[go: up one dir, main page]
Next Issue
Volume 12, April-1
Previous Issue
Volume 12, March-1
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Volume 12
Issue 6
applsci-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Appl. Sci., Volume 12, Issue 6 (March-2 2022) – 451 articles

Cover Story (view full-size image): Criminals often conceal corrosive solutions in inconspicuous plastic bottles in order to incapacitate a victim while committing a robbery or to cause physical harm. There is currently no method available to law enforcement for the safe identification of these corrosive substances without being exposed to them. In this work, the feasibility of a near infrared (NIR) handheld spectrometer for the screening of corrosive inorganic solutions through plastic bottles is investigated. The models designed identified the corrosive substances in scenarios of concentrated solutions, showcasing the potential capability of this technique for the pre-screening of corrosive substances. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
14 pages, 2230 KiB  
Article
Binding of Arsenic by Common Functional Groups: An Experimental and Quantum-Mechanical Study
by Donatella Chillé, Viviana Mollica-Nardo, Ottavia Giuffrè, Rosina Celeste Ponterio, Franz Saija, Jiří Sponer, Sebastiano Trusso, Giuseppe Cassone and Claudia Foti
Appl. Sci. 2022, 12(6), 3210; https://doi.org/10.3390/app12063210 - 21 Mar 2022
Cited by 4 | Viewed by 2433
Abstract
Arsenic is a well-known contaminant present in different environmental compartments and in human organs and tissues. Inorganic As(III) represents one of the most dangerous arsenic forms. Its toxicity is attributed to its great affinity with the thiol groups of proteins. Considering the simultaneous [...] Read more.
Arsenic is a well-known contaminant present in different environmental compartments and in human organs and tissues. Inorganic As(III) represents one of the most dangerous arsenic forms. Its toxicity is attributed to its great affinity with the thiol groups of proteins. Considering the simultaneous presence in all environmental compartments of other common functional groups, we here present a study aimed at evaluating their contribution to the As(III) complexation. As(III) interactions with four (from di- to hexa-) carboxylic acids, five (from mono- to penta-) amines, and four amino acids were evaluated via experimental methods and, in simplified systems, also by quantum-mechanical calculations. Data were analyzed also with respect to those previously reported for mixed thiol-carboxylic ligands to evaluate the contribution of each functional group (-SH, -COOH, and -NH2) toward the As(III) complexation. Formation constants of As(III) complex species were experimentally determined, and data were analyzed for each class of ligand. An empirical relationship was reported, taking into account the contribution of each functional group to the complexation process and allowing for a rough estimate of the stability of species in systems where As(III) and thiol, carboxylic, or amino groups are involved. Quantum-mechanical calculations allowed for the evaluation and the characterization of the main chelation reactions of As(III). The potential competitive effects of the investigated groups were evaluated using cysteine, a prototypical species possessing all the functional groups under investigation. Results confirm the higher binding capabilities of the thiol group under different circumstances, but also indicate the concrete possibility of the simultaneous binding of As(III) by the thiol and the carboxylic groups. Full article
(This article belongs to the Special Issue Complexation of Metals in Natural Fluids: Simulations, Experiments and Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Ligands under study.</p> Full article ">Figure 2
<p>Distribution diagrams of (<b>a</b>) As(III)-<span class="html-italic">asp</span> and (<b>b</b>) As(III)-<span class="html-italic">cys</span> systems. Conditions: C<sub>As</sub> = 3 mmol/L, C<sub>L</sub> = 6 mmol/L, I = 0.15 mol/L (NaCl), T = 298.15 K.</p> Full article ">Figure 3
<p>Calculated logK values for As(III) -S, -O-, and -N donor ligand interactions vs. experimental ones.</p> Full article ">Figure 4
<p>Molecular structures of the complexes formed by the direct one-to-one binding of As(III) with the thiol (<b>a</b>), amino (<b>b</b>), and carboxylic (<b>c</b>) groups optimized at the B3LYP/6-311++G(d,p) under implicit solvation. Relevant optimal interatomic distances are shown in Å.</p> Full article ">Figure 5
<p>Molecular structures of the complexes formed by the two-to-one binding of As(III) with the thiol and amino (<b>a</b>), thiol and carboxylic (<b>b</b>), and amino and carboxylic (<b>c</b>) groups optimized at the B3LYP/6-311++G(d,p) under implicit solvation. Relevant optimal interatomic distances are shown in Å.</p> Full article ">
16 pages, 285 KiB  
Article
Using Feature Selection with Machine Learning for Generation of Insurance Insights
by Ayman Taha, Bernard Cosgrave and Susan Mckeever
Appl. Sci. 2022, 12(6), 3209; https://doi.org/10.3390/app12063209 - 21 Mar 2022
Cited by 19 | Viewed by 4368
Abstract
Insurance is a data-rich sector, hosting large volumes of customer data that is analysed to evaluate risk. Machine learning techniques are increasingly used in the effective management of insurance risk. Insurance datasets by their nature, however, are often of poor quality with noisy [...] Read more.
Insurance is a data-rich sector, hosting large volumes of customer data that is analysed to evaluate risk. Machine learning techniques are increasingly used in the effective management of insurance risk. Insurance datasets by their nature, however, are often of poor quality with noisy subsets of data (or features). Choosing the right features of data is a significant pre-processing step in the creation of machine learning models. The inclusion of irrelevant and redundant features has been demonstrated to affect the performance of learning models. In this article, we propose a framework for improving predictive machine learning techniques in the insurance sector via the selection of relevant features. The experimental results, based on five publicly available real insurance datasets, show the importance of applying feature selection for the removal of noisy features before performing machine learning techniques, to allow the algorithm to focus on influential features. An additional business benefit is the revelation of the most and least important features in the datasets. These insights can prove useful for decision making and strategy development in areas/business problems that are not limited to the direct target of the downstream algorithms. In our experiments, machine learning techniques based on a set of selected features suggested by feature selection algorithms outperformed the full feature set for a set of real insurance datasets. Specifically, 20% and 50% of features in our five datasets had improved downstream clustering and classification performance when compared to whole datasets. This indicates the potential for feature selection in the insurance sector to both improve model performance and to highlight influential features for business insights. Full article
(This article belongs to the Topic Machine and Deep Learning)
14 pages, 1984 KiB  
Article
Biological Effect of Gamma Rays According to Exposure Time on Germination and Plant Growth in Wheat
by Min Jeong Hong, Dae Yeon Kim, Yeong Deuk Jo, Hong-Il Choi, Joon-Woo Ahn, Soon-Jae Kwon, Sang Hoon Kim, Yong Weon Seo and Jin-Baek Kim
Appl. Sci. 2022, 12(6), 3208; https://doi.org/10.3390/app12063208 - 21 Mar 2022
Cited by 31 | Viewed by 4430
Abstract
Gamma rays as a type of ionizing radiation constitute a physical mutagen that induces mutations and could be effectively used in plant breeding. To compare the effects of gamma and ionizing irradiation according to exposure time in common wheat (Keumgang, IT 213100), seeds [...] Read more.
Gamma rays as a type of ionizing radiation constitute a physical mutagen that induces mutations and could be effectively used in plant breeding. To compare the effects of gamma and ionizing irradiation according to exposure time in common wheat (Keumgang, IT 213100), seeds were exposed to 60Co gamma rays at different dose rates. To evaluate the amount of free radical content, we used electron spin resonance spectroscopy. Significantly more free radicals were generated in the case of long-term compared with short-term gamma-ray exposure at the same dose of radiation. Under short-term exposure, shoot and root lengths were slightly reduced compared with those of the controls, whereas long-term exposure caused severe growth inhibition. The expression of antioxidant-related and DNA-repair-related genes was significantly decreased under long-term gamma-ray exposure. Long-term exposure caused higher radiosensitivity than short-term exposure. The results of this study could help plant breeders select an effective mutagenic induction dose rate in wheat. Full article
(This article belongs to the Special Issue Plant Biotechnology in Agriculture)
►▼ Show Figures

Figure 1

Figure 1
<p>Measurements of the free radical content in wheat seeds following short- and long-term gamma irradiation using electron spin resonance (ESR). Con, non-irradiated seeds (0 Gy); S, short-term irradiation; L, long-term irradiation; 100, 300, and 500, gamma irradiation doses. Each bar represents the mean ± standard deviation (SD). Values with different letters are significantly different using Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>Gamma radiation dose and exposure time effects on seed germination percentage. The seed germination percentages were determined daily for 4 days. DAG, days after germination. Each bar represents the mean ± SD.</p> Full article ">Figure 3
<p>Gamma irradiation effect on plant growth under different doses and exposure times. (<b>A</b>) Image of plant growth under different irradiation conditions. Scale bar: 2 cm. (<b>B</b>) Shoot and (<b>C</b>) root length 10 days after germination. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 10). Values with different letters are significantly different using Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Determination of total chlorophyll, chlorophyll <span class="html-italic">a</span>, and chlorophyll <span class="html-italic">b</span> contents upon gamma irradiation. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 3). Values with different letters are significantly different using Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 5
<p>DNA-repair-related gene expression profiling in wheat seedlings determined by RT-qPCR. (<b>A</b>) XRCC, (<b>B</b>) KU70, (<b>C</b>) DnMT2, and (<b>D</b>) MSH6. RT-qPCR was performed with three biological replicates, and each bar represents the mean ± SD (<span class="html-italic">n</span> = 10). Values with different letters are significantly different using Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 6
<p>Antioxidant-related gene expression profiling in wheat seedlings determined by RT-qPCR. (<b>A</b>) <span class="html-italic">APX</span>, (<b>B</b>) <span class="html-italic">CAT</span>, (<b>C</b>) <span class="html-italic">MnSOD</span>, (<b>D</b>) <span class="html-italic">CuZnSOD</span>, (<b>E</b>) <span class="html-italic">GR</span>, (<b>F</b>) <span class="html-italic">GPX</span>, (<b>G</b>) <span class="html-italic">DHAR</span>, and (<b>H</b>) <span class="html-italic">MDHAR</span>. RT-qPCR was performed with three biological replicates, and each bar represents the mean ± SD (<span class="html-italic">n</span> = 10). Values with different letters are significantly different using Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 7
<p>Short- and long-term gamma irradiation effects on (<b>A</b>) APX, (<b>B</b>) CAT, (<b>C</b>) SOD, and (<b>D</b>) POD antioxidant enzyme activities. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 3). Values with different letters are significantly different using Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 8
<p>Different gamma radiation dose and exposure time effects on the (<b>A</b>) total phenolic content and (<b>B</b>) DPPH free radical scavenging activity. Each bar represents the mean ± SD (<span class="html-italic">n</span> = 3). Values with different letters are significantly different using Duncan’s multiple range test (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
27 pages, 4194 KiB  
Article
Technology Prediction for Acquiring a Must-Have Mobile Device for Military Communication Infrastructure
by Sungil Kim, Byungki Jung, Dongyun Han and Choonjoo Lee
Appl. Sci. 2022, 12(6), 3207; https://doi.org/10.3390/app12063207 - 21 Mar 2022
Viewed by 2109
Abstract
The smartphone is a must-have mobile device for the military forces to accomplish critical missions and protect critical infrastructures. This paper explores the applicability of a technology prediction methodology to manage technological obsolescence while pursuing the acquisition of advanced commercial technology for military [...] Read more.
The smartphone is a must-have mobile device for the military forces to accomplish critical missions and protect critical infrastructures. This paper explores the applicability of a technology prediction methodology to manage technological obsolescence while pursuing the acquisition of advanced commercial technology for military use. It reviews the Technology Forecasting using Data Envelopment Analysis (TFDEA) methodology and applies an author-written Stata program for smartphone technology forecasting using TFDEA. We analyzed smartphone launch data from 2005 to 2020 to predict the adoption of smartphone technology and discuss the pace of technological change. The study identifies that the market is undergoing reorganization as new smartphone models expand the market and increase their technical performance. The average rate of technological change, the efficiency change, and the technology change were 1.079, 1.004, and 1.011 each, respectively, which means that the technology progressed over the period. When dividing before and after 2017, technological change and efficiency change generally regressed except for Huawei, Xiaomi, and Oppo. This means that Chinese smartphones are expanding the global market in all directions and the technology is reaching maturity and market competition is accelerating. Full article
(This article belongs to the Special Issue Innovative Protection Facility and CBRNE Effects)
►▼ Show Figures

Figure 1

Figure 1
<p>Desired defense production frontier.</p> Full article ">Figure 2
<p>Mean efficiency and technology changes for the period of 2013–2020: (<b>a</b>) MPI; (<b>b</b>) Global MPI.</p> Full article ">Figure 2 Cont.
<p>Mean efficiency and technology changes for the period of 2013–2020: (<b>a</b>) MPI; (<b>b</b>) Global MPI.</p> Full article ">Figure 3
<p>Comparison of productivity changes by period using global MPI: (<b>a</b>) transition of technology change and efficiency change. The figure is displayed in the form of “Company name year, efficiency change, technological change”. For example, “Samsung1317, 1.0, 1.1” means “Samsung’s average efficiency change from 2013 to 2017 is 1.0, and technological change is 1.1”; (<b>b</b>) productivity change for the period of 2013–2017 and 2017–2020; (<b>c</b>) technology change for the period of 2013–2017 and 2017–2020; (<b>d</b>) efficiency change for the period of 2013–2017 and 2017–2020.</p> Full article ">Figure 3 Cont.
<p>Comparison of productivity changes by period using global MPI: (<b>a</b>) transition of technology change and efficiency change. The figure is displayed in the form of “Company name year, efficiency change, technological change”. For example, “Samsung1317, 1.0, 1.1” means “Samsung’s average efficiency change from 2013 to 2017 is 1.0, and technological change is 1.1”; (<b>b</b>) productivity change for the period of 2013–2017 and 2017–2020; (<b>c</b>) technology change for the period of 2013–2017 and 2017–2020; (<b>d</b>) efficiency change for the period of 2013–2017 and 2017–2020.</p> Full article ">Figure 4
<p>Dynamics of efficiency and technology changes: (<b>a</b>) Xiaomi; (<b>b</b>) Oppo; (<b>c</b>) Vivo; (<b>d</b>) Huawei; (<b>e</b>) Samsung; (<b>f</b>) Apple; (<b>g</b>) Motorola; (<b>h</b>) Sony; (<b>i</b>) LG.</p> Full article ">Figure 4 Cont.
<p>Dynamics of efficiency and technology changes: (<b>a</b>) Xiaomi; (<b>b</b>) Oppo; (<b>c</b>) Vivo; (<b>d</b>) Huawei; (<b>e</b>) Samsung; (<b>f</b>) Apple; (<b>g</b>) Motorola; (<b>h</b>) Sony; (<b>i</b>) LG.</p> Full article ">Figure 4 Cont.
<p>Dynamics of efficiency and technology changes: (<b>a</b>) Xiaomi; (<b>b</b>) Oppo; (<b>c</b>) Vivo; (<b>d</b>) Huawei; (<b>e</b>) Samsung; (<b>f</b>) Apple; (<b>g</b>) Motorola; (<b>h</b>) Sony; (<b>i</b>) LG.</p> Full article ">Figure 4 Cont.
<p>Dynamics of efficiency and technology changes: (<b>a</b>) Xiaomi; (<b>b</b>) Oppo; (<b>c</b>) Vivo; (<b>d</b>) Huawei; (<b>e</b>) Samsung; (<b>f</b>) Apple; (<b>g</b>) Motorola; (<b>h</b>) Sony; (<b>i</b>) LG.</p> Full article ">
10 pages, 1723 KiB  
Article
Effects of Nonlinear Damping on Vibrations of Microbeam
by Kun Huang, Tianpeng Li, Wei Xu and Liang Cao
Appl. Sci. 2022, 12(6), 3206; https://doi.org/10.3390/app12063206 - 21 Mar 2022
Cited by 6 | Viewed by 1468
Abstract
The present paper develops a new Bernoulli–Euler theory of microbeams for the consideration of small-scale effects and nonlinear terms, which are induced by the axial elongation of the beam and Kelvin–Voigt damping. The non-resonance and primary resonance of microbeams are researched through the [...] Read more.
The present paper develops a new Bernoulli–Euler theory of microbeams for the consideration of small-scale effects and nonlinear terms, which are induced by the axial elongation of the beam and Kelvin–Voigt damping. The non-resonance and primary resonance of microbeams are researched through the application of Galerkin and multiple scale methods to the new model. The results suggest the following: (1) Nonlinear damping slightly affects the vibration amplitudes under the non-resonance condition; (2) nonlinear damping can significantly change the bifurcation points that induce a jump in the vibration amplitudes under the primary resonance condition. The current researches indicate that nonlinear damping is necessary for an accurate description of microbeam vibrations. Full article
(This article belongs to the Section Acoustics and Vibrations)
►▼ Show Figures

Figure 1

Figure 1
<p>Portrait of the structure: (<b>a</b>) The undeformed coordinate system, (<b>b</b>) cross-section, (<b>c</b>) the displacement and rotation angle.</p> Full article ">Figure 2
<p>Time evolution of the microbeam’s midpoint displacement of the non-resonance for <math display="inline"><semantics> <mrow> <msub> <mo>Ω</mo> <mn>1</mn> </msub> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <mo> </mo> <mo> </mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>The phase portraits of non-resonant excitations for <math display="inline"><semantics> <mrow> <msub> <mo>Ω</mo> <mn>1</mn> </msub> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Amplitude of the response as a function of the excitation amplitude of primary resonance with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> for several damping parameters.</p> Full article ">Figure 5
<p>Frequency-response curves of primary resonance for <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Amplitude of the response as a function of the excitation amplitude of primary resonance with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <mn>10</mn> </mrow> </semantics></math> for several damping parameters.</p> Full article ">Figure 7
<p>Time evolution of the microbeam’s midpoint displacement of primary resonance for <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Time evolution of the microbeam’s midpoint displacement of primary resonance for <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <mn>10</mn> </mrow> </semantics></math>.</p> Full article ">
9 pages, 1040 KiB  
Article
Assessment of Two Commonly used Dermal Regeneration Templates in a Swine Model without Skin Grafting
by Wiebke Eisler, Jan-Ole Baur, Manuel Held, Afshin Rahmanian-Schwarz, Adrien Daigeler and Markus Denzinger
Appl. Sci. 2022, 12(6), 3205; https://doi.org/10.3390/app12063205 - 21 Mar 2022
Cited by 2 | Viewed by 2117
Abstract
In the medical care of partial and full-thickness wounds, autologous skin grafting is still the gold standard of dermal replacement. In contrast to spontaneous reepithelializing of superficial wounds, deep dermal wounds often lead to disturbing scarring, with cosmetically or functionally unsatisfactory results. However, [...] Read more.
In the medical care of partial and full-thickness wounds, autologous skin grafting is still the gold standard of dermal replacement. In contrast to spontaneous reepithelializing of superficial wounds, deep dermal wounds often lead to disturbing scarring, with cosmetically or functionally unsatisfactory results. However, modern wound dressings offer promising approaches to surface reconstruction. Against the background of our future aim to develop an innovative skin substitute, we investigated the behavior of two established dermal substitutes, a crosslinked and a non-crosslinked collagen biomatrix. The products were applied topically on a total of 18 full-thickness skin defects paravertebrally on the back of female Göttingen Minipigs—six control wounds remained untreated. The evaluation was carried out planimetrically (wound closure time) and histologically (neoepidermal cell number and epidermis thickness). Both treatment groups demonstrated significantly faster reepithelialization than the controls. The histologic examination verified the highest epidermal thickness in the crosslinked biomatrix-treated wounds, whereas the non-crosslinked biomatrix-treated wounds showed a higher cell density. Our data presented a positive influence on epidermal regeneration with the chosen dermis substitutes even without additional skin transplantation and, thus, without additional donor site morbidity. Therefore, it can be stated that the single biomatrix application might be used in a clinical routine with small wounds, which needs to be investigated further in a clinical setting to determine the size and depths of a suitable wound bed. Nevertheless, currently available products cannot solely achieve wound healing that is equal to or superior to autologous tissue. Thus, the overarching aim still is the development of an innovative skin substitute to manage surface reconstruction without additional skin grafting. Full article
(This article belongs to the Special Issue Recent Developments and Applications in Tissue Mechanics and Tissue Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Macroscopic and microscopic images from tissue stained with hematoxylin and eosin of the excised former sore center in a representative (<b>a</b>) untreated wound and a wound treated with (<b>b</b>) a crosslinked and (<b>c</b>) a non-crosslinked biomatrix at 20 days after a single dressing application. All biomaterials presented a continuous epidermal layer and complete wound closure.</p> Full article ">Figure 2
<p>Time in days to complete epithelialization of control wounds and treated wounds with the crosslinked and non-crosslinked biomatrices. The wound closure was significantly increased in the treatment groups. * Statistically significant results.</p> Full article ">Figure 3
<p>Epidermal thickness of untreated wounds versus crosslinked and non-crosslinked biomatrices-derived neoepidermis in three different sections per sample with an interval of 100 µm in micrometers. Histologic examination verified highest epidermal thickness in the crosslinked biomatrix-treated wounds. * Statistically significant results.</p> Full article ">Figure 4
<p>Neoepidermal cell density in untreated, the crosslinked and non-crosslinked biomatrices-derived neoepidermis within a section of 100 µm width taken repetitively in three different sections. Histologic examination demonstrated the densest colonization of neoepidermal cells in the non-crosslinked biomatrix-treated wounds. * Statistically significant results.</p> Full article ">
17 pages, 2417 KiB  
Article
A WSN Framework for Privacy Aware Indoor Location
by Aleksandar Tošić, Niki Hrovatin and Jernej Vičič
Appl. Sci. 2022, 12(6), 3204; https://doi.org/10.3390/app12063204 - 21 Mar 2022
Cited by 6 | Viewed by 2212
Abstract
In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, [...] Read more.
In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, with the recent COVID-19 pandemic, indoor location systems have gained considerable attention for detecting violations in physical distancing requirements and monitoring restrictions on occupant capacity. However, existing systems that rely on wearable devices, cameras, or sound signal analysis are intrusive and often violate privacy. In this research, we propose a new framework for indoor location. We present an innovative, non-intrusive implementation of indoor location based on wireless sensor networks. Further, we introduce a new protocol for querying and performing computations in wireless sensor networks (WSNs) that preserves sensor network anonymity and obfuscates computation by using onion routing. We also consider the single point of failure (SPOF) of sink nodes in WSNs and substitute them with a blockchain-based application through smart contracts. Our set of smart contracts is able to build the onion data structure and store the results of computation. Finally, a role-based access control contract is used to secure access to the system. Full article
(This article belongs to the Special Issue Recent Trends and Applications of Blockchain and IoT Technologies to Combat COVID-19)
►▼ Show Figures

Figure 1

Figure 1
<p>High level view of the presented architecture.</p> Full article ">Figure 2
<p>Bottom side of the foam tile.</p> Full article ">Figure 3
<p>A detailed view of the male connector.</p> Full article ">Figure 4
<p>Upper side of the foam tile.</p> Full article ">Figure 5
<p>Grid-based connection of individual force sensors.</p> Full article ">Figure 6
<p>Wireless-enabled force sensors.</p> Full article ">Figure 7
<p>The figure displays the data acquisition layer relying on the privacy-preserving communication protocol in [<a href="#B13-applsci-12-03204" class="html-bibr">13</a>]. The environment representation highlights where the social distancing was violated (red-colored squares).</p> Full article ">Figure 8
<p>Required time for an OM to travel the selected path length. Measurements do not include the OM processing delay. Statistics are computed for 30 OMs at each OM path length.</p> Full article ">
16 pages, 4074 KiB  
Article
Citation Context Analysis Using Combined Feature Embedding and Deep Convolutional Neural Network Model
by Musarat Karim, Malik Muhammad Saad Missen, Muhammad Umer, Saima Sadiq, Abdullah Mohamed and Imran Ashraf
Appl. Sci. 2022, 12(6), 3203; https://doi.org/10.3390/app12063203 - 21 Mar 2022
Cited by 14 | Viewed by 3090
Abstract
Citation creates a link between citing and the cited author, and the frequency of citation has been regarded as the basic element to measure the impact of research and knowledge-based achievements. Citation frequency has been widely used to calculate the impact factor, H [...] Read more.
Citation creates a link between citing and the cited author, and the frequency of citation has been regarded as the basic element to measure the impact of research and knowledge-based achievements. Citation frequency has been widely used to calculate the impact factor, H index, i10 index, etc., of authors and journals. However, for a fair evaluation, the qualitative aspect should be considered along with the quantitative measures. The sentiments expressed in citation play an important role in evaluating the quality of the research because the citation may be used to indicate appreciation, criticism, or a basis for carrying on research. In-text citation analysis is a challenging task, despite the use of machine learning models and automatic sentiment annotation. Additionally, the use of deep learning models and word embedding is not studied very well. This study performs several experiments with machine learning and deep learning models using fastText, fastText subword, global vectors, and their blending for word representation to perform in-text sentiment analysis. A dimensionality reduction technique called principal component analysis (PCA) is utilized to reduce the feature vectors before passing them to the classifier. Additionally, a customized convolutional neural network (CNN) is presented to obtain higher classification accuracy. Results suggest that the deep learning CNN coupled with fastText word embedding produces the best results in terms of accuracy, precision, recall, and F1 measure. Full article
(This article belongs to the Special Issue Application of Machine Learning in Text Mining)
►▼ Show Figures

Figure 1

Figure 1
<p>The architecture of the proposed methodology for in-text citation sentiment analysis.</p> Full article ">Figure 2
<p>Distribution for negative, neutral, and positive citations from dataset-1 for articles with ≥100 citations.</p> Full article ">Figure 3
<p>Sentiment distribution of the collected dataset.</p> Full article ">Figure 4
<p>Performance comparison of machine and deep learning models with different features, (<b>a</b>) accuracy, (<b>b</b>) precision, (<b>c</b>) recall, and (<b>d</b>) F1 score.</p> Full article ">
9 pages, 2492 KiB  
Article
An Approach for Modelling Harnesses in the Extreme near Field for Low Frequencies
by Anargyros T. Baklezos, Theodoros N. Kapetanakis, Ioannis O. Vardiambasis, Christos N. Capsalis and Christos D. Nikolopoulos
Appl. Sci. 2022, 12(6), 3202; https://doi.org/10.3390/app12063202 - 21 Mar 2022
Viewed by 1629
Abstract
A key part of every space science mission, in the system-level approach, is the detailed study and modeling of the emissions from transmission lines. Harnesses usually emit electromagnetic fields due to the currents (of common and/or differential modes) that flow on their shields. [...] Read more.
A key part of every space science mission, in the system-level approach, is the detailed study and modeling of the emissions from transmission lines. Harnesses usually emit electromagnetic fields due to the currents (of common and/or differential modes) that flow on their shields. These fields can be identified via conducted emissions measurements. Relying on the operating frequency, any cable can be considered as a dipole or a traveling-wave antenna. Limited work can be found in the literature regarding modeling methodologies for cable topologies, especially in the low frequency (ELF, SLF, VLF, LF) domain. This work intends to provide perceptions for the precise estimation of harness radiated emissions, consider a mission-specific measurement point (where the sensors are placed), and follow ESA’s recent science mission studies for electromagnetic cleanliness applications. For the low frequencies considered herein, any linear cable path is considered as a point source (infinitesimal dipole) and we evaluate its effect on the calculated electric field extremely close to the source. For such distances, it is shown that the dipole representation is not accurate. To remedy this phenomenon, this article proposes a methodology, which can be easily expanded to complex cable geometry cases. Full article
(This article belongs to the Collection Electromagnetic Antennas for HF, VHF, and UHF Band Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>Application of the near-field dipole segmentation technique in a complex cable geometry.</p> Full article ">Figure 2
<p>Dipole segmentation for near-field approximation study.</p> Full article ">Figure 3
<p>Comparison of the magnitude of the electric field (<span class="html-italic">E<sub>x</sub><sup>2</sup></span> + <span class="html-italic">E<sub>y</sub><sup>2</sup></span> + <span class="html-italic">E<sub>z</sub><sup>2</sup></span>)<sup>1/2</sup> versus the <span class="html-italic">ratio</span> of the measurement distance to the cable length, for <span class="html-italic">N</span> = 0 (straight blue line), 5 (bulleted red line), 50 (x marks), and 50,000 (dashed line) segments.</p> Full article ">Figure 4
<p>Comparison of the real and imaginary parts of the electric field component <span class="html-italic">E<sub>x</sub></span> versus the <span class="html-italic">ratio</span> of the measurement distance to the cable length, for the Single Dipole Case (x marks) and the Segmented Dipole Case for <span class="html-italic">N</span> = 50 segments (dashed lines).</p> Full article ">Figure 5
<p>Comparison of the real and imaginary parts of the electric field component <span class="html-italic">E<sub>x</sub></span> versus the <span class="html-italic">ratio</span> of the measurement distance to the cable length, for the Single Dipole Case (circles), the Segmented Dipole Case for <span class="html-italic">N</span> = 50 segments (dashed lines), and the Single Dipole Case with Near Field Approximation (x marks).</p> Full article ">Figure 6
<p>Comparison of the real and imaginary parts of the electric field component <span class="html-italic">E<sub>x</sub></span> versus the <span class="html-italic">ratio</span> of the measurement distance to the cable length, for the Segmented Cable Case (star marks) and the Segmented Cable Case with Near Field Approximation with <span class="html-italic">N</span> = 50 segments (dashed lines).</p> Full article ">Figure 7
<p>Comparison of the magnitude of the electric field (<span class="html-italic">E<sub>x</sub><sup>2</sup></span> + <span class="html-italic">E<sub>y</sub><sup>2</sup></span> + <span class="html-italic">E<sub>z</sub><sup>2</sup></span>)<sup>1/2</sup> versus the <span class="html-italic">ratio</span> of the measurement distance to the cable length, for the Single Dipole Case (dashed line) and the Segmented Cable Case with <span class="html-italic">N</span> = 50 segments (solid line).</p> Full article ">Figure 8
<p>Deviation percentage between the two modeling approaches.</p> Full article ">
7 pages, 630 KiB  
Review
Further Advances in Atrial Fibrillation Research: A Metabolomic Perspective
by Laura Arbeloa-Gómez, Jaime Álvarez-Vidal and Jose Luis Izquierdo-García
Appl. Sci. 2022, 12(6), 3201; https://doi.org/10.3390/app12063201 - 21 Mar 2022
Viewed by 2248
Abstract
Atrial fibrillation involves an important type of heart arrhythmia caused by a lack of control in the electrical signals that arrive in the heart, produce an irregular auricular contraction, and induce blood clotting, which finally can lead to stroke. Atrial fibrillation presents some [...] Read more.
Atrial fibrillation involves an important type of heart arrhythmia caused by a lack of control in the electrical signals that arrive in the heart, produce an irregular auricular contraction, and induce blood clotting, which finally can lead to stroke. Atrial fibrillation presents some specific characteristics, but it has been treated and prevented using conventional methods similar to those applied to other cardiovascular diseases. However, due to the influence of this pathology on the mortality caused by cerebrovascular accidents, further studies on the molecular mechanism of atrial fibrillation are required. Our aim here is provide a compressive review of the use of metabolomics on this condition, from the study of the metabolic profile of plasma to the development of animal models. In summary, most of the reported studies highlighted alterations in the energetic pathways related to the development of the condition. Full article
(This article belongs to the Special Issue Metabolomic Analysis in Human Diseases: Latest Advances and Prospects)
►▼ Show Figures

Figure 1

Figure 1
<p>Cardiac electric signals in normal and AF heart.</p> Full article ">
23 pages, 1697 KiB  
Review
Application of Nanofluids in CO2 Absorption: A Review
by Babak Aghel, Sara Janati, Falah Alobaid, Adel Almoslh and Bernd Epple
Appl. Sci. 2022, 12(6), 3200; https://doi.org/10.3390/app12063200 - 21 Mar 2022
Cited by 30 | Viewed by 4346
Abstract
The continuous release of CO2 into the atmosphere as a major cause of increasing global warming has become a growing concern for the environment. Accordingly, CO2 absorption through an approach with maximum absorption efficiency and minimum energy consumption is of paramount [...] Read more.
The continuous release of CO2 into the atmosphere as a major cause of increasing global warming has become a growing concern for the environment. Accordingly, CO2 absorption through an approach with maximum absorption efficiency and minimum energy consumption is of paramount importance. Thanks to the emergence of nanotechnology and its unique advantages in various fields, a new approach was introduced using suspended particles in a base liquid (suspension) to increase CO2 absorption. This review article addresses the performance of nanofluids, preparation methods, and their stability, which is one of the essential factors preventing sedimentation of nanofluids. This article aims to comprehensibly study the factors contributing to CO2 absorption through nanofluids, which mainly addresses the role of the base liquids and the reason behind their selection. Full article
(This article belongs to the Special Issue Thermochemical Conversion Processes for Solid Fuels and Renewable Energies: Volume II)
►▼ Show Figures

Figure 1

Figure 1
<p>Different methods of preparing nanofluids.</p> Full article ">Figure 2
<p>Methods for improving the stability of nanofluids.</p> Full article ">Figure 3
<p>The schematic of (<b>a</b>) Bubble breaking, (<b>b</b>) Brownian motion, and (<b>c</b>) Grazing effect mechanisms in the improvement of CO<sub>2</sub> absorption.</p> Full article ">Figure 4
<p>The main parameters of nanoparticles in CO<sub>2</sub> absorption.</p> Full article ">
17 pages, 3912 KiB  
Article
Dog Behavior Recognition Based on Multimodal Data from a Camera and Wearable Device
by Jinah Kim and Nammee Moon
Appl. Sci. 2022, 12(6), 3199; https://doi.org/10.3390/app12063199 - 21 Mar 2022
Cited by 18 | Viewed by 7551
Abstract
Although various studies on monitoring dog behavior have been conducted, methods that can minimize or compensate data noise are required. This paper proposes multimodal data-based dog behavior recognition that fuses video and sensor data using a camera and a wearable device. The video [...] Read more.
Although various studies on monitoring dog behavior have been conducted, methods that can minimize or compensate data noise are required. This paper proposes multimodal data-based dog behavior recognition that fuses video and sensor data using a camera and a wearable device. The video data represent the moving area of dogs to detect the dogs. The sensor data represent the movement of the dogs and extract features that affect dog behavior recognition. Seven types of behavior recognition were conducted, and the results of the two data types were used to recognize the dog’s behavior through a fusion model based on deep learning. Experimentation determined that, among FasterRCNN, YOLOv3, and YOLOv4, the object detection rate and behavior recognition accuracy were the highest when YOLOv4 was used. In addition, the sensor data showed the best performance when all statistical features were selected. Finally, it was confirmed that the performance of multimodal data-based fusion models was improved over that of single data-based models and that the CNN-LSTM-based model had the best performance. The method presented in this study can be applied for dog treatment or health monitoring, and it is expected to provide a simple way to estimate the amount of activity. Full article
(This article belongs to the Special Issue Intelligent Computing for Big Data)
►▼ Show Figures

Figure 1

Figure 1
<p>Process of dog behavior recognition based on multimodal data.</p> Full article ">Figure 2
<p>Overall model structure based on the multimodal data for dog behavior recognition.</p> Full article ">Figure 3
<p>Model structures of CNN, LSTM, and CNN-LSTM for feature fusion.</p> Full article ">Figure 4
<p>The process of collecting sensor data from a manufactured wearable device.</p> Full article ">Figure 5
<p>Result of dog detection for “sitting”: (<b>a</b>) FasterRCNN; (<b>b</b>) YOLOv3; (<b>c</b>) YOLOv4.</p> Full article ">Figure 6
<p>Distribution of detection by method according to the amount of data.</p> Full article ">Figure 7
<p>Distribution of detection by dog behavior per method: (<b>a</b>) FasterRCNN; (<b>b</b>) YOLOv3; (<b>c</b>) YOLOv4.</p> Full article ">Figure 8
<p>Confusion matrix of behavior recognition by dog detection methods using video data: (<b>a</b>) FasterRCNN; (<b>b</b>) YOLOv3; (<b>c</b>) YOLOv4. The bluer the cell, the higher the accuracy of dog recognition.</p> Full article ">Figure 9
<p>Confusion matrix of behavior recognition by selected features using sensor data: (<b>a</b>) Confusion matrix with no selected features; (<b>b</b>) Confusion matrix with selected features of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>n</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>f</mi> <mrow> <mi>v</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>f</mi> <mrow> <mi>s</mi> <mi>t</mi> <mi>d</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>f</mi> <mrow> <mi>a</mi> <mi>m</mi> <mi>p</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>f</mi> <mrow> <mi>s</mi> <mi>k</mi> <mi>e</mi> <mi>w</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 10
<p>Confusion matrix of behavior recognition by fusion models based on multimodal data: (<b>a</b>) Only concatenate model with FasterRCNN; (<b>b</b>) Only concatenate model with YOLOv3; (<b>c</b>) Only concatenate model with YOLOv4; (<b>d</b>) CNN-LSTM model with FasterRCNN; (<b>e</b>) CNN-LSTM model with YOLOv3; (<b>f</b>) CNN-LSTM model with YOLOv4.</p> Full article ">
17 pages, 4069 KiB  
Article
Materials Separation via the Matrix Method Employing Energy-Discriminating X-ray Detection
by Viona S. K. Yokhana, Benedicta D. Arhatari and Brian Abbey
Appl. Sci. 2022, 12(6), 3198; https://doi.org/10.3390/app12063198 - 21 Mar 2022
Cited by 6 | Viewed by 2037
Abstract
The majority of lab-based X-ray sources are polychromatic and are not easily tunable, which can make the 3D quantitative analysis of multi-component samples challenging. The lack of effective materials separation when using conventional X-ray tube sources has motivated the development of a number [...] Read more.
The majority of lab-based X-ray sources are polychromatic and are not easily tunable, which can make the 3D quantitative analysis of multi-component samples challenging. The lack of effective materials separation when using conventional X-ray tube sources has motivated the development of a number of potential solutions including the application of dual-energy X-ray computed tomography (CT) as well as the use of X-ray filters. Here, we demonstrate the simultaneous decomposition of two low-density materials via inversion of the linear attenuation matrices using data from the energy-discriminating PiXirad detector. A key application for this method is soft-tissue differentiation which is widely used in biological and medical imaging. We assess the effectiveness of this approach using both simulation and experiment noting that none of the materials investigated here incorporate any contrast enhancing agents. By exploiting the energy discriminating properties of the detector, narrow energy bands are created resulting in multiple quasi-monochromatic images being formed using a broadband polychromatic source. Optimization of the key parameters for materials separation is first demonstrated in simulation followed by experimental validation using a phantom test sample in 2D and a small-animal model in 3D. Full article
(This article belongs to the Special Issue X-ray Medical and Biological Imaging)
►▼ Show Figures

Figure 1

Figure 1
<p>The measured X-ray source spectrum for a tungsten target at a 40 kVp tube voltage, with the four chosen threshold energies <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mn>3</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mn>4</mn> </msub> </mrow> </semantics></math> (indicated by dashed vertical blue lines) which together, following image subtraction, create two discrete energy bands shown by the blue shaded areas (<math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>E</mi> <mi>a</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>E</mi> <mi>b</mi> </msub> </mrow> </semantics></math>).</p> Full article ">Figure 2
<p>Schematic of the simulated phantom in the XY plane (top row) and in the XZ plane (bottom row) consisting of (<b>a</b>) epoxy and HA400, (<b>b</b>) epoxy and HA800, and (<b>c</b>) epoxy and HA1200. The plane orientation is depicted in the top left corner.</p> Full article ">Figure 3
<p>Workflow for obtaining the separate thickness of the epoxy and HA400 components using a simulated test phantom. The distance along the <span class="html-italic">X</span>-axis of the plots corresponds to the <span class="html-italic">X</span>-axis direction in <a href="#applsci-12-03198-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 4
<p>Example simulation result comparing the retrieved thickness from the matrix approach compared to the ‘actual’ value based on the known properties of the simulated sample for the (<b>a</b>) HA400 insert and (<b>b</b>) surrounding epoxy, using a 6 keV bandwidth, with (<b>c</b>,<b>d</b>) show their corresponding difference (actual-retrieved) in (<b>a</b>,<b>b</b>), respectively.</p> Full article ">Figure 5
<p>Calculated <math display="inline"><semantics> <mrow> <msup> <mo>χ</mo> <mn>2</mn> </msup> </mrow> </semantics></math> error plots as a function of the energy bandwidth in the absence of noise (left-hand column) and with 1% noise included (right-hand column), showing the separation of (<b>a</b>,<b>b</b>) HA400, (<b>c</b>,<b>d</b>) HA800, and (<b>e</b>,<b>f</b>) HA1200 from the surrounding epoxy matrix.</p> Full article ">Figure 6
<p>The experimental set-up consisting of the X-ray source, sample stage, and the PiXirad detector.</p> Full article ">Figure 7
<p>Comparison of the retrieved thickness (red solid lines) to the ‘actual’ thickness (blue dashed lines) determined from the manufacturer’s specifications for the separation of (<b>a</b>,<b>b</b>) HA400, (<b>c</b>,<b>d</b>) HA800, and (<b>e</b>,<b>f</b>) HA1200 from the surrounding epoxy matrix. The sudden drop in intensity in (<b>b</b>,<b>d</b>,<b>f</b>) are due to the application of a mask at the detector plane to remove the transmission due to the other inserts. Note that 100 vertical pixels were binned to improve the experimental SNR. The calculated χ<sup>2</sup> error inside the indicated distance range shown by black arrows, can be seen on each plot. The experimental data of (<b>c</b>–<b>f</b>) were taken at a tube voltage of 80 kVp.</p> Full article ">Figure 8
<p>Results for a single projection of the mouse hand sample at energy thresholds (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>16</mn> <mrow> <mo> </mo> <mi>keV</mi> </mrow> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>21.1</mn> <mrow> <mo> </mo> <mi>keV</mi> </mrow> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mn>3</mn> </msub> <mo>=</mo> </mrow> </semantics></math> 23.9 keV and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mn>4</mn> </msub> <mo>=</mo> <mn>29.7</mn> <mrow> <mo> </mo> <mi>keV</mi> </mrow> </mrow> </semantics></math>. The corresponding result for the retrieved thickness after applying the matrix method is shown for (<b>e</b>) soft tissue and (<b>f</b>) bone.</p> Full article ">Figure 9
<p>Tomographic reconstruction of the mouse hand. Left column: result corresponding to the 16 keV energy threshold. Middle column: retrieved thickness from bone and Right column: retrieved thickness from soft tissue after applying the matrix method.</p> Full article ">
10 pages, 4864 KiB  
Article
Acute Effects of Different Intensities during Bench Press Exercise on the Mechanical Properties of Triceps Brachii Long Head
by Robert Trybulski, Grzegorz Wojdała, Dan Iulian Alexe, Zuzanna Komarek, Piotr Aschenbrenner, Michał Wilk, Adam Zając and Michał Krzysztofik
Appl. Sci. 2022, 12(6), 3197; https://doi.org/10.3390/app12063197 - 21 Mar 2022
Cited by 13 | Viewed by 2257
Abstract
This study aimed to analyze acute changes in the muscle mechanical properties of the triceps brachii long head after bench press exercise performed at different external loads and with different intensities of effort along with power performance. Ten resistance-trained males (age: 27.7 ± [...] Read more.
This study aimed to analyze acute changes in the muscle mechanical properties of the triceps brachii long head after bench press exercise performed at different external loads and with different intensities of effort along with power performance. Ten resistance-trained males (age: 27.7 ± 3.7 yr, body mass: 90.1 ± 17.1 kg, height: 184 ± 4 cm; experience in resistance training: 5.8 ± 2.6 yr, relative one-repetition maximum (1RM) in the bench press: 1.23 ± 0.22 kg/body mass) performed two different testing conditions in a randomized order. During the experimental session, participants performed four successive sets of two repetitions of the bench press exercise at: 50, 70, and 90% 1RM, respectively, followed by a set at 70% 1RM performed until failure, with a 4 min rest interval between each set. Immediately before and after each set, muscle mechanical properties of the dominant limb triceps brachii long head were assessed via a Myoton device. To determine fatigue, peak and average barbell velocity were measured at 70% 1RM and at 70% 1RM until failure (only first and second repetition). In the control condition, only muscle mechanical properties at the same time points after the warm-up were assessed. The intraclass correlation coefficients indicated “poor” to “excellent” reliability for decrement, relaxation time, and creep. Therefore, these variables were excluded from further analysis. Three-way ANOVAs (2 groups × 2 times × 4 loads) indicated a statistically significant group × time interaction for muscle tone (p = 0.008). Post hoc tests revealed a statistically significant increase in muscle tone after 70% 1RM (p = 0.034; ES = 0.32) and 90% 1RM (p = 0.011; ES = 0.56). No significant changes were found for stiffness. The t-tests indicated a significant decrease in peak (p = 0.001; ES = 1.02) and average barbell velocity (p = 0.008; ES = 0.8) during the first two repetitions of a set at 70% 1RM until failure in comparison to the set at 70% 1RM. The results indicate that low-volume, high-load resistance exercise immediately increases muscle tone but not stiffness. Despite no significant changes in the mechanical properties of the muscle being registered simultaneously with a decrease in barbell velocity, there was a trend of increased muscle tone. Therefore, further studies with larger samples are required to verify whether muscle tone could be a sensitive marker to detect acute muscle fatigue. Full article
(This article belongs to the Special Issue Epigenetic and Transcriptional Regulation in Muscle Cells)
►▼ Show Figures

Figure 1

Figure 1
<p>Study design. MMT—myotonometry assessment; 1RM—one-repetition maximum; UF—until failure.</p> Full article ">Figure 2
<p>Inter-individual changes in the peak (<b>a</b>) and average (<b>b</b>) barbell velocity during the bench press set at 70% 1RM and the first two repetitions in the set at 70% 1RM performed until failure. * Significant difference <span class="html-italic">p</span> &lt; 0.05. The dashed line represents the group mean response.</p> Full article ">
18 pages, 16074 KiB  
Article
Stochastic Models and Control of Anchoring Mechanisms for Grasping in Microgravity
by Qingpeng Wen, Jun He and Feng Gao
Appl. Sci. 2022, 12(6), 3196; https://doi.org/10.3390/app12063196 - 21 Mar 2022
Cited by 3 | Viewed by 1701
Abstract
Robots equipped with anchoring mechanisms have attractive applications in asteroid exploration. However, complex application scenarios bring great challenges to the modeling and control of anchoring mechanisms. This paper presents a grasping model and control method for an anchoring mechanism for asteroid exploration. First, [...] Read more.
Robots equipped with anchoring mechanisms have attractive applications in asteroid exploration. However, complex application scenarios bring great challenges to the modeling and control of anchoring mechanisms. This paper presents a grasping model and control method for an anchoring mechanism for asteroid exploration. First, the structure of the anchoring mechanism is demonstrated. Second, stochastic grasping models based on surface properties are established. The effectiveness of the grasping model is verified by experiments. A stiffness-modeling method of the microspine is proposed. On this basis, the stochastic grasping model of the anchoring mechanism is established, and the grasping cloud diagram of the anchoring mechanism is drawn. Third, in order to reduce the collision force between the anchor mechanism and the asteroid surface, a control method for the anchoring mechanism in the movement process is proposed based on the motion mode of the asteroid-exploration robot. Finally, a prototype is developed, and the experimental results validate the motion ability of the robot and the control method. Full article
(This article belongs to the Special Issue Mechanisms and Robotics in Astronautic and Deep Space Exploration)
►▼ Show Figures

Figure 1

Figure 1
<p>3D model and prototype of the anchoring mechanism. (<b>a</b>,<b>b</b>) are front and bottom views of the anchoring mechanism; (<b>c</b>) 3D model of asteroid exploration robot; (<b>d</b>) The anchoring mechanism grabs the trunk (<b>e</b>) The microspine array grabs the trunk.</p> Full article ">Figure 2
<p>Two-dimensional model of asperities.</p> Full article ">Figure 3
<p>The discrete model of the non-ideal region.</p> Full article ">Figure 4
<p>Influence of surface properties on grasping probability.</p> Full article ">Figure 5
<p>Experimental results and prediction curves. (<b>a</b>) The microspine travels on the surface of the brick, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.62</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>. (<b>b</b>) the microspine travels on sandpaper, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.65</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>0.14</mn> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Relationship between the pull force and the adhesion probability.</p> Full article ">Figure 7
<p>Model and analysis of the microspine.</p> Full article ">Figure 8
<p>Cloud diagram of grasping capacity of anchoring mechanism. X. Y, Z, respectively, represent the angle of the driving motor of the anchoring mechanism (°), the included angle between the direction vector of the external force and the direction vector of the ground (°), and the maximum force that the anchoring mechanism can bear (N).</p> Full article ">Figure 9
<p>Relationship between stiffness and displacement of different recovery springs. The stiffness of 1 to 5 in the figure is 1.5, 2, 3, 4 and 5 (N/mm) respectively.</p> Full article ">Figure 10
<p>The motion process of the anchoring mechanism when stepping.</p> Full article ">Figure 11
<p>Control algorithm of asteroid-exploration robot.</p> Full article ">Figure 12
<p>Walking experiment of asteroid-exploration robot.</p> Full article ">Figure 13
<p>Pitch angle of anchoring mechanism of left legs. (<b>a</b>) and (<b>b</b>) represent the pitch angles of the anchoring mechanism installed on the left front leg and the left rear leg, respectively.</p> Full article ">
29 pages, 62705 KiB  
Article
Aerodynamic Optimization and Analysis of Low Reynolds Number Propeller with Gurney Flap for Ultra-High-Altitude Unmanned Aerial Vehicle
by Yuan Yao, Dongli Ma, Liang Zhang, Xiaopeng Yang and Yayun Yu
Appl. Sci. 2022, 12(6), 3195; https://doi.org/10.3390/app12063195 - 21 Mar 2022
Cited by 5 | Viewed by 2483
Abstract
Ultra-high-altitude unmanned aerial vehicles have created a high demand for the performance of propellers under low Reynolds numbers, while the efficiency of such propellers by the existing design framework has reached a bottleneck. This paper explores the possibility of extending the Gurney flap [...] Read more.
Ultra-high-altitude unmanned aerial vehicles have created a high demand for the performance of propellers under low Reynolds numbers, while the efficiency of such propellers by the existing design framework has reached a bottleneck. This paper explores the possibility of extending the Gurney flap on low Reynolds number propellers to achieve efficiency breakthrough. An iterative optimization strategy for propellers with Gurney flaps is established, in which cross-sectional airfoils can be continuously optimized under updated Reynolds numbers and lift coefficients. A computational fluid dynamics (CFD) simulation based on the γ-Reθ model was used as an aerodynamic analysis method. Propellers with and without Gurney flaps were optimized successively. Optimal results were analyzed using the CFD method. Results showed that an optimal propeller with a Gurney flap can achieve an efficiency of 82.0% in cruising conditions, which is 1.8% higher than an optimal propeller without a Gurney flap. Compared with the latter, the consumed power of the optimal propeller with a Gurney flap can be reduced by 2.2% with the same advance speed. Furthermore, the variation of the improvement by the Gurney flap propeller, along with its Reynolds number, was studied. A wind tunnel test indicates that the performance of the propellers obtained by the CFD method are in good agreement with the test results. Full article
(This article belongs to the Section Aerospace Science and Engineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Computation grid of Epller387: (<b>a</b>) Computational domain; (<b>b</b>) Near-wall grids distribution.</p> Full article ">Figure 2
<p>CFD and experimental results for Eppler 387 at <span class="html-italic">Re</span> = 1.0 × 10<sup>5</sup>: (<b>a</b>) Lift coefficient vs. angle of attack; (<b>b</b>) Drag coefficient vs. angle of attack; (<b>c</b>) Laminar separation location vs. angle of attack.</p> Full article ">Figure 3
<p>Chord and pitch distributions of the test propeller.</p> Full article ">Figure 4
<p>Three-dimensional shape of the test propeller.</p> Full article ">Figure 5
<p>Ground experiment of the test propeller.</p> Full article ">Figure 6
<p>Computational mesh of the test propeller: (<b>a</b>) Computational domain; (<b>b</b>) Near-wall grids distribution.</p> Full article ">Figure 7
<p>Comparison of calculation results of five grids.</p> Full article ">Figure 8
<p>Experimental data and CFD results at an altitude of 10 km: (<b>a</b>) Thrust coefficient vs. advance ratio; (<b>b</b>) Power coefficient vs. advance ratio; (<b>c</b>) Propeller efficiency vs. advance ratio.</p> Full article ">Figure 9
<p>Parametric method for a Gurney flap airfoil.</p> Full article ">Figure 10
<p>Force diagram for a blade element.</p> Full article ">Figure 11
<p>Computation grid of the cross-sectional airfoil with a Gurney flap: (<b>a</b>) Grid around the airfoil; (<b>b</b>) Grid near the Gurney flap.</p> Full article ">Figure 12
<p>Parametric method for the propeller with a Gurney flap.</p> Full article ">Figure 13
<p>Computation grid of the propeller with a Gurney flap: (<b>a</b>) Adjustable blocks around the propeller; (<b>b</b>) Grid around cross-sectional airfoil with a Gurney flap.</p> Full article ">Figure 14
<p>Flowchart of iterative optimization strategy for low Reynolds number propeller.</p> Full article ">Figure 15
<p>The ultra-high-altitude unmanned aerial vehicle for case study.</p> Full article ">Figure 16
<p>Convergence history of two optimized propellers in the optimization of chord and pitch distribution: (<b>a</b>) Prop A; (<b>b</b>) Prop B.</p> Full article ">Figure 17
<p>The geometries of the optimized propellers: (<b>a</b>) Prop A; (<b>b</b>) Prop B.</p> Full article ">Figure 18
<p>Blade chord distribution of the two optimized propellers.</p> Full article ">Figure 19
<p>Blade pitch distribution of the two optimized propellers.</p> Full article ">Figure 20
<p>Aerodynamic performances of Prop A and Prop B near the design point: (<b>a</b>) Thrust vs. rotational velocity; (<b>b</b>) Torque vs. rotational velocity; (<b>c</b>) Efficiency vs. advance ratio; (<b>d</b>) Efficiency vs. thrust.</p> Full article ">Figure 20 Cont.
<p>Aerodynamic performances of Prop A and Prop B near the design point: (<b>a</b>) Thrust vs. rotational velocity; (<b>b</b>) Torque vs. rotational velocity; (<b>c</b>) Efficiency vs. advance ratio; (<b>d</b>) Efficiency vs. thrust.</p> Full article ">Figure 21
<p>Aerodynamic performances of two optimized propellers under an average Reynolds number of about 1.8 × 10<sup>5</sup>: (<b>a</b>) Thrust vs. rotational velocity; (<b>b</b>) Torque vs. rotational velocity; (<b>c</b>) Efficiency vs. advance ratio; (<b>d</b>) Efficiency vs. thrust.</p> Full article ">Figure 22
<p>Aerodynamic performances of Prop A and Prop B at their design advance ratios.</p> Full article ">Figure 23
<p>Wind tunnel.</p> Full article ">Figure 24
<p>The test models.</p> Full article ">Figure 25
<p>The detail of the Gurney flap on the test model.</p> Full article ">Figure 26
<p>Test propellers installed in the wind tunnel: (<b>a</b>) The test propeller without a Gurney flap (Prop A); (<b>b</b>) The test propeller with a Gurney flap (Prop B).</p> Full article ">Figure 27
<p>Results of the repeated test: (<b>a</b>) Thrust coefficient vs. advance ratio; (<b>b</b>) Power coefficient vs. advance ratio.</p> Full article ">Figure 28
<p>Comparison of the test and CFD results under <span class="html-italic">Re</span> ≈ 4 × 10<sup>4</sup>: (<b>a</b>) Thrust coefficient vs. advance ratio; (<b>b</b>) Power coefficient vs. advance ratio; (<b>c</b>) Propeller efficiency vs. advance ratio.</p> Full article ">Figure 29
<p>Comparison of the test and CFD results under <span class="html-italic">Re</span> ≈ 1.6 × 10<sup>5</sup>: (<b>a</b>) Thrust coefficient vs. advance ratio; (<b>b</b>) Power coefficient vs. advance ratio; (<b>c</b>) Propeller efficiency vs. advance ratio.</p> Full article ">Figure 30
<p>Geometries of the cross-sectional airfoils on Prop A and Prop B at 0.75 R.</p> Full article ">Figure 31
<p>Aerodynamic performance of Airfoil A, and Airfoil B at different Reynolds numbers: (<b>a</b>) Lift coefficient vs. angle of attack; (<b>b</b>) Drag coefficient vs. angle of attack; (<b>c</b>) Lift-to-drag ratio vs. angle of attack.</p> Full article ">Figure 31 Cont.
<p>Aerodynamic performance of Airfoil A, and Airfoil B at different Reynolds numbers: (<b>a</b>) Lift coefficient vs. angle of attack; (<b>b</b>) Drag coefficient vs. angle of attack; (<b>c</b>) Lift-to-drag ratio vs. angle of attack.</p> Full article ">Figure 32
<p>Pressure coefficient and streamline distributions at <span class="html-italic">Re</span> = 4.0 × 10<sup>4</sup>: (<b>a</b>) Airfoil A; (<b>b</b>) Airfoil B.</p> Full article ">Figure 33
<p>Pressure coefficient and streamline distributions at <span class="html-italic">Re</span> = 2.0 × 10<sup>5</sup>: (<b>a</b>) Airfoil A; (<b>b</b>) Airfoil B.</p> Full article ">Figure 34
<p>Aerodynamic characteristics along the blade of two propellers at State 1: (<b>a</b>) Distributions of blade element thrust; (<b>b</b>) Distributions of blade element efficiency.</p> Full article ">Figure 35
<p>Aerodynamic characteristics along the blade of two propellers at State 2: (<b>a</b>) Distributions of blade element thrust; (<b>b</b>) Distributions of blade element efficiency.</p> Full article ">Figure 36
<p>Pressure coefficient distribution and streamlines on different cross-sections of two propellers at State 1: (<b>a</b>) Prop A; (<b>b</b>) Prop B.</p> Full article ">Figure 37
<p>Pressure coefficient distribution and streamlines on different cross-sections of two propellers at State 2: (<b>a</b>) Prop A; (<b>b</b>) Prop B.</p> Full article ">Figure 38
<p>Pressure coefficient distribution on different cross-sectional airfoils at State 1: (<b>a</b>) <span class="html-italic">r</span>/<span class="html-italic">R</span> = 0.5; (<b>b</b>) <span class="html-italic">r</span>/<span class="html-italic">R</span> = 0.75; (<b>c</b>) <span class="html-italic">r</span>/<span class="html-italic">R</span> = 0.95.</p> Full article ">Figure 39
<p>Pressure coefficient distribution on different cross-sectional airfoils at State 2: (<b>a</b>) <span class="html-italic">r</span>/<span class="html-italic">R</span> = 0.5; (<b>b</b>) <span class="html-italic">r</span>/<span class="html-italic">R</span> = 0.75; (<b>c</b>) <span class="html-italic">r</span>/<span class="html-italic">R</span> = 0.95.</p> Full article ">
26 pages, 5154 KiB  
Article
Thermal–Structural Coupling Analysis of Subsea Connector Sealing Contact
by Feihong Yun, Dong Liu, Xiujun Xu, Kefeng Jiao, Xiaoquan Hao, Liquan Wang, Zheping Yan, Peng Jia, Xiangyu Wang and Bin Liang
Appl. Sci. 2022, 12(6), 3194; https://doi.org/10.3390/app12063194 - 21 Mar 2022
Cited by 6 | Viewed by 2639
Abstract
Taking a subsea collet connector as an example, the contact characteristics of the sealing structure of the subsea connector under thermal–structural coupling were studied. Considering the heat transfer problem of the subsea connector in deep water, the heat transfer model of seawater layer [...] Read more.
Taking a subsea collet connector as an example, the contact characteristics of the sealing structure of the subsea connector under thermal–structural coupling were studied. Considering the heat transfer problem of the subsea connector in deep water, the heat transfer model of seawater layer between sealing structures was established, and the relationship between equivalent thermal conductivity, composite heat transfer coefficient, and temperature was determined. The steady-state temperature field distribution of the connector under the action of the internal high-temperature oil and gas and external low-temperature seawater was obtained. Considering the stress and deformation of the subsea connector under the thermal load, the thermal–structural coupling analysis model of the steady-state temperature field was established, and the thermal stress theoretical analysis and numerical simulation of the key sealing structures of the connector were compared and verified. Analysis of coupled stress calculation, for example, under a steady-state temperature field, was carried out on the sealing structure of the subsea connector. At the same time, the pressure shock mode under a steady temperature field was analyzed, which showed that the lenticular sealing gasket is sensitive to high pressure under high-temperature conditions. Full article
(This article belongs to the Special Issue Structural Design and Computational Methods)
►▼ Show Figures

Figure 1

Figure 1
<p>The subsea connector. (<b>a</b>) Installation tool and top connector; (<b>b</b>) the structure of the subsea connector.</p> Full article ">Figure 2
<p>Heat transfer model of the subsea collet connector.</p> Full article ">Figure 3
<p>Heat transfer of the seawater between the hub and collet.</p> Full article ">Figure 4
<p>Relationship between <span class="html-italic">λ<sub>e</sub></span><sub>1</sub> and the temperature.</p> Full article ">Figure 5
<p>Heat transfer between the hub and the lenticular sealing gasket. (<b>a</b>) Schematic diagram of heat transfer; (<b>b</b>) Simple diagram of heat transfer.</p> Full article ">Figure 6
<p>Relationship between <span class="html-italic">λ<sub>e</sub></span><sub>2</sub> and the temperature.</p> Full article ">Figure 7
<p>Heat transfer of the collet outer surface and the press ring outer surface.</p> Full article ">Figure 8
<p>Relationship between the composite thermal conductivity <span class="html-italic">h<sub>e</sub></span> and the temperature.</p> Full article ">Figure 9
<p>Contact principle diagram of the seal gasket.</p> Full article ">Figure 10
<p>Stress state analysis near the contact position. (<b>a</b>) The contact position; (<b>b</b>) The micro-body; (<b>c</b>) Orthographic projection of the micro-body; (<b>d</b>) The orthographic projection obtained by intercepting the micro-body with a plane parallel to <span class="html-italic">Oxy</span>; (<b>e</b>) The orthographic projection obtained by intercepting the micro-body with a plane parallel to <span class="html-italic">Oyz</span>.</p> Full article ">Figure 11
<p>Coordinate transformation. (<b>a</b>) Coordinate system transformed; (<b>b</b>) The cylindrical coordinate system.</p> Full article ">Figure 12
<p>Steady-state temperature field of the whole subsea collet connector.</p> Full article ">Figure 13
<p>Steady-state temperature distribution of the subsea collet connector on the center path. (<b>a</b>) Temperature path <span class="html-italic">P<sub>i</sub>P<sub>o</sub></span>; (<b>b</b>) The temperature change at each point along the path <span class="html-italic">P<sub>i</sub>P<sub>o</sub></span>.</p> Full article ">Figure 14
<p>Steady-state temperature field of the lens seal gasket. (<b>a</b>) Temperature field nephogram; (<b>b</b>) Path of the temperature field; (<b>c</b>) Temperature distribution along the path.</p> Full article ">Figure 15
<p>Simulation and calculation model of the sealing structure. (<b>a</b>) Simulation model; (<b>b</b>) Location of stress calculation.</p> Full article ">Figure 16
<p>Stress distribution in the sealing width when <span class="html-italic">z</span><sub>0</sub> is 0.27 mm. (<b>a</b>) Distribution of <span class="html-italic">σ<sub>r</sub></span> at <span class="html-italic">z</span><sub>0</sub> = 0.27 mm; (<b>b</b>) Distribution of <span class="html-italic">σ<sub>θ</sub></span> at <span class="html-italic">z</span><sub>0</sub> = 0.27 mm; (<b>c</b>) Distribution of <span class="html-italic">σ<sub>z</sub></span> at <span class="html-italic">z</span><sub>0</sub> = 0.27 mm.</p> Full article ">Figure 17
<p>Pressure shock mode.</p> Full article ">Figure 18
<p>Maximum contact stress and equivalent stress changing with the oil and gas pressure in the pressure shock mode. (<b>a</b>) Maximum contact stress; (<b>b</b>) Maximum equivalent stress.</p> Full article ">
9 pages, 4908 KiB  
Article
Inkjet-Printed Flexible Strain-Gauge Sensor on Polymer Substrate: Topographical Analysis of Sensitivity
by Hyunkyoo Kang, Seokjin Kim, Jaehak Shin and Sunglim Ko
Appl. Sci. 2022, 12(6), 3193; https://doi.org/10.3390/app12063193 - 21 Mar 2022
Cited by 8 | Viewed by 2208
Abstract
Inkjet-printed strain gauges on flexible substrates have recently been investigated for biomedical motion detection as well as the monitoring of structural deformation. This study performed a topographical analysis of an inkjet-printed strain gauge constructed using silver conductive ink on a PET (polyethylene terephthalate) [...] Read more.
Inkjet-printed strain gauges on flexible substrates have recently been investigated for biomedical motion detection as well as the monitoring of structural deformation. This study performed a topographical analysis of an inkjet-printed strain gauge constructed using silver conductive ink on a PET (polyethylene terephthalate) substrate. Serpentine strain-gauge sensors of various thicknesses and widths were fabricated using inkjet printing and oven sintering. The fabricated gauge sensors were attached to curved surfaces, and gauge factors ranging from 2.047 to 3.098 were recorded. We found that the cross-sectional area of the printed strain gauge was proportional to the gauge factor. The correlation was mathematically modelled as y = 0.4167ln(x) + 1.3837, for which the coefficient of determination (R2) was 0.8383. Full article
(This article belongs to the Special Issue The Latest Developments and Applications of Printed Electronics)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Deposited single droplet on the substrate. The droplet diameter was approximately 51.4 μm. (<b>b</b>) The designed strain-gauge pattern. <span class="html-italic">W</span>, <span class="html-italic">P</span> and <span class="html-italic">h</span> are the width, pitch and height of the pattern, respectively.</p> Full article ">Figure 2
<p>(<b>a</b>) Three-dimensional printer. (<b>b</b>) Three-dimensionally printed pillars with various arc-shaped cross sections.</p> Full article ">Figure 3
<p>(<b>a</b>) Design of the printed strain gauge. (<b>b</b>) Resistance variations of the printed strain gauges. Blue and orange bars are for stacked layers and different widths, respectively. Data are mean and standard deviation values.</p> Full article ">Figure 4
<p>Cross-sectional profiles of printed strain-gauge patterns as a function of (<b>a</b>) the number of stacked layers and (<b>b</b>) the width.</p> Full article ">Figure 5
<p>Interferometer images of printed strain gauges with stacked layers: (<b>a</b>) single layer, (<b>b</b>) three layers and (<b>c</b>) five layers.</p> Full article ">Figure 6
<p>Interferometer images of printed strain gauges according to the pattern width: (<b>a</b>) two droplets, (<b>b</b>) four droplets and (<b>c</b>) seven droplets.</p> Full article ">Figure 7
<p>Gauge factor as a function of the number of stacked layers of printed strain-gauge patterns: (<b>a</b>) 2.471 for a single layer, (<b>b</b>) 3.051 for three layers and (<b>c</b>) 3.098 for five layers.</p> Full article ">Figure 8
<p>Gauge factor as a function of the width of printed strain-gauge patterns: (<b>a</b>) 2.047 for two droplets, (<b>b</b>) 2.471 for four droplets and (<b>c</b>) 2.589 for seven droplets.</p> Full article ">Figure 9
<p>Gauge factor as a function of cross-sectional area of printed strain-gauge patterns. The black dots and green squares are for the width and the number of stacked layers, respectively.</p> Full article ">
42 pages, 7185 KiB  
Review
Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
by Homa Maleki, Bahareh Azimi, Saeed Ismaeilimoghadam and Serena Danti
Appl. Sci. 2022, 12(6), 3192; https://doi.org/10.3390/app12063192 - 21 Mar 2022
Cited by 55 | Viewed by 7325
Abstract
Poly(lactic acid)(PLA) is an aliphatic polyester that can be derived from natural and renewable resources. Owing to favorable features, such as biocompatibility, biodegradability, good thermal and mechanical performance, and processability, PLA has been considered as one of the most promising biopolymers for biomedical [...] Read more.
Poly(lactic acid)(PLA) is an aliphatic polyester that can be derived from natural and renewable resources. Owing to favorable features, such as biocompatibility, biodegradability, good thermal and mechanical performance, and processability, PLA has been considered as one of the most promising biopolymers for biomedical applications. Particularly, electrospun PLA nanofibers with distinguishing characteristics, such as similarity to the extracellular matrix, large specific surface area and high porosity with small pore size and tunable mechanical properties for diverse applications, have recently given rise to advanced spillovers in the medical area. A variety of PLA-based nanofibrous structures have been explored for biomedical purposes, such as wound dressing, drug delivery systems, and tissue engineering scaffolds. This review highlights the recent advances in electrospinning of PLA-based structures for biomedical applications. It also gives a comprehensive discussion about the promising approaches suggested for optimizing the electrospun PLA nanofibrous structures towards the design of specific medical devices with appropriate physical, mechanical and biological functions. Full article
(This article belongs to the Special Issue Women in Oral Medicine, Oral Biology, Oral Microbiology and Oral Bioengineering)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic to explain the recent progress in electrospinning diverse PLA-based structures, with a focus on their biomedical applications (IVC: inferior vena cava; Ao: aorta).</p> Full article ">Figure 2
<p>Stereoisomers of the polymer start from different lactides. Reproduced with permission from [<a href="#B28-applsci-12-03192" class="html-bibr">28</a>] (License number: 5235811357024).</p> Full article ">Figure 3
<p>Schematic illustration of the electrospinning set-up.</p> Full article ">Figure 4
<p>SEM images of fibers electrospun from PLA solutions using different solvents of (<b>a</b>) HFIP (unpublished original picture by the authors), (<b>b</b>) TFE (unpublished original picture by the authors), (<b>c</b>) CHCl<sub>3</sub> (unpublished original picture by the authors), (<b>d</b>) DCM (unpublished original picture by the authors).</p> Full article ">Figure 5
<p>SEM images of electrospun yarn structures from PLA solutions using different solvents; (<b>a</b>) TFE (unpublished original picture by the authors), (<b>b</b>) DCM (unpublished original picture by the authors), (<b>c</b>) CHCl<sub>3</sub> (unpublished original picture by the authors), (<b>d</b>) Stress–strain curves of electrospun structures (unpublished original picture by the authors).</p> Full article ">Figure 6
<p>Different morphologies of PLA fibers are obtained by tuning the electrospinning parameters: (<b>a</b>) bead-on-string morphology (unpublished original picture by the authors), (<b>b</b>) cylindrical shape with a smooth surface (reproduced from an open access paper [<a href="#B4-applsci-12-03192" class="html-bibr">4</a>] distributed under the terms of the Creative Commons CC BY license, (<b>c</b>) flat ribbon-like morphology, reproduced from an open access paper [<a href="#B4-applsci-12-03192" class="html-bibr">4</a>] distributed under the terms of the Creative Commons CC BY license, (<b>d</b>) porous reproduced from an open access paper [<a href="#B4-applsci-12-03192" class="html-bibr">4</a>] distributed under the terms of the Creative Commons CC BY license, (<b>e</b>) bead with the porous surface (unpublished original picture by the authors), (<b>f</b>) core–shell structure reproduced from an open access paper [<a href="#B9-applsci-12-03192" class="html-bibr">9</a>] distributed under the terms of the Creative Commons CC BY license.</p> Full article ">Figure 7
<p>Different arrangements of fiber deposits produced via electrospinning of PLA: (<b>a</b>) random. Reproduced with permission from [<a href="#B40-applsci-12-03192" class="html-bibr">40</a>] (License number: 5235950005044), (<b>b</b>) oriented, reproduced from an open access paper [<a href="#B4-applsci-12-03192" class="html-bibr">4</a>] distributed under the terms of the Creative Commons CC BY license, and (<b>c</b>) yarn, Reproduced with permission from [<a href="#B45-applsci-12-03192" class="html-bibr">45</a>] (License number: 5235871036609).</p> Full article ">Figure 8
<p>(<b>a</b>) Double-nozzle electrospinning set-up to produce continuous twisted yarns, Reproduced with permission from [<a href="#B45-applsci-12-03192" class="html-bibr">45</a>] (License number: 5235871036609), (<b>b</b>) SEM images of the PLA twisted yarn prepared by double-nozzle electrospinning set-up Reproduced with permission from [<a href="#B45-applsci-12-03192" class="html-bibr">45</a>] (License number: 5235871036609), (<b>c</b>) cross-section image of electrospun PLA yarn with core–shell structure, reproduced from an open access paper [<a href="#B17-applsci-12-03192" class="html-bibr">17</a>] distributed under the terms of the Creative Commons CC BY license, (<b>d</b>) SEM image of the cross-section of hollow PLA nanofiber yarn reproduced from [<a href="#B56-applsci-12-03192" class="html-bibr">56</a>], distributed under the terms of the Creative Commons CC BY license, (<b>e</b>) electrospinning set-up composed of a rotating collector having a plurality of point electrodes to produce, adapted from [<a href="#B16-applsci-12-03192" class="html-bibr">16</a>], reprinted with permission from [<a href="#B16-applsci-12-03192" class="html-bibr">16</a>] Copyright @2022 American Chemical Society. (<b>f</b>) Dual plied PLA suture yarn, reprinted (adapted) with permission from [<a href="#B16-applsci-12-03192" class="html-bibr">16</a>] Copyright @2022 American Chemical Society.</p> Full article ">Figure 9
<p>(<b>a</b>) A schematic illustration of the melt electrospinning apparatus, reproduced with permission from [<a href="#B57-applsci-12-03192" class="html-bibr">57</a>] (License number: 5236501189506), (<b>b</b>) Morphology of PLA melt-electrospun nanofibers Reproduced with permission from [<a href="#B57-applsci-12-03192" class="html-bibr">57</a>] (License number: 5236501189506), (<b>c</b>) Macroscopic view of PLA tubular model produce via melt electrospinning procedure, reproduced from an open access paper [<a href="#B58-applsci-12-03192" class="html-bibr">58</a>] distributed under the terms of the Creative Commons CC BY license.</p> Full article ">Figure 10
<p>(<b>a</b>) Intermolecular ordering for stereocomplex formation in the electrospun PLA fibers Reproduced with permission from [<a href="#B67-applsci-12-03192" class="html-bibr">67</a>] (License number: 5237071296979), (<b>b</b>) SEM micrographs of electrospun Sc-PLA structures, unpublished original picture by the authors, (<b>c</b>) Stress–Elongation curves of PLA-based electrospun yarns, unpublished original picture by the authors (<b>d</b>) DSC curves of electrospun PLLA and Sc-PLA fibers, unpublished original picture by the authors.</p> Full article ">Figure 11
<p>SEM images of the PLA e(PLA-D) fibrous mats (<b>a</b>) pure, (<b>b</b>) with 14% of BET and (<b>c</b>) DEX, reproduced with permission from [<a href="#B2-applsci-12-03192" class="html-bibr">2</a>] (License number: 5232480112869), SEM images of neat (<b>d</b>) and curcumin-loaded PLLA fiber mats (<b>e</b>) reproduced with permission from [<a href="#B74-applsci-12-03192" class="html-bibr">74</a>] (License number: 5232490308135). (<b>f</b>) SEM micrographs of electrospun PLA/CUR/PEG nanofibers containing 10 wt% of PEG with a molecular weight of 400, reproduced with permission from [<a href="#B70-applsci-12-03192" class="html-bibr">70</a>] (License number: 5232490801255), Morphology of (<b>g</b>) the pure PLA nanofibers and (<b>h</b>) PLA nanofibers with 5% DCH contents, reproduced with permission from [<a href="#B78-applsci-12-03192" class="html-bibr">78</a>] (License number: 5232491264115). (<b>i</b>) SEM images of Ag2[HBTC][im]-PLA composite fibrous mat Reproduced with permission from [<a href="#B79-applsci-12-03192" class="html-bibr">79</a>] (License number: 5232491480582). SEM images of the electrospun fibers of (<b>j</b>) neat PLLA, (<b>k</b>) PLLA/POSS and (<b>l</b>) PLLA/POSS/pAng, reproduced with permission from [<a href="#B87-applsci-12-03192" class="html-bibr">87</a>] (License number: 5272940375894).</p> Full article ">Figure 12
<p>TEM images of core–shell nanofibers: (<b>a</b>) with compact core structure; (<b>b</b>) with porous core structure Reproduced with permission from [<a href="#B6-applsci-12-03192" class="html-bibr">6</a>] (License number: 5232520656228), (<b>c</b>) SEM image of coaxial PLA-PVA-CTGF membranes, reproduced from an open access paper [<a href="#B91-applsci-12-03192" class="html-bibr">91</a>] distributed under the terms of the Creative Commons Attribution—Non-Commercial (v3.0) License.</p> Full article ">Figure 13
<p>The scheme image of the different methods for loading the drug in nanofibers, reproduced from an open access paper [<a href="#B71-applsci-12-03192" class="html-bibr">71</a>], under the terms of Creative Commons Attribution 4.0 International (CC BY 4.0) License.</p> Full article ">Figure 14
<p>Illustration of doxorubicin drug release mechanisms from a PLA fiber matrix. (<b>a</b>) PLA–HCl; (<b>b</b>) PLA–base; (<b>c</b>) PLA–HCl (DMSO). M1, dissolution of drug molecules; M2, water permeation followed by drug diffusion; M3, polymer degradation followed by drug dissolution/diffusion. Reproduced from an open access paper [<a href="#B107-applsci-12-03192" class="html-bibr">107</a>], distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</p> Full article ">Figure 15
<p>(<b>a</b>) In situ combination of encapsulated particles and fibers via electrospinning and electrospray techniques. (<b>b</b>) SEM (<b>left</b>) and confocal microscopy (<b>middle</b> and <b>right</b>) images of particle-fiber composites produced with different solvent compositions: EA:BA = 1:9, Reproduced with permission from [<a href="#B110-applsci-12-03192" class="html-bibr">110</a>] (License number: 5232980582910).</p> Full article ">Figure 16
<p>Effect of fiber alignment on the morphology of BMSCs on electrospun membranes (<b>a<sub>1</sub></b>–<b>a<sub>4</sub></b>) Immunofluorescence images of AN, AM, RN and RM. (<b>a<sub>5</sub></b>–<b>a<sub>8</sub></b>) SEM images of AN, AM, RN and RM, Reproduced with permission from [<a href="#B130-applsci-12-03192" class="html-bibr">130</a>] (License number: 5232540107019). SEM images of (<b>b<sub>1</sub></b>) SiNPs, (<b>b<sub>2</sub></b>) porous PLLA membrane after DOP surface modification and porous PLLA/DOP/SiNP membranes with different SiNP concentrations (<b>b<sub>3</sub></b> = 0.05, <b>b<sub>4</sub></b> = 0.10) Reproduced with permission from [<a href="#B132-applsci-12-03192" class="html-bibr">132</a>] (License number: 5232550661164), SEM image of (<b>c<sub>1</sub></b>) nanoHA-coated nanofibers, (<b>c<sub>2</sub></b>) liposome-loaded scaffolds (liposcaffolds), Reproduced with permission from [<a href="#B133-applsci-12-03192" class="html-bibr">133</a>] (License number: 5232550143142). SEM images of the internal morphological structures of (<b>d<sub>1</sub></b>) PLA/GEL, (<b>d<sub>2</sub></b>) nHA/PLA/GEL, and (<b>d<sub>3</sub></b>) nHA/PLA/GEL-PEP 3D nanofibrous scaffolds, respectively, Reproduced with permission from [<a href="#B134-applsci-12-03192" class="html-bibr">134</a>] (License number: 5232541047315). SEM images of surface morphology of the P3C1 scaffolds after biomineralization for (<b>e<sub>1</sub></b>) 7 days, (<b>e<sub>2</sub></b>) SEM image of mineral clusters after 7 days mineralization in SBF with high magnification, reproduced with permission from [<a href="#B140-applsci-12-03192" class="html-bibr">140</a>] (License number: 5232551100684). SEM images of electrospun fibers prepared at different temperatures (<b>f<sub>1</sub></b> = 35 °C), (<b>f<sub>2</sub></b> = 50 °C), (<b>f<sub>3</sub></b> = 60 °C) using a PLA/CS 70:30 weight ratio; Reprinted (adapted) with permission from [<a href="#B141-applsci-12-03192" class="html-bibr">141</a>]. Copyright (2022) American Chemical Society.</p> Full article ">Figure 17
<p>(<b>a</b>) Structure of TEVG. Implanted TEVG was composed of biodegradable electrospun PLA nanofibers with a length of approximately 3 mm and an inner luminal diameter of 500–600 μm. Scanning electron microscopy (SEM) demonstrates macro- and micro-arrangement of graft. (<b>b</b>) TEVG after surgical implantation (IVC: inferior vena cava; Ao: aorta). (<b>c</b>,<b>d</b>) High-resolution post-mortem microCT angiography at 12 months post-implantation. The results indicated a smooth endoluminal surface and absence of aneurysmal dilatation or stenosis. Yellow bar indicates the approximate location of the vascular graft. TEVG resembles native aorta on microCT throughout the 12 month-experiment; (<span class="html-italic">n</span> = 5 in each group), reproduced from [<a href="#B147-applsci-12-03192" class="html-bibr">147</a>], distributed under the terms of Creative Commons Attribution License.</p> Full article ">Figure 18
<p>SEM image of SMCs on the surface of the scaffolds after 120 h (<b>a</b>) PLLA alone, (<b>b</b>) nonwoven PLLA with 5% gelatin, (<b>c</b>) aligned PLLA with 5% gelatin, (<b>d</b>) nonwoven PLLA with 10% gelatin, (<b>e</b>) aligned PLLA with 10% gelatin (<b>f</b>) nonwoven PLLA with 20% gelatin and (<b>g</b>) aligned PLLA with 20% gelatin. (<b>h</b>)The electrospun PLLA/gelatin tubular constructs. Reproduced with permission from [<a href="#B149-applsci-12-03192" class="html-bibr">149</a>] (License number: 5232561220454).</p> Full article ">
18 pages, 1150 KiB  
Article
An Efficient Method for Biomedical Entity Linking Based on Inter- and Intra-Entity Attention
by Mamatjan Abdurxit, Turdi Tohti and Askar Hamdulla
Appl. Sci. 2022, 12(6), 3191; https://doi.org/10.3390/app12063191 - 21 Mar 2022
Cited by 3 | Viewed by 2493
Abstract
Biomedical entity linking is an important research problem for many downstream tasks, such as biomedical intelligent question answering, information retrieval, and information extraction. Biomedical entity linking is the task of mapping mentions in medical texts to standard entities in a given knowledge base. [...] Read more.
Biomedical entity linking is an important research problem for many downstream tasks, such as biomedical intelligent question answering, information retrieval, and information extraction. Biomedical entity linking is the task of mapping mentions in medical texts to standard entities in a given knowledge base. Recently, BERT-based models have achieved state-of-the-art results on the biomedical entity linking task. Although this type of method is effective, it brings challenges for fine-tuning and online services in practical industries due to a large number of model parameters and long inference time. In addition, due to the numerous surface variants of biomedical mentions, it is difficult for a single matching module to achieve good results. To address the challenge, we propose an efficient biomedical entity linking method that integrates inter- and intra-entity attention to better capture the information between medical entity mentions and candidate entities themselves and each other, and the model in this paper is more lightweight. Experimental results show that our method achieves competitive performance on two biomedical benchmark datasets, NCBI and ADR, with an accuracy rate of 91.28% and 93.13%, respectively. Moreover, it also achieves comparable or even better results compared to the BERT-based entity linking method while having far fewer model parameters and very high inference speed. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>The architecture of our candidate ranking model.</p> Full article ">Figure 2
<p>Calculation process of self-attention for <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mi>i</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Cross-Attention module.</p> Full article ">Figure 4
<p>The impact of different margin values.</p> Full article ">Figure 5
<p>Effects of different data sizes on performance of our model.</p> Full article ">
22 pages, 14460 KiB  
Article
A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
by Jin Hyun Kim, Eunah Jo, Seungjae Ryu, Sohee Nam, Somin Song, Yong Seop Han, Tae Seen Kang, Woongsup Lee, Seongjin Lee, Kyong Hoon Kim, Hyunju Choi and Seunghwan Lee
Appl. Sci. 2022, 12(6), 3190; https://doi.org/10.3390/app12063190 - 21 Mar 2022
Cited by 7 | Viewed by 2659
Abstract
Visual acuity (VA) is a measure of the ability to distinguish shapes and details of objects at a given distance and is a measure of the spatial resolution of the visual system. Vision is one of the basic health indicators closely related to [...] Read more.
Visual acuity (VA) is a measure of the ability to distinguish shapes and details of objects at a given distance and is a measure of the spatial resolution of the visual system. Vision is one of the basic health indicators closely related to a person’s quality of life. It is one of the first basic tests done when an eye disease develops. VA is usually measured by using a Snellen chart or E-chart from a specific distance. However, in some cases, such as the unconsciousness of patients or diseases, i.e., dementia, it can be impossible to measure the VA using such traditional chart-based methodologies. This paper provides a machine learning-based VA measurement methodology that determines VA only based on fundus images. In particular, the levels of VA, conventionally divided into 11 levels, are grouped into four classes and three machine learning algorithms, one SVM model and two CNN models, are combined into an ensemble method in order to predict the corresponding VA level from a fundus image. Based on a performance evaluation conducted using randomly selected 4000 fundus images, we confirm that our ensemble method can estimate with 82.4% of the average accuracy for four classes of VA levels, in which each class of Class 1 to Class 4 identifies the level of VA with 88.5%, 58.8%, 88%, and 94.3%, respectively. To the best of our knowledge, this is the first paper on VA measurements based on fundus images using deep machine learning. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Medicine Practice)
►▼ Show Figures

Figure 1

Figure 1
<p>A normal fundus photograph of a right eye.</p> Full article ">Figure 2
<p>Pre-processing of fundus images. (<b>a</b>) Original; (<b>b</b>) Salt and pepper; (<b>c</b>) Gamma correction; (<b>d</b>) Remove noise; (<b>e</b>) Gamma correction + Salt and pepper; and (<b>f</b>) Gamma correction + Remove noise.</p> Full article ">Figure 3
<p>Before and after cropping fundus images. (<b>a</b>) before cropping; (<b>b</b>) after cropping.</p> Full article ">Figure 4
<p>Confusion matrices from 4-Class VA Classifier’s classification results for 11 classes of fundus images. (<b>a</b>) Class-0.0; (<b>b</b>) Class-0.1; (<b>c</b>) Class-0.2; (<b>d</b>) Class-0.3; (<b>e</b>) Class-0.4; (<b>f</b>) Class-0.5; (<b>g</b>) Class-0.6; (<b>h</b>) Class-0.7; (<b>i</b>) Class-0.8; (<b>j</b>) Class-0.9; (<b>k</b>) Class-1.0.</p> Full article ">Figure 5
<p>Proposed ensemble method.</p> Full article ">Figure 6
<p>VGG19-based CNN model for VA classification.</p> Full article ">Figure 7
<p>Training results of VGG19-based CNN for Step-1. (<b>a</b>) Accuracy; (<b>b</b>) Loss; (<b>c</b>) Confusion matrix; (<b>d</b>) Classification report.</p> Full article ">Figure 8
<p>Hyperplanes in SVM. (<b>a</b>) Hard-margin; (<b>b</b>) soft-margin.</p> Full article ">Figure 9
<p>Fundus images compressed into <math display="inline"><semantics> <mrow> <mn>32</mn> <mo>×</mo> <mn>32</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> for SVM (RBF kernel).</p> Full article ">Figure 10
<p>Confusion matrix of SVM-based classifier for Step-2-1.</p> Full article ">Figure 11
<p>MBConv of EfficientNet [<a href="#B29-applsci-12-03190" class="html-bibr">29</a>]. (<b>a</b>) MBConv1; (<b>b</b>) MBConv6.</p> Full article ">Figure 12
<p>EfficientNet-B7 CNN model for VA classification in Step-2-2.</p> Full article ">Figure 13
<p>Training results of EfficientNet-B7-based CNN for Step-2-2. (<b>a</b>) Accuarcy; (<b>b</b>) Loss; (<b>c</b>) Confusion matrix; (<b>d</b>) Classification report.</p> Full article ">Figure 14
<p>Verification results of 4-Class VA Classifier. (<b>a</b>) Confusion matrix; (<b>b</b>) Classification report.</p> Full article ">Figure 15
<p>Examples of misclassification of Class A and the probabilities of Softmax: The pair of numbers in parenthesis are the probability values from Softmax. The first value is the probability that the image is in Class A and the second value is the probability that the image is in Class B. (<b>a</b>) Class 1 of Class A; (<b>b</b>) Class 2 of Class A.</p> Full article ">Figure 16
<p>Examples of misclassification of Class B and the probabilities of Softmax: The pair of numbers in parenthesis are the probability values from Softmax. (<b>a</b>) Class 3 of Class B; (<b>b</b>) Class 4 of Class B.</p> Full article ">Figure 17
<p>Validation results of SVM-RBF-Kernel classification for Step-2-1. (<b>a</b>) Confusion matrix; (<b>b</b>) AUC (ROC) report.</p> Full article ">Figure 18
<p>Examples of misclassification of Classes 1 and 2 by the SVM-RBF-Kernel. (<b>a</b>) Class 1; (<b>b</b>) Class 2.</p> Full article ">Figure 19
<p>Validation results of EfficientNet-B7-based CNN classification for Step-2-2. (<b>a</b>) Confusion matrix; (<b>b</b>) AUC (ROC) report.</p> Full article ">Figure 20
<p>Examples of misclassification of Classes 3 and 4 by EfficientNet-B7. (<b>a</b>) Class 3; (<b>b</b>) Class 4.</p> Full article ">Figure 21
<p>Overall accuracy report of 4-Class VA classifier based on our ensemble method.</p> Full article ">
17 pages, 546 KiB  
Article
Iterative Dynamic Critical Path Scheduling: An Efficient Technique for Offloading Task Graphs in Mobile Edge Computing
by Bo Xu, Yi Hu, Menglan Hu, Feng Liu, Kai Peng and Lan Liu
Appl. Sci. 2022, 12(6), 3189; https://doi.org/10.3390/app12063189 - 21 Mar 2022
Cited by 3 | Viewed by 1785
Abstract
Recent years have witnessed a paradigm shift from centralized cloud computing to decentralized edge computing. As a key enabler technique in edge computing, computation offloading migrates computation-intensive tasks from resource-limited devices to nearby devices, optimizing service latency and energy consumption. In this paper, [...] Read more.
Recent years have witnessed a paradigm shift from centralized cloud computing to decentralized edge computing. As a key enabler technique in edge computing, computation offloading migrates computation-intensive tasks from resource-limited devices to nearby devices, optimizing service latency and energy consumption. In this paper, we investigate the problem of offloading task graphs in edge computing scenarios. Previous work based on list-scheduling heuristics is likely to suffer from severe processor time wastage due to intricate task dependencies and data transfer requirements. To this end, we propose a novel offloading algorithm, referred to as Iterative Dynamic Critical Path Scheduling (IDCP). IDCP minimizes the makespan by iteratively migrating tasks to keep shortening the dynamic critical path. Through IDCP, what is managed are essentially the sequences among tasks, including task dependencies and scheduled sequences on processors. Since we only schedule sequences here, the actual start time of each task is not fixed during the scheduling process, which effectively helps to avoid unfavorable schedules. Such flexibilities also offer us much space for continuous scheduling optimizations. Our experimental results show that our algorithm significantly outperforms existing list-scheduling heuristics in various scenarios, which demonstrates the effectiveness and competitiveness of our algorithm. Full article
(This article belongs to the Collection Energy-efficient Internet of Things (IoT))
►▼ Show Figures

Figure 1

Figure 1
<p>An offloading example of a task graph.</p> Full article ">Figure 2
<p>An offloading model of a MEC system.</p> Full article ">Figure 3
<p>A topology representation of task graphs.</p> Full article ">Figure 4
<p>An example of node migration process.</p> Full article ">Figure 5
<p>Time cost of different topologies under the number of nodes.</p> Full article ">Figure 6
<p>Time cost of different topologies under the number of processors.</p> Full article ">Figure 7
<p>Time cost of different topologies under communication Computation Ratio (CCR).</p> Full article ">
19 pages, 3830 KiB  
Article
Content Curation in E-Learning: A Case of Study with Spanish Engineering Students
by Juan D. Aguilar-Peña, Catalina Rus-Casas, Dolores Eliche-Quesada, Francisco José Muñoz-Rodríguez and M. Dolores La Rubia
Appl. Sci. 2022, 12(6), 3188; https://doi.org/10.3390/app12063188 - 21 Mar 2022
Cited by 4 | Viewed by 3998
Abstract
Over the last decade, e-learning and the use of digital tools have received a great boost in higher education. This paper presents a content curation methodology to assess the acquisition of specific content and soft skills during the attainment of a Degree in [...] Read more.
Over the last decade, e-learning and the use of digital tools have received a great boost in higher education. This paper presents a content curation methodology to assess the acquisition of specific content and soft skills during the attainment of a Degree in Industrial Electronic Engineering at the University of Jaén. In this teaching–learning experience, 101 engineering students were involved in activities with digital tools related to content curation, and four steps were proposed: search, select, sense making, and share. As evaluation tools, a rubric and a questionnaire of the digital tools were proposed. Moreover, a curation index was defined in order to assess the degree of achievement of the content curation. The academic results after using the rubric were better than previous years. The average content curation index obtained was 53.53. Of the four evaluated steps, search and sense making had the lowest scores and, therefore, these steps should be further developed in the future. In addition, the Kaiser–Meyer–Olkin test and Pearson’s correlation were used for analyzing the results of the questionnaires. It was concluded that the experience had a great impact on the skills related to collaborative work, digital information management, and lifelong learning, which are transversal skills at the university level. Thus, the results highlight the great educational potential of content curation. Full article
(This article belongs to the Special Issue Application of Technologies in E-learning Assessment)
►▼ Show Figures

Figure 1

Figure 1
<p>Methodology.</p> Full article ">Figure 2
<p>Content Curation Toolkit recommended to the students.</p> Full article ">Figure 3
<p>Rubric.</p> Full article ">Figure 4
<p>Example of a Symbaloo webmix as search step result.</p> Full article ">Figure 5
<p>Example of a Pocket panel for the temporary storage of information.</p> Full article ">Figure 6
<p>Example of a Scoop.it panel and a New Scoop screen for the <span class="html-italic">sense making</span> step.</p> Full article ">Figure 7
<p>Example of the groups’ results dissemination.</p> Full article ">Figure 8
<p>Academic results.</p> Full article ">
18 pages, 4887 KiB  
Article
Benchmarking Various Pseudo-Measurement Data Generation Techniques in a Low-Voltage State Estimation Pilot Environment
by Gergő Bendegúz Békési, Lilla Barancsuk, István Táczi and Bálint Hartmann
Appl. Sci. 2022, 12(6), 3187; https://doi.org/10.3390/app12063187 - 21 Mar 2022
Cited by 3 | Viewed by 2041
Abstract
Distribution system state estimation (DSSE) is a valuable step for DSOs toward tackling the challenges of transitioning to a more sustainable energy system and the evolution and proliferation of electric cars and power electronic devices. However, on the LV level, implementation has only [...] Read more.
Distribution system state estimation (DSSE) is a valuable step for DSOs toward tackling the challenges of transitioning to a more sustainable energy system and the evolution and proliferation of electric cars and power electronic devices. However, on the LV level, implementation has only taken place in a few pilot projects. In this paper, an LV DSSE method is presented and implemented in four real Hungarian LV supply areas, according to well-defined scenarios. Pseudo-measurement datasets are generated from AACs and SLPs, which have been used in different combinations on networks built with different accuracies in terms of load placement. The paper focuses on the critical aspects of finding accurate and coherent information on network topology with automated management of information systems, real LV network implementation for power flow calculation and managing portions of the network characterized by uncertain or inconsistent line lengths. A refining algorithm is implemented for the integrated network information system (INIS) models. The published method estimates node voltages with a relative error of less than 1% when using AACs, and a meter-placement method to reduce the maximum value of relative errors in future scenarios is also presented. It is shown that the observation of node voltages can be improved with the usage of AACs and SLPs, and with optimal meter placement. Full article
(This article belongs to the Special Issue Data Science Applications in Medium/Low Voltage Smart Grids)
►▼ Show Figures

Figure 1

Figure 1
<p>Flowchart of the Gauss–Newton algorithm.</p> Full article ">Figure 2
<p>Architecture of the DSSE tool. Modules inside the red box perform the run of DSSE, while those outside the red box perform data management and preprocessing.</p> Full article ">Figure 3
<p>Length correction algorithm.</p> Full article ">Figure 4
<p>Average relative error of voltages for Area 18,680 with Method 1 and 1m.</p> Full article ">Figure 5
<p>Average relative error of voltages for Area 18,680 with Method 2.</p> Full article ">Figure 6
<p>Average relative error of voltages for Area 18,680 with Method 2m.</p> Full article ">Figure 7
<p>Average relative error of voltages for Area 44,333.</p> Full article ">Figure 8
<p>Average relative error of voltages for Area 44,600.</p> Full article ">Figure 9
<p>Temporality of the relative error of voltages for Area 20,667.</p> Full article ">Figure A1
<p>Topology of area 18680 (MV/LV represents the supplying medium/low-voltage transformer).</p> Full article ">Figure A2
<p>Topology of area 44333 (MV/LV represents the supplying medium/low-voltage transformer).</p> Full article ">Figure A3
<p>Topology of area 44600 (MV/LV represents the supplying medium/low-voltage transformer).</p> Full article ">Figure A4
<p>Topology of area 20667 (MV/LV represents the supplying medium/low-voltage transformer).</p> Full article ">
29 pages, 6893 KiB  
Article
Low-Cost Sensors Accuracy Study and Enhancement Strategy
by Seyedmilad Komarizadehasl, Behnam Mobaraki, Haiying Ma, Jose-Antonio Lozano-Galant and Jose Turmo
Appl. Sci. 2022, 12(6), 3186; https://doi.org/10.3390/app12063186 - 21 Mar 2022
Cited by 15 | Viewed by 4919
Abstract
Today, low-cost sensors in various civil engineering sectors are gaining the attention of researchers due to their reduced production cost and their applicability to multiple nodes. Low-cost sensors also have the advantage of easily connecting to low-cost microcontrollers such as Arduino. A low-cost, [...] Read more.
Today, low-cost sensors in various civil engineering sectors are gaining the attention of researchers due to their reduced production cost and their applicability to multiple nodes. Low-cost sensors also have the advantage of easily connecting to low-cost microcontrollers such as Arduino. A low-cost, reliable acquisition system based on Arduino technology can further reduce the price of data acquisition and monitoring, which can make long-term monitoring possible. This paper introduces a wireless Internet-based low-cost data acquisition system consisting of Raspberry Pi and several Arduinos as signal conditioners. This study investigates the beneficial impact of similar sensor combinations, aiming to improve the overall accuracy of several sensors with an unknown accuracy range. The paper then describes an experiment that gives valuable information about the standard deviation, distribution functions, and error level of various individual low-cost sensors under different environmental circumstances. Unfortunately, these data are usually missing and sometimes assumed in numerical studies targeting the development of structural system identification methods. A measuring device consisting of a total of 75 contactless ranging sensors connected to two microcontrollers (Arduinos) was designed to study the similar sensor combination theory and present the standard deviation and distribution functions. The 75 sensors include: 25 units of HC-SR04 (analog), 25 units of VL53L0X, and 25 units of VL53L1X (digital). Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Structural Health Monitoring)
►▼ Show Figures

Figure 1

Figure 1
<p>The growth of low-cost sensors in civil engineering sector.</p> Full article ">Figure 2
<p>The principle behind distance measurement with sonar sensors.</p> Full article ">Figure 3
<p>The triangulation procedure of IR sensor.</p> Full article ">Figure 4
<p>The used low-cost sensors of the project: HC-SR04 (ultrasonic sensor), DHT22 (temperature and humidity sensor for calibrating the ultrasonic sensor), TCA9548A (multiplexor), VL53L0X (ToF sensor), and VL53L1X (ToF sensor).</p> Full article ">Figure 5
<p>The components of the distance measuring device: (<b>a</b>) PVC sheet for attaching the sensors and the data acquisition equipment, (<b>b</b>) designed 3D-printed base for holding the various sensors together at a known height, (<b>c</b>) sensor allocation, (<b>d</b>) wiring the system together, (<b>e</b>) the experiment platform for validating the accuracy of several different low-cost sensors and (<b>f</b>) flowchart of the construction of the proposed measurement device.</p> Full article ">Figure 5 Cont.
<p>The components of the distance measuring device: (<b>a</b>) PVC sheet for attaching the sensors and the data acquisition equipment, (<b>b</b>) designed 3D-printed base for holding the various sensors together at a known height, (<b>c</b>) sensor allocation, (<b>d</b>) wiring the system together, (<b>e</b>) the experiment platform for validating the accuracy of several different low-cost sensors and (<b>f</b>) flowchart of the construction of the proposed measurement device.</p> Full article ">Figure 6
<p>Scheme of the connections between the microcontrollers and the Raspberry Pi.</p> Full article ">Figure 7
<p>The laboratory experiment equipment.</p> Full article ">Figure 8
<p>Excluding the outliers of HC-SR04 estimations.</p> Full article ">Figure 9
<p>Filtered output of distance sensors for an experiment: (<b>a</b>) results of VL53L0X, (<b>b</b>) results of VL53L1X, and (<b>c</b>) results of HC-SR04.</p> Full article ">Figure 9 Cont.
<p>Filtered output of distance sensors for an experiment: (<b>a</b>) results of VL53L0X, (<b>b</b>) results of VL53L1X, and (<b>c</b>) results of HC-SR04.</p> Full article ">Figure 10
<p>Combined outputs of similar sensors.</p> Full article ">Figure 11
<p>Comparing the worst-case sensor combination with different distance measurement technologies: (<b>a</b>) VL53L0X distance estimations affected by an excessive light source, (<b>b</b>) VL53L0X distance estimations in darkness, (<b>c</b>) VL53L1X distance estimations affected by an excessive light source, (<b>d</b>) VL53L1X distance estimations in darkness, and (<b>e</b>) HC-SR04 distance measurements.</p> Full article ">Figure 11 Cont.
<p>Comparing the worst-case sensor combination with different distance measurement technologies: (<b>a</b>) VL53L0X distance estimations affected by an excessive light source, (<b>b</b>) VL53L0X distance estimations in darkness, (<b>c</b>) VL53L1X distance estimations affected by an excessive light source, (<b>d</b>) VL53L1X distance estimations in darkness, and (<b>e</b>) HC-SR04 distance measurements.</p> Full article ">Figure 12
<p>The normal distribution function of VL53L0X for tests with ambient light.</p> Full article ">Figure 13
<p>The normal distribution function of VL53L0X for tests with no excessive ambient light.</p> Full article ">Figure 14
<p>The normal distribution function of VL53L1X for tests with ambient light.</p> Full article ">Figure 14 Cont.
<p>The normal distribution function of VL53L1X for tests with ambient light.</p> Full article ">Figure 15
<p>The normal distribution function of VL53L1X for tests with no excessive ambient light.</p> Full article ">Figure 16
<p>The normal distribution function of HC-SR04 for various distance measurements.</p> Full article ">Figure 16 Cont.
<p>The normal distribution function of HC-SR04 for various distance measurements.</p> Full article ">
10 pages, 2335 KiB  
Article
Experimental Analysis on the Impact of Current on the Strength and Lifespan of a Ni-Ti Element
by Cernusca Dumitru, Laurențiu Dan Milici, Radu Dumitru Pentiuc, Pavel Atănăsoae, Constantin Ungureanu and Eugen Hopulele
Appl. Sci. 2022, 12(6), 3185; https://doi.org/10.3390/app12063185 - 21 Mar 2022
Cited by 1 | Viewed by 1524
Abstract
Intelligent materials, especially materials with shape memory, are an important discovery, with technical applications in the medical and aerospace field, among others, which led to the development of systems and applications with multiple advantages and disadvantages due to ignorance about their functionality. This [...] Read more.
Intelligent materials, especially materials with shape memory, are an important discovery, with technical applications in the medical and aerospace field, among others, which led to the development of systems and applications with multiple advantages and disadvantages due to ignorance about their functionality. This paper presents an application developed in the research laboratory for determining and monitoring the behavior of a material element with Ni-Ti shape memory, and its lifespan. The application allows the stress level of the Ni-Ti element subjected to numerous repeated cycles of deformation to be determined by supplying it to a constant electric current. Thus, the results show the variation of the Ni-Ti element force, in the form of a spring, at the ambient temperature variations as well as force variations at different numbers of attempts. The Ni-Ti alloy has both shape retention and superelasticity properties, being the most common in the fields of applicability. Due to its unique properties, it can be used in the most demanding applications in the medical field, usually involving difficult conditions of resistance to fatigue. Full article
(This article belongs to the Special Issue New Materials and Advanced Procedures of Obtaining and Processing II)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic stress-superelastic deformation curve.</p> Full article ">Figure 2
<p>Experimental stand for testing nitinol fatigue resistance. 1—nitinol element; 2—microcontroller system; 3—force sensor; 4—adapter for force sensor; 5—temperature sensor; 6—power relay self-heating system Ni-Ti; 7, 7′—supply terminals.</p> Full article ">Figure 3
<p>Temperature distribution on the nitinol spring recorded with a thermal imaging camera: (<b>a</b>) operating period; (<b>b</b>) rest period.</p> Full article ">Figure 4
<p>The force variation generated by the Ni-Ti during the test period (19 days of continuous operation) at the applied electric current, I<sub>1</sub>.</p> Full article ">Figure 5
<p>The influence of ambient temperature on the force generated by the Ni-Ti.</p> Full article ">Figure 6
<p>Temperature distribution on the nitinol spring recorded with a thermal imaging camera, tested in the second stage: (<b>a</b>) operating period; (<b>b</b>) rest period.</p> Full article ">Figure 7
<p>The force variation generated by the Ni-Ti during the test period (5 days of continuous operation) at the applied electric current, I<sub>2</sub>.</p> Full article ">Figure 8
<p>Ni-Ti spring after testing phase with I<sub>1</sub> current (<b>a</b>) and after testing phase I<sub>2</sub> current (<b>b</b>).</p> Full article ">
20 pages, 12497 KiB  
Article
Study on Mechanical Properties of Modified Polyurethane Concrete at Different Temperatures
by Jianhua Lei, Fan Feng, Shu Xu, Weibin Wen and Xuhui He
Appl. Sci. 2022, 12(6), 3184; https://doi.org/10.3390/app12063184 - 21 Mar 2022
Cited by 13 | Viewed by 2625
Abstract
The objective of the present research was to study the effect of temperature on the mechanical properties, failure mode and uniaxial compression constitutive relationship of a modified polyurethane concrete. A total of 24 cube and 27 prism specimens were fabricated, and the uniformity [...] Read more.
The objective of the present research was to study the effect of temperature on the mechanical properties, failure mode and uniaxial compression constitutive relationship of a modified polyurethane concrete. A total of 24 cube and 27 prism specimens were fabricated, and the uniformity of the polyurethane concrete was checked. The compressive test, splitting tensile test and static uniaxial compression test were carried out at 0, 15, 40 and 60 °C. The failure mode, cube compressive strength, splitting tensile strength, axial compressive strength, elastic modulus and the compressive stress–strain curves of the modified polyurethane concrete were obtained. Based on the experimental results, a uniaxial compression constitutive model of the modified polyurethane concrete considering temperature characteristics was proposed. The results show that the elastic modulus, cubic compressive strength, splitting tensile strength and axial compressive strength of the modified polyurethane concrete decrease with the increase of temperature, and the peak strain and ultimate strain increase significantly. When the temperature rises from 0 to 60 °C, the cubic compressive strength, splitting tensile strength and axial compressive strength are decreased by 67.1%, 66.4% and 73.3%, respectively. The calculation results of the proposed constitutive model are in good agreement with the test results. The results are expected to guide the application of the modified polyurethane concrete in bridge deck pavement. Full article
(This article belongs to the Special Issue Road Materials and Sustainable Pavement Design)
►▼ Show Figures

Figure 1

Figure 1
<p>Classification of sand grains based on particle sizes.</p> Full article ">Figure 2
<p>Equipment used in the pouring process of specimens. (<b>a</b>) Concrete mixer. (<b>b</b>) Concrete shaking table. (<b>c</b>) Cube specimen after pouring. (<b>d</b>) Prismatic specimen after pouring.</p> Full article ">Figure 2 Cont.
<p>Equipment used in the pouring process of specimens. (<b>a</b>) Concrete mixer. (<b>b</b>) Concrete shaking table. (<b>c</b>) Cube specimen after pouring. (<b>d</b>) Prismatic specimen after pouring.</p> Full article ">Figure 3
<p>Experiment specimens. (<b>a</b>) Cube specimens. (<b>b</b>) Prismatic specimens.</p> Full article ">Figure 4
<p>Section of experiment specimen. (<b>a</b>) Concrete slice I. (<b>b</b>) Concrete slice II.</p> Full article ">Figure 5
<p>Constant temperature box.</p> Full article ">Figure 6
<p>Loading diagram of cube compressive experiment. (<b>a</b>) Test setup. (<b>b</b>) Schematic diagram.</p> Full article ">Figure 7
<p>Loading diagram of prism compressive experiment. (<b>a</b>) Test setup. (<b>b</b>) Schematic diagram (unit: mm).</p> Full article ">Figure 8
<p>Loading diagram of cube splitting tensile experiment. (<b>a</b>) Test setup. (<b>b</b>) Schematic diagram.</p> Full article ">Figure 9
<p>Compression failure modes of cube specimens. (<b>a</b>) 0 °C. (<b>b</b>) 15 °C. (<b>c</b>) 40 °C. (<b>d</b>) 60 °C.</p> Full article ">Figure 10
<p>Failure mode of specimens of splitting tensile test. (<b>a</b>) 0 °C. (<b>b</b>) 15 °C. (<b>c</b>) 40 °C. (<b>d</b>) 60 °C.</p> Full article ">Figure 10 Cont.
<p>Failure mode of specimens of splitting tensile test. (<b>a</b>) 0 °C. (<b>b</b>) 15 °C. (<b>c</b>) 40 °C. (<b>d</b>) 60 °C.</p> Full article ">Figure 11
<p>Fracture surfaces. (<b>a</b>) Fracture surfaces I. (<b>b</b>) Fracture surfaces II.</p> Full article ">Figure 12
<p>Compression failure modes of prism specimens. (<b>a</b>) 0 °C. (<b>b</b>) 15 °C. (<b>c</b>) 40 °C. (<b>d</b>) 60 °C.</p> Full article ">Figure 13
<p>Relationship between compressive strength and temperature.</p> Full article ">Figure 14
<p>Relationship between splitting tensile strength and temperature.</p> Full article ">Figure 15
<p>Relationship between prism compressive strength and temperature.</p> Full article ">Figure 16
<p>Relationship between <span class="html-italic">f</span><sub>c,t</sub>/<span class="html-italic">f</span><sub>cu</sub> and temperature.</p> Full article ">Figure 17
<p>Relationship between peak strain and temperature.</p> Full article ">Figure 18
<p>Relationship between elastic modulus and temperature.</p> Full article ">Figure 19
<p>Dimensionless stress–strain curve.</p> Full article ">Figure 20
<p>The relation between the temperature <span class="html-italic">T</span> and the shape parameter <span class="html-italic">A</span>, <span class="html-italic">B</span>. (<b>a</b>) Fitting of shape parameter <span class="html-italic">A</span>. (<b>b</b>) Fitting of shape parameter <span class="html-italic">B</span>.</p> Full article ">Figure 21
<p>Comparison of constitutive model with experimental results.</p> Full article ">
22 pages, 870 KiB  
Review
Effect of Magnetic and Electrical Fields on Yield, Shelf Life and Quality of Fruits
by Bogdan Saletnik, Grzegorz Zaguła, Aneta Saletnik, Marcin Bajcar, Ewelina Słysz and Czesław Puchalski
Appl. Sci. 2022, 12(6), 3183; https://doi.org/10.3390/app12063183 - 21 Mar 2022
Cited by 13 | Viewed by 6881
Abstract
The presented article is a review of the literature reports on the influence of magnetic and electric fields on the growth, yield, ripening, and durability of fruits and their quality. The article shows the potential application of MF and EF in agricultural production. [...] Read more.
The presented article is a review of the literature reports on the influence of magnetic and electric fields on the growth, yield, ripening, and durability of fruits and their quality. The article shows the potential application of MF and EF in agricultural production. Magnetic and electrical fields increase the shelf life of the fruit and improve its quality. Alternating magnetic fields (AMF) with a value of 0.1–200 mT and a power frequency of 50 Hz or 60 Hz improve plant growth parameters. MF cause an increase in firmness, the rate of maturation, the content of beta-carotene, lycopene, and fructose, sugar concentration, and a reduction in acidity and respiration. The most common is a high-voltage electric field (HVEF) of 2–3.61 kV/cm. These fields extend the shelf life and improve the quality of fruit by decreasing respiration rate and ethylene production. The presented methods seem to be a promising way to increase the quantity and quality of crops in agricultural and fruit production. They are suitable for extending the shelf life of fruit and vegetables during their storage. Further research is needed to develop an accessible and uncomplicated way of applying MF and AEF in agricultural and fruit production. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture)
►▼ Show Figures

Figure 1

Figure 1
<p>Magnetic field affecting the morphophysiological properties on a fruit plant with possible mechanisms.</p> Full article ">Figure 2
<p>Magnetic and electric field affecting the morphophysiological properties of fruit, with possible mechanisms.</p> Full article ">
19 pages, 3576 KiB  
Article
Enhanced Adsorption of Methyl Orange by Mongolian Montmorillonite after Aluminum Pillaring
by Jiajun Chen, Jianzun Lu, Lingcheng Su, Huada Ruan, Yijia Zhao, Chiuhong Lee, Zongwei Cai, Zhihui Wu and Yanan Jiang
Appl. Sci. 2022, 12(6), 3182; https://doi.org/10.3390/app12063182 - 21 Mar 2022
Cited by 5 | Viewed by 1884
Abstract
This article studies the enhancement of methyl orange (MO) adsorption by Mongolian montmorillonite (MMt) modified by the intercalation of the Keggin Al13 complex, followed by calcination during the pillaring process. The properties of MMt, Al-intercalated MMt (P-MMt), and Al-pillared MMt (P-MMt-C) were [...] Read more.
This article studies the enhancement of methyl orange (MO) adsorption by Mongolian montmorillonite (MMt) modified by the intercalation of the Keggin Al13 complex, followed by calcination during the pillaring process. The properties of MMt, Al-intercalated MMt (P-MMt), and Al-pillared MMt (P-MMt-C) were determined using X-ray diffraction (XRD), thermogravimetric analysis (TGA), surface-area analysis, and a field emission scanning electron microscope (FE-SEM). The MO adsorption by modified MMt was subsequently evaluated. The XRD basal distance (d001) and the specific surface area (SSA) increased after the modification of MMt. The TGA results revealed that P-MMt and P-MMt-C had better thermal stability than MMt. The Al-pillared MMt obtained after calcination (e.g., P-MMt-C400) showed a larger basal distance and surface area than that without pillaring. The MO adsorption process of P-MMt-C400 was supposed to be dominated by chemisorption and heterogeneous multilayer adsorption, according to the kinetic and isotherm studies. The maximum adsorption capacity of P-MMt-C400 is 6.23 mg/g. The MO adsorption ability of Al-pillared MMt was contributed by the Keggin Al13 complex attracting MO and the increase in the surface area of macro-, meso- and micro-pores (>1.2 nm). The Al-pillared MMt in this study could be applied as an adsorbent in a water purification system to remove MO or other dye elements. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>The XRD patterns of MMt, P-MMt, and P-MMt-C400.</p> Full article ">Figure 2
<p>XRD patterns of P-MMt (<b>A</b>) and MMt (<b>B</b>) calcined at different temperatures.</p> Full article ">Figure 3
<p>The effect of the calcination temperature on the SSA of P-MMt (no calcination at the first point).</p> Full article ">Figure 4
<p>Micropore distribution of MMt, P-MMt, and P-MMt-C400 (calculated by the Horvath–Kawazoe model).</p> Full article ">Figure 5
<p>The TG and DTG curves of MMt, P-MMt, and P-MMt-C400.</p> Full article ">Figure 6
<p>Surface structure and particle size of (<b>a</b>,<b>b</b>) MMt, (<b>c</b>,<b>d</b>) P-MMt, and (<b>e</b>,<b>f</b>) P-MMt-C400 in the field emission scanning electron microscope (FE-SEM) images.</p> Full article ">Figure 7
<p>The MO adsorption (50 mL of 5 mg/L solution) of MMt, P-MMt, and P-MMt-C400 at different contact time intervals; SD is added as an error bar to each point, n = 3. The x-axial of the inserted figure is in log scale.</p> Full article ">Figure 8
<p>MO adsorption of P-MMt-C400 in different contact time intervals, fitted by pseudo-first-order and pseudo-second-order models.</p> Full article ">Figure 9
<p>MO adsorption by MMt, P-MMt, and P-MMt-C400 in different equilibrium concentrations of MO; SD is added to each point as an error bar, n = 3.</p> Full article ">Figure 10
<p>MO adsorption by P-MMt-C400 in different equilibrium concentrations, fitted by the Langmuir, Freundlich, and Redlich–Peterson isotherm models.</p> Full article ">
16 pages, 15472 KiB  
Article
Deep Deterministic Policy Gradient with Reward Function Based on Fuzzy Logic for Robotic Peg-in-Hole Assembly Tasks
by Ziyue Wang, Fengming Li, Yu Men, Tianyu Fu, Xuting Yang and Rui Song
Appl. Sci. 2022, 12(6), 3181; https://doi.org/10.3390/app12063181 - 21 Mar 2022
Cited by 3 | Viewed by 2150
Abstract
Robot automatic assembly of weak stiffness parts is difficult due to potential deformation during assembly. The robot manipulation cannot adapt to the dynamic contact changes during the assembly process. A robot assembly skill learning system is designed by combining the compliance control and [...] Read more.
Robot automatic assembly of weak stiffness parts is difficult due to potential deformation during assembly. The robot manipulation cannot adapt to the dynamic contact changes during the assembly process. A robot assembly skill learning system is designed by combining the compliance control and deep reinforcement, which could acquire a better robot assembly strategy. In this paper, a robot assembly strategy learning method based on variable impedance control is proposed to solve the robot assembly contact tasks. During the assembly process, the quality evaluation is designed based on fuzzy logic, and the impedance parameters in the assembly process are studied with a deep deterministic policy gradient. Finally, the effectiveness of the method is verified using the KUKA iiwa robot in the weak stiffness peg-in-hole assembly. Experimental results show that the robot obtains the robot assembly strategy with variable compliant in the process of weak stiffness peg-in-hole assembly. Compared with the previous methods, the assembly success rate of the proposed method reaches 100%. Full article
(This article belongs to the Topic Industrial Robotics)
►▼ Show Figures

Figure 1

Figure 1
<p>The framework of the proposed method.</p> Full article ">Figure 2
<p>Actor network structure.</p> Full article ">Figure 3
<p>Critic network structure.</p> Full article ">Figure 4
<p>The assembly quality evaluation with fuzzy logic.</p> Full article ">Figure 5
<p>Communication structure of assembly system.</p> Full article ">Figure 6
<p>The first layer of membership function: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>F</mi> <mi>y</mi> </msub> </semantics></math> membership function; (<b>b</b>) <math display="inline"><semantics> <msub> <mi>F</mi> <mi>z</mi> </msub> </semantics></math> membership function; (<b>c</b>) <span class="html-italic">Z</span> membership function; (<b>d</b>) <math display="inline"><semantics> <msub> <mi>D</mi> <mi>Z</mi> </msub> </semantics></math> membership function.</p> Full article ">Figure 7
<p>The first layer of fuzzy logic system output: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>F</mi> <mi>y</mi> </msub> <mo>−</mo> <msub> <mi>F</mi> <mi>z</mi> </msub> </mrow> </semantics></math> fuzzy logic system output value; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>Z</mi> <mo>−</mo> <msub> <mi>D</mi> <mi>Z</mi> </msub> </mrow> </semantics></math> fuzzy logic system output value.</p> Full article ">Figure 8
<p>The second layer of membership function: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>F</mi> <mi>y</mi> </msub> <mo>−</mo> <msub> <mi>F</mi> <mi>z</mi> </msub> </mrow> </semantics></math> membership function; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>Z</mi> <mo>−</mo> <msub> <mi>D</mi> <mi>Z</mi> </msub> </mrow> </semantics></math> membership function.</p> Full article ">Figure 9
<p>The second layer of fuzzy logic system output.</p> Full article ">Figure 10
<p>The assembly strategy learning process: (<b>a</b>) loss value change during training; (<b>b</b>) reward value change during training; (<b>c</b>) step value change during training; (<b>d</b>–<b>f</b>) represent the changes of the impedance values in the <span class="html-italic">x</span>, <span class="html-italic">y</span>, and <span class="html-italic">z</span> directions when the impedance parameters are adjusted by the learning method and the fuzzy logic method, respectively.</p> Full article ">Figure 11
<p>The force variation curve of each experiment. (<b>a</b>) Without soft; (<b>b</b>) soft with DDPG; (<b>c</b>) soft of fuzzy.</p> Full article ">Figure 12
<p>Results of each model performance: (<b>a</b>) reward; (<b>b</b>) step; (<b>c</b>,<b>d</b>): stiffness.</p> Full article ">Figure 13
<p>The trajectory of the end of the peg during insertion.</p> Full article ">Figure 14
<p>The torque of seven joints during assembly.</p> Full article ">Figure 15
<p>The end contact force and torque during assembly.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-451
Previous Issue
Next Issue
Back to TopTop