the plate to anchor the oral pelvi muscles.</p> Full article ">Figure 4
<p>Superimposition of planning and post-operative CT scan obtaining a color map.</p> Full article ">Figure 5
<p>Fixation of the reconstructive plate.</p> Full article ">Figure 6
<p>Superimposition and color map of accuracy. Green color represents best accuracy level. In green is displayed the best accuracy between planning and result, in red the worst accuracy obtained.</p> Full article ">
6 pages, 1978 KiB  
Article
Comparison between Self-Raman Nd:YVO4 Lasers and NdYVO4/KGW Raman Lasers at Lime and Orange Wavelengths
by Chi-Chun Lee, Chien-Yen Huang, Hao-Yun Huang, Chao-Ming Chen and Chia-Han Tsou
Appl. Sci. 2021, 11(22), 11068; https://doi.org/10.3390/app112211068 - 22 Nov 2021
Cited by 6 | Viewed by 2148
Abstract
The comparison of output powers between self-Raman Nd:YVO4 lasers and Nd:YVO4/KGW Raman lasers operating at lime and orange wavelengths is presented. We exploit the LBO crystal with cutting angle θ = 90° and φ = 8° for the lime wavelengths, [...] Read more.
The comparison of output powers between self-Raman Nd:YVO4 lasers and Nd:YVO4/KGW Raman lasers operating at lime and orange wavelengths is presented. We exploit the LBO crystal with cutting angle θ = 90° and φ = 8° for the lime wavelengths, and then we change the angle to θ = 90° and φ = 3.9° for the orange wavelengths. In self-Raman Nd:YVO4 lasers, experimental results reveal that thermal loading can impact on the output performances, especially at the high pump power. However, by using a KGW crystal as Raman medium can remarkably share the thermal loading from gain medium. Besides, the designed coating for high reflectively at the Stokes field on the surface of KGW also improved the beam quality and reduced the lasing threshold. For self-Raman Nd:YVO4 lasers, we have achieved the output powers of 6.54 W and 5.12 W at 559 nm and 588 nm, respectively. For Nd:YVO4/KGW Raman lasers, the output powers at 559 nm and 589 nm have been increased to 9.1 W and 7.54 W, respectively. All lasers operate at a quasi-CW regime with the repetition rate 50 Hz and the duty cycle 50%. Full article
(This article belongs to the Special Issue Optoelectronics for Lasers: Latest Advances and Prospects)
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<p>Experimental setup of the diode-pumped (<b>a</b>) self-Raman Nd:YVO<sub>4</sub> lasers and (<b>b</b>) Nd:YVO<sub>4</sub>/KGW Raman lasers with intracavity SFG and SHG for accomplishing the lime and orange wavelengths.</p> Full article ">Figure 2
<p>(<b>a</b>) The Reflectance spectrum of coating on the facet of KGW crystal toward the Nd:YVO<sub>4</sub> crystal; (<b>b</b>) The other facet of KGW crystal coating highly reflective at visible.</p> Full article ">Figure 3
<p>(<b>a</b>) The output power at 559 nm versus the incident pump power. Optical spectra of 559 nm wavelength and corresponding transverse mode patterns in the (<b>b</b>) self-Raman Nd:YVO<sub>4</sub> laser and (<b>c</b>) Nd:YVO<sub>4</sub>/KGW Raman laser.</p> Full article ">Figure 4
<p>(<b>a</b>) The output power at 588 nm and 589 nm versus the incident pump power. Optical spectra of 588 nm and 589 nm wavelengths and corresponding transverse mode patterns in the (<b>b</b>) self-Raman Nd:YVO<sub>4</sub> laser and (<b>c</b>) Nd:YVO<sub>4</sub>/KGW Raman laser.</p> Full article ">
20 pages, 8740 KiB  
Article
Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
by Hui Sun, Hongguang Jia, Lina Wang, Fang Xu and Jinghong Liu
Appl. Sci. 2021, 11(22), 11067; https://doi.org/10.3390/app112211067 - 22 Nov 2021
Cited by 3 | Viewed by 2291
Abstract
In order to improve the geo-location accuracy of the airborne optoelectronic platform and eliminate the influence of assembly systematic error on the accuracy, a systematic geo-location error correction method is proposed. First, based on the kinematic characteristics of the airborne optoelectronic platform, the [...] Read more.
In order to improve the geo-location accuracy of the airborne optoelectronic platform and eliminate the influence of assembly systematic error on the accuracy, a systematic geo-location error correction method is proposed. First, based on the kinematic characteristics of the airborne optoelectronic platform, the geo-location model was established. Then, the error items that affect the geo-location accuracy were analyzed. The installation error between the platform and the POS was considered, and the installation error of platform’s pitch and azimuth was introduced. After ignoring higher-order infinitesimals, the least square form of systematic error is obtained. Therefore, the systematic error can be obtained through a series of measurements. Both Monte Carlo simulation analysis and in-flight experiment results show that this method can effectively obtain the systematic error. Through correction, the root-mean-square value of the geo-location error have reduced from 45.65 m to 12.62 m, and the mean error from 16.60 m to 1.24 m. This method can be widely used in systematic error correction of relevant photoelectric equipment. Full article
(This article belongs to the Section Aerospace Science and Engineering)
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<p>The geo-location system introduced in this article (The top left figure shows the ground control station and the figure on the bottom left shows the inside of the station; the top right figure shows the UAV with the airborne optoelectronic platform, and the bottom right figure shows the detail of the airborne optoelectronic platform).</p> Full article ">Figure 2
<p>The geo-location process of system.</p> Full article ">Figure 3
<p>The imaging coordinate frame and aircraft coordinate frame.</p> Full article ">Figure 4
<p>The aircraft coordinate frame and navigation coordinate frame.</p> Full article ">Figure 5
<p>The geodetic coordinate frame and the navigation coordinate frame.</p> Full article ">Figure 6
<p>Error items that affect the accuracy of target positioning.</p> Full article ">Figure 7
<p>The iteration curve of the installing errors for simulation data.</p> Full article ">Figure 8
<p>Simulation result of geo-location before and after correction: (<b>a</b>) Latitude error of geo-location before correction; (<b>b</b>) Latitude error of geo-location after correction; (<b>c</b>) Longitude error of geo-location before correction; (<b>d</b>) Longitude error of geo-location after correction; (<b>e</b>) Altitude error of geo-location before correction; (<b>f</b>) Altitude error of geo-location after correction; (<b>g</b>) Box plot of latitude error of geo-location before and after correction; (<b>h</b>) Box plot of longitude error of geo-location before and after correction; (<b>i</b>) Box plot of altitude error of geo-location before and after correction; (<b>j</b>) Error of geo-location before and after correction.</p> Full article ">Figure 8 Cont.
<p>Simulation result of geo-location before and after correction: (<b>a</b>) Latitude error of geo-location before correction; (<b>b</b>) Latitude error of geo-location after correction; (<b>c</b>) Longitude error of geo-location before correction; (<b>d</b>) Longitude error of geo-location after correction; (<b>e</b>) Altitude error of geo-location before correction; (<b>f</b>) Altitude error of geo-location after correction; (<b>g</b>) Box plot of latitude error of geo-location before and after correction; (<b>h</b>) Box plot of longitude error of geo-location before and after correction; (<b>i</b>) Box plot of altitude error of geo-location before and after correction; (<b>j</b>) Error of geo-location before and after correction.</p> Full article ">Figure 9
<p>The flight routes and target points of experiments.</p> Full article ">Figure 10
<p>Target positioning flight experiment: (<b>a</b>) The plane flied on the left side of the target; (<b>b</b>) The plane flew on the right side of the target.</p> Full article ">Figure 11
<p>The iteration curve of the installing errors for flight data.</p> Full article ">Figure 12
<p>The computation results of the installing errors: (<b>a</b>) Box plot of latitude error of geo-location before and after correction; (<b>b</b>) Box plot of longitude error of geo-location before and after correction; (<b>c</b>) Box plot of altitude error of geo-location before and after correction; (<b>d</b>) Error of geo-location before and after correction.</p> Full article ">Figure 13
<p>The computation results of the installing errors.</p> Full article ">Figure 14
<p>The computation results of the installing errors.</p> Full article ">
17 pages, 8649 KiB  
Article
Shape Optimization of Discontinuous Armature Arrangement PMLSM for Reduction of Thrust Ripple
by Jun-Hwan Kwon, Jae-Kyung Kim and Euy-Sik Jeon
Appl. Sci. 2021, 11(22), 11066; https://doi.org/10.3390/app112211066 - 22 Nov 2021
Cited by 1 | Viewed by 1630
Abstract
The aim of this paper is to present the optimal design process and an optimized model for a discontinuous armature arrangement permanent magnet linear synchronous motor (PMLSM). The stator tooth shapes are optimized to reduce detent force. When the shape of the stator [...] Read more.
The aim of this paper is to present the optimal design process and an optimized model for a discontinuous armature arrangement permanent magnet linear synchronous motor (PMLSM). The stator tooth shapes are optimized to reduce detent force. When the shape of the stator is changed to reduce the detent force, the saturation magnetic flux density and the back electromotive force characteristics change. Multi-objective optimization is used to search for the local lowest point that can improve the detent force, saturation magnetic flux density, and back EMF characteristics. To reduce the detent force generated at the outlet edge, a trapezoidal auxiliary tooth was installed and the performance was analyzed. The experiment’s response surface methodology is used as an optimization method and all the experimental samples are obtained from finite-element analysis. The validity of this method is verified by comparing the optimized FEA model to the initial FEA model. Full article
(This article belongs to the Special Issue Electrical Machines for Automotive Applications: Dedicated Design Methods and Their Results, Power Electronics and Control)
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<p>Discontinuous armature arrangement PMLSM: (<b>a</b>) Schematic diagram of PMLSM; (<b>b</b>) Forces exerted in the PMLSM.</p> Full article ">Figure 2
<p>2D finite elements analysis model.</p> Full article ">Figure 3
<p>FEM result of detent force at no load: (<b>a</b>) Detent force caused by end effect; (<b>b</b>) Harmonic components of detent force.</p> Full article ">Figure 4
<p>No-load voltage back EMF: (<b>a</b>) Back EMF of initial model; (<b>b</b>) Harmonic components of back EMF.</p> Full article ">Figure 5
<p>Field distribution and flux density contours of the circuit.</p> Full article ">Figure 6
<p>Optimization process flow chart.</p> Full article ">Figure 7
<p>Pareto chart of the standardized effects: (<b>a</b>) Detent force; (<b>b</b>) Back EMF; (<b>c</b>) Magnetic flux density.</p> Full article ">Figure 8
<p>Main effects plot for objective functions obtained from the fractional factorial design: (<b>a</b>) Detent force; (<b>b</b>) Back EMF; (<b>c</b>) Magnetic flux density.</p> Full article ">Figure 9
<p>Design variables for optimization.</p> Full article ">Figure 10
<p>Main effect plot.</p> Full article ">Figure 11
<p>Response surface of an interaction term. (<b>a</b>) Detent force (<b>b</b>) Magnetic flux density (<b>c</b>) Back EMF THD.</p> Full article ">Figure 12
<p>Detent force by slot effect of initial model and optimized model.</p> Full article ">Figure 13
<p>No-load voltage back EMF: (<b>a</b>) Back EMF of initial model; (<b>b</b>) Harmonic components of back EMF.</p> Full article ">Figure 14
<p>Field distribution and flux density contours of Optimized Circuit.</p> Full article ">Figure 15
<p>End edge auxiliary teeth.</p> Full article ">Figure 16
<p>Pareto chart for end effect detent force.</p> Full article ">Figure 17
<p>Response surface of A<sub>t</sub> (mm) vs. A<sub>e</sub> (mm), Detent force (N).</p> Full article ">Figure 18
<p>Optimization of detent force using response surface methodology.</p> Full article ">Figure 19
<p>FEM results of end effect detent force at no load (<b>a</b>) Detent force of optimized model; (<b>b</b>) Detent force harmonic components of optimized model.</p> Full article ">
19 pages, 1925 KiB  
Article
Design and Control of an Omnidirectional Mobile Wall-Climbing Robot
by Zhengyu Zhong, Ming Xu, Junhao Xiao and Huimin Lu
Appl. Sci. 2021, 11(22), 11065; https://doi.org/10.3390/app112211065 - 22 Nov 2021
Cited by 10 | Viewed by 3549
Abstract
Omnidirectional mobile wall-climbing robots have better motion performance than traditional wall-climbing robots. However, there are still challenges in designing and controlling omnidirectional mobile wall-climbing robots, which can attach to non-ferromagnetic surfaces. In this paper, we design a novel wall-climbing robot, establish the robot’s [...] Read more.
Omnidirectional mobile wall-climbing robots have better motion performance than traditional wall-climbing robots. However, there are still challenges in designing and controlling omnidirectional mobile wall-climbing robots, which can attach to non-ferromagnetic surfaces. In this paper, we design a novel wall-climbing robot, establish the robot’s dynamics model, and propose a nonlinear model predictive control (NMPC)-based trajectory tracking control algorithm. Compared against state-of-the-art, the contribution is threefold: First, the combination of three-wheeled omnidirectional locomotion and non-contact negative pressure air chamber adhesion achieves omnidirectional locomotion on non-ferromagnetic vertical surfaces. Second, the critical slip state has been employed as an acceleration constraint condition, which could improve the maximum linear acceleration and the angular acceleration by 164.71% and 22.07% on average, respectively. Last, an NMPC-based trajectory tracking control algorithm is proposed. According to the simulation experiment results, the tracking accuracy is higher than the traditional PID controller. Full article
(This article belongs to the Topic Motion Planning and Control for Robotics)
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<p>Schematic diagram of the three-wheeled omnidirectional robotic system.</p> Full article ">Figure 2
<p>Schematic diagram of the negative pressure air chamber adhesion.</p> Full article ">Figure 3
<p>Schematic diagram of the robot.</p> Full article ">Figure 4
<p>Schematic diagram of the forces on the wall of the robot system.</p> Full article ">Figure 5
<p>The relation among the acceleration, orientation, and the direction of the acceleration.</p> Full article ">Figure 6
<p>Maximum angular acceleration—orientation curve.</p> Full article ">Figure 7
<p>Schematic diagram of the dual-loop structure.</p> Full article ">Figure 8
<p>Acceleration–time curve of the non-slip state.</p> Full article ">Figure 9
<p>Acceleration–time curve of the critical slip state.</p> Full article ">Figure 10
<p>Acceleration–time curve of the slip state.</p> Full article ">Figure 11
<p>Schematic diagram of the PID controller’s structure.</p> Full article ">Figure 12
<p>The result of PID controller in the simulation experiment. (<b>a</b>) X-directional position–time curve in PID experiment. (<b>b</b>) Y-directional position–time curve in PID experiment. (<b>c</b>) Orientation–time curve in PID experiment. (<b>d</b>) Error of X-directional position–time curve in PID experiment. (<b>e</b>) Error of Y-directional position–time curve in PID experiment. (<b>f</b>) Error of Orientation–time curve in PID experiment.</p> Full article ">Figure 13
<p>The result of NMPC controller in the simulation experiment. (<b>a</b>) X-directional position–time curve in NMPC experiment. (<b>b</b>) Y-directional position–time curve in NMPC experiment. (<b>c</b>) Orientation–time curve in NMPC experiment. (<b>d</b>) Error of X-directional position–time curve in NMPC experiment. (<b>e</b>) Error of Y-directional position–time curve in NMPC experiment. (<b>f</b>) Error of Orientation–time curve in NMPC experiment.</p> Full article ">
21 pages, 787 KiB  
Review
Internet of Things (IoT) Technologies for Managing Indoor Radon Risk Exposure: Applications, Opportunities, and Future Challenges
by Paulo Barros, António Curado and Sérgio Ivan Lopes
Appl. Sci. 2021, 11(22), 11064; https://doi.org/10.3390/app112211064 - 22 Nov 2021
Cited by 10 | Viewed by 3485
Abstract
Radon gas is a harmful pollutant with a well-documented adverse influence on public health. In poorly ventilated environments, that are often prone to significant radon levels, studies indicate a known relationship between human radon exposure and lung cancer. Recent technology advances, notably on [...] Read more.
Radon gas is a harmful pollutant with a well-documented adverse influence on public health. In poorly ventilated environments, that are often prone to significant radon levels, studies indicate a known relationship between human radon exposure and lung cancer. Recent technology advances, notably on the Internet of Things (IoT) ecosystem, allow the integration of sensors, computing, and communication capabilities into low-cost and small-scale devices that can be used for implementing specific cyber-physical systems (CPS) for online and real-time radon management. These technologies are crucial for improving the overall building indoor air quality (IAQ), contributing toward the so-called cognitive buildings, where human-based control is tending to decline, and building management systems (BMS) are focused on balancing critical factors, such as energy efficiency, human radon exposure management, and user experience, to achieve a more transparent and harmonious integration between technology and the built environment. This work surveys recent IoT technologies for indoor radon exposure management (monitoring, assessment and mitigation), and discusses its main challenges and opportunities, by focusing on methods, techniques, and technologies to answer the following questions: (i) What technologies have been recently in use for radon exposure management; (ii) how they operate; (iii) what type of radon detection mechanisms do they use; and (iv) what type of system architectures, components, and communication technologies have been used to assist the referred technologies. This contribution is relevant to pave the way for designing more intelligent and sustainable systems that rely on IoT and Information and Communications Technology (ICT), to achieve an optimal balance between these two critical factors: human radon exposure management and building energy efficiency. Full article
(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 5.0 Applications)
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<p>Flow diagram of the adopted methodology.</p> Full article ">
19 pages, 8112 KiB  
Article
The Effect of Collagen-I Coatings of 3D Printed PCL Scaffolds for Bone Replacement on Three Different Cell Types
by Lucas Weingärtner, Sergio H. Latorre, Dirk Velten, Anke Bernstein, Hagen Schmal and Michael Seidenstuecker
Appl. Sci. 2021, 11(22), 11063; https://doi.org/10.3390/app112211063 - 22 Nov 2021
Cited by 8 | Viewed by 2925
Abstract
Introduction The use of scaffolds in tissue engineering is becoming increasingly important as solutions need to be found to preserve human tissues such as bone or cartilage. Various factors, including cells, biomaterials, cell and tissue culture conditions, play a crucial role in tissue [...] Read more.
Introduction The use of scaffolds in tissue engineering is becoming increasingly important as solutions need to be found to preserve human tissues such as bone or cartilage. Various factors, including cells, biomaterials, cell and tissue culture conditions, play a crucial role in tissue engineering. The in vivo environment of the cells exerts complex stimuli on the cells, thereby directly influencing cell behavior, including proliferation and differentiation. Therefore, to create suitable replacement or regeneration procedures for human tissues, the conditions of the cells’ natural environment should be well mimicked. Therefore, current research is trying to develop 3-dimensional scaffolds (scaffolds) that can elicit appropriate cellular responses and thus help the body regenerate or replace tissues. In this work, scaffolds were printed from the biomaterial polycaprolactone (PCL) on a 3D bioplotter. Biocompatibility testing was used to determine whether the printed scaffolds were suitable for use in tissue engineering. Material and Methods An Envisiontec 3D bioplotter was used to fabricate the scaffolds. For better cell-scaffold interaction, the printed polycaprolactone scaffolds were coated with type-I collagen. Three different cell types were then cultured on the scaffolds and various tests were used to investigate the biocompatibility of the scaffolds. Results Reproducible scaffolds could be printed from polycaprolactone. In addition, a coating process with collagen was developed, which significantly improved the cell-scaffold interaction. Biocompatibility tests showed that the PCL-collagen scaffolds are suitable for use with cells. The cells adhered to the surface of the scaffolds and as a result extensive cell growth was observed on the scaffolds. The inner part of the scaffolds, however, remained largely uninhabited. In the cytotoxicity studies, it was found that toxicity below 20% was present in some experimental runs. The determination of the compressive strength by means of the universal testing machine Z005 by ZWICK according to DIN EN ISO 604 of the scaffolds resulted in a value of 68.49 ± 0.47 MPa. Full article
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<p>Overview of the 3D printed Scaffolds; (<b>a</b>): CAD model; (<b>b</b>): two layers of the 3D printed scaffold, image taken directly while printing from the Envisiontec 3D Bioplotter; (<b>c</b>): 3D Laserscan Image of the scaffolds; (<b>d</b>): 3D reconstruction of the 3D lasaerscanning microscopy data.</p> Full article ">Figure 2
<p>Comparison of surface roughness of (<b>a</b>): uncoated (<b>b</b>): collagen coated PCL scaffolds; images taken with KEYENCE VK-X210 3D Laserscanning microscope.</p> Full article ">Figure 3
<p>ESEM Images of the PCL scaffolds; (<b>a</b>): uncoated PCL; (<b>b</b>): collagen coated PCL (arrow); (<b>c</b>): MG-63 cells (blue arrows) on collagen coated PCL scaffolds; (<b>d</b>): MLO cells (red arrows) on collagen coated PCL scaffolds and (<b>e</b>): MSC (white arrows) on collagen coated PCL scaffolds; the cells were cultivated for 3 days on the scaffolds prior to ESEM measurements; Images taken with ESEM FEI Quanta 250 FEG, Large Field Detector, 20 kV acceleration voltage; 130 Pa; HFW 373 µm (<a href="#applsci-11-11063-f003" class="html-fig">Figure 3</a>a 746 µm).</p> Full article ">Figure 3 Cont.
<p>ESEM Images of the PCL scaffolds; (<b>a</b>): uncoated PCL; (<b>b</b>): collagen coated PCL (arrow); (<b>c</b>): MG-63 cells (blue arrows) on collagen coated PCL scaffolds; (<b>d</b>): MLO cells (red arrows) on collagen coated PCL scaffolds and (<b>e</b>): MSC (white arrows) on collagen coated PCL scaffolds; the cells were cultivated for 3 days on the scaffolds prior to ESEM measurements; Images taken with ESEM FEI Quanta 250 FEG, Large Field Detector, 20 kV acceleration voltage; 130 Pa; HFW 373 µm (<a href="#applsci-11-11063-f003" class="html-fig">Figure 3</a>a 746 µm).</p> Full article ">Figure 4
<p>Fluorescense Images of (<b>a</b>): uncoated PCL scaffolds; (<b>b</b>): collagen-I coated PCL scaffolds; immunoassay with Alexa Fluor 488 (excitation at 495 nm (blue), emission 519 nm (green); Images taken with Olympus BX 53 Fluorescense Microscope, 10x magnification; (<b>c</b>): ESEM image of collagen-I coated scaffold Magnification 6400x (HFW 46.6 µm); (<b>d</b>):enlargement of a specific area of the ESEM image (red square in <a href="#applsci-11-11063-f004" class="html-fig">Figure 4</a>c) of the collagen-I coating 12800x (HFW 23.3 µm), ESEM images taken with FEI Quanta 250 FEG with Large Field Detector, 10 kV acceleration voltage; 130 Pa.</p> Full article ">Figure 5
<p>Comparison of total, living and dead cells of three different cell types (MG-63, MLO-4 and MSC) on the surface of the collagen coated PCL scaffolds at 3 different time points, images taken with Olympus BX-53 flourescense microscope.</p> Full article ">Figure 5 Cont.
<p>Comparison of total, living and dead cells of three different cell types (MG-63, MLO-4 and MSC) on the surface of the collagen coated PCL scaffolds at 3 different time points, images taken with Olympus BX-53 flourescense microscope.</p> Full article ">Figure 6
<p>Cytotoxicity of the collagen-coated PCL scaffolds at defined times for the 3 different cell lines; neg. control = cells only; pos. control = Triton X.</p> Full article ">Figure 7
<p>Overview over the cell proliferation assays for the 3 different cell types; (<b>a</b>): MG-63; (<b>b</b>): MLO-Y4; (<b>c</b>): MSC; C + = Positive control/cells on Thermanox, Scaff = Cells on scaffold; C + R = remaining cells in the control cell culture plate, Scaff + R = Remaining cells in the TCP sample cell culture plate.</p> Full article ">Figure A1
<p>Additional Images: (<b>a</b>): Plasma treatment with Relyon Piezobrush PZ3; (<b>b</b>): fluorescence image of immunoassay on a different position of the scaffold, image taken with fluorescence microscope Olympus BX-53, 100 ms exposure time.</p> Full article ">
38 pages, 15450 KiB  
Article
ATON: An Open-Source Framework for Creating Immersive, Collaborative and Liquid Web-Apps for Cultural Heritage
by Bruno Fanini, Daniele Ferdani, Emanuel Demetrescu, Simone Berto and Enzo d’Annibale
Appl. Sci. 2021, 11(22), 11062; https://doi.org/10.3390/app112211062 - 22 Nov 2021
Cited by 42 | Viewed by 6941
Abstract
The web and its recent advancements represent a great opportunity to build universal, rich, multi-user and immersive Web3D/WebXR applications targeting Cultural Heritage field—including 3D presenters, inspection tools, applied VR games, collaborative teaching tools and much more. Such opportunity although, introduces additional challenges besides [...] Read more.
The web and its recent advancements represent a great opportunity to build universal, rich, multi-user and immersive Web3D/WebXR applications targeting Cultural Heritage field—including 3D presenters, inspection tools, applied VR games, collaborative teaching tools and much more. Such opportunity although, introduces additional challenges besides common issues and limitations typically encountered in this context. The “ideal” Web3D application should be able to reach every device, automatically adapting its interface, rendering and interaction models—resulting in a single, liquid product that can be consumed on mobile devices, PCs, Museum kiosks and immersive AR/VR devices, without any installation required for final users. The open-source ATON framework is the result of research and development activities carried out during the last 5 years through national and international projects: it is designed around modern and robust web standards, open specifications and large open-source ecosystems. This paper describes the framework architecture and its components, assessed and validated through different case studies. ATON offers institutions, researchers, professionals a scalable, flexible and modular solution to craft and deploy liquid web-applications, providing novel and advanced features targeting Cultural Heritage field in terms of 3D presentation, annotation, immersive interaction and real-time collaboration. Full article
(This article belongs to the Special Issue Virtual Reality and Its Application in Cultural Heritage II)
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<p>Top row (from left to right): water bottle sample with PBR materials from Khronos glTF samples, detail of a public domain CC0 model from Malopolska’s Virtual Museums (<a href="https://sketchfab.com/WirtualneMuzeaMalopolski" target="_blank">https://sketchfab.com/WirtualneMuzeaMalopolski</a>, accessed on 22 November 2021) and support for real-time volumetric refraction and absorption (sample glTF model “Dragon” by Khronos). Bottom row: Cesium 3D Tiles specification and NASA AMMOS open-source 3D Tiles renderer.</p> Full article ">Figure 2
<p>Overview of the ATON Framework architecture.</p> Full article ">Figure 3
<p>Deployment hardware (<b>top</b>) and (<b>A</b>–<b>C</b>) three different deployment scenarios (<b>bottom</b>).</p> Full article ">Figure 4
<p>Scenes and Collections.</p> Full article ">Figure 5
<p>Shu back-end. Authentication (<b>A</b>); private scenes gallery (<b>B</b>); web-applications gallery (<b>C</b>); public landing page on standard browser (<b>D</b>) and immersive browser (<b>E</b>) using the Oculus Quest 2.</p> Full article ">Figure 6
<p>Sample JSON patches (add node, update material) sent over time to the server to apply partial modifications to the 3D scene descriptor.</p> Full article ">Figure 7
<p>Device classes to consume ATON content.</p> Full article ">Figure 8
<p>A few captures from interactive 3D scenes in ATON. <b>Top row</b>: real-time shadows and advanced effects (bloom, ambient occlusion); <b>Middle row</b>: multiple light-probes system and PBR materials; <b>Bottom row</b>: depth-of-field effects, real-time volumetric refraction and absorption (Khronos glTF extension) and multi-resolution dataset (Cesium 3D Tiles).</p> Full article ">Figure 9
<p>Different navigation modes. <b>Top row</b>: orbit (left) and first person (right); <b>Middle row</b>: device orientation mode; <b>Bottom row</b>: sample immersive view-aligned query/pointing on 3-DoF devices like cardboards (bottom left) and through 6-DoF VR controllers for locomotion on high-end HMDs (bottom right).</p> Full article ">Figure 10
<p>Sample BVH trees in ATON (green) to accelerate 3D queries.</p> Full article ">Figure 11
<p><b>Top row</b>: basic (spherical) annotations interactively added using current 3D selector location and radius, with multiple shapes under the same semantic node ID (e.g., “eyes”, top right). <b>Bottom row</b>: free-form semantic annotations interactively created at runtime using multiple surface points at different scales.</p> Full article ">Figure 12
<p>A few applications of spatial UI elements. <b>Top row</b> (from left to right): 3D toolbars in the virtual space with custom events, multiple measurements, 3D floating labels. <b>Bottom row</b> (from left to right): immersive VR hands, semantic labels (VR), wrist interfaces and immersive VR measurements.</p> Full article ">Figure 13
<p>A collaborative session with ID “m0nt3b311u” involving multiple remote users, with different devices.</p> Full article ">Figure 14
<p>Sample captures from Hathor front-end. (<b>A</b>) sample scene presentation and basic UI; (<b>B</b>) layer switching; (<b>C</b>) multiple measurements added by the user; (<b>D</b>) HTML5 built-in editor and vocal notes for semantic annotations; (<b>E</b>) user-created rich HTML5 content; (<b>F</b>) semantic shapes export; (<b>G</b>) sharing options; (<b>H</b>) environment and lighting settings; (<b>I</b>) viewpoint options.</p> Full article ">Figure 15
<p>Sample collaborative sessions in Hathor with 3 users (red, yellow and green). (<b>A</b>) User 0 (red, left view) is streaming its focus to other participants, right is yellow user view. (<b>B</b>) User 1 (yellow) is streaming its focus, and changed lighting settings at runtime (red view left, yellow view right). (<b>C</b>,<b>D</b>) All three users perform annotations and measurement tasks at different scales.</p> Full article ">Figure 16
<p>Full Chrysippus 3D model workflow.</p> Full article ">Figure 17
<p>Chrysippus model rendered using a physically, path tracing, based production renderer (Cycles within Blender 2.93) and environment lights used: (<b>A</b>) a spot light similar to the museum exposition and (<b>B</b>) a reconstructed environment (E.Demetrescu) of the Forum of Pacis.</p> Full article ">Figure 18
<p>Interactive visualization in ATON of the same item using two different environments and lighting setups: modern lighting (<b>A</b>,<b>A’</b>) and original context (<b>B</b>,<b>B’</b>).</p> Full article ">Figure 19
<p>A different setup to assess multiple light-probes on 3 instances of the 3D model.</p> Full article ">Figure 20
<p><b>Top row</b>: vocal annotation workflow: free-form shape annotation of the floor decoration, new annotation ID and voice recording. <b>Bottom row</b>: The Aiano 3D scene with audio playback on user activation of annotated areas, from PC and HMD (Oculus Quest) using VR controllers.</p> Full article ">Figure 21
<p>Captures from the collaborative session using a web browser. (<b>A</b>) Participants enter the session and gather as the virtual guide (red) starts the explanation; (<b>B</b>) The guide progressively activates reconstruction layers (semi-transparent volumes) and uses focus streaming to raise attention on specific hotspots; (<b>C</b>) virtual discussion phases; (<b>D</b>) The 3D scene with all layers activated.</p> Full article ">Figure 22
<p>Nora collaborative experiment results.</p> Full article ">Figure 23
<p>(<b>A</b>) AR Presentation of a 3D scene; (<b>B</b>) A PBR asset directly exported from Unreal Engine 4; (<b>C</b>) A 3D scene referencing an external 3D model from Smithsonian CC0 collection; (<b>D</b>) Inline copyright from glTF; (<b>E</b>) Workload distribution among available cores; (<b>F</b>) Instance content statistics</p> Full article ">
38 pages, 5575 KiB  
Article
OntoTouTra: Tourist Traceability Ontology Based on Big Data Analytics
by Juan Francisco Mendoza-Moreno, Luz Santamaria-Granados, Anabel Fraga Vázquez and Gustavo Ramirez-Gonzalez
Appl. Sci. 2021, 11(22), 11061; https://doi.org/10.3390/app112211061 - 22 Nov 2021
Cited by 2 | Viewed by 3014
Abstract
Tourist traceability is the analysis of the set of actions, procedures, and technical measures that allows us to identify and record the space–time causality of the tourist’s touring, from the beginning to the end of the chain of the tourist product. Besides, the [...] Read more.
Tourist traceability is the analysis of the set of actions, procedures, and technical measures that allows us to identify and record the space–time causality of the tourist’s touring, from the beginning to the end of the chain of the tourist product. Besides, the traceability of tourists has implications for infrastructure, transport, products, marketing, the commercial viability of the industry, and the management of the destination’s social, environmental, and cultural impact. To this end, a tourist traceability system requires a knowledge base for processing elements, such as functions, objects, events, and logical connectors among them. A knowledge base provides us with information on the preparation, planning, and implementation or operation stages. In this regard, unifying tourism terminology in a traceability system is a challenge because we need a central repository that promotes standards for tourists and suppliers in forming a formal body of knowledge representation. Some studies are related to the construction of ontologies in tourism, but none focus on tourist traceability systems. For the above, we propose OntoTouTra, an ontology that uses formal specifications to represent knowledge of tourist traceability systems. This paper outlines the development of the OntoTouTra ontology and how we gathered and processed data from ubiquitous computing using Big Data analysis techniques. Full article
(This article belongs to the Special Issue Knowledge Retrieval and Reuse Ⅱ)
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<p>Tourist traceability system: use case.</p> Full article ">Figure 2
<p>Snippet of the image of the upper levels of OntoTouTra (using WebVOWL [<a href="#B45-applsci-11-11061" class="html-bibr">45</a>]).</p> Full article ">Figure 3
<p>OntoTouTra architecture.</p> Full article ">Figure 4
<p>OntoTouTra development model.</p> Full article ">Figure 5
<p>Web scraping class.</p> Full article ">Figure 6
<p>Listing of Data link to GeoNames for obtaining city coordinates.</p> Full article ">Figure 7
<p>Results of data link to GeoNames for obtaining city coordinates.</p> Full article ">Figure 8
<p>Distribution of the scores of the top 10 nationalities of reviewers of Colombia’s tourist reviews dataset obtained from OntoTouTra (language: English).</p> Full article ">Figure 9
<p>An example of transformation rules from the Cities spreadsheet.</p> Full article ">Figure 10
<p>Big Data lifecycle [<a href="#B16-applsci-11-11061" class="html-bibr">16</a>].</p> Full article ">Figure 11
<p>Python code snippet about OTA web scraping.</p> Full article ">Figure 12
<p>Example of ontology visualization: Main tourist destinations in Colombia.</p> Full article ">Figure 13
<p>Example of the visualization of tourist destinations in Colombia from OntoTouTra.</p> Full article ">Figure 14
<p>Application of sentiment analysis techniques to determine the Satisfaction KPI in Colombia.</p> Full article ">Figure 15
<p>Example of satisfaction KPI (Colombia): positive reviews of the destinations. Obtained from OntoTouTra.</p> Full article ">Figure 16
<p>Example of the polarity and subjectivity of the reviews about the Colombian destinations. Obtained from OntoTouTra.</p> Full article ">Figure 17
<p>Architecture diagram for the data pipeline.</p> Full article ">Figure 18
<p>Review data stream: unstructured.</p> Full article ">Figure 19
<p>Rating predictor algorithm.</p> Full article ">Figure 20
<p>Performance of the rating prediction model.</p> Full article ">
22 pages, 1780 KiB  
Article
Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
by Simone Monaco, Salvatore Greco, Alessandro Farasin, Luca Colomba, Daniele Apiletti, Paolo Garza, Tania Cerquitelli and Elena Baralis
Appl. Sci. 2021, 11(22), 11060; https://doi.org/10.3390/app112211060 - 22 Nov 2021
Cited by 13 | Viewed by 3437
Abstract
Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, [...] Read more.
Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem. Full article
(This article belongs to the Special Issue Decision Support Systems and Their Applications)
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<p>Double-Step deep learning Framework architecture.</p> Full article ">Figure 2
<p>Loss function selection experiments. For each configuration (right column) the binary and regression losses are connected with a line of the same color.</p> Full article ">Figure 3
<p>Visual representation of the Spatial Attention block of AttentionUNet.</p> Full article ">Figure 4
<p>Distribution of the severity levels for each fold and globally. Unburnt pixels percentages are not shown and correspond to the complementary of the shown bars.</p> Full article ">Figure 5
<p>Visualization of the last binary attention level compared to the network prediction. The first column is the RGB visualization of the input, the second is the binary ground truth, the third is the attention map (the intensity scale goes from blue to red for lower to higher attention values, respectively), while the last is the prediction. Rows (<b>a</b>) and (<b>b</b>) are two satellite examples from coral and cyan fold, respectively.</p> Full article ">Figure 6
<p>Values distributions for each attention level of the regression backbone. The dashed lines represents the medians of the two distributions.</p> Full article ">Figure 7
<p>Comparison of the binary prediction for each channel group between the normal case and the different occlusion strategies. The <span class="html-italic">normal</span> (blue line) distribution is the same for each row, while the y-scale is different across columns in order to fit the other curves.</p> Full article ">Figure 8
<p>Visualization of the all_occlusion results for the different Multi-bands. Rows (<b>a</b>) and (<b>b</b>) are two satellite examples from coral and cyan fold, respectively.</p> Full article ">Figure 9
<p>Visualization of the all_occlusion results for the different Multi-bands. Yellow contours indicate the severity scale region of the normal prediction. The perturbed prediction is indicated via the colored regions as in the legend.</p> Full article ">
13 pages, 1498 KiB  
Article
Chemical Composition of White Wines Produced from Different Grape Varieties and Wine Regions in Slovakia
by Silvia Jakabová, Martina Fikselová, Andrea Mendelová, Michal Ševčík, Imrich Jakab, Zuzana Aláčová, Jana Kolačkovská and Violeta Ivanova-Petropulos
Appl. Sci. 2021, 11(22), 11059; https://doi.org/10.3390/app112211059 - 22 Nov 2021
Cited by 13 | Viewed by 4699
Abstract
In this work, chemical parameters such as sugar (glucose and fructose) content, organic acid (total acids, malic and tartaric acids), total phenolic content and the antioxidant activity of 12 white wines (chardonnay, pinot blanc and pinot gris) from various wine regions in Slovakia [...] Read more.
In this work, chemical parameters such as sugar (glucose and fructose) content, organic acid (total acids, malic and tartaric acids), total phenolic content and the antioxidant activity of 12 white wines (chardonnay, pinot blanc and pinot gris) from various wine regions in Slovakia were studied in order to identify differences among the varieties and wine-growing regions. The wine samples were examined by Fourier-transform infrared spectroscopy (FTIR) and UV-VIS spectrophotometry (for determination of total polyphenolic content (TPC) and total antioxidant activity (TAA)) methods. Content of alcohol ranged between 11.50% and 13.80% with the mean value 12.52%. Mean content of total acids varied between 4.63 ± 0.09 and 6.63 ± 0.05 g.L−1, tartaric acid varied between 1.62 ± 0.09 and 2.93 ± 0.03 g L−1, malic acid was found in the concentrations ranged from 0.07 ± 0.05 and 2.50 ± 0.08 g L−1 and lactic acid was present between 1.53 and 0.01 g L−1. The content of fructose was, in general, higher in the samples from the Južnoslovenská and Nitrianska wine regions and glucose was higher in the Malokarpatská wine region. Chardonnay wines showed the highest content of total polyphenols and the antioxidant activity in the samples ranged from 51.06 ± 027 to 72.53 ± 0.35% inhibition of DPPH. The PCA analysis based on chemical descriptors distinguished the Nitrianska and Stredoslovenská wine regions. According to similarities among the wine samples, four main classes were formed by cluster analysis. Full article
(This article belongs to the Special Issue Wine Chemistry)
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<p>Location of wine manufacturers.</p> Full article ">Figure 2
<p>PCA bi-plot of wines with chemical parameters grouped by wine regions.</p> Full article ">Figure 3
<p>Estimated marginal means of scaled data with 95% CI.</p> Full article ">
26 pages, 9934 KiB  
Article
Shallow S-Wave Velocity Structure in the Middle-Chelif Basin, Algeria, Using Ambient Vibration Single-Station and Array Measurements
by Abdelouahab Issaadi, Fethi Semmane, Abdelkrim Yelles-Chaouche, Juan José Galiana-Merino and Anis Mazari
Appl. Sci. 2021, 11(22), 11058; https://doi.org/10.3390/app112211058 - 22 Nov 2021
Cited by 4 | Viewed by 2394
Abstract
In order to better assess the seismic hazard in the northern region of Algeria, the shear-wave velocity structure in the Middle-Chelif Basin is estimated using ambient vibration single-station and array measurements. The Middle-Chelif Basin is located in the central part of the Chelif [...] Read more.
In order to better assess the seismic hazard in the northern region of Algeria, the shear-wave velocity structure in the Middle-Chelif Basin is estimated using ambient vibration single-station and array measurements. The Middle-Chelif Basin is located in the central part of the Chelif Basin, the largest of the Neogene sedimentary basins in northern Algeria. This basin hosts the El-Asnam fault, one of the most important active faults in the Mediterranean area. In this seismically active region, most towns and villages are built on large unconsolidated sedimentary covers. Application of the horizontal-to-vertical spectral ratio (HVSR) technique at 164 sites, and frequency–wavenumber (F–K) analysis at 7 other sites, allowed for the estimation of the ground resonance frequencies, shear-wave velocity profiles, and sedimentary cover thicknesses. The electrical resistivity tomography method was used at some sites to further constrain the thickness of the superficial sedimentary layers. The soil resonance frequencies range from 0.75 Hz to 12 Hz and the maximum frequency peak amplitude is 6.2. The structure of the estimated shear-wave velocities is presented in some places as 2D profiles to help interpret the existing faults. The ambient vibration data allowed us to estimate the maximum depth in the Middle-Chelif Basin, which is 760 m near the city of El-Abadia. Full article
(This article belongs to the Special Issue Geohazards: Risk Assessment, Mitigation and Prevention)
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<p>Situation of the Middle-Chelif Basin. LCB: Lower-Chelif Basin. MCB: Middle-Chelif Basin. UCB: Upper-Chelif Basin. OF: Oued-Fodda. AB: El-Abadia. AT: El-Attaf. AM: El-Amra. RO: Rouina. AD: Ain-Defla.</p> Full article ">Figure 2
<p>Geological map of the Middle-Chelif Basin. Modified and compiled from [<a href="#B33-applsci-11-11058" class="html-bibr">33</a>,<a href="#B36-applsci-11-11058" class="html-bibr">36</a>]. The lithological cross-section, AA′, is digitalized from [<a href="#B35-applsci-11-11058" class="html-bibr">35</a>]; BB′, CC′, and DD′ are from [<a href="#B32-applsci-11-11058" class="html-bibr">32</a>].</p> Full article ">Figure 3
<p>Zonation map of the study area. Red dots correspond to single-station measurements. Red polygons correspond to the limits of the different cities.</p> Full article ">Figure 4
<p>Compiled data in Zone 1.</p> Full article ">Figure 5
<p>Compiled data in Zone 2.</p> Full article ">Figure 6
<p>Compiled data in Zone 3.</p> Full article ">Figure 7
<p>Some examples of the calculated HVSR curves at each zone.</p> Full article ">Figure 8
<p>Theoretical wavenumbers obtained at each array recording site.</p> Full article ">Figure 9
<p>(<b>A</b>) Fundamental frequencies obtained from the HVSR analysis. (<b>B</b>) Amplitudes of the HVSR fundamental frequency peaks. Red circles represent the cities. The blue line represents the surface trace of the El-Asnam fault. OF: Oued-Fodda. AB: El-Abadia. AT: El-Attaf. RO: Rouina. AM: El-Amra. AD: Ain-Defla.</p> Full article ">Figure 10
<p>Surface wave dispersion curves.</p> Full article ">Figure 11
<p>Resistivity profiles. The shear-wave velocity models correspond to the HVSR points, P77 and P78. The top left panel represents the resistivity scale for the Middle-Chelif Basin [<a href="#B7-applsci-11-11058" class="html-bibr">7</a>]. The bottom left panel represents the lithological units identified in the resistivity profiles.</p> Full article ">Figure 12
<p>Examples of the inversion results. For each site, the left panels for each site represent the Vs models. The black line corresponds to the best fit model, the dark grey represents models with minimum misfit + 10%. All the tested models are in light grey. In the right panels, we show the computed fundamental mode of the Rayleigh wave ellipticity curve (dark grey curve), and the inverted part of the HVSR curve (black dotted curve). For sites AR1 and AR3, the bottom of the right panel represents the surface wave dispersion curve.</p> Full article ">Figure 13
<p>2D Shear-wave velocity profiles for Zone 1. Qt: Quaternary. Pl: Pliocene. Mi: Miocene. Cr: Cretaceous.</p> Full article ">Figure 14
<p>Shear-wave velocity profiles for Zone 2. Qt: Quaternary. Pl: Pliocene. Mi: Miocene. Cr: Cretaceous. Ju: Jurassic.</p> Full article ">Figure 15
<p>Shear-wave velocity profiles for Zone 3. Qt: Quaternary. Pl: Pliocene. Mi: Miocene. Cr: Cretaceous. Ju: Jurassic.</p> Full article ">Figure 16
<p>Bedrock depths of the Middle-Chelif Basin. Red circles represent the cities. OF: Oued-Fodda. AB: El-Abadia. AT: El-Attaf. RO: Rouina. AM: El-Amra. AD: Ain-Defla.</p> Full article ">
15 pages, 728 KiB  
Article
Effect of Different Essential Oils on the Properties of Edible Coatings Based on Yam (Dioscorea rotundata L.) Starch and Its Application in Strawberry (Fragaria vesca L.) Preservation
by Paula Gómez-Contreras, Kelly J. Figueroa-Lopez, Joaquín Hernández-Fernández, Misael Cortés Rodríguez and Rodrigo Ortega-Toro
Appl. Sci. 2021, 11(22), 11057; https://doi.org/10.3390/app112211057 - 22 Nov 2021
Cited by 25 | Viewed by 3784
Abstract
Every year the world loses about 50% of fruits and vegetables post-harvest and in the supply chain. The use of biodegradable coatings and films with antioxidant properties has been considered an excellent alternative to extend the shelf life of food. Therefore, the objective [...] Read more.
Every year the world loses about 50% of fruits and vegetables post-harvest and in the supply chain. The use of biodegradable coatings and films with antioxidant properties has been considered an excellent alternative to extend the shelf life of food. Therefore, the objective of this work was to develop a coating based on yam (Dioscorea rotundata L.) starch-containing lime, fennel, and lavender essential oils to extend the shelf life of strawberries (Fragaria vesca l.). The tensile properties, barrier properties (water vapour permeability (WVP) and oxygen permeability (OP)), moisture content, water-solubility, absorption capacity, water contact angle, optical properties, the antioxidant activity of the resultant starch-based coatings were evaluated. After that, the active properties of the coatings were assessed on strawberries inoculated with Aspergillus niger during 14 days of storage at 25 °C. The results showed that the incorporation of essential oils improved the elongation and WVP and provided antioxidant capacity and antimicrobial activity in the films. In particular, the essential oil of lime showed higher antioxidant activity. This fact caused the unwanted modification of other properties, such as the decrease in tensile strength, elastic modulus and increase in OP. The present study revealed the potential use of lime, fennel, and lavender essential oils incorporated into a polymeric yam starch matrix to produce biodegradable active films (antioxidant and antimicrobial). Obtained films showed to be a viable alternative to increase the shelf life of strawberries and protect them against Aspergillus niger. Full article
(This article belongs to the Section Food Science and Technology)
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<p>Mean values and standard deviation of the tensile strength (<b>a</b>), elastic modulus (<b>b</b>), and elongation at a breakpoint (<b>c</b>) for the different films stored for one week at 53% RH and 25 °C. The formulations: 1 (without essential oil), 2 (essential oil of lime), 3 (essential oil of fennel), 4 (essential oil of lavender), 5 (essential oil of lime, fennel and lavender).</p> Full article ">Figure 2
<p>Weight loss of strawberries with (●) and without (○) coating (F5 formulation) stored under 85% RH at 25 °C for two weeks.</p> Full article ">
30 pages, 16944 KiB  
Article
Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement
by Ehsan Taheri, Peyman Mehrabi, Shervin Rafiei and Bijan Samali
Appl. Sci. 2021, 11(22), 11056; https://doi.org/10.3390/app112211056 - 22 Nov 2021
Cited by 44 | Viewed by 2266
Abstract
This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The [...] Read more.
This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The finite element method (FEM) was employed to increase the available datasets and evaluate the proposed reinforcement method in different geometrical types of sections. The most influential factors on the axial strength were investigated using a feature-selection (FS) method within a multi-layer perceptron (MLP) algorithm. The MLP algorithm was developed by particle swarm optimization (PSO) and FEM results as input. In terms of accuracy evaluation, some of the rolling criteria including results showed that geometrical parameters have almost the same contribution in compression capacity and displacement of the specimens. According to the performance evaluation indexes, the best model was detected and specified in the paper and optimised by tuning other parameters of the algorithm. As a result, the normalised ultimate load and displacement were predicted successfully. Full article
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<p>The reinforcement system and the constituent elements: (<b>a</b>) graphical section detail and (<b>b</b>) upright column in tests.</p> Full article ">Figure 1 Cont.
<p>The reinforcement system and the constituent elements: (<b>a</b>) graphical section detail and (<b>b</b>) upright column in tests.</p> Full article ">Figure 2
<p>Upright configuration details (dimensions are in millimeters).</p> Full article ">Figure 3
<p>(<b>a</b>) Schematic of compressive test on uprights; (<b>b</b>) testing rig.</p> Full article ">Figure 4
<p>(<b>a</b>) Ball bearing; (<b>b</b>) cap plates.</p> Full article ">Figure 5
<p>Interaction and connection properties of a typical model.</p> Full article ">Figure 6
<p>A typical model with a mesh matrix view on the polygon and circular perforations.</p> Full article ">Figure 7
<p>Designation of models.</p> Full article ">Figure 8
<p>Comparison of the experimental and numerical results of normalised load for (<b>a</b>) 3600L-1.6T, (<b>b</b>)3600L-2.5T.</p> Full article ">Figure 9
<p>Comparison of experimental and numerical results of normalised load for (<b>a</b>) 3000L-1.6T, (<b>b</b>) 3000L-2.5T.</p> Full article ">Figure 10
<p>Comparison of experimental and numerical results of normalised load for (<b>a</b>) 2400L-1.6T, (<b>b</b>) 2400L-2.5T.</p> Full article ">Figure 11
<p>Comparison of experimental and numerical results of normalised load for (<b>a</b>) 1800L-1.6T, (<b>b</b>) 1800L-2.5T.</p> Full article ">Figure 12
<p>Normalised load-displacement diagrams of the FE results for: (<b>a</b>) 3600L-1.6T, (<b>b</b>) 3600L-2.0T, (<b>c</b>) 3600L-2.5T, and (<b>d</b>) 3600L-3.0T models.</p> Full article ">Figure 13
<p>Normalised load-displacement diagrams of the FE results for: (<b>a</b>) 3000L-1.6T, (<b>b</b>) 3000L-2.0T, (<b>c</b>) 3000L-2.5T, and (<b>d</b>) 3000L-3.0T models.</p> Full article ">Figure 14
<p>Normalised load-displacement diagrams of the FE results for: (<b>a</b>) 2400L-1.6T, (<b>b</b>) 2400L-2.0T, (<b>c</b>) 2400L-2.5T, and (<b>d</b>) 2400L-3.0T models.</p> Full article ">Figure 15
<p>Normalised load-displacement diagrams of the FE results for: (<b>a</b>) 1800L-1.6T, (<b>b</b>) 1800L-2.0T, (<b>c</b>) 1800L-2.5T, and (<b>d</b>) 1800L-3.0T models.</p> Full article ">Figure 16
<p>Normalised load-displacement diagrams of the FE results for: (<b>a</b>) 3600L-50B, (<b>b)</b> 3000L-50B, (<b>c</b>) 2400L-50B, and (<b>d</b>) 1800L-50B models.</p> Full article ">Figure 16 Cont.
<p>Normalised load-displacement diagrams of the FE results for: (<b>a</b>) 3600L-50B, (<b>b)</b> 3000L-50B, (<b>c</b>) 2400L-50B, and (<b>d</b>) 1800L-50B models.</p> Full article ">Figure 17
<p>Ultimate load capacities based on thickness and reinforcement spacing for (<b>a</b>) 1800 mm models and (<b>b</b>) 2400 mm models.</p> Full article ">Figure 18
<p>Ultimate load capacities based on thickness and reinforcement spacing for (<b>a</b>) 3000 mm models and (<b>b</b>) 3600 mm models.</p> Full article ">Figure 19
<p>Schematic representation of MLP neuron.</p> Full article ">Figure 20
<p>Flowchart of typical single line hidden layer MLP for identifying a problem.</p> Full article ">Figure 21
<p>PSO sequential flowchart.</p> Full article ">Figure 22
<p>Feature selection technique steps.</p> Full article ">Figure 23
<p>The flowchart of the sequential combination of hybrid MLP-PSO-FS algorithm.</p> Full article ">Figure 24
<p>Regression of the training (above charts) and testing (below charts) phase results with measured values of displacement for (<b>a</b>) one input, (<b>b</b>) two inputs, (<b>c</b>) three inputs, (<b>d</b>) four inputs, (<b>e</b>) five inputs.</p> Full article ">Figure 25
<p>Tolerance diagram of the displacement prediction corresponding to the MPF model with five inputs: (<b>above</b>) training phase, and (<b>below</b>) testing phase.</p> Full article ">Figure 26
<p>Error histograms for displacement prediction by the MPF model with five inputs: (<b>above</b>) training phase, and (<b>below</b>) testing phase.</p> Full article ">Figure 27
<p>Regression of the training (above charts) and testing (below charts) phase results with measured values of normalised load for (<b>a</b>) one input, (<b>b</b>) two inputs, (<b>c</b>) three inputs, (<b>d</b>) four inputs, (<b>e</b>) five inputs.</p> Full article ">Figure 28
<p>The MPF (five inputs) prediction vs experimental diagram for ultimate load: (<b>above</b>) training phase, (<b>below</b>) testing phase.</p> Full article ">Figure 29
<p>The MPF (five inputs) error histograms for ultimate load prediction: (<b>above</b>) training phase and (<b>bellow</b>) testing phase.</p> Full article ">
16 pages, 41858 KiB  
Case Report
Conceptual and Preliminary Design of a Shoe Manufacturing Plant
by Jorge Borrell Méndez, David Cremades, Fernando Nicolas, Carlos Perez-Vidal and Jose Vicente Segura-Heras
Appl. Sci. 2021, 11(22), 11055; https://doi.org/10.3390/app112211055 - 22 Nov 2021
Cited by 1 | Viewed by 6249
Abstract
This article presents a procedure for designing footwear production plants with a Decision Support System combined with an expert system and a simulation approach. The footwear industry has many operations and is labour intensive. Optimisation of plant layout, machinery, and human resources is [...] Read more.
This article presents a procedure for designing footwear production plants with a Decision Support System combined with an expert system and a simulation approach. The footwear industry has many operations and is labour intensive. Optimisation of plant layout, machinery, and human resources is very important to design the footwear manufacturing system, making adequate investment in space and equipment. In the industry it is essential to reduce the process time, so the research is based on a Decision Support System combined with an expert system and simulation to improve the design of the manufacturing plan. This work contains two case studies, direct injection manufacturing and assembly and carburising methods, which are compared to analyse all the necessary resources to have the best cost–benefit ratio. In each case, a precise knowledge of the type and quantity of machinery and human resources is needed to estimate the production. This comparison has been done through simulations and using a knowledge base of an expert system. The conclusions are presented in which an improvement in production time is obtained by applying the methodology developed in the study. Full article
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<p>Steps to produce a pair of shoes. Courtesy of Simplicity Works Europe.</p> Full article ">Figure 2
<p>DESMA rotary machine equipped with two injectors and robotic cells. Courtesy of DESMA Schuh-maschinen GmbH.</p> Full article ">Figure 3
<p>Class diagram of the conceptual reference framework.</p> Full article ">Figure 4
<p>(<b>a</b>) System parameters (with a green frame the parameters that represent agent characteristics are outlined while parameters framed in red represent the main times of the system). This representation is associated with the direct-injection process. (<b>b</b>) Agents denned (agents framed in red are necessary in order to store logical blocks). These agents are associated with the direct-injection process.</p> Full article ">Figure 5
<p>Logical blocks in ‘Rotary’ agent and configuration of the transition between the two phases. These blocks are associated with the direct-injection process.</p> Full article ">Figure 6
<p>Block diagram of the direct-injection process.</p> Full article ">Figure 7
<p>Spatial representation of the direct-injection system.</p> Full article ">Figure 8
<p>Logical blocks of the characteristic operations of the mounting-and-cementing process.</p> Full article ">Figure 9
<p>Spatial representation of the mounting-and-cementing process.</p> Full article ">
22 pages, 7885 KiB  
Article
Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine
by Khaled Yousef Almansi, Abdul Rashid Mohamed Shariff, Ahmad Fikri Abdullah and Sharifah Norkhadijah Syed Ismail
Appl. Sci. 2021, 11(22), 11054; https://doi.org/10.3390/app112211054 - 22 Nov 2021
Cited by 14 | Viewed by 3766
Abstract
Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is [...] Read more.
Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation. Full article
(This article belongs to the Special Issue Remote Sensing and GIS in Environmental Monitoring)
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<p>Neural network (MLP) model.</p> Full article ">Figure 2
<p>Map of the study area showing Palestine (<b>a</b>) and the Gaza Strip (<b>b</b>).</p> Full article ">Figure 3
<p>Overall methodological flowchart.</p> Full article ">Figure 4
<p>Conditioning factors considered for the hospital site suitability; (<b>a</b>) elevation altitude, (<b>b</b>) slope surface, (<b>c</b>) plan curvature, (<b>d</b>) topographic wetness index, (<b>e</b>) topographic roughness index, (<b>f</b>) stream power index, (<b>g</b>) distance from road, (<b>h</b>) distance from river, (<b>i</b>) distance from main road (<b>j</b>) distance from residential, (<b>k</b>) distance from agriculture, (<b>l</b>) distance from refugee camps (<b>m</b>) population size, (<b>n</b>) population density, and (<b>o</b>) no−go zone.</p> Full article ">Figure 4 Cont.
<p>Conditioning factors considered for the hospital site suitability; (<b>a</b>) elevation altitude, (<b>b</b>) slope surface, (<b>c</b>) plan curvature, (<b>d</b>) topographic wetness index, (<b>e</b>) topographic roughness index, (<b>f</b>) stream power index, (<b>g</b>) distance from road, (<b>h</b>) distance from river, (<b>i</b>) distance from main road (<b>j</b>) distance from residential, (<b>k</b>) distance from agriculture, (<b>l</b>) distance from refugee camps (<b>m</b>) population size, (<b>n</b>) population density, and (<b>o</b>) no−go zone.</p> Full article ">Figure 4 Cont.
<p>Conditioning factors considered for the hospital site suitability; (<b>a</b>) elevation altitude, (<b>b</b>) slope surface, (<b>c</b>) plan curvature, (<b>d</b>) topographic wetness index, (<b>e</b>) topographic roughness index, (<b>f</b>) stream power index, (<b>g</b>) distance from road, (<b>h</b>) distance from river, (<b>i</b>) distance from main road (<b>j</b>) distance from residential, (<b>k</b>) distance from agriculture, (<b>l</b>) distance from refugee camps (<b>m</b>) population size, (<b>n</b>) population density, and (<b>o</b>) no−go zone.</p> Full article ">Figure 4 Cont.
<p>Conditioning factors considered for the hospital site suitability; (<b>a</b>) elevation altitude, (<b>b</b>) slope surface, (<b>c</b>) plan curvature, (<b>d</b>) topographic wetness index, (<b>e</b>) topographic roughness index, (<b>f</b>) stream power index, (<b>g</b>) distance from road, (<b>h</b>) distance from river, (<b>i</b>) distance from main road (<b>j</b>) distance from residential, (<b>k</b>) distance from agriculture, (<b>l</b>) distance from refugee camps (<b>m</b>) population size, (<b>n</b>) population density, and (<b>o</b>) no−go zone.</p> Full article ">Figure 5
<p>Comparative plot of the ROC curve for MLP, SVM, and LR.</p> Full article ">Figure 6
<p>Suitability map produced using (<b>a</b>) multilayer perceptron, (<b>b</b>) support ector machine, and (<b>c</b>) linear regression models.</p> Full article ">Figure 6 Cont.
<p>Suitability map produced using (<b>a</b>) multilayer perceptron, (<b>b</b>) support ector machine, and (<b>c</b>) linear regression models.</p> Full article ">
21 pages, 1599 KiB  
Article
Evaluation of HMDs by QFD for Augmented Reality Applications in the Maxillofacial Surgery Domain
by Alessandro Carpinello, Enrico Vezzetti, Guglielmo Ramieri, Sandro Moos, Andrea Novaresio, Emanuele Zavattero and Claudia Borbon
Appl. Sci. 2021, 11(22), 11053; https://doi.org/10.3390/app112211053 - 22 Nov 2021
Cited by 9 | Viewed by 2570
Abstract
Today, surgical operations are less invasive than they were a few decades ago and, in medicine, there is a growing trend towards precision surgery. Among many technological advancements, augmented reality (AR) can be a powerful tool for improving the surgery practice through its [...] Read more.
Today, surgical operations are less invasive than they were a few decades ago and, in medicine, there is a growing trend towards precision surgery. Among many technological advancements, augmented reality (AR) can be a powerful tool for improving the surgery practice through its ability to superimpose the 3D geometrical information of the pre-planned operation over the surgical field as well as medical and instrumental information gathered from operating room equipment. AR is fundamental to reach new standards in maxillofacial surgery. The surgeons will be able to not shift their focus from the patients while looking to the monitors. Osteotomies will not require physical tools to be fixed on patient bones as guides to make resections. Handling grafts and 3D models directly in the operating room will permit a fine tuning of the procedure before harvesting the implant. This article aims to study the application of AR head-mounted displays (HMD) in three operative scenarios (oncological and reconstructive surgery, orthognathic surgery, and maxillofacial trauma surgery) by the means of quantitative logic using the Quality Function Deployment (QFD) tool to determine their requirements. The article provides an evaluation of the readiness degree of HMD currently on market and highlights the lacking features. Full article
(This article belongs to the Topic eHealth and mHealth: Challenges and Prospects)
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<p>The virtuality continuum described by Milgram and Kishino.</p> Full article ">Figure 2
<p>Device specification relative importance for oncological and reconstructive surgery.</p> Full article ">Figure 3
<p>Devices’ evaluation chart for oncological and reconstruction surgery.</p> Full article ">Figure 4
<p>Device specification relative importance for orthognathic surgery.</p> Full article ">Figure 5
<p>Devices’ evaluation chart for orthognathic surgery.</p> Full article ">Figure 6
<p>Device specification relative importance for maxillofacial trauma surgery.</p> Full article ">Figure 7
<p>Device evaluation chart for maxillofacial trauma surgery.</p> Full article ">Figure 8
<p>Specification relative importance comparison for the three analysed scenarios.</p> Full article ">
19 pages, 5625 KiB  
Review
Substrate-Driven Atomic Layer Deposition of High-κ Dielectrics on 2D Materials
by Emanuela Schilirò, Raffaella Lo Nigro, Fabrizio Roccaforte and Filippo Giannazzo
Appl. Sci. 2021, 11(22), 11052; https://doi.org/10.3390/app112211052 - 22 Nov 2021
Cited by 13 | Viewed by 5737
Abstract
Atomic layer deposition (ALD) of high-κ dielectrics on two-dimensional (2D) materials (including graphene and transition metal dichalcogenides) still represents a challenge due to the lack of out-of-plane bonds on the pristine surfaces of 2D materials, thus making the nucleation process highly disadvantaged. The [...] Read more.
Atomic layer deposition (ALD) of high-κ dielectrics on two-dimensional (2D) materials (including graphene and transition metal dichalcogenides) still represents a challenge due to the lack of out-of-plane bonds on the pristine surfaces of 2D materials, thus making the nucleation process highly disadvantaged. The typical methods to promote the nucleation (i.e., the predeposition of seed layers or the surface activation via chemical treatments) certainly improve the ALD growth but can affect, to some extent, the electronic properties of 2D materials and the interface with high-κ dielectrics. Hence, direct ALD on 2D materials without seed and functionalization layers remains highly desirable. In this context, a crucial role can be played by the interaction with the substrate supporting the 2D membrane. In particular, metallic substrates such as copper or gold have been found to enhance the ALD nucleation of Al2O3 and HfO2 both on monolayer (1 L) graphene and MoS2. Similarly, uniform ALD growth of Al2O3 on the surface of 1 L epitaxial graphene (EG) on SiC (0001) has been ascribed to the peculiar EG/SiC interface properties. This review provides a detailed discussion of the substrate-driven ALD growth of high-κ dielectrics on 2D materials, mainly on graphene and MoS2. The nucleation mechanism and the influence of the ALD parameters (namely the ALD temperature and cycle number) on the coverage as well as the structural and electrical properties of the deposited high-κ thin films are described. Finally, the open challenges for applications are discussed. Full article
(This article belongs to the Special Issue Applications of Graphene Family Materials for Environmental Sensing)
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<p>(<b>a</b>) Schematic of a typical cycle in ALD growth. (<b>b</b>) Schematic diagram showing the adsorption and desorption processes of a TMA precursor during the ALD deposition of Al<sub>2</sub>O<sub>3</sub> on the 2D materials [<a href="#B32-applsci-11-11052" class="html-bibr">32</a>]. (<b>c</b>) AFM image of the ALD-Al<sub>2</sub>O<sub>3</sub> layer deposited on a pristine MoS<sub>2</sub> surface [<a href="#B33-applsci-11-11052" class="html-bibr">33</a>].</p> Full article ">Figure 2
<p>AFM images and height line profiles of ALD Al<sub>2</sub>O<sub>3</sub> films deposited on (<b>a</b>–<b>d</b>) MoS<sub>2</sub>, (<b>e</b>–<b>h</b>) WS<sub>2</sub>, and (<b>i</b>–<b>l</b>) WSe<sub>2</sub> multilayer samples at different temperatures (150, 200, and 250 °C). Images adapted with permission from [<a href="#B32-applsci-11-11052" class="html-bibr">32</a>], copyright the Royal Society of Chemistry, 2017.</p> Full article ">Figure 3
<p>(<b>a</b>) Schematic of a top-gated graphene transistor with an ALD Al<sub>2</sub>O<sub>3</sub> gate dielectric. (<b>b</b>) Capacitance voltage (C-V<sub>TG</sub>) measurements performed by sweeping V<sub>TG</sub> from negative to positive values and back using a progressively increasing sweep range. (<b>c</b>) Dirac point position (left axis) as a function of the bias sweep range for the forward and backward sweep and the evaluated density N<sub>ot</sub> of the trapped negative charges by near-interface oxide traps (right axis). Images adapted with permission from [<a href="#B42-applsci-11-11052" class="html-bibr">42</a>], copyright American Chemical Society, 2017.</p> Full article ">Figure 4
<p>SEM images of 10 nm Al<sub>2</sub>O<sub>3</sub> grown by ALD on HOPG (<b>a</b>), on monolayer CVD-graphene transferred on SiO<sub>2</sub> (<b>b</b>), and on monolayer CVD-graphene on the native metal substrate (Cu) (<b>c</b>), and an image of 3 nm Al<sub>2</sub>O<sub>3</sub> on monolayer CVD-graphene on Cu (<b>d</b>). Images adapted with permission from [<a href="#B21-applsci-11-11052" class="html-bibr">21</a>], copyright the American Institute of Physics, 2012.</p> Full article ">Figure 5
<p>(<b>a</b>) The coverage percentage of the Al<sub>2</sub>O<sub>3</sub> grown by ALD on different graphene materials as a function of the growth temperature. (<b>b</b>) Schematic of the precursor (H<sub>2</sub>O) adsorption mechanism in the cases of 1 L graphene/metal and HOPG or 1 L graphene/SiO<sub>2</sub> samples. Water molecules are more efficiently adsorbed on the surface of the 1 L graphene/metal system. Images adapted with permission from [<a href="#B21-applsci-11-11052" class="html-bibr">21</a>], copyright the American Institute of Physics, 2012.</p> Full article ">Figure 6
<p>SEM and AFM images of the graphene/Cu system before (<b>a</b>) and after Al<sub>2</sub>O<sub>3</sub> growth by ALD at 200 °C with 12 (<b>b</b>) and 100 (<b>c</b>) TMA/H<sub>2</sub>O cycles. Images adapted with permission from [<a href="#B53-applsci-11-11052" class="html-bibr">53</a>], copyright the American Chemical Society, 2016.</p> Full article ">Figure 7
<p>AFM morphology of graphene/copper without (<b>a</b>) and with an Al<sub>2</sub>O<sub>3</sub> layer (<b>b</b>). Current maps (<b>c</b>,<b>d</b>) and local I-V curves (<b>e</b>,<b>f</b>) acquired on graphene/copper and Al<sub>2</sub>O<sub>3</sub>/graphene/copper.</p> Full article ">Figure 8
<p>AFM morphology of the as-grown epitaxial graphene surface (<b>a</b>) and after 10 (<b>b</b>), 40 (<b>c</b>), and 80 (<b>d</b>) ALD cycles for Al<sub>2</sub>O<sub>3</sub> deposition, as well as the percentage of the graphene surface covered by the deposited Al<sub>2</sub>O<sub>3</sub> as a function of the number of ALD cycles (<b>e</b>). Schematic representation of the evolution of the deposited Al<sub>2</sub>O<sub>3</sub> on epitaxial graphene with an increasing number of ALD cycles (<b>f</b>). Images adapted with permission from [<a href="#B27-applsci-11-11052" class="html-bibr">27</a>], copyright Elsevier, 2020.</p> Full article ">Figure 9
<p>C-AFM morphology (<b>a</b>) and current map (<b>b</b>) acquired on a scratch of the deposited Al<sub>2</sub>O<sub>3</sub> (40 cycles) on EG. Line scans of the height (<b>c</b>), from which the dielectric thickness of ~2.4 nm has been determined, and of the current (<b>d</b>), showing the insulating properties of the deposited film. Images adapted with permission from [<a href="#B27-applsci-11-11052" class="html-bibr">27</a>], copyright Elsevier, 2020.</p> Full article ">Figure 10
<p>AFM morphologies of Al<sub>2</sub>O<sub>3</sub> deposited by ALD on the 1 L MoS<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub>/Si sample (<b>a</b>) and on the 1 L MoS<sub>2</sub>/Au sample (<b>b</b>). The deposited Al<sub>2</sub>O<sub>3</sub> on 1 L MoS<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub>/Si and 1 L MoS<sub>2</sub>/Au are schematically illustrated in (<b>c</b>,<b>d</b>). Images adapted with permission from [<a href="#B22-applsci-11-11052" class="html-bibr">22</a>], copyright Wiley, 2021.</p> Full article ">Figure 11
<p>Schematic of the C-AFM measurement set-up (<b>a</b>). AFM image of a step between the Al<sub>2</sub>O<sub>3</sub>/1 L MoS<sub>2</sub> stack and the underlying Au substrate, from which a deposited Al<sub>2</sub>O<sub>3</sub> thickness of ~3.6 nm was estimated (<b>b</b>). C-AFM current map acquired with a bias V = 3 V (<b>c</b>). Local I-V characteristics collected at different positions on the Al<sub>2</sub>O<sub>3</sub>/MoS<sub>2</sub>/Au stack, showing the current breakdown at biases between 3.7 and 4.5 V (<b>d</b>). C-AFM current map collected on the Al<sub>2</sub>O<sub>3</sub>/MoS<sub>2</sub>/Au stack at bias values of 3 V (upper region), 4 V (middle region), and 3 V (bottom region). A significant increase in the current leakage with the appearance of localized breakdown spots is observed at 4 V (<b>e</b>). Images adapted with permission from [<a href="#B22-applsci-11-11052" class="html-bibr">22</a>], copyright Wiley, 2021.</p> Full article ">Figure 12
<p>Schematic representation of the transfer process from copper to the required substrate of the dielectric/graphene stack. Image adapted with permission from [<a href="#B75-applsci-11-11052" class="html-bibr">75</a>], copyright IOP, 2017.</p> Full article ">Figure 13
<p>Electrical characteristics (I<sub>D</sub> -V<sub>G</sub>) of back-gated field effect transistors fabricated using (i) 1 L graphene without Al<sub>2</sub>O<sub>3</sub> on top (no Al<sub>2</sub>O<sub>3</sub>), (ii) 1 L graphene with Al<sub>2</sub>O<sub>3</sub> deposited after being transferred onto SiO<sub>2</sub> (Al<sub>2</sub>O<sub>3</sub> ALD after Gr transfer), and (iii) 1 L graphene with Al<sub>2</sub>O<sub>3</sub> deposited before the transfer (Al<sub>2</sub>O<sub>3</sub> ALD before Gr transfer). Image adapted with permission from [<a href="#B75-applsci-11-11052" class="html-bibr">75</a>], copyright IOP, 2017.</p> Full article ">
19 pages, 10365 KiB  
Article
Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine
by Taeyoung Yoo, Seongjae Lee and Taehyoun Kim
Appl. Sci. 2021, 11(22), 11051; https://doi.org/10.3390/app112211051 - 22 Nov 2021
Cited by 6 | Viewed by 6114
Abstract
A reverse vending machine motivates citizens to bring recyclable waste by rewarding them, which is a viable solution to increase the recycling rate. Reverse vending machines generally use near-infrared sensors, barcode sensors, or cameras to classify recycling resources. However, sensor-based reverse vending machines [...] Read more.
A reverse vending machine motivates citizens to bring recyclable waste by rewarding them, which is a viable solution to increase the recycling rate. Reverse vending machines generally use near-infrared sensors, barcode sensors, or cameras to classify recycling resources. However, sensor-based reverse vending machines suffer from a high configuration cost and the limited scope of target objects, and conventional single image-based reverse vending machines usually make erroneous predictions about intentional fraud objects. This paper proposes a dual image-based convolutional neural network ensemble model to address these problems. For this purpose, we first created a prototype reverse vending machine and constructed an image dataset containing two cross-sections of objects, top and front view. Then, we chose convolutional neural network models widely used in image classification as the candidates for building an accurate and lightweight ensemble model. Considering the size and classification performance of candidates, we constructed the best-fit ensemble combination and evaluated its classification performance. The final ensemble model showed a classification accuracy higher than 95% for all target classes, including fraud objects. This result proves that our approach achieves better robustness against intentional fraud objects than single image-based models and thus can broaden the scope for target resources. The measurement results on lightweight embedded platforms also demonstrated that our model provides a short inference time that is enough to facilitate the real-time execution of reverse vending machines based on low-cost edge artificial intelligence devices. Full article
(This article belongs to the Special Issue Smart Cities in Applied Sciences)
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<p>Workflow of choosing optimized classifiers and framework of the proposed CNN ensemble model.</p> Full article ">Figure 2
<p>Hardware setup for constructing dataset: (<b>a</b>) Prototype RVM; (<b>b</b>) Settings for capturing dual image.</p> Full article ">Figure 3
<p>Dataset and its composition.</p> Full article ">Figure 4
<p>Actual images from the dataset: (<b>a</b>) Top view of PET and Can classes; (<b>b</b>) Top view of Non-target class with printed images of PET and can, PET label-only object, and IAO such as paper cup; (<b>c</b>) Front view of PET and Can classes; (<b>d</b>) Front view of Non-target class with printed images of PET and can, and IAO such as whiteboard eraser.</p> Full article ">Figure 5
<p>Building blocks of ResNet and DenseNet: (<b>a</b>) Bottleneck building block for ResNet 50/101/152 where the 1 × 1 layers reduce and increase dimensions, and 3 × 3 layer extracts features; (<b>b</b>) Simple dense block of DenseNet. The multiple dense blocks are connected with the transition layers and compose the DenseNet.</p> Full article ">Figure 6
<p>Building blocks of SqueezeNet and MobileNetV2: (<b>a</b>) Fire module of SqueezeNet with three 1 × 1 convolutional filters in the squeeze layer, four 1 × 1 convolutional filters in the expand layer, and four 3 × 3 convolutional filters in the expand layer; (<b>b</b>) Bottleneck depthwise separable convolution with residuals, which is a basic building block of MobileNetV2. This figure represents the block where <math display="inline"><semantics> <mrow> <mi>s</mi> <mi>t</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> <mi>e</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Simple scheme of stacking ensemble learning procedure.</p> Full article ">Figure 8
<p>Framework of the proposed CNN ensemble model.</p> Full article ">Figure 9
<p>Grad-CAM visualization results using MobileNetV2 for target objects: (<b>a</b>) Properly classified objects; (<b>b</b>) Misclassified objects. The predicted class and its probability are shown below each Grad-CAM figure.</p> Full article ">Figure 10
<p>Front view of five different aluminum cans.</p> Full article ">Figure 11
<p>Kernel density estimation for prediction result of fraud objects: (<b>a</b>) MobileNetV2 trained only with top view images; (<b>b</b>) MobileNetV2 trained only with front view images; (<b>c</b>) The proposed CNN ensemble model.</p> Full article ">
20 pages, 5715 KiB  
Article
An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors
by Hye-Jin Lee, Sun-Young Ihm, So-Hyun Park and Young-Ho Park
Appl. Sci. 2021, 11(22), 11050; https://doi.org/10.3390/app112211050 - 22 Nov 2021
Cited by 1 | Viewed by 2256
Abstract
Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of [...] Read more.
Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves violently. A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. This paper introduces the YouTube-C7B dataset consisting of dog behaviors in various environments. Based on a method that visualizes the Conv-layer filters in analyzable feature maps, we add reliability to the results derived by the model. We can identify the joint parts, i.e., those represented as rows of input images showing behaviors, learned by the proposed model mainly for making decisions. Finally, the performance of the proposed method is compared to those of the LSTM, GRU, and RNN models. The experimental results demonstrate that the proposed TRT-Net method classifies dog behaviors more effectively, with improved accuracy and F1 scores of 7.9% and 7.3% over conventional models. Full article
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<p>Three typical multi-modality fusion strategies. (<b>a</b>) Simple concatenation [<a href="#B11-applsci-11-11050" class="html-bibr">11</a>], (<b>b</b>) late fusion [<a href="#B12-applsci-11-11050" class="html-bibr">12</a>,<a href="#B13-applsci-11-11050" class="html-bibr">13</a>], (<b>c</b>) AV-TFN [<a href="#B14-applsci-11-11050" class="html-bibr">14</a>], (<b>d</b>) MTLN [<a href="#B16-applsci-11-11050" class="html-bibr">16</a>], and (<b>e</b>) the proposed method (TRT-Net).</p> Full article ">Figure 2
<p>Overall process of TRT-Net.</p> Full article ">Figure 3
<p>Some samples from the Youtube-C7B dataset. (<b>a</b>) Siberian Husky, (<b>b</b>) Retriever and (<b>c</b>) French Bulldog.</p> Full article ">Figure 4
<p>Configuration of the companion dog parts.</p> Full article ">Figure 5
<p>Histogram-based backtracking algorithm through RGB color representation: (<b>a</b>) Structures of CNN Networks(step3 of <a href="#applsci-11-11050-f002" class="html-fig">Figure 2</a>), (<b>b</b>) 32 feature maps in each layer, (<b>c</b>) Generated images through a color quantization method using K-means clustering, (<b>d</b>) The values of the pixels corresponding to the extracted positions are mapped to the respective RGB channels and the skeleton matrix, (<b>e</b>) The histogram according to the joint frequency is produced based on the extracted joint names and numbers and (<b>f</b>) Joints that influence the model’s decision on the behavioral image are extracted.</p> Full article ">Figure 6
<p>Structure of compared models: (<b>a</b>) LSTM, (<b>b</b>) GRU, (<b>c</b>) RNN, and (<b>d</b>) TRT-Net (our approach).</p> Full article ">Figure 7
<p>Expressing the color location and value according to each color model. (<b>a</b>) RGB color model, (<b>b</b>) HSV color model and (<b>c</b>) HSL color model.</p> Full article ">Figure 8
<p>Graphs showing (<b>a</b>) F1 score, (<b>b</b>) accuracy, and (<b>c</b>) error rate of four models (LSTM, GRU, RNN, TRT-Net (our)).</p> Full article ">Figure 9
<p>(<b>a</b>) Yao et al. [<a href="#B7-applsci-11-11050" class="html-bibr">7</a>], 14 skeleton joints; (<b>b</b>) Kearny et al. [<a href="#B5-applsci-11-11050" class="html-bibr">5</a>], 18 skeleton joints; and (<b>c</b>) our method, 20 skeleton joints.</p> Full article ">Figure 10
<p>(<b>a</b>) <span class="html-italic">F</span>1 <span class="html-italic">score</span>, (<b>b</b>) <span class="html-italic">accuracy</span>, and (<b>c</b>) error rate (<span class="html-italic">MSE</span>) according to the number of skeleton joints: Yao et al. [<a href="#B7-applsci-11-11050" class="html-bibr">7</a>], 14 skeleton joints; Kearny et al. [<a href="#B5-applsci-11-11050" class="html-bibr">5</a>], 18 skeleton joints; and our method, 20 skeleton joints.</p> Full article ">
14 pages, 2494 KiB  
Review
Potential New Treatments for Knee OA: A Prospective Review of Registered Trials
by Marius Ioniţescu, Dinu Vermeşan, Bogdan Andor, Cristian Dumitrascu, Musab Al-Qatawneh, Vlad Bloanca, Andrei Dumitrascu and Radu Prejbeanu
Appl. Sci. 2021, 11(22), 11049; https://doi.org/10.3390/app112211049 - 22 Nov 2021
Cited by 2 | Viewed by 2145
Abstract
We aimed to evaluate potential new treatments for knee osteoarthritis (OA). The National Institute of Health ClinicalTrials.gov database was searched for “Osteoarthritis, Knee”. We found 565 ongoing interventional studies with a total planned enrollment of 111,276 subjects. Ongoing studies for knee OA represent [...] Read more.
We aimed to evaluate potential new treatments for knee osteoarthritis (OA). The National Institute of Health ClinicalTrials.gov database was searched for “Osteoarthritis, Knee”. We found 565 ongoing interventional studies with a total planned enrollment of 111,276 subjects. Ongoing studies for knee OA represent a very small fraction of the registered clinical trials, but they are over a quarter of all knee trials and over two thirds of all OA studies. The most researched topic was arthroplasty, with aspects such as implant design changes, cementless fixation, robotic guidance, pain management, and fast track recovery. Intraarticular injections focused on cell therapies with mesenchymal stem cells sourced from adipose tissue, bone marrow, or umbilical cord. We could see the introduction of the first disease modifying drugs with an impact on knee OA, as well as new procedures such as geniculate artery embolization and geniculate nerve ablation. Full article
(This article belongs to the Special Issue Frontiers in Orthopedic Surgery)
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<p>Status of registered studies for “Osteoarthritis, Knee”.</p> Full article ">Figure 2
<p>Interventional trials categories.</p> Full article ">Figure 3
<p>Geographic distribution of interventional trials (green low–red high).</p> Full article ">Figure 3 Cont.
<p>Geographic distribution of interventional trials (green low–red high).</p> Full article ">Figure 4
<p>Starting year.</p> Full article ">Figure 5
<p>Study design.</p> Full article ">Figure 6
<p>Clinical applicability distribution.</p> Full article ">
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