Applied Sciences

15 pages, 1026 KiB

Open AccessArticle

Metabolic Syndrome and Its Components among Taxi Drivers in the City of Tshwane, South Africa

by Eaglet Moditsa Mabetwa, Kebogile Elizabeth Mokwena, Peter Modupi Mphekgwana and Perpetua Modjadji

Appl. Sci. 2022, 12(3), 1767; https://doi.org/10.3390/app12031767 - 08 Feb 2022

Cited by 7 | Viewed by 2886

The occupation of taxi driving predisposes drivers to health risks, including obesity, cardiovascular and metabolic disorders. Although individual components of metabolic syndrome (MetS) are documented, data is scarce on concurrent metabolic disturbances among commercial drivers. The prevalence of MetS and its components were [...] Read more.

The occupation of taxi driving predisposes drivers to health risks, including obesity, cardiovascular and metabolic disorders. Although individual components of metabolic syndrome (MetS) are documented, data is scarce on concurrent metabolic disturbances among commercial drivers. The prevalence of MetS and its components were determined in a cross-sectional study among taxi drivers (n = 362) in the City of Tshwane, South Africa. Sociodemographic, occupational, and lifestyle factors were assessed using a structured questionnaire. Anthropometry, blood pressure, and glucose were measured. MetS was defined based on BMI strata, hypertension, and glucose levels. Data was analyzed using SPSS. The mean age of taxi drivers was 42 ± 10.9 years. Overall prevalence of MetS was 17.1%, with higher prevalence observed among older taxi drivers (24.2%) and those with longer experience in the industry (22.9%). Individual components of MetS were obesity (36%), hypertension (36%) and diabetes (46%), while smoking (30%), alcohol use (59%), and physical inactivity (71%) were observed. MetS was associated with duration in the taxi industry, and family history of diabetes among taxi drivers. The presence of MetS and its components among taxi drivers calls for early identification of cardiometabolic risks in the taxi industry and efforts towards achieving a healthier workforce. Full article

(This article belongs to the Special Issue Prevention and Treatments of Cardiovascular Diseases)

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26 pages, 4850 KiB

Open AccessArticle

Dracocephalum palmatum S. and Dracocephalum ruyschiana L. Originating from Yakutia: A High-Resolution Mass Spectrometric Approach for the Comprehensive Characterization of Phenolic Compounds

by Zhanna M. Okhlopkova, Mayya P. Razgonova, Konstantin S. Pikula, Alexander M. Zakharenko, Wojciech Piekoszewski, Yuri A. Manakov, Sezai Ercisli and Kirill S. Golokhvast

Appl. Sci. 2022, 12(3), 1766; https://doi.org/10.3390/app12031766 - 08 Feb 2022

Cited by 6 | Viewed by 2466

Abstract

Dracocephalum palmatum S. and Dracocephalum ruyschiana L. contain a large number of target analytes, which are biologically active compounds. High performance liquid chromatography (HPLC) in combination with an ion trap (tandem mass spectrometry) was used to identify target analytes in extracts of D. [...] Read more.

Dracocephalum palmatum S. and Dracocephalum ruyschiana L. contain a large number of target analytes, which are biologically active compounds. High performance liquid chromatography (HPLC) in combination with an ion trap (tandem mass spectrometry) was used to identify target analytes in extracts of D. palmatum S. and D. ruyschiana L. originating from Yakutia. The results of initial studies revealed the presence of 114 compounds, of which 92 were identified for the first time in the genus Dracocephalum. New identified metabolites belonged to 17 classes, including 16 phenolic acids and their conjugates, 18 flavones, 5 flavonols, 2 flavan-3-ols, 1 flavanone, 2 stilbenes, 10 anthocyanins, 1 condensed tannin, 2 lignans, 6 carotenoids, 3 oxylipins, 2 amino acids, 3 sceletium alkaloids, 3 carboxylic acids, 8 fatty acids, 1 sterol, and 3 terpenes, along with 6 miscellaneous compounds. It was shown that extracts of D. palmatum are richer in the spectrum of polyphenolic compounds compared with extracts of D. ruyschiana, according to a study of the presence of these compounds in extracts, based on the results of mass spectrometric studies. Full article

(This article belongs to the Special Issue Advances in Natural Bioactive Compounds and Biological Effects)

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15 pages, 3509 KiB

Open AccessArticle

Bridge Natural Frequencies, Numerical Solution versus Experiment

by Veronika Valašková and Jozef Melcer

Appl. Sci. 2022, 12(3), 1765; https://doi.org/10.3390/app12031765 - 08 Feb 2022

Viewed by 1585

Abstract

The article is dedicated to a numerical and experimental analysis of the basic natural frequencies of a bridge structure. It presents the results obtained using the finite element method and the frequency response functions applied in two variants, using the lumped mass model [...] Read more.

The article is dedicated to a numerical and experimental analysis of the basic natural frequencies of a bridge structure. It presents the results obtained using the finite element method and the frequency response functions applied in two variants, using the lumped mass model and the model with a continuously distributed mass, as well as the results obtained using the energy method. It describes a simple experiment to measure the response of a bridge to random excitations from rail traffic, and compares the values of selected natural frequencies obtained by numerical and experimental methods. It offers engineers alternative solutions for their applications in engineering practice. It tries to bring a complicated theory closer to engineering practice in the simplest possible way and, at the same time, arouses interest in its deeper study. Full article

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16 pages, 1440 KiB

Open AccessArticle

Deep Reinforcement Learning-Based Spectrum Allocation Algorithm in Internet of Vehicles Discriminating Services

by Zheng Guan, Yuyang Wang and Min He

Appl. Sci. 2022, 12(3), 1764; https://doi.org/10.3390/app12031764 - 08 Feb 2022

Cited by 8 | Viewed by 2342

Abstract

With the rapid development of global automotive industry intelligence and networking, the Internet of Vehicles (IoV) service, as a key communication technology, has been faced with an increasing spectrum of resources shortage. In this paper, we consider a spectrum utilization problem, in which [...] Read more.

With the rapid development of global automotive industry intelligence and networking, the Internet of Vehicles (IoV) service, as a key communication technology, has been faced with an increasing spectrum of resources shortage. In this paper, we consider a spectrum utilization problem, in which a number of co-existing cellular users (CUs) and prioritized device-to-device (D2D) users are equipped in a single antenna vehicle-mounted communication network. To ensure a business-aware spectrum access mechanism with delay granted in a complex dynamic environment, we consider optimizing a metric that maintains a trade off between maximizing the total capacity of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) links and minimizing the interference of high priority links. A low complexity priority-based spectrum allocation scheme based on the deep reinforcement learning method is developed to solve the proposed formulation. We trained our algorithm using the deep Q-learning network (DQN) over a set of public bandwidths. Simulation results show that the proposed scheme can allocate spectrum resources quickly and effectively in a high dynamic vehicle network environment. Concerning improved channel transmission rate, the V2V link rate in this scheme is 2.54 times that of the traditional random spectrum allocation scheme, and the V2I link rate is 13.5% higher than that of the traditional random spectrum allocation scheme. The average total interference received by priority links decreased by 14.2 dB compared to common links, realized service priority distinction and has good robustness to communication noise. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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16 pages, 758 KiB

Open AccessArticle

Microwave- and Ultrasound-Assisted Extraction of Cucurbita pepo Seeds: A Comparison Study of Antioxidant Activity, Phenolic Profile, and In-Vitro Cells Effects

by Catarina Macedo, Ana Margarida Silva, Ana Sofia Ferreira, Manuela M. Moreira, Cristina Delerue-Matos and Francisca Rodrigues

Appl. Sci. 2022, 12(3), 1763; https://doi.org/10.3390/app12031763 - 08 Feb 2022

Cited by 3 | Viewed by 2365

Abstract

Nowadays there is a growing demand for nutraceuticals to prevent diseases related to redox imbalances, such as atherosclerosis and diabetes, being crucial to search for new matrixes rich in bioactive compounds. This work aims to characterize the value-added compounds extracted from Curcubita pepo [...] Read more.

Nowadays there is a growing demand for nutraceuticals to prevent diseases related to redox imbalances, such as atherosclerosis and diabetes, being crucial to search for new matrixes rich in bioactive compounds. This work aims to characterize the value-added compounds extracted from Curcubita pepo seeds using green methodologies, namely microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE), employing water as an extracting solvent for two ratios (condition 1: 1 mg/20 mL; condition 2: 2.5 mg/20 mL). The extract with the best antioxidant/antiradical activity in FRAP (71.09 μmol FSE/g DW) and DPPH (5.08 mg TE/g DW) assays was MAE condition 1, while MAE condition 2 exhibited the highest activity in the ABTS assay (13.29 mg AAE/g DW) and TPC (16.89 mg GAE/g DW). A remarkable scavenging capacity was observed, particularly for HOCl, with IC50 values ranging from 1.88–13.50 μg/mL. A total of 21 phenolic compounds were identified, being catechin (4.567–7.354 mg/g DW), caffeine (1.147–2.401 mg/g DW) and gallic acid (0.945–1.337 mg/g DW) predominant. No adverse effects were observed on Caco-2 viability after exposure to MAE extracts, while the other conditions led to a slight viability decrease in NSC-34. These results highlighted that the extract from MAE condition 2 is the most promising as a potential nutraceutical ingredient. Full article

(This article belongs to the Special Issue Advances in Natural Bioactive Compounds and Biological Effects)

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11 pages, 4063 KiB

Open AccessArticle

SCADA-Compatible and Scaleable Visualization Tool for Corrosion Monitoring of Offshore Wind Turbine Structures

by Joachim Verhelst, Inge Coudron and Agusmian Partogi Ompusunggu

Appl. Sci. 2022, 12(3), 1762; https://doi.org/10.3390/app12031762 - 08 Feb 2022

Cited by 5 | Viewed by 2376

Abstract

The exploitation of offshore windfarms (WFs) goes hand in hand with large capital expenditures (CAPEX) and operational expenditures (OPEX), as these mechanical installations operate continuously for multiple decades in harsh, saline conditions. OPEX can account for up to 30% of the levelised cost [...] Read more.

The exploitation of offshore windfarms (WFs) goes hand in hand with large capital expenditures (CAPEX) and operational expenditures (OPEX), as these mechanical installations operate continuously for multiple decades in harsh, saline conditions. OPEX can account for up to 30% of the levelised cost of energy (LCoE) for a deployed offshore wind farm. To maintain the cost-competitiveness of deployed offshore WFs versus other renewable energy sources, their LCoE has to be kept in check, both by minimising the OPEX and optimising the offshore wind energy production. As corrosion, in particular uniform corrosion, is a major cause of failure of offshore wind turbine structures, there is an urgent need for corrosion management systems for deployed offshore wind turbine structures (WTs). Despite the fact that initial corrosion protection solutions are already integrated on some critical structural components such as WT towers, WT transition pieces or WT sub-structure (fixed or floating platforms), these components can still be harshly damaged by the corrosive environmental offshore conditions. The traditional preventive maintenance strategy, in which regular manual inspections by experts are necessary, is widely implemented nowadays in wind farm applications. Unfortunately, for such challenging operating environments, regular human inspections have a significant cost, which eventually increase the OPEX. To minimise the OPEX, remote corrosion monitoring solutions combined with supporting software (SW) tools are thus necessary. This paper focuses on the development of a software (SW) tool for the visualisation of corrosion measurement data. To this end, criteria for efficient structural corrosion analysis were identified, namely a scaleable, SCADA-compatible, secure, web accessible tool that can visualise 3D relationships. In order to be effective, the SW tool requires a tight integration with decision support tools. This paper provides three insights: Firstly, through a literature study and non-exhaustive market study, it is shown that a combined visualisation and decision SW tool is currently non-existing in the market. This gap motivates a need for the development of a custom SW tool. Secondly, the capabilities of the developed custom software tool, consisting of a backend layer and visualisation browser designed for this task are demonstrated and discussed in this paper. This indicates that a SCADA-compatible visualisation software tool is possible, and can be a major stepping stone towards a semi-automated decision support toolchain for offshore wind turbine corrosion monitoring. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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20 pages, 7973 KiB

Open AccessArticle

3D Finite Element Simulation and Experimental Validation of a Mole Rat’s Digit Inspired Biomimetic Potato Digging Shovel

by Jiangtao Yu, Yunhai Ma, Shuo Wang, Zihe Xu, Xuanting Liu, Hubiao Wang, Hongyan Qi, Lijun Han and Jian Zhuang

Appl. Sci. 2022, 12(3), 1761; https://doi.org/10.3390/app12031761 - 08 Feb 2022

Cited by 7 | Viewed by 1903

Abstract

To reduce the draught force of a traditional planar potato digging shovel (DZ), a biomimetic potato digging shovel (YS), inspired by the mole rat’s digits, is designed, using the biomimetic macroscopic surface modification method. The finite element simulations, soil bin experiments, and field [...] Read more.

To reduce the draught force of a traditional planar potato digging shovel (DZ), a biomimetic potato digging shovel (YS), inspired by the mole rat’s digits, is designed, using the biomimetic macroscopic surface modification method. The finite element simulations, soil bin experiments, and field experiments for DZ and YS are conducted to explore the factors affecting draught force and to verify the feasibility and effectiveness of the biomimetic potato digging shovel. Results show that the soil–shovel interaction models predict the draught force well, but the simulations for the soil rupture distance ratio need to be further improved. The studied factors all have a great influence on the draught force of DZ and YS and they follow the order of cutting speed > digging depth > mounting angle. For the single shovels, YS, compared with DZ, increases the draught force at a low mounting angle but decreases the draught force by over 8.41% when the mounting angle is higher than 30°; for the grouped shovels, the draught force and fuel consumption of YS, compared with those of DZ, decline by over 13.33% and 9.18%, respectively. The reasons for the reduction in the draught force of YS are to make the soil mass tend to move upward and to change the soil’s state of motion and stress continually; thus, the compaction to the soil is reduced, and the soil becomes easier to be broken. Full article

(This article belongs to the Section Mechanical Engineering)

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21 pages, 48622 KiB

Open AccessArticle

Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping

by Zelang Miao, Renfeng Peng, Wei Wang, Qirong Li, Shuai Chen, Anshu Zhang, Minghui Pu, Ke Li, Qinqin Liu and Changhao Hu

Appl. Sci. 2022, 12(3), 1760; https://doi.org/10.3390/app12031760 - 08 Feb 2022

Cited by 5 | Viewed by 2039

Abstract

Earthquakes induce landslides worldwide every year that may cause massive fatalities and financial losses. Precise and timely landslide susceptibility mapping (LSM) is significant for landslide hazard assessment and mitigation in earthquake-affected areas. State-of-the-art LSM approaches connect causative factors from various sources without considering [...] Read more.

Earthquakes induce landslides worldwide every year that may cause massive fatalities and financial losses. Precise and timely landslide susceptibility mapping (LSM) is significant for landslide hazard assessment and mitigation in earthquake-affected areas. State-of-the-art LSM approaches connect causative factors from various sources without considering the fusion of different information at the data modal level. To exploit the complementary information of different modalities and boost LSM accuracy, this study presents a new LSM model that integrates data modality and machine learning methods. The presented method first groups causative factors into different modal types based on their intrinsic characteristics, followed by the calculation of the pairwise similarity of modal data. The similarities of different modalities are fused using nonlinear graph fusion to generate a unified graph, which is subsequently classified using different machine learning methods to produce final LSM. Experimental results suggest that the presented method achieves higher performance than existing LSM methods. This study provides a new solution for producing precise LSM from a fusion perspective that can be applied to minimize the potential landslide risk and for sustainable use of erosion-prone slopes. Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

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20 pages, 660 KiB

Open AccessFeature PaperArticle

Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection

by Francesco Carrera, Vincenzo Dentamaro, Stefano Galantucci, Andrea Iannacone, Donato Impedovo and Giuseppe Pirlo

Appl. Sci. 2022, 12(3), 1759; https://doi.org/10.3390/app12031759 - 08 Feb 2022

Cited by 17 | Viewed by 3342

Abstract

The 0-day attack is a cyber-attack based on vulnerabilities that have not yet been published. The detection of anomalous traffic generated by such attacks is vital, as it can represent a critical problem, both in a technical and economic sense, for a smart [...] Read more.

The 0-day attack is a cyber-attack based on vulnerabilities that have not yet been published. The detection of anomalous traffic generated by such attacks is vital, as it can represent a critical problem, both in a technical and economic sense, for a smart enterprise as for any system largely dependent on technology. To predict this kind of attack, one solution can be to use unsupervised machine learning approaches, as they guarantee the detection of anomalies regardless of their prior knowledge. It is also essential to identify the anomalous and unknown behaviors that occur within a network in near real-time. Three different approaches have been proposed and benchmarked in exactly the same condition: Deep Autoencoding with GMM and Isolation Forest, Deep Autoencoder with Isolation Forest, and Memory Augmented Deep Autoencoder with Isolation Forest. These approaches are thus the result of combining different unsupervised algorithms. The results show that the addition of the Isolation Forest improves the accuracy values and increases the inference time, although this increase does not represent a relevant problematic factor. This paper also explains the features that the various models consider most important for classifying an event as an attack using the explainable artificial intelligence methodology called Shapley Additive Explanations (SHAP). Experiments were conducted on KDD99, NSL-KDD, and CIC-IDS2017 datasets. Full article

(This article belongs to the Special Issue Applied AI for cybersecurity in smart enterprises)

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13 pages, 2557 KiB

Open AccessArticle

Experimental Analysis of Transformer Core Vibration and Noise under Inter-Harmonic Excitation

by Qiang He, Chao Fan, Guang Yang, Huiqi Li, Jiadong Li and Xiaogang Chen

Appl. Sci. 2022, 12(3), 1758; https://doi.org/10.3390/app12031758 - 08 Feb 2022

Cited by 3 | Viewed by 1940

Abstract

With the wide application of power electronic devices and nonlinear loads, the resulting inter-harmonic pollution is becoming more and more serious in the power system. As important equipment in power systems, transformers have always been the focus of research. However, there are few [...] Read more.

With the wide application of power electronic devices and nonlinear loads, the resulting inter-harmonic pollution is becoming more and more serious in the power system. As important equipment in power systems, transformers have always been the focus of research. However, there are few studies on the abnormal increase in vibration and noise caused by inter-harmonic excitation. In this work, a transformer core vibration and noise measurement platform that can generate arbitrary inter-harmonic excitations was built. The real-time vibration displacement waveforms of the core model under normal and inter-harmonic conditions were experimentally measured as well as the surrounding noise level amplitude and spectrum analysis result. The influence law of excitation of intermediate harmonic content and frequency on core vibration displacement and surrounding sound pressure level was summarized. The work of this paper lays a theoretical foundation for studying the vibration and noise of power transformers and other equipment under inter-harmonics. Full article

(This article belongs to the Special Issue 5th Anniversary of Acoustics and Vibrations Section—Recent Advances in Acoustics and Vibration)

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11 pages, 4131 KiB

Open AccessArticle

Enhancement-Mode Heterojunction Vertical β-Ga₂O₃ MOSFET with a P-Type Oxide Current-Blocking Layer

by Yuwen Huang, Xiaoping Xie, Zeyulin Zhang, Peng Dong, Zhe Li, Dazheng Chen, Weidong Zhu, Shenglei Zhao, Qian Feng, Jincheng Zhang, Chunfu Zhang and Yue Hao

Appl. Sci. 2022, 12(3), 1757; https://doi.org/10.3390/app12031757 - 08 Feb 2022

Cited by 5 | Viewed by 2771

Abstract

The vertical heterojunction Ga₂O₃ MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) with the p-type oxide as the current-blocking layer (CBL) is investigated for the first time using SILVACO simulation software. The results show that the threshold voltage of the device is easily positive, [...] Read more.

The vertical heterojunction Ga₂O₃ MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) with the p-type oxide as the current-blocking layer (CBL) is investigated for the first time using SILVACO simulation software. The results show that the threshold voltage of the device is easily positive, which means that the device works in the enhancement mode. By adjusting the doping concentration (from 2 × 10¹⁷ cm⁻³ to 2 × 10¹⁸ cm⁻³) and thickness (from 0.4 um to 2 um) of p-SnO CBL, the threshold voltage is around from 2.4 V to 2.8 V and the breakdown voltage of the device can be increased from 361 V to 518 V. Compared with the original homojunction Ga₂O₃ vertical MOSFET with CBL, the p-SnO CBL can greatly improve the performance of the device. Other p-type oxides are also investigated as the CBL and show promising performances. This work has a certain guiding significance for the design of a vertical enhanced current-blocking layer MOSFET device and for the development of a Ga₂O₃ heterojunction power device. Full article

(This article belongs to the Special Issue Advanced Compound Semiconductor)

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17 pages, 4752 KiB

Open AccessArticle

Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm

by Ying Sun, Lin Lv, Peng Lee and Yunkai Cai

Appl. Sci. 2022, 12(3), 1756; https://doi.org/10.3390/app12031756 - 08 Feb 2022

Cited by 2 | Viewed by 1750

Abstract

In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; various uncertain factors will bring errors [...] Read more.

In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; various uncertain factors will bring errors to test results, and the time of an engine test is so long that the test results cannot meet the real-time requirement. Therefore, it is necessary to develop technology with high accuracy and a fast response to predict the in-cylinder pressure of diesel engines. In this paper, the in-cylinder pressure values of a high-speed diesel engine under different conditions are used to train the extreme gradient boosting model, and the sparrow search algorithm—which belongs to the swarm intelligence optimization algorithm—is introduced to optimize the hyper parameters of the model. The research results show that the extreme gradient boosting model combined with the sparrow search algorithm can predict the in-cylinder pressure under each verification condition with high accuracy, and the proportion of the samples which prediction error is less than 10% in the validation set is 94%. In the process of model optimization, it is found that compared with the grid search method, the sparrow search algorithm has stronger hyper parameter optimization ability, which reduces the mean square error of the prediction model by 27.99%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Engineering in Engineering Applications)

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27 pages, 2452 KiB

Open AccessReview

Benefits and Challenges of Virtual-Reality-Based Industrial Usability Testing and Design Reviews: A Patents Landscape and Literature Review

by Fabio Vinicius de Freitas, Marcus Vinicius Mendes Gomes and Ingrid Winkler

Appl. Sci. 2022, 12(3), 1755; https://doi.org/10.3390/app12031755 - 08 Feb 2022

Cited by 17 | Viewed by 5404

Abstract

With the introduction of new devices, industries are turning to virtual reality to innovate their product development processes. However, before the technology’s possibilities can be fully harnessed, certain constraints must be overcome. This study identifies the benefits and challenges of virtual-reality-based usability testing [...] Read more.

With the introduction of new devices, industries are turning to virtual reality to innovate their product development processes. However, before the technology’s possibilities can be fully harnessed, certain constraints must be overcome. This study identifies the benefits and challenges of virtual-reality-based usability testing and design reviews in industry through a patents and articles review. We searched Derwent Innovation, Scopus, and Web of Science and identified 7 patent filings and 20 articles. We discovered an increase in patent filings since 2016 and strong development in the technology space, offering opportunities to enter an area while it is still young. The most frequently researched field is the automotive industry and the most used device is the HTC VIVE head-mounted display, which is frequently paired with motion capture systems and Unity 3D game engines. Virtual reality benefits design reviews and usability testing by providing the visualization of new angles that stimulate novel insights, increasing team engagement, offering more intuitive interactions for non-CAD specialists, saving redesign cost and time, and increasing participants’ safety. The challenges faced by virtual-reality-based prototypes are a lack of realism due to unnatural tactile and visual interactions, latency and registration issues, communication difficulties between teams, and unpleasant symptoms. While these constraints prevent virtual reality from replacing conventional design reviews and usability testing in the near future, it is already a valuable contribution to the industrial product development process. Full article

(This article belongs to the Special Issue Innovative Solutions for Augmented and Virtual Reality Applications)

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14 pages, 1815 KiB

Open AccessArticle

Plant-Based Milks: Alternatives to the Manufacture and Characterization of Ice Cream

by Ana Leahu, Sorina Ropciuc and Cristina Ghinea

Appl. Sci. 2022, 12(3), 1754; https://doi.org/10.3390/app12031754 - 08 Feb 2022

Cited by 12 | Viewed by 6664

Abstract

This study investigated the potential use of dietary fibers (psyllium and pectin fibers added in different proportions of 0–10%) to improve the rheological, textural, and sensory characteristics of vegetable ice cream using vegetable milk (almond and hemp milk). Hemp milk was obtained from [...] Read more.

This study investigated the potential use of dietary fibers (psyllium and pectin fibers added in different proportions of 0–10%) to improve the rheological, textural, and sensory characteristics of vegetable ice cream using vegetable milk (almond and hemp milk). Hemp milk was obtained from the peeled seeds of the industrial hemp plant, which includes varieties of Cannabis sativa, which have a low content of the psychotropic substance tetrahydrocannabinol (THC) and are grown for food. The rheological characteristics of the mix and ice cream were determined by using the Haake Mars rheometer. Compared with the control sample, the viscosities of the mix in all samples analyzed were enhanced with the addition of dietary fibers, due to the occurrence of interactions and stabilizations. The viscoelastic modules G′ G″ were determined on ice cream samples at a temperature of −10 °C. The elastic and viscous modulus showed high values with the increase of the addition of 6% dietary fibers. The textural characteristics were assessed by the shear strength of a layer of ice cream at a temperature of −4 °C. Hardness, firmness, and adhesiveness were influenced by the size of their ice crystals, the fat content, and the percentage of dietary fibers added. The sensory analysis of the ice cream showed higher overall scores for the almond milk ice cream, because the sweet taste was appreciated with a maximum score, while the hemp milk ice cream was evaluated for flavor and taste. Full article

(This article belongs to the Special Issue Selected Papers from the 8th International Conference of Biotechnologies, Present and Perspectives)

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18 pages, 1552 KiB

Open AccessArticle

Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

by Panagiotis G. Asteris, Fariz Iskandar Mohd Rizal, Mohammadreza Koopialipoor, Panayiotis C. Roussis, Maria Ferentinou, Danial Jahed Armaghani and Behrouz Gordan

Appl. Sci. 2022, 12(3), 1753; https://doi.org/10.3390/app12031753 - 08 Feb 2022

Cited by 46 | Viewed by 4239

Abstract

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers [...] Read more.

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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14 pages, 1954 KiB

Open AccessArticle

Trademark Image Similarity Detection Using Convolutional Neural Network

by Hayfa Alshowaish, Yousef Al-Ohali and Abeer Al-Nafjan

Appl. Sci. 2022, 12(3), 1752; https://doi.org/10.3390/app12031752 - 08 Feb 2022

Cited by 10 | Viewed by 4386

Abstract

A trademark is any recognizable sign that identifies products/services and distinguishes them from others. Many regional and international intellectual property offices are dedicated to dealing with trademark registration processes. The registration process involves examining the trademark to ensure there is no confusion or [...] Read more.

A trademark is any recognizable sign that identifies products/services and distinguishes them from others. Many regional and international intellectual property offices are dedicated to dealing with trademark registration processes. The registration process involves examining the trademark to ensure there is no confusion or interference similarity to any other prior registered trademark. Due to the increasing number of registered trademarks annually, the current manual examining approach is becoming insufficient and more susceptible to human error. As such, there is potential for machine learning applications and deep learning, in particular, to enhance the examination process by providing an automated image detection system to be used by the examiners to facilitate and improve the accuracy of the examination process. Therefore, this paper proposed a trademark similarity detection system using deep-learning techniques to extract image features automatically in order to retrieve a trademark based on shape similarity. Two pretrained convolutional neural networks (VGGNet and ResNet) were individually used to extract image features. Then, their performance was compared. Subsequently, the extracted features were used to calculate the similarity between a new trademark and each of those registered using the Euclidean distance. Thereafter, the system retrieved the most similar trademark to the query according to the smallest distances. As a result, the system achieved an average rank of 67,067.788, a normalized average rank of 0.0725, and a mean average precision of 0.774 on the Middle East Technical University dataset, which displays a promising application in detecting trademark similarity. Full article

(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods)

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15 pages, 36100 KiB

Open AccessArticle

Compressor Surge Mitigation in Turbocharged Spark-Ignition Engines without an Anti-Surge Control System during Load-Decrease Operation

by José Galindo, Héctor Climent, Joaquín de la Morena, David González-Domínguez, Stéphane Guilain and Thomas Besançon

Appl. Sci. 2022, 12(3), 1751; https://doi.org/10.3390/app12031751 - 08 Feb 2022

Cited by 2 | Viewed by 2079

Abstract

Automotive manufacturers are showing an increasing preference for hybrid powertrains based on advanced gasoline engines. The most extended solution to improve fuel economy in these engines consists in downsizing with direct injection, while turbocharging is required to compensate the consequent power loss. However, [...] Read more.

Automotive manufacturers are showing an increasing preference for hybrid powertrains based on advanced gasoline engines. The most extended solution to improve fuel economy in these engines consists in downsizing with direct injection, while turbocharging is required to compensate the consequent power loss. However, turbocharging is associated with different issues, such as compressor surge. It can appear during fast throttle closings (tip-outs), when the engine air flow is abruptly reduced. A usual strategy to manage this kind of maneuver is the installation of an anti-surge valve (ASV) that connects the compressor inlet and outlet when approaching the surge limit. In pursuit of cost reduction, the removal of the ASV system was assessed in this research. To this end, tip-outs without ASV were tested in a turbocharged gasoline engine equipped with a low-pressure EGR loop, and two strategies were analyzed: throttle closure optimization and reduction of the compressor inlet pressure through the intake flap (located upstream of the compressor to increase the EGR rate). The instantaneous compressor outlet pressure and its time derivative were used for surge detection. Experimental tip-outs without ASV revealed that applying a certain intake flap closing combined with an optimized throttle actuation led to a fast torque decrease, similar to that observed for the reference case with ASV, without compressor instabilities. Full article

(This article belongs to the Special Issue Advances in Efficiency and Emission Control in Internal Combustion Engines)

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14 pages, 3259 KiB

Open AccessArticle

Atrial Fibrillation and Anterior Cerebral Artery Absence Reduce Cerebral Perfusion: A De Novo Hemodynamic Model

by Timothy J. Hunter, Jermiah J. Joseph, Udunna Anazodo, Sanjay R. Kharche, Christopher W. McIntyre and Daniel Goldman

Appl. Sci. 2022, 12(3), 1750; https://doi.org/10.3390/app12031750 - 08 Feb 2022

Cited by 2 | Viewed by 2103

Abstract

Background: Atrial fibrillation is a prevalent cardiac arrhythmia and may reduce cerebral blood perfusion augmenting the risk of dementia. We hypothesize that geometric variations in the cerebral arterial structure called the Circle of Willis (CoW) play an important role in influencing cerebral perfusion. [...] Read more.

Background: Atrial fibrillation is a prevalent cardiac arrhythmia and may reduce cerebral blood perfusion augmenting the risk of dementia. We hypothesize that geometric variations in the cerebral arterial structure called the Circle of Willis (CoW) play an important role in influencing cerebral perfusion. The objective of this work was to develop a novel cardio-cerebral lumped parameter hemodynamic model to investigate the role of CoW variants on cerebral blood flow dynamics under atrial fibrillation conditions. Methods: A computational blood flow model was developed by coupling whole-body and detailed cerebral circulation descriptions, modified to represent six common variations of the CoW. Cerebral blood flow dynamics were simulated in common CoW variants, under control and imposed atrial fibrillation conditions. Risk was assessed based on the frequency of beat-wise hypoperfusion events, and sensitivity analysis was performed with respect to this model output. Results: It was found that the geometry of the CoW influenced the frequency of hypoperfusion events at different heart rates, with the variant missing a P1 segment having the highest risk. Sensitivity analysis revealed that intrinsic heart rate is most associated with the considered outcome. Conclusions: Our results suggest that CoW geometry plays an important role in influencing cerebral hemodynamics during atrial fibrillation. The presented study may assist in guiding our future clinical-imaging research. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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14 pages, 305 KiB

Open AccessArticle

Comparison of Selection Criteria for Model Selection of Support Vector Machine on Physiological Data with Inter-Subject Variance

by Minho Choi and Jae Jin Jeong

Appl. Sci. 2022, 12(3), 1749; https://doi.org/10.3390/app12031749 - 08 Feb 2022

Cited by 4 | Viewed by 1387

Abstract

Support vector machines (SVMs) utilize hyper-parameters for classification. Model selection (MS) is an essential step in the construction of the SVM classifier as it involves the identification of the appropriate parameters. Several selection criteria have been proposed for MS, but their usefulness is [...] Read more.

Support vector machines (SVMs) utilize hyper-parameters for classification. Model selection (MS) is an essential step in the construction of the SVM classifier as it involves the identification of the appropriate parameters. Several selection criteria have been proposed for MS, but their usefulness is limited for physiological data exhibiting inter-subject variance (ISV) that makes different characteristics between training and test data. To identify an effective solution for the constraint, this study considered a leave-one-subject-out cross validation-based selection criterion (LSSC) with six well-known selection criteria and compared their effectiveness. Nine classification problems were examined for the comparison, and the MS results of each selection criterion were obtained and analyzed. The results showed that the SVM model selected by the LSSC yielded the highest average classification accuracy among all selection criteria in the nine problems. The average accuracy was 2.96% higher than that obtained with the conventional K-fold cross validation-based selection criterion. In addition, the advantage of the LSSC was more evident for data with larger ISV. Thus, the results of this study can help optimize SVM classifiers for physiological data and are expected to be useful for the analysis of physiological data to develop various medical decision systems. Full article

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11 pages, 5516 KiB

Open AccessArticle

Nano Scaled Checkerboards: A Long Range Ordering in NiCoMnAl Magnetic Shape Memory Alloy Thin Films with Martensitic Intercalations

by Daniela Ramermann, Andreas Becker, Björn Büker, Andreas Hütten and Inga Ennen

Appl. Sci. 2022, 12(3), 1748; https://doi.org/10.3390/app12031748 - 08 Feb 2022

Cited by 1 | Viewed by 1460

Abstract

Magnetic shape memory Heusler alloys, such as NiCoMnAl, are considered as promising candidates for magnetocaloric cooling applications. Grown in thin film systems of adjacent layers with austenite and martensite crystal structures of almost equal thicknesses, a long-range ordering phenomenon in the shape of [...] Read more.

Magnetic shape memory Heusler alloys, such as NiCoMnAl, are considered as promising candidates for magnetocaloric cooling applications. Grown in thin film systems of adjacent layers with austenite and martensite crystal structures of almost equal thicknesses, a long-range ordering phenomenon in the shape of a 3D checkerboard pattern occurs in NiCoMnAl samples. The crystallographic origin of the pattern is proven by transmission electron microscopy (TEM) techniques. The darker fields of the arrangement consist of martensite nuclei superposed with austenite, while the purely austenite regions appear bright in TEM cross sections. The nucleation process is presumably triggered by inhomogeneous local elastic stray fields of primary martensitic nuclei in the austenite matrix and limited by the thicknesses of the martensite and austenite thin films. Full article

(This article belongs to the Special Issue Advances in Magnetic Nanomaterials and Nanostructures)

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20 pages, 8708 KiB

Open AccessArticle

Influence of Wind Barriers with Different Curvatures on Crosswind Aerodynamic Characteristics of a Train-Bridge System

by Ping Lou, Wei Tao, Chenzhi Cai, Xuhui He, Yunfeng Zou and Yuyan Ai

Appl. Sci. 2022, 12(3), 1747; https://doi.org/10.3390/app12031747 - 08 Feb 2022

Cited by 4 | Viewed by 1704

Abstract

Wind barriers can effectively reduce the risk of overturning and derailment of high-speed trains running on a bridge under crosswind. However, it can adversely affect the wind resistance of the bridge. There are few studies on the aerodynamic performance of curved wind barriers. [...] Read more.

Wind barriers can effectively reduce the risk of overturning and derailment of high-speed trains running on a bridge under crosswind. However, it can adversely affect the wind resistance of the bridge. There are few studies on the aerodynamic performance of curved wind barriers. In this paper, the effects of curved wind barriers with four curvatures (0, 0.2, 0.35, and 0.50) and different train-bridge combinations on the crosswind aerodynamic characteristics of a train-bridge system are investigated. The results show that the curved wind barrier can significantly reduce the wind speed below a certain height on the bridge deck. The curved wind barrier with small curvature can better reduce the aerodynamic force of the train; however, it greatly increases the aerodynamic force of the bridge. A wind barrier with a curvature of 0.35 is recommended because it takes into account the aerodynamic characteristics of the train and bridge at the same time. The porosity of a wind barrier greatly influences the aerodynamic performance of the train on the track of the windward side of the bridge, while the wind barrier has little effects on the train on the track of the leeward side of the bridge. The aerodynamic performance of the train on the track of the windward side of the bridge is less affected by whether or not a train on the track of the leeward side of the bridge is present. Full article

(This article belongs to the Special Issue Wind Engineering for Bridge Structures: Latest Advances and Prospects)

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12 pages, 10410 KiB

Open AccessFeature PaperReview

S-Adenosylmethionine, a Promising Antitumor Agent in Oral and Laryngeal Cancer

by Laura Mosca, Francesca Vitiello, Martina Pagano, Alessandra Coppola, Roberta Veglia Tranchese, Roberta Grillo, Giovanna Cacciapuoti and Marina Porcelli

Appl. Sci. 2022, 12(3), 1746; https://doi.org/10.3390/app12031746 - 08 Feb 2022

Cited by 3 | Viewed by 2018

Abstract

Squamous cell carcinoma of the head and neck (HNSCC), which includes cancers of the oral cavity and larynx, is one of the most common and highly aggressive malignancies worldwide, despite significant efforts committed in recent decades in its detection, prevention, and treatment. The [...] Read more.

Squamous cell carcinoma of the head and neck (HNSCC), which includes cancers of the oral cavity and larynx, is one of the most common and highly aggressive malignancies worldwide, despite significant efforts committed in recent decades in its detection, prevention, and treatment. The intrinsic or acquired drug resistance during treatment is the main limitation to chemotherapy, increasing mortality and cancer recurrence. Therefore, there is a growing scientific interest in identifying and developing adjuvant chemotherapies able to improve currently available treatments. S-Adenosylmethionine (AdoMet), a safe and nontoxic natural cofactor with pleiotropic effects on multiple cellular processes and the main biological methyl donor in transmethylation reactions, has been considerably studied as a therapeutic compound. Its application, alone or in combination with other drugs, is emerging as a potentially effective strategy for cancer treatment and for chemoprevention. This review summarizes the structural, pharmacological, and clinical aspects of AdoMet and provides an overview of the recent results highlighting its anticancer activity in the treatment of oral and laryngeal cancer, with particular emphasis on its molecular mechanisms and the promising chemoprotective and synergistic effects exerted in combination with cisplatin and specific microRNAs. Full article

(This article belongs to the Special Issue Women in Oral Medicine, Oral Biology, Oral Microbiology and Oral Bioengineering)

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16 pages, 867 KiB

Open AccessArticle

Framework for Preparation of Engaging Online Educational Materials—A Cognitive Approach

by Žolt Namestovski and Attila Kovari

Appl. Sci. 2022, 12(3), 1745; https://doi.org/10.3390/app12031745 - 08 Feb 2022

Cited by 13 | Viewed by 2743

Abstract

This study examines the process of creating successful, engaging, interactive, and activity-based online educational materials, while taking the cognitive aspects of learners into account. The quality of online educational materials has become increasingly important in the recent period, and it is crucial that [...] Read more.

This study examines the process of creating successful, engaging, interactive, and activity-based online educational materials, while taking the cognitive aspects of learners into account. The quality of online educational materials has become increasingly important in the recent period, and it is crucial that content is created that allows our students to learn effectively and enjoyably. In this paper, we present the milestones of curriculum creation and the resulting model, the criteria of selecting online learning environments, technical requirements, and the content of educational videos, interactive contents, and other methodological solutions. In addition, we also introduce some principles of instructional design, as well as a self-developed model that can be used to create effective online learning materials and online courses. There was a need for a self-developed, milestone-based, practice-oriented model because the models examined so far were too general and inadequate to meet the needs of a decentralized developer team, who work on different schedules, with significant geographical distances between them and do not place enough emphasis on taking cognitive factors into account. In these processes, special attention should be paid to having a clear and user-friendly interface, support for individual learning styles, effective multimedia, ongoing assistance and tracking of students’ progress, as well as interactivity and responsive appearance. Full article

(This article belongs to the Special Issue Applied Cognitive Sciences)

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17 pages, 3288 KiB

Open AccessArticle

Force Measurement with a Strain Gauge Subjected to Pure Bending in the Fluid–Wall Interaction of Open Water Channels

by Luis Santana, Diego Rivera and Eric Forcael

Appl. Sci. 2022, 12(3), 1744; https://doi.org/10.3390/app12031744 - 08 Feb 2022

Cited by 2 | Viewed by 2507

Abstract

An experimental method to measure forces of small magnitude with a strain gauge as a force sensor in the fluid–wall interaction of open water channels is presented. Six uniaxial strain gauges were employed for this purpose, which were embedded across the entire sensing [...] Read more.

An experimental method to measure forces of small magnitude with a strain gauge as a force sensor in the fluid–wall interaction of open water channels is presented. Six uniaxial strain gauges were employed for this purpose, which were embedded across the entire sensing area and subjected to pure bending, employing two-point bending tests. Sixteen two-point bending tests were performed to determine the existence of a direct relationship between the load and the instrument signal. Furthermore, a regression analysis was used to estimate the parameters of the model. A data acquisition system was developed to register the behavior of the strain gauge relative to the lateral displacement induced by the loading nose of the universal testing machine. The results showed a significant linear relationship between the load and the instrumental signal, provided that the strain gauge was embedded between 30% and 45% of the central axis in the sensing area of the sensor (R² > 0.99). Thus, the proposed sensor can be employed to measure forces of small magnitude. Additionally, the linear relationship between the load and the instrumental signal can be used as a calibration equation, provided that the strain gauge is embedded close to the central axis of the sensing area. Full article

(This article belongs to the Special Issue Non-destructive Testing in Civil Engineering)

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20 pages, 784 KiB

Open AccessArticle

Efficient Fake News Detection Mechanism Using Enhanced Deep Learning Model

by Tahir Ahmad, Muhammad Shahzad Faisal, Atif Rizwan, Reem Alkanhel, Prince Waqas Khan and Ammar Muthanna

Appl. Sci. 2022, 12(3), 1743; https://doi.org/10.3390/app12031743 - 08 Feb 2022

Cited by 18 | Viewed by 4802

Abstract

The spreading of accidental or malicious misinformation on social media, specifically in critical situations, such as real-world emergencies, can have negative consequences for society. This facilitates the spread of rumors on social media. On social media, users share and exchange the latest information [...] Read more.

The spreading of accidental or malicious misinformation on social media, specifically in critical situations, such as real-world emergencies, can have negative consequences for society. This facilitates the spread of rumors on social media. On social media, users share and exchange the latest information with many readers, including a large volume of new information every second. However, updated news sharing on social media is not always true.In this study, we focus on the challenges of numerous breaking-news rumors propagating on social media networks rather than long-lasting rumors. We propose new social-based and content-based features to detect rumors on social media networks. Furthermore, our findings show that our proposed features are more helpful in classifying rumors compared with state-of-the-art baseline features. Moreover, we apply bidirectional LSTM-RNN on text for rumor prediction. This model is simple but effective for rumor detection. The majority of early rumor detection research focuses on long-running rumors and assumes that rumors are always false. In contrast, our experiments on rumor detection are conducted on real-world scenario data set. The results of the experiments demonstrate that our proposed features and different machine learning models perform best when compared to the state-of-the-art baseline features and classifier in terms of precision, recall, and F1 measures. Full article

(This article belongs to the Special Issue Natural Language Processing: Recent Development and Applications)

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12 pages, 2532 KiB

Open AccessArticle

A Serial Attention Frame for Multi-Label Waste Bottle Classification

by Jingyu Xiao, Jiayu Xu, Chunwei Tian, Peiyi Han, Lei You and Shichao Zhang

Appl. Sci. 2022, 12(3), 1742; https://doi.org/10.3390/app12031742 - 08 Feb 2022

Cited by 12 | Viewed by 2221

Abstract

The multi-label recognition of damaged waste bottles has important significance in environmental protection. However, most of the previous methods are known for their poor performance, especially in regards to damaged waste bottle classification. In this paper, we propose the use of a serial [...] Read more.

The multi-label recognition of damaged waste bottles has important significance in environmental protection. However, most of the previous methods are known for their poor performance, especially in regards to damaged waste bottle classification. In this paper, we propose the use of a serial attention frame (SAF) to overcome the mentioned drawback. The proposed network architecture includes the following three parts: a residual learning block (RB), a mixed attention block (MAB), and a self-attention block (SAB). The RB uses ResNet to pretrain the SAF to extract more detailed information. To address the effect of the complex background of waste bottle recognition, a serial attention mechanism containing MAB and SAB is presented. MAB is used to extract more salient category information via the simultaneous use of spatial attention and channel attention. SAB exploits the obtained features and its parameters to enable the diverse features to improve the classification results of waste bottles. The experimental results demonstrate that our proposed model exhibited good recognition performance in the collected waste bottle datasets, with eight labels of three classifications, i.e., the color, whether the bottle was damage, and whether the wrapper had been removed, as well as public image classification datasets. Full article

(This article belongs to the Topic Applied Computer Vision and Pattern Recognition)

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10 pages, 1687 KiB

Open AccessArticle

PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration

by Cuixia Li, Shanshan Yang, Li Shi, Yue Liu and Yinghao Li

Appl. Sci. 2022, 12(3), 1741; https://doi.org/10.3390/app12031741 - 08 Feb 2022

Cited by 2 | Viewed by 2024

Abstract

Existing end-to-end cloud registration methods are often inefficient and susceptible to noise. We propose an end-to-end point cloud registration network model, Point Transformer for Registration Network (PTRNet), that considers local and global features to improve this behavior. Our model uses point clouds as [...] Read more.

Existing end-to-end cloud registration methods are often inefficient and susceptible to noise. We propose an end-to-end point cloud registration network model, Point Transformer for Registration Network (PTRNet), that considers local and global features to improve this behavior. Our model uses point clouds as inputs and applies a Transformer method to extract their global features. Using a K-Nearest Neighbor (K-NN) topology, our method then encodes the local features of a point cloud and integrates them with the global features to obtain the point cloud’s strong global features. Comparative experiments using the ModelNet40 data set show that our method offers better results than other methods, with a mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) between the ground truth and predicted values lower than those of competing methods. In the case of multi-object class without noise, the rotation average absolute error of PTRNet is reduced to 1.601 degrees and the translation average absolute error is reduced to 0.005 units. Compared to other recent end-to-end registration methods and traditional point cloud registration methods, the PTRNet method has less error, higher registration accuracy, and better robustness. Full article

(This article belongs to the Topic Applied Computer Vision and Pattern Recognition)

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15 pages, 8063 KiB

Open AccessArticle

A Filtering Method for Suppressing the Lift-Off Interference in Magnetic Flux Leakage Detection of Rail Head Surface Defect

by Yinliang Jia, Yichen Lu, Longhui Xiong, Yuhua Zhang, Ping Wang and Huangjian Zhou

Appl. Sci. 2022, 12(3), 1740; https://doi.org/10.3390/app12031740 - 08 Feb 2022

Cited by 7 | Viewed by 1779

Abstract

Magnetic flux leakage (MFL) detection is a common nondestructive detection method which is usually used to detect the surface defects of steel pipes and rails. To suppress the interference of lift-off on the detection signal of the defects in rail head surfaces, a [...] Read more.

Magnetic flux leakage (MFL) detection is a common nondestructive detection method which is usually used to detect the surface defects of steel pipes and rails. To suppress the interference of lift-off on the detection signal of the defects in rail head surfaces, a filtering method is proposed according to the distribution characteristics of the defect leakage magnetic field (LMF) in different directions. The sensor array is used to confirm the reference signal according to the difference between the signals in x and z directions. The installation mode of the sensors is deduced according to the distribution of the defect LMF. The experimental results show that this method can effectively suppress the lift-off interference in the MFL signal of the defects in the rail head surfaces. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures)

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9 pages, 4457 KiB

Open AccessArticle

Study on Mid-Infrared Energy Conversion of a Doubly Resonant Optical Parametric Oscillator Using Aperiodically Poled Lithium Niobate

by Zijian Wang, Bingyang Li, Yuheng Wang, Renzhe Han, Yaru Yang, Yongji Yu and Guangyong Jin

Appl. Sci. 2022, 12(3), 1739; https://doi.org/10.3390/app12031739 - 08 Feb 2022

Cited by 2 | Viewed by 1491

Abstract

This paper presents an external-cavity dual-wavelength mid-infrared multiple optical parametric oscillator based on a single MgO:APLN crystal, which is pumped by a pulsed 1.064 μm laser. The output power and beam qualities of parametric lasers at different repetition rates and transmittance were studied. [...] Read more.

This paper presents an external-cavity dual-wavelength mid-infrared multiple optical parametric oscillator based on a single MgO:APLN crystal, which is pumped by a pulsed 1.064 μm laser. The output power and beam qualities of parametric lasers at different repetition rates and transmittance were studied. When the pump power of the 1.064 μm laser was 34.5 W, the repetition rate was 63 kHz, the maximum output powers of 2.79 W@3.30 μm and 4.92 W@3.84 μm were obtained with the transmittance T = 60%@1.57 μm, corresponding to optical–optical conversion efficiencies of 8.1% and 14.3%, respectively. Meanwhile, the beam qualities of two mid-infrared laser beams were effectively optimized and the pulse widths of 9.72 ns@3.30 μm and 9.67 ns@3.84 μm were obtained synchronously. Full article

(This article belongs to the Special Issue Advances in Middle Infrared (Mid-IR) Lasers and Their Application)

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13 pages, 2375 KiB

Open AccessArticle

Reaction Center of Rhodobacter Sphaeroides, a Photoactive Protein for pH Sensing: A Theoretical Investigation of Charge Transport Properties

by Eleonora Alfinito and Lino Reggiani

Appl. Sci. 2022, 12(3), 1738; https://doi.org/10.3390/app12031738 - 08 Feb 2022

Viewed by 1219

Abstract

In the perspective of an increasing attention to ecological aspects of science and technology, it is of interest to design devices based on architectures of modular, low cost, and low-pollutant elements, each of them able to perform simple duties. Elemental devices may be [...] Read more.

In the perspective of an increasing attention to ecological aspects of science and technology, it is of interest to design devices based on architectures of modular, low cost, and low-pollutant elements, each of them able to perform simple duties. Elemental devices may be themselves green as, for example, proteins able to make simple actions, like sensing. To this aim, photosensitive proteins are often considered because of the possibility of transferring their specific reaction to visible light into electronic signals. Here, we investigate the expected electrical response of the photoactive protein Reaction Center (bRC) of Rhodobacter Sphaeroides within the proteotronics, a recent branch of molecular electronics that evaluates the electrical properties of a protein by using an impedance network protein analog based on the protein tertiary structure and the degree of electrical connectivity between neighboring amino acids. To this purpose, the linear and nonlinear regimes of the electrical response to an applied bias are studied when the protein is in its native state or in an active state. In the linear response regime, results evidence a significant difference in the electrical properties of bRC when the pH value of the solution in which the protein is embedded changes from acid to basic. In the non-linear response regime, the current-voltage characteristics experimentally reported in the recent literature are interpreted in terms of a sequential tunneling mechanism of charge transfer. The qualitative agreement of present findings with available experiments strongly suggests the use of this protein as a bio-rheostat or a pH sensor. Full article

(This article belongs to the Special Issue Frontiers in Optical Materials)

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