Journal Description
Applied Sciences
Applied Sciences
is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.9 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our authors say about Applied Sciences.
- Companion journals for Applied Sciences include: Applied Nano, AppliedChem, Applied Biosciences, Virtual Worlds, Spectroscopy Journal and JETA.
Impact Factor:
2.7 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
Appl. Sci. 2024, 14(9), 3809; https://doi.org/10.3390/app14093809 (registering DOI) - 29 Apr 2024
Abstract
In the specific application scenario of pepper product supply chain traceability, with the advancement of pepper product production, the expansion of links, and the increase of nodes, the quantity of data will become more and more enormous. The single-chain model is less efficient
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In the specific application scenario of pepper product supply chain traceability, with the advancement of pepper product production, the expansion of links, and the increase of nodes, the quantity of data will become more and more enormous. The single-chain model is less efficient for querying if the data are all stored into the same blockchain. In order to improve the efficiency of blockchain data querying, this paper proposes a traceability model with one main chain and multiple side chain structures, which separate the uplinked data from each link and use multi-chain transactions to improve the efficiency of data queries. This model builds an indexing mechanism with a product traceability code, using one main chain and multiple side chains. The main and side chains form a one-to-many mapping relationship, storing the mapping relationship between the traceability code and the transaction address of the side chain traceability information in the main chain. This enables information to travel through the main chain traversal query based on the mapping relationship and then query the direct index out of the side chain , to achieve fast traceability query and improve the efficiency of querying.
Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
Open AccessArticle
The Impact of Cardiorespiratory and Metabolic Parameters on Match Running Performance (MRP) in National-Level Football Players: A Multiple Regression Analysis
by
Radivoje Radaković, Borko Katanić, Mima Stanković, Bojan Mašanović and Suzana Žilić Fišer
Appl. Sci. 2024, 14(9), 3807; https://doi.org/10.3390/app14093807 (registering DOI) - 29 Apr 2024
Abstract
The aim of the study was to examine the association between cardiorespiratory and metabolic parameters and match running performance (MRP) in highly trained football players. The sample of participants consisted of 41 national-level football players (aged 23.20 ± 3.40 yrs, body height 182.00
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The aim of the study was to examine the association between cardiorespiratory and metabolic parameters and match running performance (MRP) in highly trained football players. The sample of participants consisted of 41 national-level football players (aged 23.20 ± 3.40 yrs, body height 182.00 ± 5.15 cm, and body mass 76.86 ± 6.06 kg) from the Serbian Super league. For the purposes of this research, the following measurements were applied. A maximal multistage progressive treadmill test, with a direct measurement of maximal oxygen consumption (VO2max) (using Fitmate MED, Cosmed, Rome, Italy) was conducted, alongside continuous heart rate monitoring. Capillary blood samples were taken from the hyperemic area using specific test strips, and, after sample collection, lactate concentration was immediately determined using a lactate analyzer. MRP variables were analyzed according to the BioIRC model of motion structure analysis, based on existing standards for profiling movement intensity. The results of multiple regression analysis indicated an association between cardiac parameters and total distance (R2 = 54.3%, p = 0.000), high-speed running (R2 = 46.4%, p = 0.000), and jogging (R2 = 33.6%, p = 0.004). Regression analysis revealed an association between cardiorespiratory parameters and total distance (R2 = 24.8%, p = 0.014), and high-speed running (R2 = 20%, p = 0.039). Meanwhile, no association was found between lactate concentration and running performance. The explanation for these regression analysis results is based on the observation that functional abilities represent significant potential for expressing movement performance, a crucial condition for success in football.
Full article
(This article belongs to the Special Issue Human Performance and Health in Sport and Exercise)
Open AccessArticle
Study of the Photo-Response of Doped GaAs with Aging
by
Samuel Zambrano Rojas and Gerardo Fonthal
Appl. Sci. 2024, 14(9), 3806; https://doi.org/10.3390/app14093806 (registering DOI) - 29 Apr 2024
Abstract
The aging of semiconductor materials is a topic of current interest. We studied the photo-response of epitaxial samples of GaAs doped with Ge and Sn up to 1 × 1019 atoms cm−3. These samples were stored in a dry and
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The aging of semiconductor materials is a topic of current interest. We studied the photo-response of epitaxial samples of GaAs doped with Ge and Sn up to 1 × 1019 atoms cm−3. These samples were stored in a dry and dark environment for 26 years. We realized photoluminescence measurements at different temperatures and photoreflectance spectra at 300 K in three periods: 1995, 2001 and 2021. We found that environmental oxygen formed defects in GaAs, leaving lattice vacancies that provoked changes in the optical photo-response. In addition, we found that the vacancy concentrations could be as large as 5 × 1017 atoms cm−3 over the 26 years. In this work, we demonstrate that the aging of semiconductor materials occurs even when they are not used within a functioning circuit, with the changes being greater when the material is not doped. Knowing about the aging of materials is important for the industry, particularly for the semiconductor industry, because aging-induced deterioration influences prices and guarantees.
Full article
(This article belongs to the Section Materials Science and Engineering)
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Open AccessArticle
Injury Incidence in Traineras: Analysis of Traditional Rowing by Competitive Level and Gender
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Patxi León-Guereño, Alfonso Penichet-Tomas, Arkaitz Castañeda-Babarro and Jose M. Jimenez-Olmedo
Appl. Sci. 2024, 14(9), 3805; https://doi.org/10.3390/app14093805 (registering DOI) - 29 Apr 2024
Abstract
The growing interest in “Traineras”, a traditional competitive rowing modality prevalent in Northern Spain, underscores the need for a comprehensive analysis of the injury incidence associated with this sporting practice. Despite rowing’s significance in the international sports arena and its inclusion since the
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The growing interest in “Traineras”, a traditional competitive rowing modality prevalent in Northern Spain, underscores the need for a comprehensive analysis of the injury incidence associated with this sporting practice. Despite rowing’s significance in the international sports arena and its inclusion since the beginnings of the modern Olympic Games, research into injuries in this sport, especially in traditional modalities such as Traineras, has been limited. This study aimed to identify and describe the predominant injuries among Traineras rowers, analyzing their epidemiology, characteristics, affected body regions, and diagnoses, further differentiated by competitive level and gender. A retrospective survey completed by 773 rowers (24% women, 76% men) participating in various leagues (ACT, ARC1, ARC2, LGT1, LGT2, ETE, and LGT-F) during the season revealed that 68.2% suffered from at least one injury, predominantly due to overuse (91.1% in men, 83.1% in women). The most affected regions were the lower back and shoulders, with the main diagnoses being muscle cramps and tendinitis, showing statistically significant differences between sexes. The findings of this study not only provide a deeper understanding of the etiology and origin of injuries in this sport but also lay the groundwork for developing specific injury prevention plans, thereby contributing to the safety and optimal performance of athletes.
Full article
(This article belongs to the Special Issue Applied Biomechanics in Sports Performance, Injury Prevention and Rehabilitation)
Open AccessArticle
Research on Driving Scenario Knowledge Graphs
by
Ce Zhang, Liang Hong, Dan Wang, Xinchao Liu, Jinzhe Yang and Yier Lin
Appl. Sci. 2024, 14(9), 3804; https://doi.org/10.3390/app14093804 (registering DOI) - 29 Apr 2024
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Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited
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Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of a driving scenario knowledge graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable driving scenario knowledge graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.
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Open AccessEditorial
Concrete Structures: Latest Advances and Prospects for a Sustainable Future
by
Mariella Diaferio and Francisco B. Varona
Appl. Sci. 2024, 14(9), 3803; https://doi.org/10.3390/app14093803 (registering DOI) - 29 Apr 2024
Abstract
Along with structural steel, structural concrete is probably one of the most widely used construction materials worldwide for building construction and civil engineering infrastructures [...]
Full article
(This article belongs to the Section Civil Engineering)
Open AccessArticle
Analysis of the Occurrent Models of Potential Debris-Flow Sources in the Watershed of Ching-Shuei River
by
Ji-Yuan Lin, Jen-Chih Chao and Lung-Kun Yang
Appl. Sci. 2024, 14(9), 3802; https://doi.org/10.3390/app14093802 (registering DOI) - 29 Apr 2024
Abstract
The areas around the Ching-Shuei River saw numerous landslides (2004–2017) after the Jiji earthquake, profoundly harming the watershed’s geological environment. The 33 catchment areas in the Ching-Shuei River watershed and five typhoon and rainstorm events, with a total of 165 occurrences and non-occurrences,
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The areas around the Ching-Shuei River saw numerous landslides (2004–2017) after the Jiji earthquake, profoundly harming the watershed’s geological environment. The 33 catchment areas in the Ching-Shuei River watershed and five typhoon and rainstorm events, with a total of 165 occurrences and non-occurrences, were analyzed, and the training and validation were categorized into 70% training and 30% validation. A landslide disaster is deemed, for the purposes of this research, to have taken place if SPOT satellite images taken before and after an incident show a Normalized Difference Vegetation Index difference larger than 0.25, a slope of less than 30 degrees, and a number of connected grids greater than 10. The analysis was carried out using the instability index method analysis with Rogers regression analysis and artificial neural network. The accuracy rates of neural network, logit regression, and instability index analyses were, respectively, 93.3%, 80.6%, and 70.9%. The neural network’s area under the curve was 0.933, indicating excellent discrimination ability; that of the logit regression analysis was 0.794, which is considered good; and that of the instability index analysis was 0.635, or fair. This suggests that any of the three models are suitable for the danger assessment of large post-earthquake debris flows. The results of this study also provide a reference and evidence for specific sites’ potential susceptibility to debris flows.
Full article
(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)
Open AccessArticle
Research on Online Review Information Classification Based on Multimodal Deep Learning
by
Jingnan Liu, Yefang Sun, Yueyi Zhang and Chenyuan Lu
Appl. Sci. 2024, 14(9), 3801; https://doi.org/10.3390/app14093801 (registering DOI) - 29 Apr 2024
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The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery
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The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery timeliness, hygiene standards, and environmental considerations. Traditional text data mining techniques primarily focus on consumers’ emotional traits, disregarding pertinent information pertaining to the online products themselves. In light of these aforementioned issues in current research methodologies, this paper introduces the Bert BiGRU Softmax model combined with multimodal features to enhance the efficacy of sentiment classification in data analysis. Comparative experiments conducted using existing data demonstrate that the accuracy rate of the model employed in this study reaches 90.9%. In comparison to single models or combinations of three models with the highest accuracy rate of 7.7%, the proposed model exhibits superior accuracy and proves to be highly applicable to online reviews.
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Open AccessReview
Mechanical Properties of Aramid Fiber Fabrics and Composites Enhanced by Phthalic Anhydride Catalyzed with Anhydrous Aluminum Chloride
by
Yi Xiao, Yibo E, Hanmei Gao, Honggang Li, Guowen Xu and Xuhong Qiang
Appl. Sci. 2024, 14(9), 3800; https://doi.org/10.3390/app14093800 (registering DOI) - 29 Apr 2024
Abstract
The surface modification of aramid fiber plain fabric (PPTA) was conducted through phthalic anhydride treatment and anhydrous aluminum chloride (AlCl3) catalysis, aiming to enhance the interfacial bonding strength between aramid fiber fabric and bisphenol A diglycidyl ether (DGEBA) resin. The surface
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The surface modification of aramid fiber plain fabric (PPTA) was conducted through phthalic anhydride treatment and anhydrous aluminum chloride (AlCl3) catalysis, aiming to enhance the interfacial bonding strength between aramid fiber fabric and bisphenol A diglycidyl ether (DGEBA) resin. The surface morphologies and structures of PPTA fiber before and after modification were characterized using scanning electron microscopy, atomic force microscopy, X-ray photoelectron spectroscopy, and X-ray diffractometry. The mechanical properties of the PPTA/DGEBA composite were evaluated using a universal mechanical testing machine. The results demonstrate that when the concentration of phthalic anhydride is 0.3 mol/L, the tensile strength, bending strength and interlaminar shear strength of PPTA/DGEBA composites reach the maximum value, which are increased by 17.94%, 44.18%, and 15.94% compared with the unmodified sample, respectively. After a 0.5-h catalytic modification, the PPTA/DGEBA composites exhibited significantly enhanced tensile strength, bending strength, and interlaminar shear strength, achieving respective increments of 32.28%, 24.91%, and 29.10% compared to the modified samples without catalyst addition. Moreover, the overall mechanical properties of the aramid fiber fabrics and composites were substantially improved, which are more suitable for structural applications.
Full article
(This article belongs to the Section Civil Engineering)
Open AccessArticle
A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers
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Inessa Ainbinder, Evgeni Temnikov and Miriam Allalouf
Appl. Sci. 2024, 14(9), 3799; https://doi.org/10.3390/app14093799 (registering DOI) - 29 Apr 2024
Abstract
A virtual marketplace or service-providing system must ensure minimal task response times. Varying working rates among the human workers in the system can lead to longer delays for certain tasks. The waiting time in the queue is crucially affected by the queueing architecture
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A virtual marketplace or service-providing system must ensure minimal task response times. Varying working rates among the human workers in the system can lead to longer delays for certain tasks. The waiting time in the queue is crucially affected by the queueing architecture used in the system, whether global or local. Studies generally favor global queue systems over local ones, assuming similar processing rates. However, system behavior changes when workers are heterogeneous. In this research, we used simulation to compare the waiting times of tasks assigned to three categories of processing rates in both architectures and with various routing policies in local queues. We found that when using random tie-breaking, there was a correlation between waiting time duration and the proportion of tie-breaking events. Performance is improved when controlling these events using scheduling awareness of the workers’ processing rates. The global queue outperforms local queues when the workers are homogeneous. However, the push mechanisms that control the assignment processes and heterogeneity-aware algorithms improve local queue system waiting times and load balance. It is better than global queues when tasks are assigned to medium and fast workers, but it also enables specific slow workers’ assignments.
Full article
(This article belongs to the Special Issue Technologies, Algorithms and Applications for Planning, Scheduling and Optimization)
Open AccessArticle
Multi-Step Multidimensional Statistical Arbitrage Prediction Using PSO Deep-ConvLSTM: An Enhanced Approach for Forecasting Price Spreads
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Sensen Tu, Panke Qin, Mingfu Zhu, Zeliang Zeng, Shenjie Cheng and Bo Ye
Appl. Sci. 2024, 14(9), 3798; https://doi.org/10.3390/app14093798 (registering DOI) - 29 Apr 2024
Abstract
Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary
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Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary research predominantly focuses on projections of single indicators for the subsequent temporal juncture, and devising efficacious arbitrage strategies often necessitates the examination of multiple indicators across timeframes. To tackle the aforementioned challenge, our methodology leverages a PSO Deep-ConvLSTM network, which, through particle swarm optimization (PSO), refines hyperparameters, including layer architectures and learning rates, culminating in superior predictive performance. By analyzing temporal-spatial data within financial markets through ConvLSTM, the model captures intricate market patterns, performing better in forecasting than traditional models. Multistep forward simulation experiments and extensive ablation studies using future data from the Shanghai Futures Exchange in China validate the effectiveness of the integrated model. Compared with the gate recurrent unit (GRU), long short-term memory (LSTM), Transformer, and FEDformer, this model exhibits an average reduction of 39.8% in root mean squared error (RMSE), 42.5% in mean absolute error (MAE), 45.6% in mean absolute percentage error (MAPE), and an average increase of 1.96% in coefficient of determination (R2) values.
Full article
(This article belongs to the Special Issue Advances in Neural Networks and Deep Learning)
Open AccessArticle
Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
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Peter Tettey Yamak, Yujian Li, Ting Zhang and Pius K. Gadosey
Appl. Sci. 2024, 14(9), 3797; https://doi.org/10.3390/app14093797 (registering DOI) - 29 Apr 2024
Abstract
In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a
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In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a type of convolutional neural network (CNN). We propose a tripartite classification system to accurately represent Bitcoin price trends. In addition, we demonstrate the effectiveness of Wide-TSNet through various experiments, in which it achieves an Accuracy of approximately 94% and an F1 score of 90%. It is also shown that lightweight CNN models, such as SqueezeNet and EfficientNet, can be as effective as complex models under certain conditions. Furthermore, we investigate the efficacy of other image transformation methods, such as Gramian angular fields, in capturing the trends and volatility of Bitcoin prices and revealing patterns that are not visible in the raw data. Moreover, we assess the effect of image resolution on model performance, emphasizing the importance of this factor in image-based time-series classification. Our findings explore the intersection between finance, image processing, and deep learning, providing a robust methodology for financial time-series classification.
Full article
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence, Data Analytics and Soft Computing)
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Open AccessArticle
Numerical Simulation on the Leakage-Induced Collapse of Segmental Tunnels
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Qihao Sun, Xian Liu, Wouter De Corte and Luc Taerwe
Appl. Sci. 2024, 14(9), 3796; https://doi.org/10.3390/app14093796 (registering DOI) - 29 Apr 2024
Abstract
Sudden leakage during tunnel construction poses a great threat to the safety of the tunnel. There are relatively few studies on the mechanism of structural collapse induced by tunnel leakage, so it is difficult to propose effective control measures. To solve this problem,
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Sudden leakage during tunnel construction poses a great threat to the safety of the tunnel. There are relatively few studies on the mechanism of structural collapse induced by tunnel leakage, so it is difficult to propose effective control measures. To solve this problem, a coupled fluid–solid strata analysis model and a nonlinear FEM tunnel model were established based on model test results to analyze the mechanism of tunnel collapse. The following conclusions were drawn: (1) A DEM-based coupled fluid–solid model combined with a nonlinear FEM tunnel model can effectively simulate the physical process of tunnel collapse. (2) The mechanism of tunnel leakage-induced strata response is the continuous destabilization and reappearance of the soil arching effect, which restricts the erosion of the soil and results in macroscopic soil caves, and finally leads to the impact load of the destabilized soil. (3) The process of the tunnel structure collapse is as follows: firstly, a large deformation of the tunnel structure is caused by the redistribution of external loads generated by the earth arching effect; then, due to the multiple impact loads from the destabilization of the soil, plastic hinges are generated at the tunnel joints, and the tunnel collapses.
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(This article belongs to the Special Issue Advances in Tunnel and Underground Construction)
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Open AccessArticle
Optimal Planning of Battery Swapping Stations Incorporating Dynamic Network Reconfiguration Considering Technical Aspects of the Power Grid
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Waleed Khalid Mahmood Al-Zaidi and Aslan Inan
Appl. Sci. 2024, 14(9), 3795; https://doi.org/10.3390/app14093795 (registering DOI) - 29 Apr 2024
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In order to drive electric vehicle adoption and bolster grid stability, the incorporation of battery swapping stations (BSSs) into the power grid is imperative. Conversely, network reconfiguration plays a crucial role in optimizing energy exchange within the power network, ensuring its economical and
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In order to drive electric vehicle adoption and bolster grid stability, the incorporation of battery swapping stations (BSSs) into the power grid is imperative. Conversely, network reconfiguration plays a crucial role in optimizing energy exchange within the power network, ensuring its economical and safe operation. Therefore, this study proposes an optimal planning method for battery swapping stations that integrates dynamic power distribution network reconfiguration while addressing technical aspects of the grid. The proposed method aims to concurrently optimize the placement and capacity of battery swapping stations, along with power distribution network reconfiguration, to enhance grid reliability and efficiency. The optimization model accounts for various factors including power quality, technical considerations, grid limitations, and operational expenses. A multi-objective optimization framework is devised to simultaneously reduce system losses, improve voltage stability, and mitigate environmental impacts of the power distribution network incorporating DG units. Case studies are conducted to illustrate the efficacy of the proposed approach in enhancing overall grid performance while accommodating the integration of battery swapping stations. The findings underscore the significance of considering technical factors and grid reconfiguration in battery swapping station planning to achieve optimal system operation and maximize benefits for electric vehicle users and grid operators alike.
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Open AccessArticle
Development of a Method for Estimating the Angle of Lumbar Spine X-ray Images Using Deep Learning with Pseudo X-ray Images Generated from Computed Tomography
by
Ryuma Moriya, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa and Hiroyuki Sugimori
Appl. Sci. 2024, 14(9), 3794; https://doi.org/10.3390/app14093794 (registering DOI) - 29 Apr 2024
Abstract
Background and Objectives: In lumbar spine radiography, the oblique view is frequently utilized to assess the presence of spondylolysis and the morphology of facet joints. It is crucial to instantly determine whether the oblique angle is appropriate for the evaluation and the necessity
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Background and Objectives: In lumbar spine radiography, the oblique view is frequently utilized to assess the presence of spondylolysis and the morphology of facet joints. It is crucial to instantly determine whether the oblique angle is appropriate for the evaluation and the necessity of retakes after imaging. This study investigates the feasibility of using a convolutional neural network (CNN) to estimate the angle of lumbar oblique images. Since there are no existing lumbar oblique images with known angles, we aimed to generate synthetic lumbar X-ray images at arbitrary angles from computed tomography (CT) images and to estimate the angles of these images using a trained CNN. Methods: Synthetic lumbar spine X-ray images were created from CT images of 174 individuals by rotating the lumbar spine from 0° to 60° in 5° increments. A line connecting the center of the spinal canal and the spinous process was used as the baseline to define the shooting angle of the synthetic X-ray images based on how much they were tilted from the baseline. These images were divided into five subsets and trained using ResNet50, a CNN for image classification, implementing 5-fold cross-validation. The models were trained for angle estimation regression and image classification into 13 classes at 5° increments from 0° to 60°. For model evaluation, mean squared error (MSE), root mean squared error (RMSE), and the correlation coefficient (r) were calculated for regression analysis, and the area under the curve (AUC) was calculated for classification. Results: In the regression analysis for angles from 0° to 60°, the MSE was 14.833 degree2, the RMSE was 3.820 degrees, and r was 0.981. The average AUC for the 13-class classification was 0.953. Conclusion: The CNN developed in this study was able to estimate the angle of an lumbar oblique image with high accuracy, suggesting its usefulness.
Full article
(This article belongs to the Special Issue New Frontiers in X-ray Technologies for Medical Research: Image Analysis and Disease Discovered)
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Open AccessArticle
Why Not Both? An Attention-Guided Transformer with Pixel-Related Deconvolution Network for Face Super-Resolution
by
Zhe Zhang and Chun Qi
Appl. Sci. 2024, 14(9), 3793; https://doi.org/10.3390/app14093793 (registering DOI) - 29 Apr 2024
Abstract
Transformer-based encoder-decoder networks for face super-resolution (FSR) have achieved promising success in delivering stunningly clear and detailed facial images by capturing local and global dependencies. However, these methods have certain limitations. Specifically, the deconvolution in upsampling layers neglects the relationship between adjacent pixels,
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Transformer-based encoder-decoder networks for face super-resolution (FSR) have achieved promising success in delivering stunningly clear and detailed facial images by capturing local and global dependencies. However, these methods have certain limitations. Specifically, the deconvolution in upsampling layers neglects the relationship between adjacent pixels, which is crucial in facial structure reconstruction. Additionally, raw feature maps are fed to the transformer blocks directly without mining their potential feature information, resulting in suboptimal face images. To circumvent these problems, we propose an attention-guided transformer with pixel-related deconvolution network for FSR. Firstly, we devise a novel Attention-Guided Transformer Module (AGTM), which is composed of an Attention-Guiding Block (AGB) and a Channel-wise Multi-head Transformer Block (CMTB). AGTM at the top of the encoder-decoder network (AGTM-T) promotes both local facial details and global facial structures, while AGTM at the bottleneck side (AGTM-B) optimizes the encoded features. Secondly, a Pixel-Related Deconvolution (PRD) layer is specially designed to establish direct relationships among adjacent pixels in the upsampling process. Lastly, we develop a Multi-scale Feature Fusion Module (MFFM) to fuse multi-scale features for better network flexibility and reconstruction results. Quantitative and qualitative experimental results on various datasets demonstrate that the proposed method outperforms other state-of-the-art FSR methods.
Full article
(This article belongs to the Special Issue Advances in Image Recognition and Processing Technologies)
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Open AccessArticle
K-WISC-V Processing Speed Index Analysis to Verify the Effectiveness of ADHD Symptom Improvement Using Pediatric Digital Content
by
Seon-Chil Kim
Appl. Sci. 2024, 14(9), 3792; https://doi.org/10.3390/app14093792 (registering DOI) - 29 Apr 2024
Abstract
The most common treatment approach for children diagnosed with attention deficit hyperactivity disorder (ADHD) involves drug therapy; however, persuading parents and motivating children in the early stages of treatment is challenging. Consequently, there is a growing interest among parents of children with ADHD
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The most common treatment approach for children diagnosed with attention deficit hyperactivity disorder (ADHD) involves drug therapy; however, persuading parents and motivating children in the early stages of treatment is challenging. Consequently, there is a growing interest among parents of children with ADHD in non-drug therapies. Moreover, recent advancements in information and communication technology have increased the accessibility of digital treatments for ADHD and non-drug therapy content. However, some challenges persist in confirming specific and objective effects. In this retrospective study, we developed game-type digital therapy content for children aged 6–16 years and monitored improvements in ADHD symptoms using the K-WISC-V subtest processing speed index. The analysis revealed that the rate of change in the sum of converted scores on the 14th day was 0.64% lower in the experimental group compared with the control group; however, on the 28th day, the rate of change increased by 6.93%. This suggests that the supplementary use of Neuroworld DTx therapy proved effective for visual enhancement. Furthermore, improvements were observed in visual discrimination, short-term memory, and motor cooperation abilities. Consequently, game-based digital content is an effective adjunctive therapy for children dealing with ADHD.
Full article
(This article belongs to the Section Biomedical Engineering)
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Open AccessArticle
Therapeutic Effects of 30 nm Cyclosporin A-Loaded Nanoparticles Using PLGA-PEG-PLGA Triblock Copolymer for Transdermal Delivery in Mouse Models of Psoriasis
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Akira Kagawa, Akira Sato, Kimiko Makino and Issei Takeuchi
Appl. Sci. 2024, 14(9), 3791; https://doi.org/10.3390/app14093791 (registering DOI) - 29 Apr 2024
Abstract
This study aimed to evaluate the effectiveness of poly(DL-lactide-co-glycolide)-block-poly(ethylene glycol)-block-poly(DL-lactide-co-glycolide) triblock copolymers (PLGA-PEG-PLGA) as a drug carrier in the treatment of psoriasis. Nanoparticles containing cyclosporin A (CsA) were prepared, and their cytotoxicity and
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This study aimed to evaluate the effectiveness of poly(DL-lactide-co-glycolide)-block-poly(ethylene glycol)-block-poly(DL-lactide-co-glycolide) triblock copolymers (PLGA-PEG-PLGA) as a drug carrier in the treatment of psoriasis. Nanoparticles containing cyclosporin A (CsA) were prepared, and their cytotoxicity and skin irritation properties were investigated. These results revealed that the nanoparticles themselves had no obvious cytotoxicity or skin irritation effects. Furthermore, it was shown that loading CsA into nanoparticles promoted its cellular uptake. The therapeutic effect of CsA-loaded PLGA-PEG-PLGA nanoparticles on psoriasis was evaluated using a mouse model of psoriasis induced by imiquimod. In psoriatic skin, we confirmed that nanoparticles penetrate deep into the skin. Furthermore, it was suggested that by using PLGA-PEG-PLGA, drug carriers could reach the dermal layer, which is the target site for psoriasis treatment. The observation of skin sections after the treatment experiment showed that excessively proliferated keratinocytes were restored to an almost normal state by using PLGA-PEG-PLGA nanoparticles as drug carriers. Additionally, the quantitative measurement results for cytokines revealed that the levels of TNF-α, IL-17A, and IL-22 were significantly decreased compared with those of the group to which CsA suspended in a 20% ethanol solution was administered. These results indicate that PLGA-PEG-PLGA nanoparticles are promising drug carriers for the transdermal administration of CsA.
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(This article belongs to the Section Nanotechnology and Applied Nanosciences)
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Open AccessArticle
Compressive Characteristics and Energy Absorption Capacity of Automobile Energy-Absorbing Box with Filled Porous TPMS Structures
by
Xuejin Zhao, Zhenzong Li, Yupeng Zou and Xiaoyu Zhao
Appl. Sci. 2024, 14(9), 3790; https://doi.org/10.3390/app14093790 (registering DOI) - 29 Apr 2024
Abstract
In order to meet the higher requirements of energy-absorbing structures in the lightweight automobile design, the mechanical design and impact energy absorption of porous TPMS structures are studied. Eight kinds of porous TPMS structure elements, Gyroid, Diamond, I-WP, Neovius, Primitive, Fischer-Koch S, F-RD,
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In order to meet the higher requirements of energy-absorbing structures in the lightweight automobile design, the mechanical design and impact energy absorption of porous TPMS structures are studied. Eight kinds of porous TPMS structure elements, Gyroid, Diamond, I-WP, Neovius, Primitive, Fischer-Koch S, F-RD, and PMY, are designed based on Matlab, and the porous structure samples composed of eight elements are printed and molded using SLM. The deformation mechanism, mechanical response, and energy absorption characteristics of different porous TPMS structures are investigated. Gyroid and Primitive elements are selected to fill the internal structure of the energy-absorbing automobile boxes. Traditional thin-walled energy-absorbing boxes served as a control group and were subjected to low-speed impact testing. The results show that the peak load of the energy-absorbing box filled with TPMS porous structures is almost equal to the average load under a 4.4 m/s impact, and the SEA of the energy-absorbing box filled with TPMS porous structures is higher than the traditional thin-walled energy-absorbing box. The problems of excessive peak load and inconsistent load fluctuation of traditional thin-walled energy-absorbing structures are effectively solved by porous TPMS structures with the assurance that the lightweight and energy-absorbing requirements are still met.
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(This article belongs to the Special Issue Additively Manufactured Mechanical Metamaterials: Design, Analysis, and Applications)
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Open AccessArticle
Intelligent Dynamic Power Control with Cell Range Expansion for Small-Cells in 5G HetNets
by
Ilhak Ban and Se-Jin Kim
Appl. Sci. 2024, 14(9), 3789; https://doi.org/10.3390/app14093789 (registering DOI) - 29 Apr 2024
Abstract
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In 5G heterogeneous networks (HetNets), small-cell base stations (SBSs) are deployed in the coverage of macro base stations (MBSs) to improve the system performance. However, some macro user equipments (MUEs) have strong interference from neighboring SBSs and thus the performance of MBSs decreases.
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In 5G heterogeneous networks (HetNets), small-cell base stations (SBSs) are deployed in the coverage of macro base stations (MBSs) to improve the system performance. However, some macro user equipments (MUEs) have strong interference from neighboring SBSs and thus the performance of MBSs decreases. Thus, in this paper, we propose a novel intelligent dynamic power control (DPC) with cell range expansion (CRE) to improve the downlink performance of both small-cell user equipments (SUEs) and CRE user equipments (CUEs) in 5G HetNets. That is, in the proposed DPC scheme, each MUE first collects the received signal strength indicator (RSSI) measurements from neighboring SBSs and sends them to the serving MBS. Then, the MBS finds MUEs with strong interference from neighboring SBSs based on a given target threshold of CRE and offloads a fraction of MUEs from MBSs to SBSs. In addition, SBSs divide their SUEs and CUEs into two groups, i.e., inner and outer groups, to assign different subchannels and dynamically allocate the appropriate transmission power to increase the performance of both SUEs and CUEs. Through simulation results, it is shown that the proposed DPC scheme outperforms others in terms of the capacity and outage probability of SUEs and CUEs.
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