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
Evaluating Agility in Pre-Adolescent Basketball: A Comparative Analysis of CODAT, IAT, and RAT
Appl. Sci. 2024, 14(9), 3815; https://doi.org/10.3390/app14093815 (registering DOI) - 29 Apr 2024
Abstract
Background: In basketball, agility is essential, characterized by the ability to change direction swiftly and accelerate. Traditional tests like the Illinois Agility Test (IAT) and the Reactive Agility Test (RAT) may not fully capture the agility demands specific to basketball. Purpose: This study
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Background: In basketball, agility is essential, characterized by the ability to change direction swiftly and accelerate. Traditional tests like the Illinois Agility Test (IAT) and the Reactive Agility Test (RAT) may not fully capture the agility demands specific to basketball. Purpose: This study aimed to introduce the Change of Direction and Acceleration Test (CODAT), designed specifically for young basketball players. It evaluates CODAT’s effectiveness by comparing it with IAT and RAT through comprehensive analysis. Methods: We assessed 87 pre-adolescent male basketball players, aged 9 to 13 years, with an average biological age of 11.2 years and an average estimated Peak Height Velocity (PHV) of 12.5 ± 0.5 years, using CODAT, IAT, and RAT. We employed regression analysis and the Bland–Altman method to determine CODAT’s reliability and validity. Results: The findings indicate that CODAT offers superior reliability and validity in measuring basketball-specific agility. Consistent scores highlight its potential as an effective tool for agility assessment in basketball training and talent identification. Conclusions: CODAT represents a significant advancement in agility assessment for young basketball players, advocating for its integration into sports science practices to better address the specialized demands of basketball agility.
Full article
(This article belongs to the Special Issue Advances in Sports, Exercise and Health)
Open AccessArticle
Water Pipeline Leakage Detection Based on Coherent φ-OTDR and Deep Learning Technology
by
Shuo Zhang, Zijian Xiong, Boyuan Ji, Nan Li, Zhangwei Yu, Shengnan Wu and Sailing He
Appl. Sci. 2024, 14(9), 3814; https://doi.org/10.3390/app14093814 (registering DOI) - 29 Apr 2024
Abstract
Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks.
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Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks. This method allows for the precise localization of leak points. The stability is compromised because of the wireless signal strength. The latter approach, which relies on pressure measurements to predict leak events, does not achieve precise leak point localization. To address these challenges, in this paper, a coherent optical time-domain reflectometry (φ-OTDR) system is employed to capture vibration signal phase information. Subsequently, two pre-trained neural network models based on CNN and Resnet18 are responsible for processing this information to accurately identify vibration events. In an experimental setup simulating water pipelines, phase information from both leaking and non-leaking pipe segments is collected. Using this dataset, classical CNN and ResNet18 models are trained, achieving accuracy rates of 99.7% and 99.5%, respectively. The multi-leakage point experiment results indicate that the Resnet18 model has better generalization compared to the CNN model. The proposed solution enables long-distance water-pipeline precise leak point localization and accurate vibration event identification.
Full article
(This article belongs to the Special Issue Advanced Optical-Fiber-Related Technologies)
Open AccessReview
A Critical Review of Human Jaw Biomechanical Modeling
by
Marco De Stefano and Alessandro Ruggiero
Appl. Sci. 2024, 14(9), 3813; https://doi.org/10.3390/app14093813 (registering DOI) - 29 Apr 2024
Abstract
The human jaw is a complex biomechanical system involving different anatomical components and an articulated muscular system devoted to its dynamical activation. The numerous actions exerted by the mandible, such as talking, eating or chewing, make its biomechanical comprehension absolutely indispensable. To date,
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The human jaw is a complex biomechanical system involving different anatomical components and an articulated muscular system devoted to its dynamical activation. The numerous actions exerted by the mandible, such as talking, eating or chewing, make its biomechanical comprehension absolutely indispensable. To date, even if research on this topic has achieved interesting outcomes using in vitro testing, thanks to the development of new apparatus and methods capable of performing more and more realistic experiments, theoretical modeling is still worthy of investigation. In light of this, nowadays, the Finite Element Method (FEM) approach constitutes certainly the most common tool adopted to investigate particular issues concerning stress–strain characterization of the human jaw. In addition, kinematics analyses, both direct and inverse, are also diffuse and reported in the literature. This manuscript aimed to propose a critical review of the most recurrent biomechanical models of the human mandible to give readers a comprehensive overview on the topic. In light of this, the numerical approaches, providing interesting outcomes, such as muscular activation profiles, condylar forces and stress–strain fields for the human oral cavity, are mainly differentiated between according to the joint degrees of freedom, the analytical descriptions of the muscular forces, the boundary conditions imposed, the kind of task and mandible anatomical structure modeling.
Full article
(This article belongs to the Special Issue Research and Development in Orthopaedic Biomechanics)
Open AccessReview
Analyses of Physical and Physiological Responses during Competition in Para-Footballers with Cerebral Palsy: A Systematic Review
by
Santiago Álvarez-Hernández, Daniel Castillo, José Gerardo Villa-Vicente, Javier Yanci, Diego Marqués-Jiménez and Alejandro Rodríguez-Fernández
Appl. Sci. 2024, 14(9), 3812; https://doi.org/10.3390/app14093812 (registering DOI) - 29 Apr 2024
Abstract
Background: Classification of athletes in cerebral palsy (CP) football is a key action that aims to promote the participation of all players by minimizing the impact of their physical disabilities on the outcome of the competition by establishing sports classes. As such, a
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Background: Classification of athletes in cerebral palsy (CP) football is a key action that aims to promote the participation of all players by minimizing the impact of their physical disabilities on the outcome of the competition by establishing sports classes. As such, a new research line has been included in the classification process at an international level; that is, the analysis of locomotor demands during competition helps classifiers to understand the para-footballers’ profile. Therefore, the main aim of this systematic review was to summarize the physical and physiological responses of players with CP in different sport classes during competition. Methods: A bibliographic search was conducted using PubMed, SCOPUS, and Web Of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines using the PICOS strategy. Results: Six studies meeting inclusion criteria analyzing physical (i.e., total distances, distances at different speeds, high-intensity and short-term actions, change of directions, etc.) and physiological (heart rate (HR), time spent at different zones of maximum HR, etc.) responses. Findings revealed that para-footballers with CP and minimal impairment impact covered greater total and distance above 23.04 km·h−1 and achieved higher maximum speeds during match-play. Notably, no significant differences in physiological responses were observed based on classification. Conclusions: The research suggests that para-footballers with CP and lower physical impairment may exhibit enhanced performance in terms of distance covered and speed during gameplay, highlighting their potential competence in the sport. In addition, the limited number of studies examining the physiological response of para-footballers prevents conclusive results and differentiating between classification groups.
Full article
(This article belongs to the Special Issue Human Performance and Health in Sport and Exercise)
Open AccessArticle
Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
by
Shen-Yung Lin and Chia-Jen Hsieh
Appl. Sci. 2024, 14(9), 3811; https://doi.org/10.3390/app14093811 (registering DOI) - 29 Apr 2024
Abstract
This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis,
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This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.
Full article
(This article belongs to the Special Issue Machine Tools, Advanced Manufacturing and Precision Manufacturing)
Open AccessArticle
A Building Heat Load Prediction Method Driven by a Multi-Component Fusion LSTM Ridge Regression Ensemble Model
by
Yu Zhang and Guangshu Chen
Appl. Sci. 2024, 14(9), 3810; https://doi.org/10.3390/app14093810 (registering DOI) - 29 Apr 2024
Abstract
Under the background of “double carbon”, building carbon emission reduction is urgent, and improving energy efficiency through short-term building heat load forecasting is an efficient means of building carbon emission reduction. Aiming at the characteristics of the decomposed short-term building heat load data,
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Under the background of “double carbon”, building carbon emission reduction is urgent, and improving energy efficiency through short-term building heat load forecasting is an efficient means of building carbon emission reduction. Aiming at the characteristics of the decomposed short-term building heat load data, such as complex trend changes, significant seasonal changes, and randomness, a single-step short-term building heat load prediction method driven by the multi-component fusion LSTM Ridge Regression Ensemble Model (ST-LSTM-RR) is designed and implemented. First, the trend and seasonal components of the heat load are decomposed by the STL seasonal decomposition algorithm, which are fused into the original data to construct three diversified datasets; second, three basic models, namely, the trend LSTM, the seasonal LSTM, and the original LSTM, are trained; and then, the ridge regression model is trained to fuse the predicted values of the three basic models to obtain the final predicted values. Finally, the method of this paper is applied to the heat load prediction of eight groups in a large mountain hotel park, and the root mean square error (RMSE) and mean absolute error (MAE) are used as the evaluation indexes. The experimental results show that the average RMSE and average MAE of the prediction results of the proposed method in this paper are minimized on the eight groups.
Full article
(This article belongs to the Section Computing and Artificial Intelligence)
Open AccessArticle
Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
by
Wenxuan Jin, Mingjun Zheng and Pingzeng Liu
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
Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
by
Shuai Chen, Yinwei Ma, Zhongshu Wang, Minjing Liu and Zhanjun Wu
Appl. Sci. 2024, 14(9), 3808; https://doi.org/10.3390/app14093808 (registering DOI) - 29 Apr 2024
Abstract
Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an
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Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.
Full article
(This article belongs to the Special Issue The Application of Machine Learning in Structural Health Monitoring)
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
by
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
Abstract
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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
Abstract
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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
by
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.
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(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
by
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.
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(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
by
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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