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Appl. Sci., Volume 9, Issue 10 (May-2 2019) – 207 articles

Cover Story (view full-size image): Smart energy products and services (SEPS) play a key role in the development of smart grids by enabling greater interaction between end users, home appliances, and energy suppliers. There is a need to develop innovative SEPS that achieve a better match with user expectations and demands, and simulation testing methods can be useful for evaluating the technical functioning and preferred user interaction with a new design during its early development stages. In this paper, three innovative concepts for home energy management products (HEMPs) are tested using a simulation testing environment, as well as scenario-based simulations. View Paper here.
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9 pages, 1332 KiB  
Article
Measurement of the Absolute Value of Cerebral Blood Volume and Optical Properties in Term Neonates Immediately after Birth Using Near-Infrared Time-Resolved Spectroscopy: A Preliminary Observation Study
by Aya Morimoto, Shinji Nakamura, Masashiro Sugino, Kosuke Koyano, Yinmon Htun, Makoto Arioka, Noriko Fuke, Ami Mizuo, Takayuki Yokota, Ikuko Kato, Yukihiko Konishi, Sonoko Kondo, Takashi Iwase, Saneyuki Yasuda and Takashi Kusaka
Appl. Sci. 2019, 9(10), 2172; https://doi.org/10.3390/app9102172 - 27 May 2019
Cited by 7 | Viewed by 3275
Abstract
The aim of this study was to use near-infrared time-resolved spectroscopy (TRS) to determine the absolute values of cerebral blood volume (CBV) and cerebral hemoglobin oxygen saturation (ScO2) during the immediate transition period in term neonates and the changes in optical [...] Read more.
The aim of this study was to use near-infrared time-resolved spectroscopy (TRS) to determine the absolute values of cerebral blood volume (CBV) and cerebral hemoglobin oxygen saturation (ScO2) during the immediate transition period in term neonates and the changes in optical properties such as the differential pathlength factor (DPF) and reduced scattering coefficient (μs’). CBV and ScO2 were measured using TRS during the first 15 min after birth by vaginal delivery in term neonates who did not need resuscitation. Within 2–3 min after birth, CBV showed various changes such as increases or decreases, followed by a gradual decrease until 15 min and then stability (mean (SD) mL/100 g brain: 2 min, 3.09 (0.74); 3 min, 3.01 (0.77); 5 min, 2.69 (0.77); 10 min, 2.40 (0.61), 15 min, 2.08 (0.47)). ScO2 showed a gradual increase, then kept increasing or became a stable reading. The DPF and μs’ values (mean (SD) at 762, 800, and 836 nm) were stable during the first 15 min after birth (DPF: 4.47 (0.38), 4.41 (0.32), and 4.06 (0.28)/cm; μs’: 6.54 (0.67), 5.82 (0.84), and 5.43 (0.95)/cm). Accordingly, we proved that TRS can stably measure cerebral hemodynamics, despite the dramatic physiological changes occurring at this time in the labor room. Full article
(This article belongs to the Special Issue New Horizons in Time-Domain Diffuse Optical Spectroscopy and Imaging)
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Figure 1
<p>Photograph showing the actual near-infrared time-resolved spectroscopy (TRS) setting.</p>
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<p>Cerebral blood volume (CBV) in five neonates during the first 15 min of life. Values are means. Compared with the reference value at 15 min, a significant decrease in CBV was observed at each time point.</p>
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<p>Cerebral hemoglobin oxygen saturation (ScO<sub>2</sub>) in five neonates during the first 15 min of life. Values are means. ScO<sub>2</sub> showed the same pattern as arterial Hb oxygen saturation with a gradual increase, peak at 5–10 min, and then stable values.</p>
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<p>Arterial Hb oxygen saturation (SpO<sub>2</sub>) in five neonates during the first 15 min of life. Values are means.</p>
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14 pages, 5054 KiB  
Article
Automatic Digital Modulation Classification Based on Curriculum Learning
by Min Zhang, Zhongwei Yu, Hai Wang, Hongbo Qin, Wei Zhao and Yan Liu
Appl. Sci. 2019, 9(10), 2171; https://doi.org/10.3390/app9102171 - 27 May 2019
Cited by 17 | Viewed by 4061
Abstract
Neural network shows great potential in modulation classification because of its excellent accuracy and achievability but overfitting and memorizing data noise often happen in previous researches on automatic digital modulation classifier. To solve this problem, we utilize two neural networks, namely MentorNet and [...] Read more.
Neural network shows great potential in modulation classification because of its excellent accuracy and achievability but overfitting and memorizing data noise often happen in previous researches on automatic digital modulation classifier. To solve this problem, we utilize two neural networks, namely MentorNet and StudentNet, to construct an automatic modulation classifier, which possesses great performance on the test set with −18–20 dB signal-to-noise ratio (SNR). The MentorNet supervises the training of StudentNet according to curriculum learning, and deals with the overfitting problem in StudentNet. The proposed classifier is verified in several test sets containing additive white Gaussian noise (AWGN), Rayleigh fading, carrier frequency offset and phase offset. Experimental results reveal that the overall accuracy of this classifier for common eleven modulation types was up to 99.3% while the inter-class accuracy could be up to 100%, which was much higher than many other classifiers. Besides, in the presence of interferences, the overall accuracy of this novel classifier still could reach 90% at 10 dB SNR indicting its excellent robustness, which makes it suitable for applications like military electronic warfare. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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Figure 1
<p>The architecture of the (<b>a</b>) residual block and (<b>b</b>) ResNet.</p>
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<p>The diagram of the automatic digital modulation classifier.</p>
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<p>MentorNet architecture.</p>
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<p>Average loss of actual and predictive modulation type at different signal-to-noise ratios (SNRs).</p>
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<p>The data-driven curriculum learned by MentorNet: (<b>a</b>) Epoch percentage = 20 and (<b>b</b>) epoch percentage = 90.</p>
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<p>The diagrams of (<b>a</b>) training StudentNet under the supervision of MentorNet training and (<b>b</b>) testing the performance.</p>
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<p>The performance of various classifiers under different SNRs: (<b>a</b>) Curves about the classification accuracy versus the SNR range of the training set, and (<b>b</b>) classification accuracy of different methods with −20–18 dB SNR.</p>
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<p>Curves about intra-class classification accuracy versus SNR.</p>
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<p>Confusion matrix with different SNRs: (<b>a</b>) SNR = −20 dB; (<b>b</b>) SNR = −10 dB; (<b>c</b>) SNR = 0 dB and (<b>d</b>) SNR = 10 dB.</p>
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<p>Curves about inter-class classification accuracy versus SNR.</p>
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<p>Classification accuracy of the MentorNet classifier under the interference of Rayleigh fading.</p>
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<p>Classification accuracy with (<b>a</b>) different carrier frequency offsets and (<b>b</b>) different phase offsets.</p>
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<p>Classification accuracy of various classifiers on dataset RML2016b.</p>
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17 pages, 936 KiB  
Article
Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information
by Changgyun Kim, Youngdoo Son and Sekyoung Youm
Appl. Sci. 2019, 9(10), 2170; https://doi.org/10.3390/app9102170 - 27 May 2019
Cited by 16 | Viewed by 4712
Abstract
The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is [...] Read more.
The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is missing. An advantage of Char-RNN is that it does not require any additional imputation method because it implicitly infers missing values considering the relationship with nearby data points. We applied Char-RNN to classify cases in the Korea National Health and Nutrition Examination Survey (KNHANES) VI as normal status and five chronic diseases: hypertension, stroke, angina pectoris, myocardial infarction, and diabetes mellitus. We also employed a multilayer perceptron network for the same task for comparison. The results show higher accuracy for Char-RNN than for the conventional multilayer perceptron model. Char-RNN showed remarkable performance in finding patients with hypertension and stroke. The present study utilized the KNHANES VI data to demonstrate a practical approach to predicting and managing chronic diseases with partially observed information. Full article
(This article belongs to the Special Issue Advances in Deep Learning)
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<p>Data category configuration.</p>
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<p>Overview of proposed procedure using character-recurrent neural network (Char-RNN).</p>
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<p>Data loss at (<b>a</b>) 50,000 and (<b>b</b>) 55,000 epochs.</p>
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10 pages, 1192 KiB  
Article
Solids Content of Black Liquor Measured by Online Time-Domain NMR
by Ekaterina Nikolskaya, Petri Janhunen, Mikko Haapalainen and Yrjö Hiltunen
Appl. Sci. 2019, 9(10), 2169; https://doi.org/10.3390/app9102169 - 27 May 2019
Cited by 16 | Viewed by 4503
Abstract
Black liquor, a valuable by-product of the pulp production process, is used for the recovery of chemicals and serves as an energy source for the pulp mill. Before entering the recovery unit, black liquor runs through several stages of evaporation, wherein the solids [...] Read more.
Black liquor, a valuable by-product of the pulp production process, is used for the recovery of chemicals and serves as an energy source for the pulp mill. Before entering the recovery unit, black liquor runs through several stages of evaporation, wherein the solids content (SC) can be used to control the evaporation effectiveness. In the current study, the time-domain nuclear magnetic resonance (TD-NMR) technique was applied to determine the SC of black liquor. The TD-NMR system was modified for flowing samples, so that the black liquor could be pumped through the system, followed by the measurement of the spin-spin relaxation rate, R2. A temperature correction was also applied to reduce deviations in the R2 caused by the sample temperature. The SC was calculated based on a linear model between the R2 and the SC values determined gravimetrically, where good agreement was shown. The online TD-NMR system was tested at a pulp mill for the SC estimation of weak black liquor over seven days without any fouling, which demonstrated the feasibility of the method in a harsh industrial environment. Therefore, the potential of the TD-NMR technology as a technique for controlling the black liquor evaporation process was demonstrated. Full article
(This article belongs to the Special Issue Applications of Low Field Magnetic Resonance)
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Figure 1
<p>Schematic overview of the online time-domain nuclear magnetic resonance (TD-NMR) measuring system.</p>
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<p>R<sub>2</sub> measured for the weak (triangles), intermediate (squares), and half-strong (circles) black liquor samples at different temperatures.</p>
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<p>Solids content (SCs) as a function of the spin-spin relaxation rates ln(R<sub>2TC</sub>) corrected to T<sub>ref</sub> = 60 °C for the black liquor samples.</p>
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<p>Data measured for the weak black liquor in the online test: (<b>a</b>) Sample temperature; (<b>b</b>) Measured R<sub>2</sub> values and values of the R<sub>2TC</sub> corrected to T<sub>ref</sub> = 60 °C, after applying a one-hour moving average; (<b>c</b>) Temperature correction at the beginning of the test (dotted rectangle in <a href="#applsci-09-02169-f004" class="html-fig">Figure 4</a>b): measured R<sub>2</sub> (squares), values of the R<sub>2TC</sub> corrected to T<sub>ref</sub> = 60 °C (circles), and the sample temperature T (triangles).</p>
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<p>Estimated solids content (SC) shown by a black line, and standard deviations (SD) shown by the grey lines. Average SCs and SDs were estimated within one hour, using moving data.</p>
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20 pages, 7400 KiB  
Article
A Worm-Inspired Robot Flexibly Steering on Horizontal and Vertical Surfaces
by Wenhao Yang and Wenzeng Zhang
Appl. Sci. 2019, 9(10), 2168; https://doi.org/10.3390/app9102168 - 27 May 2019
Cited by 11 | Viewed by 4484
Abstract
Based on the motion principle of bionic earthworms, we designed and fabricated a novel crawling robot driven by pneumatic power. Its structure is divided into four segments, and its motion process is periodic with high stability. Due to the pneumatic suction cups mounted [...] Read more.
Based on the motion principle of bionic earthworms, we designed and fabricated a novel crawling robot driven by pneumatic power. Its structure is divided into four segments, and its motion process is periodic with high stability. Due to the pneumatic suction cups mounted on its feet, it is able to crawl on smooth horizontal, inclined, or vertical walls. On this basis, we designed a novel underactuated steering mechanism. Through the tendons on both sides and the springs installed on the side of the robot, we accurately controlled the steering motion of the robot. We analyzed the steering process in detail, calculated the influence of external parameters on the steering process of the robot, and simulated the trajectory of the robot in the steering process. The experimental results validated our analysis. In addition, we calculate the maximum thrust that each segment of the robot can provide, and determine the maximum load that the robot can bear during climbing motions. Full article
(This article belongs to the Section Mechanical Engineering)
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Figure 1
<p>Skeleton structure unit of the first three segments of the robot. Each unit consists of three cylinders, a pair of pneumatic suckers, and a miniature motor.</p>
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<p>The position of the feet under the control of the miniature motor: (<b>a</b>) the feet are lowered down when the segment is stationary; (<b>b</b>) the feet is lifted up before the segment’s motion starts.</p>
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<p>Top view of the overall structure of the steering mechanism. The two adjacent segment units are connected by a bearing hinge. There are two tendons on the both sides of the robot.</p>
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<p>A diagram of the air circuit that consists of two pumps, nine valves, three sets of cylinders, and four pairs of suction cups: yellow represents the positive pressure generated by the first air pump, blue represents the negative pressure generated by the second air pump, gray represents the atmospheric pressure, and the direction of the arrow is the transmission direction of the air pressure signal. This figure does not cover the up and down motion of the feet controlled by motors.</p>
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<p>Four motion phases in a single cycle of the straight crawling process.</p>
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<p>Steering crawling process in a single cycle.</p>
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<p>The solenoid valves’ control signal during the motion cycle of the robot. As shown in <a href="#applsci-09-02168-f004" class="html-fig">Figure 4</a>, valves 1–3 control the pressure in suction cups, and valves 4–9 control the pressure in the cylinders of segments 1–3. If the solenoid valve receives high potential, the cylinder will be connected to the air pump, and if it’s low potential, the cylinder will be connected to the atmosphere.</p>
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<p>A simplified geometric model of the robot in Phase 1, where MN is the cylinder of Segment 1, NO is the cylinder of the Segment 2, and OP is the cylinder of Segment 3: (<b>a</b>) The state at the beginning of Phase 1; (<b>b</b>) The state at the end of Phase 1; (<b>c</b>) The simplified model after the introduction of the springs.</p>
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<p>Theoretical relationship and experimental results between the values of <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>3</mn> </msub> </mrow> </semantics></math> and the values of <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> </mrow> </semantics></math> at the end of Phase 1. From the figure, we can see that <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> is always less than <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>3</mn> </msub> </mrow> </semantics></math>, because there is a friction force on the tendon. (<b>a</b>) When <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> </mrow> </semantics></math> is 20°, the relationship between the values of <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>3</mn> </msub> </mrow> </semantics></math> and the value of <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) when <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> </mrow> </semantics></math> is <math display="inline"><semantics> <mrow> <mn>400</mn> <mrow> <mtext> </mtext> <mi>mm</mi> </mrow> </mrow> </semantics></math>, the relationship between the values of <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>3</mn> </msub> </mrow> </semantics></math> and the value of <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>(<b>a</b>) A simplified geometric model of the robot at the end of Phase 2, where ON is the cylinder of Segment 2; (<b>b</b>) A simplified geometric model of the robot at the end of Phase 3, where PO is the cylinder of Segment 3.</p>
Full article ">Figure 11
<p>The relationship between the values of <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>3</mn> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mi>φ</mi> </semantics></math> at the end of Phase 1 and the number of cycles T (at the initial phase: <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>400</mn> <mtext> </mtext> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> </mrow> </semantics></math> = 20°): (<b>a</b>) The curve of <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>3</mn> </msub> </mrow> </semantics></math> changing with the number of cycles T; (<b>b</b>) the curve of steering angle <math display="inline"><semantics> <mi>φ</mi> </semantics></math> and cumulative steering angle <math display="inline"><semantics> <mrow> <mo>∑</mo> <mi>φ</mi> </mrow> </semantics></math> changing with the number of cycles T.</p>
Full article ">Figure 12
<p>(<b>a</b>) Simulation diagram of the motion trajectory of the robot. The blue dotted line in the figure indicates the trajectory of the robot’s center point in 120 cycles (at the initial phase: <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>400</mn> <mtext> </mtext> <mi>mm</mi> </mrow> </semantics></math>). The red line in the figure indicates the specific position and posture of the robot when T = 0, T = 30, T = 60, and T = 90. The red dot in the figure is the three bearing hinge points of the robot. It can be seen that during the steering process, the motion trajectory of the robot is circular, and the radius is related to the initial state. (<b>b</b>) In a particular case, the robot can avoid an obstacle through adjusting the values of <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> </mrow> </semantics></math> at each of the following points: 1) A: <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>534</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>390</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>; 2) B: <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>400</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>523</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>; 3) C: <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>534</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>390</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>; and 4) D: <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>461.4</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>461.4</mn> <mo> </mo> <mi>mm</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 13
<p>Performance demonstration of the robot climbing a vertical glass surface: (<b>a</b>) photos of a gait sequence on a vertical glass surface; (<b>b</b>) the locomotion of the robot in two minutes on a vertical glass surface; (<b>c</b>) the locomotion of the robot in 2 minutes on a painted wooden door.</p>
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<p>A simplified force model of the robot in Phase 1 after the introduction of gravity, where O1 is the gravity center of Segment 1, O4 is the gravity center of Segment 4, and <math display="inline"><semantics> <mrow> <msup> <mi>φ</mi> <mo>′</mo> </msup> </mrow> </semantics></math> is the angle between Segment 2 and the vertical direction.</p>
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<p>(<b>a</b>) Trajectory diagram of the climbing motion on a vertical surface in 120 cycles. At the initial phase (<math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>400</mn> <mtext> </mtext> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>2</mn> </msub> <mo> </mo> </mrow> </semantics></math>= 20°), the trajectory can be regarded as a circle. (<b>b</b>) The relationship between the diameter of the circle and the length of tendon <span class="html-italic">L</span><sub>2</sub> in different motion patterns at the initial phase: <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>The force diagram at a certain point during Phase 1: (<b>a</b>) the force on Segment 1; (<b>b</b>) the force on Segment 4. In the diagram, <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>3</mn> </msub> </mrow> </semantics></math> are the deflection angles of segments 1 and 3, and x is the cylinder elongation.</p>
Full article ">Figure 17
<p>(<b>a</b>) A brief diagram of the force in Segment 2 at a certain point during Phase 2; (<b>b</b>) a brief diagram of the force in Segment 3 at a certain point during Phase 3. The dotted line in the diagram is the initial position before the movement.</p>
Full article ">Figure 18
<p>A diagram showing the relationship between the thrust, the deflection angles of each segment, and the cylinder elongation <span class="html-italic">x</span>. Segments 2 and 3 have relatively higher thrust and thus have better load capacity, while Segment 1 and Segment 4 should be specially designed to reduce their weight.</p>
Full article ">
18 pages, 6098 KiB  
Article
Reactive Black 5 Degradation on Manganese Oxides Supported on Sodium Hydroxide Modified Graphene Oxide
by Hayarpi Saroyan, Dimitra Ntagiou, Kyriazis Rekos and Eleni Deliyanni
Appl. Sci. 2019, 9(10), 2167; https://doi.org/10.3390/app9102167 - 27 May 2019
Cited by 22 | Viewed by 4349
Abstract
Sodium hydroxide-modified graphene oxide was used as manganese oxides support for the preparation of nanocomposites via a one-pot preparation route for the degradation of Reactive Black 5. The nanocomposites were characterized for their structure by X-ray diffraction, for their textural properties by Nitrogen [...] Read more.
Sodium hydroxide-modified graphene oxide was used as manganese oxides support for the preparation of nanocomposites via a one-pot preparation route for the degradation of Reactive Black 5. The nanocomposites were characterized for their structure by X-ray diffraction, for their textural properties by Nitrogen adsorption, and for their surface chemistry by Fourier transform infrared spectroscopy, potentiometric titration, and thermal analysis measurements. The nanocomposites prepared showed to possess high activity for the degradation/oxidation of Reactive Black 5 at ambient conditions, without light irradiation, which was higher than that of the precursors manganese oxides and can be attributed to the synergistic effect of the manganese oxides and the modified graphene oxide. Full article
(This article belongs to the Special Issue Innovative Approaches for Drinking- and Waste-Water Treatment)
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Figure 1
<p>XRD patterns of: (<b>a</b>) graphene oxide (GO), modified graphene oxide (NGO), manganese oxide (Mn<sub>3</sub>O<sub>4</sub>) and NGO–Mn<sub>3</sub>O<sub>4</sub> nanocomposite; (<b>b</b>) GO, modified graphene oxide (NGO), manganese oxide (MnO<sub>2</sub>) and NGO–MnO<sub>2</sub> nanocomposite.</p>
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<p>N<sub>2</sub> adsorption isotherms and pore diameter distribution (inset) for NGO–Mn<sub>3</sub>O<sub>4</sub> and NGO–MnO<sub>2</sub> modified composites.</p>
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<p>(<b>a</b>) Effect of initial pH on RB5 adsorption/degradation onto NGO, MnO<sub>2</sub>, Mn<sub>3</sub>O<sub>4</sub>, NGO–Mn<sub>3</sub>O<sub>4</sub> and NGO–MnO<sub>2</sub> nanocomposites (Co = 100 mg/L, m = 0.01 g, V = 0.02 L); (<b>b</b>) potentiometric titration results for the nanocomposite catalysts.</p>
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<p>Effect of initial concentration of RB5 (pH = 3, m = 0.01 g, V = 0.02 L).</p>
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<p>(<b>a</b>,<b>b</b>) Effect of contact time on the degradation of RB5 on NGO, MnO<sub>2</sub>, Mn<sub>3</sub>O<sub>4</sub>, NGO–Mn<sub>3</sub>O<sub>4</sub> and NGO–MnO<sub>2</sub> nanocomposites for initial RB5 concentration of 100 mg/L (<b>a</b>) in presence and (<b>b</b>) absence of H<sub>2</sub>O<sub>2</sub> (Co = 100 mg/L, pH = 3, m = 0.01 g, V = 0.02 L). (inset: decolorization after 60 min for Mn<sub>3</sub>O<sub>4</sub> in the first row and NGO–MnO<sub>2</sub> in the second row).</p>
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<p>Effect of contact time on the degradation of RB5 on NGO–Mn<sub>3</sub>O<sub>4</sub> and NGO–MnO<sub>2</sub> nanocomposites for initial RB5 concentration of 40 mg/L and 100 mg/L in presence (radical) and absence (non-radical) of H<sub>2</sub>O<sub>2</sub> (pH = 3, m = 0.01 g, V = 0.02 L).</p>
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<p>Effect of (<b>a</b>) H<sub>2</sub>O<sub>2</sub> concentration and (<b>b</b>) methanol/H<sub>2</sub>O<sub>2</sub> ratio on RB5 degradation by NGO–Mn<sub>3</sub>O<sub>4</sub> and NGO–MnO<sub>2</sub> nanocomposites (Co = 100 mg/L, pH = 3, m = 0.01 g, V = 0.02 L).</p>
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<p>Elution experiments by (<b>a</b>) deionized water (<b>b</b>) ethanol, methanol and acetonitrile of the loaded the NGO–MnO<sub>2</sub> and NGO–Mn<sub>3</sub>O<sub>4</sub> nanocomposites (Co = 100 mg/L, m = 0.01 g, V = 0.02 L).</p>
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<p>Recovery efficiency of the NGO—MnO<sub>2</sub> and NGO—Mn<sub>3</sub>O<sub>4</sub> after 5 cycles of adsorption—desorption (Co = 100 mg/L, pH = 3, m = 0.01 g, V = 0.02 L).</p>
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<p>FTIR patterns of raw (black line) and after the RB5 degradation (red line) of NGO (<b>a</b>); NGO–Mn<sub>3</sub>O<sub>4</sub> (<b>b</b>) and NGO–MnO<sub>2</sub> (<b>c</b>) in presence of H<sub>2</sub>O<sub>2</sub>.</p>
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<p>DTG curves for (<b>a</b>) NGO–Mn<sub>3</sub>O<sub>4</sub> and (<b>b</b>) NGO–MnO<sub>2</sub> modified composites in absence and presence of RB5.</p>
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<p>UV-Vis curves for RB5 on NGO–Mn<sub>3</sub>O<sub>4</sub> in 3 different RB5 concentrations (pH = 3, m = 0.01 g, V = 0.02 L).</p>
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<p>UV-Vis curves for RB5 (<b>a</b>,<b>b</b>) after degradation on NGO, MnO<sub>2</sub>, Mn<sub>3</sub>O<sub>4</sub>, NGO–Mn<sub>3</sub>O<sub>4</sub> and NGO–MnO<sub>2</sub> nanocomposites for Co = 50 mg/L (pH = 3, m = 0.01 g, V = 0.02 L).</p>
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13 pages, 2650 KiB  
Article
Enhanced Automatic Speech Recognition System Based on Enhancing Power-Normalized Cepstral Coefficients
by Mohamed Tamazin, Ahmed Gouda and Mohamed Khedr
Appl. Sci. 2019, 9(10), 2166; https://doi.org/10.3390/app9102166 - 27 May 2019
Cited by 22 | Viewed by 4234
Abstract
Many new consumer applications are based on the use of automatic speech recognition (ASR) systems, such as voice command interfaces, speech-to-text applications, and data entry processes. Although ASR systems have remarkably improved in recent decades, the speech recognition system performance still significantly degrades [...] Read more.
Many new consumer applications are based on the use of automatic speech recognition (ASR) systems, such as voice command interfaces, speech-to-text applications, and data entry processes. Although ASR systems have remarkably improved in recent decades, the speech recognition system performance still significantly degrades in the presence of noisy environments. Developing a robust ASR system that can work in real-world noise and other acoustic distorting conditions is an attractive research topic. Many advanced algorithms have been developed in the literature to deal with this problem; most of these algorithms are based on modeling the behavior of the human auditory system with perceived noisy speech. In this research, the power-normalized cepstral coefficient (PNCC) system is modified to increase robustness against the different types of environmental noises, where a new technique based on gammatone channel filtering combined with channel bias minimization is used to suppress the noise effects. The TIDIGITS database is utilized to evaluate the performance of the proposed system in comparison to the state-of-the-art techniques in the presence of additive white Gaussian noise (AWGN) and seven different types of environmental noises. In this research, one word is recognized from a set containing 11 possibilities only. The experimental results showed that the proposed method provides significant improvements in the recognition accuracy at low signal to noise ratios (SNR). In the case of subway noise at SNR = 5 dB, the proposed method outperforms the mel-frequency cepstral coefficient (MFCC) and relative spectral (RASTA)–perceptual linear predictive (PLP) methods by 55% and 47%, respectively. Moreover, the recognition rate of the proposed method is higher than the gammatone frequency cepstral coefficient (GFCC) and PNCC methods in the case of car noise. It is enhanced by 40% in comparison to the GFCC method at SNR 0dB, while it is improved by 20% in comparison to the PNCC method at SNR −5dB. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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Figure 1
<p>Difference between the block diagrams of enhanced PNCC, PNCC, GFCC, RASTA–PLP, and MFCC feature extraction techniques.</p>
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<p>Normalized 25 gammatone filter banks.</p>
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<p>Gammatone spectrogram of the uttered word ‘one’.</p>
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<p>Gammatone spectrogram of the uttered word ‘one’.</p>
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<p>Percentage recognition accuracy under various noise conditions.</p>
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<p>Percentage recognition accuracy under various noise conditions.</p>
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<p>Percentage improvement rate of the proposed method for all noise types at SNRs −5 dB, 0 dB, and 5 dB.</p>
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17 pages, 1308 KiB  
Article
Impact of Copper Oxide Nanoparticles on Enhancement of Bioactive Compounds Using Cell Suspension Cultures of Gymnema sylvestre (Retz.) R. Br
by Ill-Min Chung, Govindasamy Rajakumar, Umadevi Subramanian, Baskar Venkidasamy and Muthu Thiruvengadam
Appl. Sci. 2019, 9(10), 2165; https://doi.org/10.3390/app9102165 - 27 May 2019
Cited by 50 | Viewed by 5027
Abstract
Gymnema sylvestre is a plant that is enriched in bioactive compounds. In particular, gymnemic acids (GA) and phenolic compounds (PC) are pharmaceutically important. There is a commercial demand for naturally occurring bioactive compounds, but their availability is limited due to geographical and seasonal [...] Read more.
Gymnema sylvestre is a plant that is enriched in bioactive compounds. In particular, gymnemic acids (GA) and phenolic compounds (PC) are pharmaceutically important. There is a commercial demand for naturally occurring bioactive compounds, but their availability is limited due to geographical and seasonal variations. The elicitation approach can enhance the biosynthesis of phytochemicals during in vitro culture of G. sylvestre. Here, to further improve gymnemic acid II (GA II) and phenolic compounds (PC) production by G. sylvestre, cell suspension cultures (CSC), which has attracted attention for the production of essential phytochemicals, was explored using copper oxide nanoparticles (CuO NPs). Callus was obtained on MS medium containing 2,4-dichlorophenoxyacetic acid, kinetin, phytoagar, and sucrose. Agar-free MS medium was used to initiate CSC, which was treated with three concentrations of CuO NPs (1, 3 or 5 mg/L). Treatment for 48 h with 3 mg/L CuO NPs resulted in the greatest yields of GA II, total phenolics, and flavonoids. The cultures also displayed pronounced antioxidant, antidiabetic, anti-inflammatory, antibacterial, antifungal, and anticancer activities. The use of CuO NPs (3 mg/L) significantly increased the production of GA II (nine-fold) and PC compared to unamended CSC. We propose that CSC and use of nanoparticles (NPs) as a new generation of elicitors, offer a suitable prospect for the production of bioactive compounds. Full article
(This article belongs to the Special Issue Bioactive Substances: Properties, Applications and or Toxicities)
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Figure 1
<p>Callus induction and biomass accumulation in <span class="html-italic">Gymnema sylvestre</span>. (<b>a</b>) Effect of different concentrations of 2,4-dichlorophenoxyacetic acid (2,4-D) in combination with 0.1 mg/L kinetin (KIN) for callus induction in <span class="html-italic">G. sylvestre</span>. (<b>b</b>) Biomass accumulation. (<b>c</b>) Gymnemic Acid II (GA II) production on MS liquid medium with 2,4-D (2 mg/L), KIN (0.1 mg/L), and sucrose (30 g/L) with time. Different letters indicate a significant difference at <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Effect of copper oxide nanoparticles (CuO NPs) on copper content and biomass accumulation in cell suspension cultures (CSC) of <span class="html-italic">G. sylvestre</span>. (<b>a</b>) Copper content, (<b>b</b>) biomass accumulation. Different letters indicate a significant difference at <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Effect of CuO NPs on bioactive compounds in CSC of <span class="html-italic">G. sylvestre</span>. (<b>a</b>) Gymnemic acid II (GA II), (<b>b</b>) Total phenolic content (TPC), (<b>c</b>) Total flavonoid content (TFC). Different letters indicate a significant difference at <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Effect of CuO NPs on antioxidant activities in CSC of <span class="html-italic">G. sylvestre</span>. (<b>a</b>) Free radical-scavenging activity by the 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) assay, (<b>b</b>) total Fe<sup>3+</sup>– Fe<sup>2+</sup> reductive potential reference antioxidants (butylated hydroxytoluene), (<b>c</b>) total antioxidant capacity (TAC) by the phosphomolybdenum method. TAC is expressed as equivalents of α-tocopherol (μg/g of extract). Different letters indicate a significant difference at <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Effect of CuO NPs on antidiabetic and anti-inflammatory activities in CSC of <span class="html-italic">G. sylvestre.</span> (<b>a</b>) In vitro α-amylase activity, (<b>b</b>) non-enzymatic glycosylation of hemoglobin activity, (<b>c</b>) lipoxygenase inhibition activity, (<b>d</b>) albumin denaturation inhibition assay. Different letters indicate a significant difference at <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Effect of CuO NPs on antimicrobial activity in CSC of <span class="html-italic">G. sylvestre</span> using the disc diffusion method. Different letters indicate a significant difference at <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Effect of CuO NPs on cell viability of MCF-7 cells (<b>a</b>) and HT-29 cells (<b>b</b>) in CSC of <span class="html-italic">G. sylvestre</span>. Different letters indicate a significant difference at <span class="html-italic">p</span> ≤ 0.05.</p>
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20 pages, 3807 KiB  
Article
Study of Heat and Mass Transfer in Electroosmotic Flow of Third Order Fluid through Peristaltic Microchannels
by Sadia Waheed, Saima Noreen and Abid Hussanan
Appl. Sci. 2019, 9(10), 2164; https://doi.org/10.3390/app9102164 - 27 May 2019
Cited by 45 | Viewed by 3948
Abstract
An analysis is carried out to evaluate the effects of heat and mass transfer in an electro-osmotic flow of third order fluid via peristaltic pumping. Solutions are derived for small wave number and Peclet number. The emerging non-linear mathematical model is solved analytically [...] Read more.
An analysis is carried out to evaluate the effects of heat and mass transfer in an electro-osmotic flow of third order fluid via peristaltic pumping. Solutions are derived for small wave number and Peclet number. The emerging non-linear mathematical model is solved analytically and compared numerically by the built-in scheme of working software. The table is inserted for shear stress distribution and a graph for comparison of solution techniques and accuracy of obtained results. The effects of various parameters of interest on pumping, trapping, temperature, heat transfer coefficient, and concentration distribution have been studied graphically. Electro-osmotic exchange of energy and mass has a role in reservoir engineering, chemical industry, and in micro-fabrication technologies. Full article
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Figure 1
<p>Schematic of the geometry of electro-osmotically modulated peristaltic flow.</p>
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<p>Comparative analysis between Perturbation solution and Numerical solution for axial velocity, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>x</mi> <mo>=</mo> <mi>π</mi> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>S</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>S</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Axial velocity u profile for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>b</mi> </mstyle> <mo>)</mo> </mrow> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>x</mi> <mo>=</mo> <mi>π</mi> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.358</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Pressure rise <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>P</mi> <mi>λ</mi> </msub> </mrow> </semantics></math> profile for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>b</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.258</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Pressure gradient profile for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> <mo> </mo> </mrow> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>b</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="sans-serif">Θ</mi> </mrow> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.4</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>0.1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Streamline distribution for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.00</mn> <mo> </mo> <mrow> <mo>(</mo> <mi mathvariant="bold">b</mi> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> </mrow> <mo>=</mo> <mn>0.02</mn> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> </mrow> <mo>=</mo> <mn>0.04</mn> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> </mrow> <mo>=</mo> <mn>0.06</mn> </mrow> </semantics></math>, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">ε</mi> <mo>=</mo> <mn>0.258</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Streamline distribution for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.00</mn> <mo> </mo> <mrow> <mo>(</mo> <mi mathvariant="bold">b</mi> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> </mrow> <mo>=</mo> <mn>0.02</mn> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> </mrow> <mo>=</mo> <mn>0.04</mn> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> </mrow> <mo>=</mo> <mn>0.06</mn> </mrow> </semantics></math>, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">ε</mi> <mo>=</mo> <mn>0.258</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>0.8</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Streamline distribution for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>→</mo> <mn>0</mn> <mo> </mo> <mrow> <mo>(</mo> <mi mathvariant="bold">b</mi> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>5</mn> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">ε</mi> <mo>=</mo> <mn>0.358</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>0.7</mn> <mo>,</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> </mrow> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>Streamline distribution for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1.0</mn> <mo> </mo> <mrow> <mo>(</mo> <mi mathvariant="bold">b</mi> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>0.0</mn> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">ε</mi> <mo>=</mo> <mn>0.758</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>0.7</mn> <mo>,</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> </mrow> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>Temperature profile for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> <mo> </mo> </mrow> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>b</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> </mrow> </semantics></math>, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>x</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>Concentration profile for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> <mo> </mo> </mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">b</mi> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">e</mi> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>S</mi> <mi>c</mi> </msub> </mrow> </semantics></math><math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">f</mi> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>S</mi> <mi>r</mi> </msub> <mo>,</mo> </mrow> </semantics></math> while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>x</mi> <mo>=</mo> <mi>π</mi> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>S</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>S</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10 Cont.
<p>Concentration profile for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> <mo> </mo> </mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">b</mi> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">e</mi> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>S</mi> <mi>c</mi> </msub> </mrow> </semantics></math><math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">f</mi> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>S</mi> <mi>r</mi> </msub> <mo>,</mo> </mrow> </semantics></math> while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>x</mi> <mo>=</mo> <mi>π</mi> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>S</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>S</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>Heat transfer coefficient for <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> <mo>.</mo> <mrow> <mo> </mo> <mi mathvariant="normal">Г</mi> <mo> </mo> </mrow> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>b</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>c</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo> </mo> <mrow> <mo>(</mo> <mstyle mathvariant="bold" mathsize="normal"> <mi>d</mi> </mstyle> <mo>)</mo> </mrow> <mo>.</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> </mrow> </semantics></math>, while other parameters are <math display="inline"><semantics> <mrow> <mi mathvariant="normal">Г</mi> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mi>ε</mi> <mo>=</mo> <mn>0.2</mn> <mo>,</mo> <mi mathvariant="sans-serif">Θ</mi> <mo>=</mo> <mn>1.2</mn> <mo>,</mo> <mo> </mo> <msub> <mi>m</mi> <mi>e</mi> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mo> </mo> <msub> <mi>U</mi> <mrow> <mi>h</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mi>B</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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10 pages, 1910 KiB  
Article
Energy Concepts and Critical Plane for Fatigue Assessment of Ti-6Al-4V Notched Specimens
by Camilla Ronchei, Andrea Carpinteri and Sabrina Vantadori
Appl. Sci. 2019, 9(10), 2163; https://doi.org/10.3390/app9102163 - 27 May 2019
Cited by 5 | Viewed by 2460
Abstract
In the present paper, the fatigue life assessment of notched structural components is performed by applying a critical plane-based multiaxial fatigue criterion. Such a criterion is formulated by using the control volume concept related to the strain energy density criterion. The verification point [...] Read more.
In the present paper, the fatigue life assessment of notched structural components is performed by applying a critical plane-based multiaxial fatigue criterion. Such a criterion is formulated by using the control volume concept related to the strain energy density criterion. The verification point is assumed to be at a given distance from the notch tip. Such a distance is taken as a function of the control volume radii around the notch tip under both Mode I and Mode III loading. The accuracy of the present criterion is evaluated through experimental data available in the literature, concerning titanium alloy notched specimens under uniaxial and multiaxial fatigue loading. Full article
(This article belongs to the Special Issue Fracture and Fatigue Assessments of Structural Components)
Show Figures

Figure 1

Figure 1
<p>Verification point position according to the control volume concept for V-notch.</p>
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<p>Discretization adopted for the finite element model.</p>
Full article ">Figure 3
<p>Geometrical sizes of the titanium alloy V-notched specimens subjected to tension and/or torsion fatigue loading.</p>
Full article ">Figure 4
<p>Accuracy of the present criterion in estimating the fatigue lifetime of Ti-6Al-4V notched specimens: (<b>a</b>) Uniaxial loading, (<b>b</b>) multiaxial proportional loading, (<b>c</b>) multiaxial non-proportional loading.</p>
Full article ">Figure 5
<p>Experimental fatigue life <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>f</mi> <mo>,</mo> <mi mathvariant="italic">exp</mi> </mrow> </msub> </mrow> </semantics></math> against equivalent strain amplitude <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mrow> <mi>e</mi> <mi>q</mi> <mo>,</mo> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>Absolute value of the error index <math display="inline"><semantics> <mi>I</mi> </semantics></math> according to the present criterion.</p>
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14 pages, 2343 KiB  
Article
Dynamic Response Analysis of a Simply Supported Double-Beam System under Successive Moving Loads
by Lizhong Jiang, Yuntai Zhang, Yulin Feng, Wangbao Zhou and Zhihua Tan
Appl. Sci. 2019, 9(10), 2162; https://doi.org/10.3390/app9102162 - 27 May 2019
Cited by 28 | Viewed by 3804
Abstract
The dynamic response of a simply supported double-beam system under moving loads was studied. First, in order to reduce the difficulty of solving the equation, a finite sin-Fourier transform was used to transform the infinite-degree-of-freedom double-beam system into a superimposed two-degrees-of-freedom system. Second, [...] Read more.
The dynamic response of a simply supported double-beam system under moving loads was studied. First, in order to reduce the difficulty of solving the equation, a finite sin-Fourier transform was used to transform the infinite-degree-of-freedom double-beam system into a superimposed two-degrees-of-freedom system. Second, Duhamel’s integral was used to obtain the analytical expression of Fourier amplitude spectrum function considering the initial conditions. Finally, based on finite sin-Fourier inverse transform, the analytical expression of dynamic response of a simply supported double-beam system under moving loads was deduced. The dynamic response under successive moving loads was calculated by the analytical method and the general FEM software ANSYS. The analysis results show that the analytical method calculation results are consistent with ANSYS’ calculation, thus validating the analytical calculation method. The simply supported double-beam system had multiple critical speeds, and the flexural rigidity significantly affected both peak vertical displacement and critical speed. Full article
(This article belongs to the Special Issue Bridge Dynamics)
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Figure 1

Figure 1
<p>Double-beam system under successive moving loads.</p>
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<p>The primary-secondary beam system under moving load-groups.</p>
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<p>The 3D dynamic graphs of vertical deflection of a simply supported double-beam system for: (<b>a</b>) primary beam; (<b>b</b>) secondary beam.</p>
Full article ">Figure 4
<p>The response of the beams obtained by different method: (<b>a</b>,<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>40</mn> <mtext> </mtext> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>; (<b>c</b>,<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>100</mn> <mtext> </mtext> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>; (<b>e</b>,<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>122</mn> <mtext> </mtext> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>; (<b>g</b>,<b>h</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>180</mn> <mtext> </mtext> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 4 Cont.
<p>The response of the beams obtained by different method: (<b>a</b>,<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>40</mn> <mtext> </mtext> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>; (<b>c</b>,<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>100</mn> <mtext> </mtext> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>; (<b>e</b>,<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>122</mn> <mtext> </mtext> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>; (<b>g</b>,<b>h</b>) <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>180</mn> <mtext> </mtext> <mrow> <mi mathvariant="normal">m</mi> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>The max response versus the speed for: (<b>a</b>) primary beam; (<b>b</b>) secondary beam.</p>
Full article ">Figure 6
<p>The max response versus the speed: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> <msub> <mi>I</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>0.0001</mn> <msub> <mi>E</mi> <mn>2</mn> </msub> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> <msub> <mi>I</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>=</mo> <mn>0.001</mn> <msub> <mi>E</mi> <mn>2</mn> </msub> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>3</mn> </mrow> </msub> <msub> <mi>I</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>3</mn> </mrow> </msub> <mo>=</mo> <mn>0.01</mn> <msub> <mi>E</mi> <mn>2</mn> </msub> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>4</mn> </mrow> </msub> <msub> <mi>I</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>4</mn> </mrow> </msub> <mo>=</mo> <mn>0.1</mn> <msub> <mi>E</mi> <mn>2</mn> </msub> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 6 Cont.
<p>The max response versus the speed: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> <msub> <mi>I</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>0.0001</mn> <msub> <mi>E</mi> <mn>2</mn> </msub> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> <msub> <mi>I</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>=</mo> <mn>0.001</mn> <msub> <mi>E</mi> <mn>2</mn> </msub> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>3</mn> </mrow> </msub> <msub> <mi>I</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>3</mn> </mrow> </msub> <mo>=</mo> <mn>0.01</mn> <msub> <mi>E</mi> <mn>2</mn> </msub> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>4</mn> </mrow> </msub> <msub> <mi>I</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>4</mn> </mrow> </msub> <mo>=</mo> <mn>0.1</mn> <msub> <mi>E</mi> <mn>2</mn> </msub> <msub> <mi>I</mi> <mn>2</mn> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 7
<p>The magnification factor under different flexural rigidity for: (<b>a</b>) primary beam; (<b>b</b>) secondary beam.</p>
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12 pages, 2908 KiB  
Article
Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
by Lin He, Xianjun Chen, Jun Li and Xiaofeng Xie
Appl. Sci. 2019, 9(10), 2161; https://doi.org/10.3390/app9102161 - 27 May 2019
Cited by 9 | Viewed by 2496
Abstract
Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in [...] Read more.
Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in the method design, which can yield many spatially homogenous subregions in an HSI scence. Conventional manifold learning methods, such as a locality preserving projection (LPP), pursue a unified projection on the entire HSI, while neglecting the local homogeneities on the HSI manifold caused by those spatially homogenous subregions. In this work, we propose a novel multiscale superpixelwise LPP (MSuperLPP) for HSI classification to overcome the challenge. First, we partition an HSI into homogeneous subregions with a multiscale superpixel segmentation. Then, on each scale, subregion specific LPPs and the associated preliminary classifications are performed. Finally, we aggregate the classification results from all scales using a decision fusion strategy to achieve the final result. Experimental results on three real hyperspectral data sets validate the effectiveness of our method. Full article
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Figure 1

Figure 1
<p>A flowchart of the proposed MSuperLPP for hyperspectral image (HSI) classification.</p>
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<p>Classification accuracy versus training size. (<b>a</b>) Indian Pines with the nearest neighbor classifier. (<b>b</b>) Indian Pines with support vector machine (SVM) classifier. (<b>c</b>) Zaoyuan with nearest neighbor classifier. (<b>d</b>) Zaoyuan with SVM classifier. (<b>e</b>) Salinas with nearest neighbor classifier. (<b>f</b>) Salinas with SVM classifier.</p>
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<p>Classification maps obtained with the Indian Pines data set. (<b>a</b>) Ground truth. (<b>b</b>) GlobalLPP-SF. (<b>c</b>) GlobalLPP-SSCF. (<b>d</b>) SuperLPP-SF. (<b>e</b>) SuperLPP-SSCF. (<b>f</b>) MSuperLPP.</p>
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<p>Classification maps obtained with the Zaoyuan data set. (<b>a</b>) Ground truth. (<b>b</b>) GlobalLPP-SF. (<b>c</b>) GlobalLPP-SSCF. (<b>d</b>) SuperLPP-SF. (<b>e</b>) SuperLPP-SSCF. (<b>f</b>) MSuperLPP.</p>
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<p>Classification maps obtained with the Salinas data set. (<b>a</b>) Ground truth. (<b>b</b>) GlobalLPP-SF. (<b>c</b>) GlobalLPP-SSCF. (<b>d</b>) SuperLPP-SF. (<b>e</b>) SuperLPP-SSCF. (<b>f</b>) MSuperLPP.</p>
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18 pages, 3063 KiB  
Article
Establishment of a Numerical Model to Design an Electro-Stimulating System for a Porcine Mandibular Critical Size Defect
by Hendrikje Raben, Peer W. Kämmerer, Rainer Bader and Ursula van Rienen
Appl. Sci. 2019, 9(10), 2160; https://doi.org/10.3390/app9102160 - 27 May 2019
Cited by 20 | Viewed by 3975
Abstract
Electrical stimulation is a promising therapeutic approach for the regeneration of large bone defects. Innovative electrically stimulating implants for critical size defects in the lower jaw are under development and need to be optimized in silico and tested in vivo prior to application. [...] Read more.
Electrical stimulation is a promising therapeutic approach for the regeneration of large bone defects. Innovative electrically stimulating implants for critical size defects in the lower jaw are under development and need to be optimized in silico and tested in vivo prior to application. In this context, numerical modelling and simulation are useful tools in the design process. In this study, a numerical model of an electrically stimulated minipig mandible was established to find optimal stimulation parameters that allow for a maximum area of beneficially stimulated tissue. Finite-element simulations were performed to determine the stimulation impact of the proposed implant design and to optimize the electric field distribution resulting from sinusoidal low-frequency ( f = 20 Hz ) electric stimulation. Optimal stimulation parameters of the electrode length h el = 25 m m and the stimulation potential φ stim = 0.5 V were determined. These parameter sets shall be applied in future in vivo validation studies. Furthermore, our results suggest that changing tissue properties during the course of the healing process might make a feedback-controlled stimulation system necessary. Full article
(This article belongs to the Special Issue Biomaterials for Bone Tissue Engineering)
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Figure 1

Figure 1
<p>(<b>a</b>) Computer tomographic pictures of the head of a minipig. To reduce unnecessary computational costs in the later simulations the upper part of the head has been removed in the modelling process. (<b>b</b>) CAD model of a defective minipig mandible and its surrounding tissues, equipped with an electro-stimulating implant. The critical size defect (region of interest) is highlighted in red. (<b>c</b>) Bipolar electro-stimulating implant consisting of stimulating electrode (“Electrode 1”), insulator and counter-electrode (“Electrode 2”). The parameters <math display="inline"><semantics> <msub> <mi>h</mi> <mi>el</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>stim</mi> </msub> </semantics></math> are to be optimized.</p>
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<p>Equivalent circuit model for the electrode–tissue interface.</p>
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<p>Boundary conditions applied in the simulation model: We assigned Dirichlet boundary conditions to the surfaces of both electrodes (black), homogeneous Neumann boundary conditions to the surface of the skin (blue), and a floating potential boundary condition to the surface enclosing the defect (red).</p>
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<p>(<b>a</b>) Simulated electric field norm <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <munder> <mi mathvariant="bold">E</mi> <mo>̲</mo> </munder> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math> in a slice through the electrically stimulated minipig mandible (<math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mi>el</mi> </msub> <mo>=</mo> <mn>25</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>stim</mi> </msub> <mo>=</mo> <mn>0.523</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">V</mi> </mrow> </semantics></math>). Scale is bounded for field strengths between 5 and 70 <math display="inline"><semantics> <mi mathvariant="normal">V</mi> </semantics></math>/<math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math> and the arrow length is normalized. (<b>b</b>) Region with beneficially (top, green) and overstimulated (bottom, red) tissue around the electro-stimulating implant. The depicted mesh on the mandible bone corresponds to the finite-element discretization used in the simulations.</p>
Full article ">Figure 5
<p>Volume of under-, beneficially, and overstimulated tissue in the ROI at a stimulation amplitude of <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>stim</mi> </msub> <mo>=</mo> <mn>1</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">V</mi> </mrow> </semantics></math> as a function of the electrode length <math display="inline"><semantics> <msub> <mi>h</mi> <mi>el</mi> </msub> </semantics></math>.</p>
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<p>Impact of changing the electrical conductivity <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>defect</mi> </msub> </semantics></math> in the defect domain on the volume of stimulated tissue and the current flowing through the electrodes.</p>
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15 pages, 2532 KiB  
Article
Influence of Time Delay in Signal Transmission on Synchronization between Two Coupled FitzHugh-Nagumo Neurons
by Bin Zhen, Zhenhua Li and Zigen Song
Appl. Sci. 2019, 9(10), 2159; https://doi.org/10.3390/app9102159 - 27 May 2019
Cited by 7 | Viewed by 2856
Abstract
In this paper, the energy method is employed to analytically investigate the influence of time delay in signal transmission on synchronization between two coupled FitzHugh-Nagumo (FHN) neurons. Unlike pre-existing methods that deal with synchronization problems, our major idea is to consider the change [...] Read more.
In this paper, the energy method is employed to analytically investigate the influence of time delay in signal transmission on synchronization between two coupled FitzHugh-Nagumo (FHN) neurons. Unlike pre-existing methods that deal with synchronization problems, our major idea is to consider the change rate of the energy of the synchronization error system, since the original system’s synchronization is equivalent to the disappearance of the energy of the error system. In rewriting the original coupled system in the corresponding energy coordinates based on the energy method, we find that the change rate of energy of the error system can be divided into two parts (periodic and non-periodic). The synchronization criterion for the original system can then be obtained by letting the non-periodic part of the change rate of the energy be less than zero. The correctness of the analysis is illustrated with numerical simulations. Our analytical results show that time delay in signal transmission has very significant effects on the synchronization between two FHN neurons. If the time delay in signal transmission is not taken into account in the two coupled FHN neurons, synchronous spikes cannot be achieved in the system for any given coupling strength. By adjusting the value of the time delay in signal transmission, the neural system can freely switch between neural rest and synchronous spikes. This means that time delay in signal transmission is crucial for the occurrence of synchronous spikes in the FHN neural system, which contributes to our understanding of the interaction between neurons. We analytically show the influence of the time delay on the synchronization between two FHN neurons, which was seldom considered by other researchers. Full article
(This article belongs to the Section Applied Biosciences and Bioengineering)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Dynamics of Equation (2) with <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>=</mo> <mn>0.16</mn> </mrow> </semantics></math>. The initial conditions are taken as <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>; (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>.</p>
Full article ">Figure 1 Cont.
<p>Dynamics of Equation (2) with <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>=</mo> <mn>0.16</mn> </mrow> </semantics></math>. The initial conditions are taken as <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>; (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>.</p>
Full article ">Figure 2
<p>Dynamics of Equation (2) with <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>=</mo> <mn>0.18</mn> </mrow> </semantics></math>. The initial conditions are taken as <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>.</p>
Full article ">Figure 2 Cont.
<p>Dynamics of Equation (2) with <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>=</mo> <mn>0.18</mn> </mrow> </semantics></math>. The initial conditions are taken as <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mn>0</mn> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. <math display="inline"><semantics> <mi>t</mi> </semantics></math>.</p>
Full article ">Figure 3
<p>Synchronization errors (<b>a</b>,<b>b</b>) and phase diagrams (<b>c</b>,<b>d</b>) for Equation (12) with <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>=</mo> <mn>0.16</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics></math>. The initial conditions are chosen as <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo stretchy="false">(</mo> <mo>−</mo> <mi>τ</mi> </mrow> </semantics></math>,<math display="inline"><semantics> <mrow> <mn>0</mn> <mo stretchy="false">]</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 3 Cont.
<p>Synchronization errors (<b>a</b>,<b>b</b>) and phase diagrams (<b>c</b>,<b>d</b>) for Equation (12) with <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>=</mo> <mn>0.16</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics></math>. The initial conditions are chosen as <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>3</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.02</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo stretchy="false">(</mo> <mo>−</mo> <mi>τ</mi> </mrow> </semantics></math>,<math display="inline"><semantics> <mrow> <mn>0</mn> <mo stretchy="false">]</mo> </mrow> </semantics></math>.</p>
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<p>Synchronization errors (<b>a</b>,<b>b</b>) and phase diagrams (<b>c</b>,<b>d</b>) for Equation (12) with <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.08</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>c</mi> <mo>=</mo> <mn>0.18</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mn>7</mn> </mrow> </semantics></math>. The initial conditions are chosen as <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>4</mn> </msub> <mo stretchy="false">(</mo> <mi>t</mi> <mo>−</mo> <mi>τ</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo stretchy="false">(</mo> <mo>−</mo> <mi>τ</mi> </mrow> </semantics></math>,<math display="inline"><semantics> <mrow> <mn>0</mn> <mo stretchy="false">]</mo> </mrow> </semantics></math>.</p>
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11 pages, 1237 KiB  
Review
Toward Long-Term Implantable Glucose Biosensors for Clinical Use
by Yun Jung Heo and Seong-Hyok Kim
Appl. Sci. 2019, 9(10), 2158; https://doi.org/10.3390/app9102158 - 27 May 2019
Cited by 23 | Viewed by 7471
Abstract
Continuous glucose monitoring (CGM) sensors have led a paradigm shift to painless, continuous, zero-finger pricking measurement in blood glucose monitoring. Recent electrochemical CGM sensors have reached two-week lifespans and no calibration with clinically acceptable accuracy. The system with the recent CGM sensors is [...] Read more.
Continuous glucose monitoring (CGM) sensors have led a paradigm shift to painless, continuous, zero-finger pricking measurement in blood glucose monitoring. Recent electrochemical CGM sensors have reached two-week lifespans and no calibration with clinically acceptable accuracy. The system with the recent CGM sensors is identified as an “integrated glucose monitoring system,” which can replace finger-pricking glucose-testing for diabetes treatment decisions. Although such innovation has brought CGM technology closer to realizing the artificial pancreas, discomfort and infection problems have arisen from short lifespans and open wounds. A fully implantable sensor with a longer-term lifespan (90 days) is considered as an alternative CGM sensor with high comfort and low running cost. However, it still has barriers, including surgery for applying and replacing and frequent calibration. If technical refinement is conducted (e.g., stability and reproducibility of sensor fabrication), fully implantable, long-term CGM sensors can open the new era of continuous glucose monitoring. Full article
(This article belongs to the Special Issue Biomaterials and Biofabrication)
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<p>Comparison of finger-pricking self-monitoring of blood glucose (SMBC) and continuous glucose monitoring (CGM).</p>
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<p>Clarke error grid. Reproduced with permission [<a href="#B22-applsci-09-02158" class="html-bibr">22</a>]. Copyright 1988, Elsevier.</p>
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<p>Glucose sensing mechanism of diboronic-acid-based fluorescence. Modified figures from those in Ref. [<a href="#B46-applsci-09-02158" class="html-bibr">46</a>].</p>
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<p>(<b>a</b>) Fluorescence hydrogel with boronic acids under the skin of mouse ear. The hydrogel glows through skin. (<b>b</b>) The fluorescence intensity of hydrogels responded to blood glucose concentration continuously after 140 days from implantation. Modified figures from those in Ref. [<a href="#B47-applsci-09-02158" class="html-bibr">47</a>].</p>
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14 pages, 2037 KiB  
Review
Overview on the Evolution of Laser Welding of Vascular and Nervous Tissues
by Diogo Francisco Gomes, Ivan Galvão and Maria Amélia Ramos Loja
Appl. Sci. 2019, 9(10), 2157; https://doi.org/10.3390/app9102157 - 27 May 2019
Cited by 11 | Viewed by 5536
Abstract
Laser welding presents a core position in the health sector. This process has had an outstanding impact on the surgical procedures from many medical areas, such as on vascular and nervous surgeries. The aim of the present research is to present an overview [...] Read more.
Laser welding presents a core position in the health sector. This process has had an outstanding impact on the surgical procedures from many medical areas, such as on vascular and nervous surgeries. The aim of the present research is to present an overview on the evolution of laser welding of vascular and nervous tissues. These surgeries present many advantages, such as an absence of foreign-body reactions and aneurysms and good tensile strengths. However, despite the sutureless nature of the process, complementary sutures have been applied to support the procedure success. An important concern in vascular and nervous laser welding is the thermal damage. The development of temperature-controlled feedback systems has reduced this concern with a very precise control of the laser parameters. The bonding strength of vascular and nerve laser welds can be enhanced with the application of solder solutions, bonding materials, and laser-activated dyes. Alternative techniques to laser welding, such as photochemical tissue bonding and electrosurgical high-frequency technologies, have also been tested for vascular and nervous repairs. Full article
(This article belongs to the Special Issue New Frontiers of Laser Welding Technology)
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<p>Schematics of laser welding of a nervous tissue [<a href="#B40-applsci-09-02157" class="html-bibr">40</a>].</p>
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<p>Instrumentation for preloaded longitudinal compression: (<b>a</b>) Vessel clamp device (prototype); (<b>b</b>) Procedure of preloaded longitudinal compression [<a href="#B48-applsci-09-02157" class="html-bibr">48</a>].</p>
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<p>Visual identification scores and adequate laser-welding end point [<a href="#B45-applsci-09-02157" class="html-bibr">45</a>,<a href="#B56-applsci-09-02157" class="html-bibr">56</a>].</p>
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<p>Schematics of laser welding of a nervous tissue, with the application of a solder and a light absorbing dye [<a href="#B40-applsci-09-02157" class="html-bibr">40</a>].</p>
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<p>Transverse cross-section of an artery repaired by laser welding, with the application of a semi-solid solder solution [<a href="#B47-applsci-09-02157" class="html-bibr">47</a>].</p>
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<p>Schematics of the laser-assisted vessel soldering [<a href="#B51-applsci-09-02157" class="html-bibr">51</a>].</p>
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14 pages, 3048 KiB  
Article
The Effect of Elevated Temperatures on the TRM-to-Masonry Bond: Comparison of Normal Weight and Lightweight Matrices
by Paraskevi D. Askouni, Catherine (Corina) G. Papanicolaou and Michael I. Kaffetzakis
Appl. Sci. 2019, 9(10), 2156; https://doi.org/10.3390/app9102156 - 27 May 2019
Cited by 15 | Viewed by 2720
Abstract
Textile Reinforced Mortar (TRM) is a composite material that has already been successfully used as an externally bonded strengthening means of existing structures. The bond of TRM with various substrates is of crucial importance for determining the degree of exploitation of the textile. [...] Read more.
Textile Reinforced Mortar (TRM) is a composite material that has already been successfully used as an externally bonded strengthening means of existing structures. The bond of TRM with various substrates is of crucial importance for determining the degree of exploitation of the textile. However, little is known on the effect of elevated/high temperatures on the TRM-to-substrate bond characteristics while relevant testing protocols are also lacking. This study focuses on the experimental assessment of the TRM-to-masonry bond after exposure of masonry wallettes unilaterally furnished with TRM strips at 120 °C and 200 °C for 1 h. The shear bond tests on cooled-down specimens were carried out using the single-lap/single-prism set-up. Two TRM systems were investigated sharing the same type of textile, which is a dry AR glass fiber one (either in a single-layer or in a double-layer configuration) and different matrices: one normal weight (TRNM) and another lightweight (TRLM) of equal compressive strengths. At control conditions (non-heated specimens) and after exposure at a nominal air temperature of 120 °C, both single-layer TRM systems exhibited similar bond capacities. After exposure at a nominal air temperature of 200 °C single-layer and double-layer TRNM overlays outperformed their TRLM counterparts. A critical discussion is based on phenomenological evidence and measured response values. Full article
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<p>Axial tensile stress versus axial tensile strain curves of: (<b>a</b>) TRNM (Textile Reinforced Normal Weight Mortar) and (<b>b</b>) TRLM (Textile Reinforced Lightweight Mortar) coupons (stress is calculated by dividing with the load-aligned fibers’ cross section).</p>
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<p>Single-lap/single-prism shear bond test set-up.</p>
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<p>Time history of specimens’ preparation and treatment.</p>
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<p>Insulated specimen (instrumented with thermocouples): (<b>a</b>) side view and (<b>b</b>) inside the electrical furnace; (K-type thermocouple: close to Loaded End, TKLE; close to Free End, TKFE; on the strip’s surface, TKM; close to specimen’s surface, TKA)</p>
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<p>Specimen at failure.</p>
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<p>Thermocouples’ temperature profiles for representative specimens: (<b>a</b>) T120S1L03, (<b>b</b>) T200S1L03, and (<b>c</b>) T200S2L02.</p>
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<p>(<b>a</b>) Maximum textile axial stress, <span class="html-italic">σ<sub>max</sub></span>, and corresponding (<b>b</b>) relative displacement, <span class="html-italic">d<sub>r,max</sub></span>, and (<b>c</b>) slip, <span class="html-italic">s<sub>max</sub></span>, versus nominal exposure temperature.</p>
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<p>Response curves of representative specimens reinforced with: (<b>a</b>) TRNM and (<b>b</b>) TRLM overlays.</p>
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13 pages, 1295 KiB  
Article
Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
by Qi Liu, Shuangxing Liu, Dan Luo and Bo Peng
Appl. Sci. 2019, 9(10), 2155; https://doi.org/10.3390/app9102155 - 27 May 2019
Cited by 21 | Viewed by 4173
Abstract
The liquid phase of foam systems plays a major role in improving the fluidity of oil, by reducing oil viscosity and stripping oil from rock surfaces during foam-flooding processes. Improving the oil displacement capacity of the foam’s liquid phase could lead to significant [...] Read more.
The liquid phase of foam systems plays a major role in improving the fluidity of oil, by reducing oil viscosity and stripping oil from rock surfaces during foam-flooding processes. Improving the oil displacement capacity of the foam’s liquid phase could lead to significant improvement in foam-flooding effects. Oil-liquid interfacial tension (IFT) is an important indicator of the oil displacement capacity of a liquid. In this study, several surfactants were used as foaming agents, and polymers were used as foam stabilizers. Foaming was induced using a Waring blender stirring method. Foam with an oil-liquid IFT of less than 10–3 mN/m was prepared after a series of adjustments to the liquid composition. This study verified the possibility of a foam system with both an ultra-low oil-liquid IFT and high foaming properties. Our results provide insight into a means of optimizing foam fluids for enhanced oil recovery. Full article
(This article belongs to the Section Chemical and Molecular Sciences)
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<p>Molecular structure of the surfactants.</p>
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<p>Flow chart of core-flooding experiment.</p>
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<p>(<b>a</b>) Viscosity versus shear rate and (<b>b</b>) viscosity versus temperature.</p>
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<p>Influence of the xanthan gum (XC) concentration on oil-liquid interfacial tension.</p>
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<p>Differential pressure varied with the injection volume.</p>
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12 pages, 942 KiB  
Article
Simple Degree-of-Freedom Modeling of the Random Fluctuation Arising in Human–Bicycle Balance
by Katsutoshi Yoshida, Keishi Sato and Yoshikazu Yamanaka
Appl. Sci. 2019, 9(10), 2154; https://doi.org/10.3390/app9102154 - 27 May 2019
Cited by 5 | Viewed by 3016
Abstract
In this study, we propose a new simple degree-of-freedom fluctuation model that accurately reproduces the probability density functions (PDFs) of human–bicycle balance motions as simply as possible. First, we measure the time series of the roll angular displacement and velocity of human–bicycle balance [...] Read more.
In this study, we propose a new simple degree-of-freedom fluctuation model that accurately reproduces the probability density functions (PDFs) of human–bicycle balance motions as simply as possible. First, we measure the time series of the roll angular displacement and velocity of human–bicycle balance motions and construct their PDFs. Next, using these PDFs as training data, we identify the model parameters by means of particle swarm optimization; in particular, we minimize the Kolmogorov–Smirnov distance between the human PDFs from the participants and the PDFs simulated by our model. The resulting PDF fitnesses were over 98.7 % for all participants, indicating that our simulated PDFs were in close agreement with human PDFs. Furthermore, the Kolmogorov–Smirnov statistical hypothesis testing was applied to the resulting human–bicycle fluctuation model, showing that the measured time responses were much better supported by our model than the Gaussian distribution. Full article
(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)
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<p>Photograph of our experimental device, a human participant, and an experimenter.</p>
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<p>Schematic front view of the bicycle during the experiment.</p>
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<p>The measured time series of the human–bicycle balance for <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>s</mi> <mo>,</mo> <mi>n</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>The measured joint probability density functions (PDFs) from all participants (<math display="inline"><semantics> <mrow> <mi>s</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <mn>8</mn> </mrow> </semantics></math>).</p>
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<p>Difference of our simulated <math display="inline"><semantics> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>s</mi> <mi>i</mi> <mi>m</mi> </mrow> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> </semantics></math> from the measured <math display="inline"><semantics> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>h</mi> <mi>u</mi> <mi>m</mi> </mrow> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> </semantics></math> (left column) and that of the equivalent Gaussian PDF <math display="inline"><semantics> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>G</mi> <mi>a</mi> <mi>u</mi> <mi>s</mi> <mi>s</mi> </mrow> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> </semantics></math> (right column), for all <span class="html-italic">s</span>.</p>
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<p>Kolmogorov–Smirnov (KS) testing results. The solid curve plots <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </semantics></math>, the KS statistic cumulative distribution function (CDF). The small circles indicate the <span class="html-italic">p</span>-values between measured <math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>h</mi> <mi>u</mi> <mi>m</mi> </mrow> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </msubsup> </semantics></math> and our proposed <math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>s</mi> <mi>i</mi> <mi>m</mi> </mrow> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </msubsup> </semantics></math>, and the cross marks indicate those between <math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>h</mi> <mi>u</mi> <mi>m</mi> </mrow> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </msubsup> </semantics></math> and Gaussian <math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>G</mi> <mi>a</mi> <mi>u</mi> <mi>s</mi> <mi>s</mi> </mrow> <mrow> <mo>(</mo> <mi>s</mi> <mo>)</mo> </mrow> </msubsup> </semantics></math>.</p>
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13 pages, 2578 KiB  
Article
Classification of Marine Vessels with Multi-Feature Structure Fusion
by Erhu Zhang, Kelu Wang and Guangfeng Lin
Appl. Sci. 2019, 9(10), 2153; https://doi.org/10.3390/app9102153 - 27 May 2019
Cited by 20 | Viewed by 3729
Abstract
The classification of marine vessels is one of the important problems of maritime traffic. To fully exploit the complementarity between different features and to more effectively identify marine vessels, a novel feature structure fusion method based on spectral regression discriminant analysis (SF-SRDA) was [...] Read more.
The classification of marine vessels is one of the important problems of maritime traffic. To fully exploit the complementarity between different features and to more effectively identify marine vessels, a novel feature structure fusion method based on spectral regression discriminant analysis (SF-SRDA) was proposed. Firstly, we selected the different convolutional neural network features that better describe the characteristics of ships, and constructed the features based on graphs by the similarity metric. Then we weighed the concatenate multi-feature and fused their structures according to the linear relationship assumption. Finally, we constructed the optimization formula to solve the fusion features and structure by using spectral regression discriminant analyses. Experiments on the VAIS dataset show that the proposed SF-SRDA method can reduce the feature dimension from the original 102,400 dimensions to 5 dimensions, that the classification accuracy of visible images can reach 87.60%, and that that of the infrared image can reach 74.68% at daytime. The experimental results demonstrate that the proposed method can not only extract the optimal features from the original redundant feature space, but also greatly reduce the dimensions of the feature. Furthermore, the classification performance of SF-SRDA also gets a promising result. Full article
(This article belongs to the Special Issue Multimodal Deep Learning Methods for Video Analytics)
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<p>The overall framework of multi-feature Structure Fusion based on Spectral Regression Discriminant Analysis (SF-SRDA). CNN: convolutional neural network, IR: Infrared images.</p>
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<p>The location of the extracted features from VGG-19 or ResNet-152.</p>
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<p>Internal structure fusion of multi-feature.</p>
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<p>Some visible image of VAIS dataset.</p>
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<p>Some infrared image of VAIS dataset.</p>
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<p>Fine-grained image examples under the sailing class.</p>
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13 pages, 6011 KiB  
Article
Modeling and Analysis of the Influence of an Edge Filter on the Combining Efficiency and Beam Quality of a 10-kW-Class Spectral Beam-Combining System
by Jun Ma, Fan Chen, Cong Wei and Rihong Zhu
Appl. Sci. 2019, 9(10), 2152; https://doi.org/10.3390/app9102152 - 27 May 2019
Cited by 6 | Viewed by 3055
Abstract
Filter-based spectral beam combining (FSBC) is a promising power-scaling concept for high-power, broad-linewidth fiber lasers, as it relaxes the requirements for linewidth control and also the sizes of the individual beams. As the combining element in the FSBC system, the steep-edge filter plays [...] Read more.
Filter-based spectral beam combining (FSBC) is a promising power-scaling concept for high-power, broad-linewidth fiber lasers, as it relaxes the requirements for linewidth control and also the sizes of the individual beams. As the combining element in the FSBC system, the steep-edge filter plays a major role in achievement of the combining efficiency and the beam quality. In this case, we combine the uncorrelated surface roughness model and the combining efficiency model, and we conduct a comprehensive analysis of the effects of surface roughness, thickness error, and incident angle on the filter’s optical properties and the combining efficiency, in order to determine the optimal configuration for the laser beam-combining system. The simulation results show a good agreement with the measured ones. Meanwhile, through the adoption of the angular spectrum theory, this paper has also conducted a preliminary analysis of the influence of the combining elements on the quality of the combined beam, and some theoretical instructions on the future design of the spectral beam-combining system are provided. Full article
(This article belongs to the Section Optics and Lasers)
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<p>(<b>a</b>) Schematic diagrams of the steep-edge filters; (<b>b</b>) the profile of the rough surface [<a href="#B21-applsci-09-02152" class="html-bibr">21</a>,<a href="#B24-applsci-09-02152" class="html-bibr">24</a>].</p>
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<p>Experimental setup for a spectral beam formed from the combination of two high-power, broad-linewidth single beams. DL, diode laser; HR FBG, highly reflective fiber Bragg grating; OC FBG, output coupler fiber Bragg grating; YDCF, ytterbium-doped double-clad fiber; CPS, cladding power stripper; HR, high reflector [<a href="#B11-applsci-09-02152" class="html-bibr">11</a>,<a href="#B24-applsci-09-02152" class="html-bibr">24</a>].</p>
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<p>Test setup for measuring the reflectance curve of the edge filter. FC, fiber collimator; ASE light source, amplified spontaneous emission light source. Inset (<b>a</b>) shows the measured spectra; Inset (<b>b</b>) shows the experimental results of the reflectance curves under different incident angles (0°, 2.5°, and 5°).</p>
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<p>(<b>a</b>) The reflectance curves of the edge filter under different surface roughness values. The simulation results of the reflectance curve of the edge filter with smooth surface, obtained with: (<b>b</b>) TFCalc; (<b>c</b>) uncorrelated surface roughness model. (<b>d</b>) The reflectance curve of the edge filter versus the thickness error.</p>
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<p>The reflectance curves of the edge filter with various random errors on each layer of the stack: (<b>a</b>) 0.1%~0.3%; (<b>b</b>) 0.4%~0.6%.</p>
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<p>The simulation and experimental results of the reflectance curves under different incident angles: (<b>a</b>) <span class="html-italic">s</span> polarization; (<b>b</b>) <span class="html-italic">p</span> polarization.</p>
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<p>(<b>a</b>) The measured normalized spectra of the 1070 nm laser (blue line) and the 1090 nm laser (red line). (<b>b</b>) The combining efficiency as a function of the surface roughness.</p>
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<p>(<b>a</b>) The combining efficiency as a function of the thickness error. (<b>b</b>) The combining efficiency as a function of the incident angle. The blue line represents the <span class="html-italic">s</span>-polarized light, and the red line represents the <span class="html-italic">p</span>-polarized light.</p>
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<p>(<b>a</b>) The encircled power versus surface roughness at a distance of 500 mm. The intensity profile of: (<b>b</b>) the ideal laser (surface roughness <span class="html-italic">σ</span> = 0 nm); (<b>c</b>) the reflected laser (surface roughness <span class="html-italic">σ</span> = 2 nm); (<b>d</b>) the transmitted laser (surface roughness <span class="html-italic">σ</span> = 2 nm), and (<b>e</b>) the combined laser (surface roughness <span class="html-italic">σ</span> = 2 nm), all at a distance of 500 mm.</p>
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<p>(<b>a</b>) The encircled power versus the propagation distance, with a surface roughness of 2 nm. The intensity profiles of the combined beam at different propagation distances <span class="html-italic">Z</span>; (<b>b</b>) <span class="html-italic">Z</span> = 0 mm; (<b>c</b>) <span class="html-italic">Z</span> = 80 mm; (<b>d</b>) <span class="html-italic">Z</span> = 160 mm; (<b>e</b>) <span class="html-italic">Z</span> = 240 mm; (<b>f</b>) <span class="html-italic">Z</span> = 320 mm; (<b>g</b>) <span class="html-italic">Z</span> = 500 mm.</p>
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<p>The measured complex amplitudes of the incident lasers.</p>
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<p>The encircled power versus surface roughness at a distance of 500 mm. The insets are the intensity profiles of the combined laser under different surface roughness values.</p>
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<p>The encircled power versus propagation distance, with a surface roughness of 0.85 nm. The insets display the intensity profiles of the combined beam at different propagation distances.</p>
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16 pages, 2730 KiB  
Article
Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty
by Pengzhi Wei, Yanqiu Li, Tie Li, Naiyuan Sheng, Enze Li and Yiyu Sun
Appl. Sci. 2019, 9(10), 2151; https://doi.org/10.3390/app9102151 - 27 May 2019
Cited by 7 | Viewed by 2579
Abstract
The continuous decrease in the size of lithographic technology nodes has led to the development of source and mask optimization (SMO) and also to the control of defocus becoming stringent in the actual lithography process. Due to multi-factor impact, defocusing is always changeable [...] Read more.
The continuous decrease in the size of lithographic technology nodes has led to the development of source and mask optimization (SMO) and also to the control of defocus becoming stringent in the actual lithography process. Due to multi-factor impact, defocusing is always changeable and uncertain in the real exposure process. But conventional SMO assumes the lithography system is ideal, which only compensates the optical proximity effect (OPE) in the best focus plane. Therefore, to solve the inverse lithography problem with more uniformity of pattern in different defocus variations, we proposed a defocus robust SMO (DRSMO) approach that is driven by a defocus sensitivity penalty function for the first time. This multi-objective optimization samples a wide range of defocus disturbances and it can be proceeded by the mini-batch gradient descent (MBGD) algorithm effectively. The simulation results showed that a more robust defocus source and mask can be designed through DRSMO optimization. The defocus sensitivity factor sβ maximally decreased 63.5% compared to conventional SMO, and due to the low error sensitivity and the depth of defocus (DOF), the process window (PW) was further enlarged effectively. Compared to conventional SMO, the exposure latitude (EL) maximally increased from 4.5% to 10.5% and DOF maximally increased 54.5% (EL = 5%), which proved the validity of the DRSMO method in improving the focusing performance. Full article
(This article belongs to the Section Optics and Lasers)
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<p>Forward calculation and inverse optimization process for defocus robust SMO (DRSMO).</p>
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<p>Two test patterns used in the simulation: (<b>a</b>) Test pattern 1; (<b>b</b>) Test pattern 2. The PW calculation positions are marked at the yellow lines.</p>
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<p>Target 2 simulation results of the initial SMO, SMO under assigned focus plane (100 nm defocusing), and DRSMO with <span class="html-italic">ω</span> = 0.2, respectively.</p>
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<p>The defocus–pattern error (PAE) curves of target 1 for initial SMO (blue curve), the DRSMO with the weight factor <span class="html-italic">ω</span> = 0 (green curve), <span class="html-italic">ω</span> = 0.1 (red curve), <span class="html-italic">ω</span> = 0.2 (azury curve), and <span class="html-italic">ω</span> = 0.3 (purple curve). Optimizations were proceeded by the MBGD algorithm.</p>
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<p>Process window (PW) of target 1 for the initial SMO (blue curve), the DRSMO with the weight factor <span class="html-italic">ω</span> = 0 (green curve), <span class="html-italic">ω</span> = 0.1 (red curve), <span class="html-italic">ω</span> = 0.2 (azury curve), and <span class="html-italic">ω</span> = 0.3 (purple curve). Optimizations were proceeded by the MBGD algorithm.</p>
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<p>The defocus–PAE curves of target 2 for initial SMO (blue curve), the DRSMO with the weight factor <span class="html-italic">ω</span> = 0 (green curve) and <span class="html-italic">ω</span> = 0.2 (azury curve), respectively. Optimizations were proceeded by the MBGD algorithm.</p>
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<p>PWs of target 2 for the initial SMO (blue curve) and the DRSMO with the weight factors <span class="html-italic">ω</span> = 0 (green curve) and <span class="html-italic">ω</span> = 0.2 (azury curve), respectively. Optimizations were proceeded by the MBGD algorithm.</p>
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<p>The defocus–PAE curves of target 1 for the DRSMO with the same weight factor <span class="html-italic">ω</span> = 0.1 which was proceeded by the SGD algorithm (jasper curve) and MBGD algorithms (red curve), respectively.</p>
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<p>PWs of target 1 for the DRSMO with the same weight factor <span class="html-italic">ω</span> = 0.1 that were proceeded by the SGD algorithm (jasper curve) and MBGD algorithms (red curve), respectively.</p>
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12 pages, 1933 KiB  
Article
Plasma-Activation of Larger Liquid Volumes by an Inductively-Limited Discharge for Antimicrobial Purposes
by Michael Schmidt, Veronika Hahn, Beke Altrock, Torsten Gerling, Ioana Cristina Gerber, Klaus-Dieter Weltmann and Thomas von Woedtke
Appl. Sci. 2019, 9(10), 2150; https://doi.org/10.3390/app9102150 - 27 May 2019
Cited by 31 | Viewed by 4030
Abstract
A new configuration of a discharge chamber and power source for the treatment of up to 1 L of liquid is presented. A leakage transformer, energizing two metal electrodes positioned above the liquid, limits the discharge current inductively by utilizing the weak magnetic [...] Read more.
A new configuration of a discharge chamber and power source for the treatment of up to 1 L of liquid is presented. A leakage transformer, energizing two metal electrodes positioned above the liquid, limits the discharge current inductively by utilizing the weak magnetic coupling between the primary and secondary coils. No additional means to avoid arcing (electric short-circuiting), e.g., dielectric barriers or resistors, are needed. By using this technique, exceeding the breakdown voltage leads to the formation of transient spark discharges, producing non-thermal plasma (NTP). These discharges effected significant changes in the properties of the treated liquids (distilled water, physiological saline solution, and tap water). Considerable concentrations of nitrite and nitrate were detected after the plasma treatment. Furthermore, all tested liquids gained strong antibacterial efficacy which was shown by inactivating suspended Escherichia coli and Staphylococcus aureus. Plasma-treated tap water had the strongest effect, which is shown for the first time. Additionally, the pH-value of tap water did not decrease during the plasma treatment, and its conductivity increased less than for the other tested liquids. Full article
(This article belongs to the Special Issue Plasma Technology for Biomedical Applications)
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<p>Experimental setup.</p>
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<p>Photograph of the 4-electrode configuration for treatment of up to 1 L water [<a href="#B28-applsci-09-02150" class="html-bibr">28</a>].</p>
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<p>Electrical characterization of one discharge event with tap water as liquid [<a href="#B30-applsci-09-02150" class="html-bibr">30</a>]: Driving voltage at the electrodes (black and grey lines) and discharge current (red and green lines), discharges take place at both electrodes simultaneously.</p>
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<p>Electrical characterization for different treated liquids measured on one electrode: (<b>a</b>) tap water σ = 0.648 mS/cm, (<b>b</b>) saline solution σ = 16.23 mS/cm, (<b>c</b>) distilled water σ = 0.013 mS/cm.</p>
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<p>Optical characterization of the discharge for different liquids, the Na-D line is almost missing for liquids not containing sodium.</p>
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<p>Change of pH-value with treatment time.</p>
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<p>Change of conductivity with treatment time.</p>
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<p>Antimicrobial effect of plasma-treated saline solution on the gram-negative bacterium E. coli for different treatment times: t<sub>treat</sub> = 10, 20, and 30 min, determined by total viable count [cfu/mL] within an exposure time t<sub>exp</sub> = 30 min.</p>
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<p>Antimicrobial effect of plasma-treated saline solution (NaCl) on E. coli (boxes) and S. aureus (triangles) as well as plasma-treated tap water (TW) on E. coli (circles) and S. aureus (rhombi), t<sub>treat</sub> = 30 min.</p>
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15 pages, 3964 KiB  
Article
Enhanced Application of Principal Component Analysis in Machine Learning for Imputation of Missing Traffic Data
by Yoon-Young Choi, Heeseung Shon, Young-Ji Byon, Dong-Kyu Kim and Seungmo Kang
Appl. Sci. 2019, 9(10), 2149; https://doi.org/10.3390/app9102149 - 26 May 2019
Cited by 13 | Viewed by 3863
Abstract
Missing value imputation approaches have been widely used to support and maintain the quality of traffic data. Although the spatiotemporal dependency-based approaches can improve the imputation performance for large and continuous missing patterns, additionally considering traffic states can lead to more reliable results. [...] Read more.
Missing value imputation approaches have been widely used to support and maintain the quality of traffic data. Although the spatiotemporal dependency-based approaches can improve the imputation performance for large and continuous missing patterns, additionally considering traffic states can lead to more reliable results. In order to improve the imputation performances further, a section-based approach is also needed. This study proposes a novel approach for identifying traffic-states of different spots of road sections that comprise, namely, a section-based traffic state (SBTS), and determining their spatiotemporal dependencies customized for each SBTS, for missing value imputations. A principal component analysis (PCA) was employed, and angles obtained from the first principal component were used to identify the SBTSs. The pre-processing was combined with a support vector machine for developing the imputation model. It was found that the segmentation of the SBTS using the angles and considering the spatiotemporal dependency for each state by the proposed approach outperformed other existing models. Full article
(This article belongs to the Section Civil Engineering)
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<p>Speed contour and <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>n</mi> <msub> <mi>g</mi> <mi>t</mi> </msub> </mrow> </semantics></math> plots for the numerical example.</p>
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<p>Study area in Korean freeway. (<b>a</b>) Geographical information. (<b>b</b>) Vehicle detection system (VDS) information.</p>
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<p>Speed contour and <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>n</mi> <msub> <mi>g</mi> <mi>t</mi> </msub> </mrow> </semantics></math> plots for empirical data on 3 March 2016.</p>
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<p><math display="inline"><semantics> <mrow> <mi>A</mi> <mi>n</mi> <msub> <mi>g</mi> <mi>t</mi> </msub> </mrow> </semantics></math> and average speed plots on 12 March 2016.</p>
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<p>Flow-speed and occupancy-flow plots at VDS 4 on March 12 2016. (<b>a</b>) Classified by <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>n</mi> <msub> <mi>g</mi> <mi>t</mi> </msub> </mrow> </semantics></math>. (<b>b</b>) Classified by average speed.</p>
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<p>Spatiotemporal dependence among variables (<b>a</b>) Section-based traffic state (SBTS) I (<b>b</b>) SBTS II (<b>c</b>) SBTS III.</p>
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<p>Traffic speed profile of real and predicted data by ANN and proposed model.</p>
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23 pages, 2172 KiB  
Article
Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology
by Chen Qiao, Lujia Lu, Lan Yang and Paul J. Kennedy
Appl. Sci. 2019, 9(10), 2148; https://doi.org/10.3390/app9102148 - 26 May 2019
Cited by 8 | Viewed by 4280
Abstract
Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial [...] Read more.
Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia. Full article
(This article belongs to the Special Issue Optical Methods for Tissue Diagnostics)
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<p>The flowchart of the hybrid feature selection method. Fre denotes frequency, <math display="inline"><semantics> <mi>τ</mi> </semantics></math> the Kendall correlation coefficient, <span class="html-italic">p</span> the <span class="html-italic">p</span>-values of test and <span class="html-italic">b</span> and <span class="html-italic">c</span> the given constants. In which, SVMRFE refers to support vector machine based on recursive feature elimination, RFFS-GI refers to the feature selection with random forest by Gini importance and RFFS-OOB refers to the feature selection with random forest by the classification accuracy on the OOB data.</p>
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<p>The flowchart of locating the abnormalities in brains for SZ. Where SBM refers to source-based morphometric, FNC refers to functional network connectivity and FS refers to feature selection.</p>
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<p>SVMRFE, RFFS-GI and RFFS-OOB results of SBM data.</p>
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<p>SVMRFE, RFFS-GI and RFFS-OOB results of FNC data.</p>
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<p>The results obtained by the Kendall correlation coefficient. The <span class="html-italic">x</span> axis corresponds to the features, and the <span class="html-italic">y</span> axis is the absolute value of the Kendall tau correlation coefficient.</p>
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<p>The results of hypothesis test for both two-sample <span class="html-italic">t</span>-tests and the permutation test. The <span class="html-italic">x</span> axis corresponds to the features, and the <span class="html-italic">y</span> axis is the significance level <math display="inline"><semantics> <mrow> <mo>(</mo> <mo>−</mo> <mi>l</mi> <mi>o</mi> <msub> <mi>g</mi> <mn>2</mn> </msub> <mi>P</mi> <mo>)</mo> </mrow> </semantics></math>. The red and green lines show the significance levels of 0.05 and 0.01, respectively. The features with <math display="inline"><semantics> <mrow> <mo>−</mo> <mi>l</mi> <mi>o</mi> <msub> <mi>g</mi> <mn>2</mn> </msub> <mi>P</mi> </mrow> </semantics></math> values above the lines have significant differences, and they are the candidates of abnormal regions or connections.</p>
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<p>Feature selection results based on statistical methods.</p>
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<p>The selected abnormal brain regions of SZ by the hybrid method. Segall et al. presented the relationships between the cortical maps and the brain regions described by the SBM features [<a href="#B47-applsci-09-02148" class="html-bibr">47</a>].</p>
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<p>The abnormal functional connections of brains with SZ. In this figure, the left table lists the selected abnormal functional connections of the regions of interest (the relationships of the regions and the labels are shown in <a href="#applsci-09-02148-f0A2" class="html-fig">Figure A2</a>), in which ML refers to machine learning methods and SM refers to statistical methods. The circular connectivity graph in the middle is a schematic map of the selected functional connections, which are listed in the fourth column of the left table. The labels in this graph correspond to the regions of interest, and the corresponding spatial maps of these regions (see [<a href="#B48-applsci-09-02148" class="html-bibr">48</a>]) are also shown in this graph. The right graph depicts the locations and their connections of the selected brain regions by the BrainNet Viewer toolbox [<a href="#B49-applsci-09-02148" class="html-bibr">49</a>].</p>
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<p>Different measuring parameters of the global and local network properties. Where <math display="inline"><semantics> <msub> <mi>t</mi> <mi>i</mi> </msub> </semantics></math> is the number of triangles around node <span class="html-italic">i</span>, <math display="inline"><semantics> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </semantics></math> is the shortest path length between node <span class="html-italic">i</span> and node <span class="html-italic">j</span>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>L</mi> <mrow> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> </mrow> </msub> </semantics></math> refer to the average clustering coefficient and characteristic path length values obtained from 100 random networks with the same number of nodes, as well as edges and the same degree of distribution as the original network, <math display="inline"><semantics> <msub> <mi>σ</mi> <mrow> <mi>j</mi> <mi>k</mi> </mrow> </msub> </semantics></math> is the number of shortest paths between <span class="html-italic">j</span> and <span class="html-italic">k</span> and <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mrow> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> is the number of shortest paths between <span class="html-italic">j</span> and <span class="html-italic">k</span> that pass through <span class="html-italic">i</span>.</p>
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<p>Twenty eight brain regions selected for the experiment according to the AAL template.</p>
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<p>The connections between the brain regions R1 and R2 corresponding to FNC features.</p>
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<p>SVMRFE and RFFS results of SBM data, where Fea represents the feature number and Fre represents the frequency at which the feature appears in 20 experiments.</p>
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<p>SVMRFE and RFFS results of FNC data, Part 1.</p>
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<p>SVMRFE and RFFS results of FNC data, Part 2.</p>
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<p>SVMRFE and RFFS results of FNC data, Part 3.</p>
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<p>The characteristic frequency distribution with a frequency greater than or equal to 50. The <span class="html-italic">x</span> axis corresponds to the frequency of occurrence, and the <span class="html-italic">y</span> axis is the number of features. We can find that when the frequency is in the red range, i.e., greater than or equal to 52 and less than or equal to 56, the number of features is quite stable. Compared with other ranges, in the red range, there exists a balance between the number of features and the frequency of occurrence, which facilitates the abnormal analysis of brain function connections and structures corresponding to diseases. Therefore, we selected features with a frequency greater than or equal to 55.</p>
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13 pages, 1501 KiB  
Article
Assessing the Benefits of Battery Energy Storage Systems for Frequency Regulation, Based on Electricity Market Price Forecasting
by Eunjung Lee and Jinho Kim
Appl. Sci. 2019, 9(10), 2147; https://doi.org/10.3390/app9102147 - 26 May 2019
Cited by 5 | Viewed by 3182
Abstract
In electricity markets, energy storage systems (ESSs) have been widely used to regulate frequency in power system operations. Frequency regulation (F/R) relates to the short-term reserve power used to balance the real-time mismatch of supply and demand. Every alternating current power system has [...] Read more.
In electricity markets, energy storage systems (ESSs) have been widely used to regulate frequency in power system operations. Frequency regulation (F/R) relates to the short-term reserve power used to balance the real-time mismatch of supply and demand. Every alternating current power system has its own unique standard frequency level, and frequency variation occurs whenever there is a mismatch of supply and demand. To cope with frequency variation, generating units—particularly base-loader generators—reduce their power outputs to a certain level, and the reduced generation outputs are used as a generation reserve whenever frequency variation occurs in the power systems. ESSs have recently been implemented as an innovative means of providing the F/R reserve previously provided by base-loader generators, because they are much faster in responding to frequency variation than conventional generators. We assess the economic benefits of ESSs for F/R, based on a new forecast of long-term electricity market price and real power system operation characteristics. For this purpose, we present case studies with respect to the South Korean electricity market as well as simulation results featuring key variables, along with their implications vis-à-vis electricity market operations. Full article
(This article belongs to the Special Issue State-of-the-Art Renewable Energy in Korea)
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<p>Economic benefits of ESSs for F/R in the electricity market [<a href="#B25-applsci-09-02147" class="html-bibr">25</a>,<a href="#B26-applsci-09-02147" class="html-bibr">26</a>].</p>
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<p>Schematic diagram of the SMP forecast methodology.</p>
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<p>Comparison of real and estimated SMP, 2001–2015.</p>
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<p>Hourly load profiles of weekday and weekend power usage.</p>
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<p>Hourly load profile of weekdays and weekends, and generation mix, in 2014.</p>
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11 pages, 2715 KiB  
Article
Optimal Measurement Speed and Its Determination Method in the Transmission Precision Evaluation of Precision Reducers
by Hang Xu, Zhaoyao Shi, Bo Yu and Hui Wang
Appl. Sci. 2019, 9(10), 2146; https://doi.org/10.3390/app9102146 - 26 May 2019
Cited by 18 | Viewed by 2816
Abstract
Transmission error is the key index for characterizing the transmission precision of precision reducers, and its accurate measurement is significant for the precision evaluation of precision reducers. Transmission error is generally measured under the conditions of zero-load and low speed. However, low speed [...] Read more.
Transmission error is the key index for characterizing the transmission precision of precision reducers, and its accurate measurement is significant for the precision evaluation of precision reducers. Transmission error is generally measured under the conditions of zero-load and low speed. However, low speed is a general concept and there is no general standard of measurement speed or solid scientific basis. Therefore, it is difficult to obtain consistent transmission precision evaluation results for the same precision reducer. The concept of optimal measurement speed in the transmission precision evaluation of precision reducers was put forward in order to reduce the influence of measurement speed. The determination method of optimal measurement speed was proposed and the calculation model of the optimal measurement speed was established, according to the Stribeck friction model of precision reducers. Taking a certain type of RV reducer as an example, the transmission error measurement experiments were carried out under different speeds. The friction torque of the RV reducer and the peak-to-peak value of the measured transmission error were the least under the optimal measurement speed. The influence of speed on the measurement results can be effectively reduced. The determination of optimal measurement speed of transmission errors could improve the measurement precision of the transmission errors for the objective evaluation of transmission precision of precision reducers. Full article
(This article belongs to the Section Mechanical Engineering)
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<p>Transmission error curve.</p>
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<p>Optimal measurement speed.</p>
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<p>Four friction stages of precision reducers: (I) static friction stage, (II) boundary lubrication stage, (III) partial fluid lubrication stage, and (IV) full fluid lubrication stage.</p>
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<p>RV reducer comprehensive performance tester. (<b>a</b>) Structure diagram; and, (<b>b</b>) Physical view.</p>
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<p>Friction torque of a RV reducer (2.0933 rad/s).</p>
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<p>Fitted Stribeck curve of a RV reducer.</p>
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<p>Transmission error curve (2.4483 rad/s).</p>
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<p>Variations of the peak-to-peak value of transmission errors with speed.</p>
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<p>Transmission error spectrum.</p>
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13 pages, 5726 KiB  
Article
High Spatial Resolution Three-Dimensional Imaging Static Unitary Detector-Based Laser Detection and Ranging Sensor
by Hyeon-June Kim, Eun-Gyu Lee, Han-Woong Choi and Choul-Young Kim
Appl. Sci. 2019, 9(10), 2145; https://doi.org/10.3390/app9102145 - 26 May 2019
Viewed by 3164
Abstract
This paper presents a static unitary detector (STUD)-based laser detection and ranging (LADAR) sensor with a 16-to-1 transimpedance-combining amplifier for high spatial resolution three-dimensional (3-D) applications. In order to readout the large size of a photodetector for better results of 3-D information without [...] Read more.
This paper presents a static unitary detector (STUD)-based laser detection and ranging (LADAR) sensor with a 16-to-1 transimpedance-combining amplifier for high spatial resolution three-dimensional (3-D) applications. In order to readout the large size of a photodetector for better results of 3-D information without any reduction of the bandwidth, the partitioning photosensitive cell method is embedded in a 16-to-1 transimpedance-combining amplifier. The effective number of partitioning photosensitive cells and signal-combining stages are selected based on the analysis of the partitioning photosensitive cell method for the optimum performance of a transimpedance-combining amplifier. A prototype chip is fabricated in a 0.18-μm CMOS technology. The input referred noise is 41.9 pA/√Hz with a bandwidth of 230 MHz and a transimpedance gain of 70.4 dB·Ω. The total power consumption of the prototype chip is approximately 86 mW from a 1.8-V supply, and the TICA consumes approximately 15.4 mW of it. Full article
(This article belongs to the Special Issue LiDAR and Time-of-flight Imaging)
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<p>Block diagram of the STUD-based LADAR sensor.</p>
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<p>Partitioning photosensitive cell method.</p>
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<p>Block diagram of the STUD-based LADAR receiver embedding the PPC method.</p>
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<p>Number of signal-combining stages according to the number of combining input current buffers.</p>
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<p>Output voltages as functions of the input current and the combining number of input current buffers.</p>
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<p>Power consumption and bandwidth according to the number of combining input current buffers.</p>
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<p>Schematic of the proposed 16-to-1 TICA with PPC method.</p>
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<p>Schematic of the balun and output buffer.</p>
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<p>Test board photographs with the prototype: (<b>a</b>) optical response with wire-bonded APD and (<b>b</b>) electrical response test board without APD.</p>
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<p>(<b>a</b>) Prototype receiver-COB schematic and (<b>b</b>) measurement setup for electrical pulse response test.</p>
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<p>Dependence of the 16-to-1 TICA output voltage amplitude as the input current sweep.</p>
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<p>Measured output rms noise of (<b>a</b>) prototype chip and (<b>b</b>) oscilloscope.</p>
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<p>Dependences of TICA output on optical laser power sweep: (<b>a</b>) time axis and (<b>b</b>) attenuation axis.</p>
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<p>Two-dimensional optical pulse scanning measurement setup.</p>
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<p>Captured 2-D intensity images from two samples of the prototype chip: (<b>a</b>) sample-1 and (<b>b</b>) sample-2.</p>
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<p>Variation of prototype APD with off-chip digital offset adjustment.</p>
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16 pages, 1691 KiB  
Article
Increased Anti-Inflammatory Effects on LPS-Induced Microglia Cells by Spirulina maxima Extract from Ultrasonic Process
by Woon Yong Choi, Jae-Hun Sim, Jung-Youl Lee, Do Hyung Kang and Hyeon Yong Lee
Appl. Sci. 2019, 9(10), 2144; https://doi.org/10.3390/app9102144 - 26 May 2019
Cited by 15 | Viewed by 3787
Abstract
The Spirulina maxima exact from a non-thermal ultrasonic process (UE) contains 17.5 mg/g of total chlorophyll, compared to 6.24 mg/g of chlorophyll derived from the conventional 70% ethanol extraction at 80 °C for 12 h (EE). The UE also showed relatively low cytotoxicity [...] Read more.
The Spirulina maxima exact from a non-thermal ultrasonic process (UE) contains 17.5 mg/g of total chlorophyll, compared to 6.24 mg/g of chlorophyll derived from the conventional 70% ethanol extraction at 80 °C for 12 h (EE). The UE also showed relatively low cytotoxicity against murine microglial cells (BV-2) and inhibited the production of the inflammatory mediators, NO and PGE2. The UE also effectively suppresses both mRNA expression and the production of pro-inflammatory cytokines, such as TNF-α, IL-6 and IL-1β, in a concentration-dependent manner. Notably, TNF-α gene and protein production were most strongly down-regulated, while IL-6 was the least affected by all ranges of treatment concentrations. This work first demonstrated a quantitative correlation between mRNA expression and the production of cytokines, showing that suppression of TNF-α gene expression was most significantly correlated with its secretion. These results clearly proved that the anti-inflammatory effects of Spirulina extract from a nonthermal ultrasonic process, which yielded high concentrations of intact forms of chlorophylls, were increased two-fold compared to those of conventional extracts processed at high temperature. Full article
(This article belongs to the Special Issue Eco-Novel Food and Feed)
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<p>Cytotoxicity of the <span class="html-italic">S. maxima</span> extracts with (black bars) and without (white bars) adding 1 µg/mL of lipopolysaccharide (LPS) against BV-2 cells. EE, 70% ethanol extraction at 80 °C for 12 h, UE, ultrasonic pretreatment with 70 % ethanol at 40 kHz and room temperature for 8 h, and further extraction at 65 °C for 4 h. Values are presented as means ±SD; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the non-treatment group.</p>
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<p>Secretion of nitric oxide from BV-2 cells by the treatment of various concentrations of the <span class="html-italic">S. maxima</span> extracts. EE, 70% ethanol extraction at 80 °C for 12 h, UE, ultrasonic pretreatment with 70% ethanol at 40 kHz and room temperature for 8 h, and further extraction at 65 °C for 4 h. Values are presented as means ±SD; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the LPS group.</p>
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<p>Comparison of PGE2 secretion from BV-2 cells by the treatment of various concentrations of the <span class="html-italic">S.maxima</span> extracts. EE, 70% ethanol extraction at 80 °C for 12 h, UE, ultrasonic pretreatment with 70% ethanol at 40 kHz and room temperature for 8 h, and further extraction at 65 °C for 4 h. Values are presented as means ±SD; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the non-treatment group.</p>
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<p>Down-regulation of mRNA expression of TNF-a from LPS-induced BV-2 cells (<b>a</b>) and the relative ratio of the gene expression by normalizing with beta-actin as a house-keeping gene (<b>b</b>) by the treatment of various concentrations of the <span class="html-italic">Spirulina</span> extracts along with the untreated control. EE, 70% ethanol extraction at 80 °C for 12 h, UE, ultrasonic pretreatment with 70% ethanol at 40 kHz and room temperature for 8 h, and further extraction at 65 °C for 4 h. Values are presented as means ±SD; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the LPS group.</p>
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<p>The secretion of TNF-a from LPS-induced BV-2 cells by the treatment of various concentrations of the <span class="html-italic">Spirulina</span> extracts. EE, 70% ethanol extraction at 80 °C for 12 h, UE, ultrasonic pretreatment with 70% ethanol at 40 kHz and room temperature for 8 h, and further extraction at 65 °C for 4 h. Values are presented as means ±SD; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the LPS group.</p>
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<p>Relative ratios of mRNA expression of IL-6 by normalizing with beta-actin as a house-keeping gene (<b>a</b>) and secretion of IL-6 (<b>b</b>) from LPS-induced BV-2 cells by the treatment of various concentrations of the <span class="html-italic">Spirulina</span> extracts. EE, 70% ethanol extraction at 80 °C for 12 h, UE, ultrasonic pretreatment with 70% ethanol at 40 kHz and room temperature for 8 h, and further extraction at 65 °C for 4 h. Values are presented as means ±SD; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the LPS group.</p>
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<p>Relative ratios of mRNA expression of IL-1beta by normalizing with beta-actin as a house-keeping gene (<b>a</b>) and secretion of IL-6 (<b>b</b>) from LPS-induced BV-2 cells by the treatment of various concentrations of the <span class="html-italic">Spirulina</span> extracts. EE, 70% ethanol extraction at 80 °C for 12 h, UE, ultrasonic pretreatment with 70% ethanol at 40 kHz and room temperature for 8 h, and further extraction at 65 °C for 4 h. Values are presented as means ±SD; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the LPS group.</p>
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<p>Quantitative comparison of the secretion and mRNA expression of TNF-a (<b>a</b>), IL-6 (<b>b</b>) and IL-1beta (<b>c</b>). EE, 70% ethanol extraction at 80 °C for 12 h, UE, ultrasonic pretreatment with 70% ethanol at 40 kHz and room temperature for 8 h, and further extraction at 65 °C for 4 h. Values are presented as means ±SD; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 compared with the non-treatment group.</p>
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20 pages, 1939 KiB  
Article
The Study on the Cultivable Microbiome of the Aquatic Fern Azolla Filiculoides L. as New Source of Beneficial Microorganisms
by Artur Banach, Agnieszka Kuźniar, Radosław Mencfel and Agnieszka Wolińska
Appl. Sci. 2019, 9(10), 2143; https://doi.org/10.3390/app9102143 - 26 May 2019
Cited by 14 | Viewed by 6288
Abstract
The aim of the study was to determine the still not completely described microbiome associated with the aquatic fern Azolla filiculoides. During the experiment, 58 microbial isolates (43 epiphytes and 15 endophytes) with different morphologies were obtained. We successfully identified 85% of [...] Read more.
The aim of the study was to determine the still not completely described microbiome associated with the aquatic fern Azolla filiculoides. During the experiment, 58 microbial isolates (43 epiphytes and 15 endophytes) with different morphologies were obtained. We successfully identified 85% of microorganisms and assigned them to 9 bacterial genera: Achromobacter, Bacillus, Microbacterium, Delftia, Agrobacterium, and Alcaligenes (epiphytes) as well as Bacillus, Staphylococcus, Micrococcus, and Acinetobacter (endophytes). We also studied an A. filiculoides cyanobiont originally classified as Anabaena azollae; however, the analysis of its morphological traits suggests that this should be renamed as Trichormus azollae. Finally, the potential of the representatives of the identified microbial genera to synthesize plant growth-promoting substances such as indole-3-acetic acid (IAA), cellulase and protease enzymes, siderophores and phosphorus (P) and their potential of utilization thereof were checked. Delftia sp. AzoEpi7 was the only one from all the identified genera exhibiting the ability to synthesize all the studied growth promoters; thus, it was recommended as the most beneficial bacteria in the studied microbiome. The other three potentially advantageous isolates (Micrococcus sp. AzoEndo14, Agrobacterium sp. AzoEpi25 and Bacillus sp. AzoEndo3) displayed 5 parameters: IAA (excluding Bacillus sp. AzoEndo3), cellulase, protease, siderophores (excluding Micrococcus sp. AzoEndo14), as well as mineralization and solubilization of P (excluding Agrobacterium sp. AzoEpi25). Full article
(This article belongs to the Section Applied Biosciences and Bioengineering)
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<p>(<b>a</b>) Culture of <span class="html-italic">A. filiculoides</span> under laboratory conditions on IRRI medium; (<b>b</b>) filaments of <span class="html-italic">A. azolla</span>e in a leaf cavity; (<b>c</b>) close up of <span class="html-italic">A. azollae</span>; both pictures taken from the light microscope at magnifications of 10x and 100x, respectively (Nikon Eclipse 80i, Nikon Instruments Europe B.V., Amsterdam, The Netherlands). Photo: A. Banach.</p>
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<p>UV microphotograph of the cyanobiont culture obtained during passage (Nikon Eclipse 80i microscope, magnification 4x, UV2A filter, Nikon Instruments Europe B.V., Amsterdam, The Netherlands). Photo: A. Banach.</p>
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<p>Examples of the most common isolates: (<b>a</b>) cream epiphyte, (<b>b</b>) yellow endophyte, and (<b>c</b>) white-cream filamentous form of epiphyte no. 37. Photo: A. Banach.</p>
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<p>Growth curves for the cultured microorganisms selected for phenotyping. <span class="html-italic">X</span>-axis presents time of incubation (hours) and <span class="html-italic">Y</span>-axis values of OD<sub>600</sub>. Logarithmic curves are fitted to the data.</p>
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