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
Residual Dense Swin Transformer for Continuous-Scale Super-Resolution Algorithm
Appl. Sci. 2024, 14(9), 3678; https://doi.org/10.3390/app14093678 (registering DOI) - 25 Apr 2024
Abstract
The single-image super-resolution task benefits has a wide range of application scenarios, so has long been a hotspot in the field of computer vision. However, designing a continuous-scale super-resolution algorithm with excellent performance is still a difficult problem to solve. In order to
[...] Read more.
The single-image super-resolution task benefits has a wide range of application scenarios, so has long been a hotspot in the field of computer vision. However, designing a continuous-scale super-resolution algorithm with excellent performance is still a difficult problem to solve. In order to solve this problem, we propose a continuous-scale SR algorithm based on a Transformer, which is called residual dense Swin Transformer (RDST). Firstly, we design a residual dense Transformer block (RDTB) to enhance the information flow before and after the network and extract local fusion features. Then, we use multilevel feature fusion to obtain richer feature information. Finally, we use the upsampling module based on the local implicit image function (LIIF) to obtain continuous-scale super-resolution results. We test RDST on multiple benchmarks. The experimental results show that RDST achieves SOTA performance in the fixed scale of super-resolution tasks in the distribution, and significantly improves (0.1∼0.6 dB) the arbitrary scale of super-resolution tasks out of distribution. Sufficient experiments show that our RDST can use fewer parameters,and its performance is better than the SOTA SR method.
Full article
(This article belongs to the Section Computing and Artificial Intelligence)
Open AccessArticle
Repetition of the Exhaustive Wrestling-Specific Test Leads to More Effective Differentiation between Quality Categories of Youth Wrestlers
by
Kreso Skugor, Hrvoje Karnincic, Nenad Zugaj, Valdemar Stajer and Barbara Gilic
Appl. Sci. 2024, 14(9), 3677; https://doi.org/10.3390/app14093677 (registering DOI) - 25 Apr 2024
Abstract
This study aimed to investigate whether wrestlers of different competitive qualities (i.e., medalists vs non-medallists) would differ in terms of specific test performance and cardiac and metabolic responses after a demanding testing protocol. The research included 29 wrestlers aged 17.62 ± 1.86 years
[...] Read more.
This study aimed to investigate whether wrestlers of different competitive qualities (i.e., medalists vs non-medallists) would differ in terms of specific test performance and cardiac and metabolic responses after a demanding testing protocol. The research included 29 wrestlers aged 17.62 ± 1.86 years divided into two performance categories: successful (medallists at the National Championships; n = 13) and less successful (non-medallists; n = 16). The variables included anthropometric indices and specific wrestling fitness test (SWFT) parameters, including the number of throws, heart rate, lactate concentration and calculated cardiac and metabolic indexes. To show differences between quality categories, Student's t-test and receiver operating characteristic curves (ROC) were calculated. Two-way ANOVA for repeated measurements was used to evaluate the differences in performance, cardiac, and metabolic characteristics between the test trials and quality categories. Wrestlers differed in the total number of throws (p < 0.01, AUC = 0.82), cardiac indices (p < 0.03, AUC = 0.73), and metabolic indices (p < 0.04, AUC = 0.75) after the second SWFT trial, with successful wrestlers reaching better results. There were no differences in the first testing trial. The findings of this study indicate that wrestlers exhibit differences in specific performance variables after undergoing an exhaustive testing protocol. Therefore, this study suggests that future research on sport-specific performance in wrestlers should include exhaustive exercise or testing protocols.
Full article
(This article belongs to the Special Issue Performance Aspects, Biomechanics, and Technology in Sports and Exercise)
Open AccessArticle
Research on the Effect of Structural Parameters on Cavitation Performance of Shear Hydrodynamic Cavitation Generator
by
Fengxia Lyu, Ming Tang, Faqi Zhou, Xintong Zhang, Saiyue Han and Sheng Zhang
Appl. Sci. 2024, 14(9), 3676; https://doi.org/10.3390/app14093676 (registering DOI) - 25 Apr 2024
Abstract
The method of cavitation is increasingly applied in water environmental protection. Based on the numerical simulation method, a study on the structural parameters of the shear-type hydrodynamic cavitation generators for wastewater treatment is proceeded. The internal flow field is described by employing the
[...] Read more.
The method of cavitation is increasingly applied in water environmental protection. Based on the numerical simulation method, a study on the structural parameters of the shear-type hydrodynamic cavitation generators for wastewater treatment is proceeded. The internal flow field is described by employing the mixed multiphase flow model and the Zwart cavitation model. Experiments were conducted by applying the wastewater from a dyeing factory as the medium. The degradation rate of COD in water characterizes the cavitation performance of the hydrodynamic cavitation generator, and the rationality of the numerical simulation approach is validated. The findings indicate that different structural parameters have a great influence on the cavitation performance. The appropriate number of tooth rows creates a flow field with a greater vapor and velocity than the other parameters. The number of teeth in a single row, the outer diameter of the hydrodynamic cavitation generator and the tooth bevel angle also affect the cavitation performance to some extent, and there is an optimal value. The study provides a reference for the applicability of a numerical simulation of the flow field inside the hydrodynamic cavitation generator and the structural optimization of the rotary hydrodynamic cavitation generator.
Full article
(This article belongs to the Special Issue Flow Analysis and Structural Control of Fluid Machinery)
Open AccessReview
A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
by
Katarzyna Samborska-Goik and Marta Pogrzeba
Appl. Sci. 2024, 14(9), 3675; https://doi.org/10.3390/app14093675 (registering DOI) - 25 Apr 2024
Abstract
The pollution of groundwater and soil by hydrocarbons is a significant and growing global problem. Efforts to mitigate and minimise pollution risks are often based on modelling. Modelling-based solutions for prediction and control play a critical role in preserving dwindling water resources and
[...] Read more.
The pollution of groundwater and soil by hydrocarbons is a significant and growing global problem. Efforts to mitigate and minimise pollution risks are often based on modelling. Modelling-based solutions for prediction and control play a critical role in preserving dwindling water resources and facilitating remediation. The objectives of this article are to: (i) to provide a concise overview of the mechanisms that influence the migration of hydrocarbons in groundwater and to improve the understanding of the processes that affect contamination levels, (ii) to compile the most commonly used models to simulate the migration and fate of hydrocarbons in the subsurface; and (iii) to evaluate these solutions in terms of their functionality, limitations, and requirements. The aim of this article is to enable potential users to make an informed decision regarding the modelling approaches (deterministic, stochastic, and hybrid) and to match their expectations with the characteristics of the models. The review of 11 1D screening models, 18 deterministic models, 7 stochastic tools, and machine learning experiments aimed at modelling hydrocarbon migration in the subsurface should provide a solid basis for understanding the capabilities of each method and their potential applications.
Full article
(This article belongs to the Special Issue Environmental Bioaccumulation and Assessment of Toxic Elements)
Open AccessArticle
Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art
by
Wentong Wan and Runcai Huang
Appl. Sci. 2024, 14(9), 3674; https://doi.org/10.3390/app14093674 (registering DOI) - 25 Apr 2024
Abstract
The emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data.
[...] Read more.
The emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data. Analyzing these data can provide useful insights into the public’s opinions on AI Art, enable the investigation of the origins of conflicts in online debates, and contribute to the sustainable development of AI Art. This paper presents a deep learning-driven framework for analyzing the characteristics of public opinion on the Weibo topic of AI Art. To classify the sentiments users expressed in Weibo posts, the linguistic feature-enhanced pre-training model (LERT) was employed to improve text representation via the fusion of syntactic features, followed by a bidirectional Simple Recurrent Unit (SRU) embedded with a soft attention module (BiSRU++) for capturing the long-range dependencies in text features, thus improving the sentiment classification performance. Furthermore, a text clustering analysis was performed across sentiments to capture the nuanced opinions expressed by Weibo users, hence providing useful insights about different online communities. The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art.
Full article
(This article belongs to the Section Computing and Artificial Intelligence)
Open AccessArticle
Estimating the Thermal Conductivity of Unsaturated Sand
by
Xuejun Liu, Yucong Gao and Yanjun Li
Appl. Sci. 2024, 14(9), 3673; https://doi.org/10.3390/app14093673 (registering DOI) - 25 Apr 2024
Abstract
A modified parallel model for estimating the thermal conductivity of unsaturated sand was proposed in this study. The heat conduction in the solid phase of sand depends mainly on the form of contacts between solid particles, while water bridges at the particle contacts
[...] Read more.
A modified parallel model for estimating the thermal conductivity of unsaturated sand was proposed in this study. The heat conduction in the solid phase of sand depends mainly on the form of contacts between solid particles, while water bridges at the particle contacts increase the contact areas and remarkably enlarge the transfer paths of heat conduction in sandy soils. However, the thermal conductivity of the solid particle itself (λs) cannot describe the influence of the form of contacts and water bridges on heat conduction through the solid phase. In this study, the equivalent thermal conductivity of the solid particle (λes) was presented which reflected the influence of the form of contacts and water bridges between particles under dry conditions or a low degree of saturation, respectively. The relationship between λes and degree of saturation was described by hyperbolic expression. The modified model was calibrated using measured values of the thermal conductivity from published datasets, including those for 41 types of sand from 15 studies. Numerical analyses of the temperature field of the energy pile were performed and validated against laboratory measurements. The results illustrated that the modified model was more applicable than the original model for predictions of sand thermal conductivity.
Full article
(This article belongs to the Special Issue Emerging Technologies and Advances in Soil Mechanics and Geotechnical Engineering)
►▼
Show Figures
Figure 1
Open AccessArticle
The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process
by
Seungeon Cha, Martin Loeser and Kyoungwon Seo
Appl. Sci. 2024, 14(9), 3672; https://doi.org/10.3390/app14093672 (registering DOI) - 25 Apr 2024
Abstract
The course-recommender system (CRS), designed to aid students’ course-selection decision-making process by suggesting courses aligned with their interests and grades, plays a crucial role in fulfilling curricular requirements, enhancing career opportunities, and fostering intellectual growth. Recent advancements in artificial intelligence (AI) have empowered
[...] Read more.
The course-recommender system (CRS), designed to aid students’ course-selection decision-making process by suggesting courses aligned with their interests and grades, plays a crucial role in fulfilling curricular requirements, enhancing career opportunities, and fostering intellectual growth. Recent advancements in artificial intelligence (AI) have empowered CRSs to deliver personalized recommendations by considering individual contexts. However, the impact of AI-based CRS on students’ course-selection decision-making process (inter alia, search and evaluation phases) is an open question. Understanding student perceptions and expectations of AI-based CRSs is key to optimizing their decision-making process in course selection. For this purpose, we employed speed dating with storyboards to gather insights from 24 students on five different types of AI-based CRS. The results revealed that students expected AI-based CRSs to play an assistive role in the search phase, helping them efficiently complete time-consuming search tasks in less time. Conversely, during the evaluation phase, students expected AI-based CRSs to play a leading role as a benchmark to address their uncertainty about course suitability, learning value, and serendipity. These findings underscore the adaptive nature of AI-based CRSs, which adjust according to the intricacies of students’ course-selection decision-making process, fostering fruitful collaboration between students and AI.
Full article
Open AccessArticle
Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
by
Özge Baltacı, Zeki Kıral, Konuralp Dalkılınç and Oğulcan Karaman
Appl. Sci. 2024, 14(9), 3671; https://doi.org/10.3390/app14093671 (registering DOI) - 25 Apr 2024
Abstract
The world’s energy demand is on the rise, leading to an increased focus on renewable energy options due to global warming and rising emissions from fossil fuels. To effectively monitor and maintain these renewable energy systems connected to electrical grids, efficient methods are
[...] Read more.
The world’s energy demand is on the rise, leading to an increased focus on renewable energy options due to global warming and rising emissions from fossil fuels. To effectively monitor and maintain these renewable energy systems connected to electrical grids, efficient methods are needed. Early detection of PV faults is vital for enhancing the efficiency, reliability, and safety of PV systems. Thermal imaging emerges as an efficient and effective technique for inspection. On the other hand, evidence indicates that monitoring inverters within a solar energy farm reduces maintenance expenses and boosts production. Optimizing the efficiency of solar energy farms necessitates comprehensive analytics and data on every inverter, encompassing voltage, current, temperature, and power. In this study, our objective was to perform two distinct fault analyses utilizing image processing techniques with thermal images and machine learning techniques using inverter and other physical data. The results show that hotspot and bypass failures on the panels can be detected successfully using these methods.
Full article
(This article belongs to the Topic Advances in Renewable Energy Technologies and Systems Solutions)
Open AccessArticle
The Investigation of Various Flange Gaps on Wind Turbine Tower Bolt Fatigue Using Finite-Element Method
by
Mingxing Liu, Rongrong Geng, Jiaqing Wang, Yong Li, Kai Long, Wenjie Ding and Yiming Zhou
Appl. Sci. 2024, 14(9), 3670; https://doi.org/10.3390/app14093670 (registering DOI) - 25 Apr 2024
Abstract
Upon careful examination, numerous wind turbine collapses can be attributed to the failure of the tower bolts. Nowadays, the Schmidt–Neuper algorithm is extensively accepted in wind turbine tower bolt design. It is not advisable to utilize the finite-element method, notwithstanding the effect of
[...] Read more.
Upon careful examination, numerous wind turbine collapses can be attributed to the failure of the tower bolts. Nowadays, the Schmidt–Neuper algorithm is extensively accepted in wind turbine tower bolt design. It is not advisable to utilize the finite-element method, notwithstanding the effect of the flange gap. To quantitatively investigate the influence of flange gaps on bolt fatigue, a nonlinear finite-element model of a flange segment incorporating bolt pretension and contact elements is herein proposed. Three distinct types of flange gaps are defined intentionally. It is possible to determine the nonlinear relationship between the wall load and bolt internal force. The fatigue damage of bolts was thus computed using the obtained nonlinear curve. Comparing with the results with those of Schmidt–Neuper method revealed the bolt fatigue damage is susceptible to a specified flange gap.
Full article
(This article belongs to the Section Mechanical Engineering)
►▼
Show Figures
Figure 1
Open AccessArticle
Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
by
Xinggu Liu, Lin Zhou and Youxi Luo
Appl. Sci. 2024, 14(9), 3669; https://doi.org/10.3390/app14093669 (registering DOI) - 25 Apr 2024
Abstract
►▼
Show Figures
In this study, we propose a filter pruning method based on MCP (Minimax Concave Penalty) regression. The convolutional process is conceptualized as a linear regression procedure, and the regression coefficients serve as indicators to assess the redundancy of channels. In the realm of
[...] Read more.
In this study, we propose a filter pruning method based on MCP (Minimax Concave Penalty) regression. The convolutional process is conceptualized as a linear regression procedure, and the regression coefficients serve as indicators to assess the redundancy of channels. In the realm of feature selection, the efficacy of sparse penalized regression gradually outperforms that of Lasso regression. Building upon this insight, MCP regression is introduced to screen convolutional channels, coupled with the coordinate descent method, to effectuate model compression. In single-layer pruning and global pruning analyses, the Top1 loss value associated with the MCP regression compression method is consistently smaller than that of the Lasso regression compression method across diverse models. Specifically, when the global pruning ratio is set to 0.3, the Top1 accuracy of the MCP regression compression method, in comparison with that of the Lasso regression compression method, exhibits improvements of 0.21% and 1.67% under the VGG19_Simple and VGG19 models, respectively. Similarly, for ResNet34, at two distinct pruning ratios, the Top1 accuracy demonstrates enhancements of 0.33% and 0.26%. Lastly, we compare and discuss the novel methods introduced in this study, considering both time and space resource consumption.
Full article
Figure 1
Open AccessArticle
Effect Mechanism of Material Ratio on Ultrasonic P-wave Velocity in Coal Based Paste Fill Materials
by
Baifu An, Jie Song, Jinfang Ren, Junmeng Li, Chenghao Cui, Jiale Wang and Wenting Bai
Appl. Sci. 2024, 14(9), 3668; https://doi.org/10.3390/app14093668 (registering DOI) - 25 Apr 2024
Abstract
This research is designed to investigate the variations in ultrasonic p-wave velocity in various coal based paste fill materials used for recovering standing pillars in closed/closing coal mines, with consideration given to the effects of numerous material-related factors. For this purpose, orthogonal tests
[...] Read more.
This research is designed to investigate the variations in ultrasonic p-wave velocity in various coal based paste fill materials used for recovering standing pillars in closed/closing coal mines, with consideration given to the effects of numerous material-related factors. For this purpose, orthogonal tests were designed. The evaluation was performed on the effects of four variables on the ultrasonic p-wave velocities in samples, using coal grains as the primary material. These variables consisted of the coal grains’ particle size (PA), high-water material content (PB), cement content (PC), and water content (PD). The experimental results show the following: (1) Ultrasonic p-wave velocity of coal based paste fill materials are measured within the range of 1.596 to 2.357 km/s, and these are classified (in descending order) as PD, PB, PC, and then PA, based on their effects on ultrasonic p-wave velocity. (2) Ultrasonic p-wave velocity is positively correlated with compressive strength and shear strength; the correlation coefficients are 0.82 and 0.69, respectively. (3) Changes in the ultrasonic p-wave velocity of coal based paste fill materials, when exposed to various factors, have been characterized by fitted formulae. It was observed that the velocity maintained a quadratic polynomial correlation with factor PB and exponential correlations with factors PA, PC, and PD. The comprehensive predictive model, reflecting the characteristics of the ultrasonic p-wave velocity in response to the combined influence of these four factors, was developed through the utilization of fitted equations pertaining to individual factor variations. Subsequently, this model underwent verification.
Full article
(This article belongs to the Special Issue Advances and Challenges in Rock Mechanics and Rock Engineering)
►▼
Show Figures
Figure 1
Open AccessArticle
Optimizing Cattle Behavior Analysis in Precision Livestock Farming: Integrating YOLOv7-E6E with AutoAugment and GridMask to Enhance Detection Accuracy
by
Hyeon-seok Sim, Tae-kyeong Kim, Chang-woo Lee, Chang-sik Choi, Jin Soo Kim and Hyun-chong Cho
Appl. Sci. 2024, 14(9), 3667; https://doi.org/10.3390/app14093667 (registering DOI) - 25 Apr 2024
Abstract
Recently, the growing demand for meat has increased interest in precision livestock farming (PLF), wherein monitoring livestock behavior is crucial for assessing animal health. We introduce a novel cattle behavior detection model that leverages data from 2D RGB cameras. It primarily employs you
[...] Read more.
Recently, the growing demand for meat has increased interest in precision livestock farming (PLF), wherein monitoring livestock behavior is crucial for assessing animal health. We introduce a novel cattle behavior detection model that leverages data from 2D RGB cameras. It primarily employs you only look once (YOLO)v7-E6E, which is a real-time object detection framework renowned for its efficiency across various applications. Notably, the proposed model enhances network performance without incurring additional inference costs. We primarily focused on performance enhancement and evaluation of the model by integrating AutoAugment and GridMask to augment the original dataset. AutoAugment, a reinforcement learning algorithm, was employed to determine the most effective data augmentation policy. Concurrently, we applied GridMask, a novel data augmentation technique that systematically eliminates square regions in a grid pattern to improve model robustness. Our results revealed that when trained on the original dataset, the model achieved a mean average precision (mAP) of 88.2%, which increased by 2.9% after applying AutoAugment. The performance was further improved by combining AutoAugment and GridMask, resulting in a notable 4.8% increase in the mAP, thereby achieving a final mAP of 93.0%. This demonstrates the efficacy of these augmentation strategies in improving cattle behavior detection for PLF.
Full article
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
Open AccessArticle
Experimental Investigation of the Influence of Longitudinal Tilt Angles on the Thermal Performance of a Small-Scale Linear Fresnel Reflector
by
Carmen López-Smeetz, Arsenio Barbón, Luis Bayón and Covadonga Bayón-Cueli
Appl. Sci. 2024, 14(9), 3666; https://doi.org/10.3390/app14093666 (registering DOI) - 25 Apr 2024
Abstract
This paper analyses the influence of the longitudinal tilt angle of the secondary system of a low-concentration photovoltaic system based on a small-scale linear Fresnel reflector. Several evaluation indicators, such as useful heat gain, thermal efficiency, incident solar irradiance gain on the photovoltaic
[...] Read more.
This paper analyses the influence of the longitudinal tilt angle of the secondary system of a low-concentration photovoltaic system based on a small-scale linear Fresnel reflector. Several evaluation indicators, such as useful heat gain, thermal efficiency, incident solar irradiance gain on the photovoltaic cells, and total useful energy gain, were evaluated for five wind speed conditions and six locations in the Northern Hemisphere. The tests were performed with two small-scale linear Fresnel reflector configurations: the classical large-scale linear Fresnel reflector configuration (base configuration) and the optimal longitudinal tilt angle configuration (longitudinal tilt configuration). An experimental platform based on an open-loop wind tunnel was designed and built for this purpose. As far as useful heat production, the longitudinal tilt configuration performs worse as the longitudinal tilt angle and wind speed increase. A useful heat gain lower than the base configuration is obtained with a wind speed of (m/s) at the (°) latitude location. Thermal efficiency decreases with increasing wind speed and longitudinal tilt angle. The thermal efficiency is between and with wind speeds of (m/s) and (m/s). The longitudinal tilt configuration shows the best increase in total useful energy gain in the absence of wind (up to at a latitude of (°)). This increase is at this same location with a wind speed of (m/s). It can be concluded that the effect of the longitudinal tilt of the secondary system has a positive effect. To highlight the importance of this work, the results obtained in the configuration comparison were used to compare a nonconcentrating photovoltaic system and a low-concentration photovoltaic system. The incident solar irradiance on the photovoltaic cells is much higher with nonconcentrating photovoltaic technology. This solar irradiance gain is over for the base configuration and for the longitudinal tilt configuration. The total useful energy gain is in the absence of wind and at the (°) latitude location in favour of the low-concentration photovoltaic system. The nonconcentrating photovoltaic system performs better with a wind speed of (m/s).
Full article
(This article belongs to the Section Energy Science and Technology)
Open AccessArticle
Numerical Investigation of Stratified Slope Failure Containing Rough Non-Persistent Joints Based on Distinct Element Method
by
Yishan Zhang, Yilin Fu, Ran Qin and Peitao Wang
Appl. Sci. 2024, 14(9), 3665; https://doi.org/10.3390/app14093665 (registering DOI) - 25 Apr 2024
Abstract
To address the critical issue of slope stability in jointed rock masses with complex and rough structural planes, a rough joint network model using the Fourier transform was proposed and applied to the Shilu open pit mine. The on-site structural plane survey results
[...] Read more.
To address the critical issue of slope stability in jointed rock masses with complex and rough structural planes, a rough joint network model using the Fourier transform was proposed and applied to the Shilu open pit mine. The on-site structural plane survey results were combined with MATLAB and PFC2D to establish numerical models for slope stability analysis considering both linear-jointed and rough-jointed rock slopes. By comparing the slip body and fracture distribution, it was found that the rough-jointed slope was stabler than the linear-jointed slope. In addition, the fracture patterns and slip displacement were significantly influenced by the inclination and undulation of the bedding planes. Slip failure patterns occurred when the angle of inclination was set at 60°. The joints played a crucial role in inducing the shear strength of slope rock masses, and the slide area was mainly observed in the shallow slope surface for inclination angles of 0° and 45°, and in the middle deep area for 60° and 90°. These results demonstrated the importance of considering rough non-persistent fractures when developing a new numerical model for slope failure modes.
Full article
(This article belongs to the Special Issue Slope Stability and Earth Retaining Structures)
Open AccessArticle
Trajectory Tracking Control Design for 4WS Vehicle Based on Particle Swarm Optimization and Phase Plane Analysis
by
Yang Sun, Haonan Ning, Haiyang Wang, Chao Wang and Jiushuai Zheng
Appl. Sci. 2024, 14(9), 3664; https://doi.org/10.3390/app14093664 (registering DOI) - 25 Apr 2024
Abstract
With the rapid development of today’s society, the traffic environment has become more and more complex. As an essential part of intelligent vehicles, trajectory tracking has attracted significant attention for its stability and safety. It is prone to poor accuracy and instability in
[...] Read more.
With the rapid development of today’s society, the traffic environment has become more and more complex. As an essential part of intelligent vehicles, trajectory tracking has attracted significant attention for its stability and safety. It is prone to poor accuracy and instability in extreme working conditions like high speed. In this paper, a trajectory tracking control strategy to ensure lateral stability at a high speed and low attachment limit conditions is proposed for distributed drive vehicles. The model predictive controller (MPC) was used to control the front wheel angle, and the particle swarm optimization (PSO) algorithm was designed to optimize the MPC control parameters adaptively. The sliding mode controller controls the rear wheel angle, and the vehicle instability degree is judged by analyzing the β − phase plane. The controllers of different instability degrees are designed herein. Finally, a torque divider is designed to consider the actuation anti-slip. The designed controller is verified by Carsim and MATLAB-Simulink co-simulation. The results show that the trajectory tracking controller designed in this paper effectively improves the tracking accuracy under the premise of ensuring stability.
Full article
(This article belongs to the Topic Vehicle Dynamics and Control)
Open AccessArticle
Comprehensive Stable Control Strategy for a Typical Underactuated Manipulator Considering Several Uncertainties
by
Zixin Huang, Wei Wang, Ba Zeng, Chengsong Yu and Yaosheng Zhou
Appl. Sci. 2024, 14(9), 3663; https://doi.org/10.3390/app14093663 (registering DOI) - 25 Apr 2024
Abstract
This article proposes a comprehensive stable control strategy for the planar multi-link underactuated manipulator (PMLUM), considering several uncertainties. According to the nilpotent approximation property, the control procedure is split into two stages. In the first stage of control, we postulate the idea of
[...] Read more.
This article proposes a comprehensive stable control strategy for the planar multi-link underactuated manipulator (PMLUM), considering several uncertainties. According to the nilpotent approximation property, the control procedure is split into two stages. In the first stage of control, we postulate the idea of model degradation, reducing the PMLUM to a planar virtual Pendubot (PVP). This occurs by controlling the active link (AL) to a specific desired position and the passive link (PL) moves along with it. When the AL moves to the desired position, the second phase of control is entered. Meanwhile, all ALs are regarded as a whole, so the PMLUM can be regarded as a mechanical arm with 2-DOF. In the second stage of control, due to the nilpotent approximation feature of the PVP, the PVP is guided to the desired angle using the iterative steering technique. Simulation experiments are carried out on active–active–passive (AAP) and active–active–active–passive (AAAP) systems under major uncertainties, which contain initial velocity and torque disturbances. The final results validate the effectiveness of the method proposed.
Full article
(This article belongs to the Section Robotics and Automation)
Open AccessReview
A Comprehensive Review and Tutorial on Confounding Adjustment Methods for Estimating Treatment Effects Using Observational Data
by
Amy X. Shi, Paul N. Zivich and Haitao Chu
Appl. Sci. 2024, 14(9), 3662; https://doi.org/10.3390/app14093662 (registering DOI) - 25 Apr 2024
Abstract
►▼
Show Figures
Controlling for confounding bias is crucial in causal inference. Causal inference using data from observational studies (e.g., electronic health records) or imperfectly randomized trials (e.g., imperfect randomization or compliance) requires accounting for confounding variables. Many different methods are currently employed to mitigate bias
[...] Read more.
Controlling for confounding bias is crucial in causal inference. Causal inference using data from observational studies (e.g., electronic health records) or imperfectly randomized trials (e.g., imperfect randomization or compliance) requires accounting for confounding variables. Many different methods are currently employed to mitigate bias due to confounding. This paper provides a comprehensive review and tutorial of common estimands and confounding adjustment approaches, including outcome regression, g-computation, propensity score, and doubly robust methods. We discuss bias and precision, advantages and disadvantages, and software implementation for each method. Moreover, approaches are illustrated empirically with a reproducible case study. We conclude that different scientific questions are better addressed by certain estimands. No estimand is uniformly more appropriate. Upon selecting an estimand, decisions on which estimator can be driven by performance and available background knowledge.
Full article
Figure 1
Open AccessFeature PaperArticle
Measurement of the Impact Loads to Reduce Injuries in Acrobatic Gymnasts: Designing a Dedicated Platform
by
Maria F. Paulino, Beatriz B. Gomes, Amílcar L. Ramalho and Ana M. Amaro
Appl. Sci. 2024, 14(9), 3661; https://doi.org/10.3390/app14093661 (registering DOI) - 25 Apr 2024
Abstract
Background: The main objective of this study was the development of a specific load platform that would meet the needs of gymnasts and acrobatic coaches. This new platform has larger dimensions and is an identical structure to the plywood floor surface normally used;
[...] Read more.
Background: The main objective of this study was the development of a specific load platform that would meet the needs of gymnasts and acrobatic coaches. This new platform has larger dimensions and is an identical structure to the plywood floor surface normally used; it was designed to make competitions with gymnasts safer and more like a real training situation. During a landing, there is high body stiffness, especially in the knees and ankles, which can cause injuries due to the number of repetitions performed in this gymnastics specialty. Methods: A group of 10 volunteers, with a mean age of 14.7 ± 2.4 years, performed at least 10 valid vertical jumps on each platform. Results: Despite being a preliminary study, this specific platform was shown to be more suitable for gymnastic use, compared to the industrial one, which represents a significant advantage for the modality. In fact, this platform is similar to the surface used for training and competition, allowing athletes to perform the jump in a similar way, and for the results to be replicable during the practice of the sport. The standard deviation values were lower, which shows that the new platform was more suitable for acrobatic gymnastics. Conclusions: As the maximum vertical load induced during landing after a jump has a significant effect on the likelihood of gymnasts suffering injuries, the development of a new load platform specifically for acrobatic gymnastics is clearly an improvement in this discipline. Knowledge of the load transmitted to the body can help coaches and athletes in defining training, and avoiding the possible occurrence of injuries. Therefore, it is necessary to use a platform that can accurately evaluate the load transmitted to the acrobatic gymnasts during real training and competition conditions, which is achieved with this new platform.
Full article
(This article belongs to the Special Issue Advances in Sports Training and Biomechanics)
Open AccessEditorial
Closing Editorial for Computer Vision and Pattern Recognition Based on Deep Learning
by
Hui Yuan
Appl. Sci. 2024, 14(9), 3660; https://doi.org/10.3390/app14093660 (registering DOI) - 25 Apr 2024
Abstract
Deep learning has demonstrated unparalleled performance in various industries [...]
Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition Based on Deep Learning)
Open AccessArticle
Construction of a Dimensional Damage Model of Reinforced Concrete Columns under Explosion Loading
by
Jia Wang, Jianping Yin, Xudong Li, Jianya Yi, Zhijun Wang and Xifeng Li
Appl. Sci. 2024, 14(9), 3659; https://doi.org/10.3390/app14093659 (registering DOI) - 25 Apr 2024
Abstract
When studying the damage law of reinforced concrete building structures under explosive loading, the direct experimental cost is too high and numerical simulations take a long time. Based on the theoretical analysis, the dimensional analysis model of reinforced concrete members under explosive loading
[...] Read more.
When studying the damage law of reinforced concrete building structures under explosive loading, the direct experimental cost is too high and numerical simulations take a long time. Based on the theoretical analysis, the dimensional analysis model of reinforced concrete members under explosive loading can be used to study the damage law of reinforced concrete members under explosive loading. It provides guidance, reduces the number of tests, improves the efficiency of the test, and has certain research significance. In this paper, a typical reinforced concrete column is taken as the main research object. Based on the dimensional analysis method, the relationship between the damage to the reinforced concrete column and the explosion equivalent and explosion distance under explosion loading is studied. The finite element simulation software LS-DYNA 18.2 is used to determine the function relationship between the disturbance in the column and the proportional explosion distance. The results show that when the proportional explosion distance Z is greater than 0.0693 m/kg1/3, the center disturbance of the blasting surface of the reinforced concrete column has a linear relationship with the reciprocal of the proportional explosion distance. The linear relationship can be used to predict the column’s center disturbance under partial explosion conditions, which provides guidance for studying the damage criterion of reinforced concrete under explosion loading.
Full article
Journal Menu
► ▼ Journal Menu-
- Applied Sciences Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Applied Sciences, Energies, Materials, Nanoenergy Advances, Nanomaterials
Applications of Nanomaterials in Energy Systems, 2nd Volume
Topic Editors: Eleftheria C. Pyrgioti, Ioannis F. Gonos, Diaa-Eldin A. MansourDeadline: 30 April 2024
Topic in
Materials, Nanomaterials, Photonics, Polymers, Applied Sciences, Sensors
Optical and Optoelectronic Properties of Materials and Their Applications
Topic Editors: Zhiping Luo, Gibin George, Navadeep ShrivastavaDeadline: 20 May 2024
Topic in
Applied Sciences, Energies, Minerals, Mining, Sustainability
Mining Innovation
Topic Editors: Krzysztof Skrzypkowski, René Gómez, Fhatuwani Sengani, Derek B. Apel, Faham Tahmasebinia, Jianhang ChenDeadline: 1 June 2024
Topic in
Applied Sciences, Electricity, Electronics, Energies, Sensors
Power System Protection
Topic Editors: Seyed Morteza Alizadeh, Akhtar KalamDeadline: 20 June 2024
Conferences
Special Issues
Special Issue in
Applied Sciences
Advances in Sustainable Materials for Engineering
Guest Editors: Richard Critchley, Rachael HazaelDeadline: 27 April 2024
Special Issue in
Applied Sciences
Hearing Loss: From Pathophysiology to Therapies and Habilitation
Guest Editors: Ronen Perez, Liat Kishon-RabinDeadline: 30 April 2024
Special Issue in
Applied Sciences
Oral and Systemic Implications of Periodontal Disease – an Integrated Approach
Guest Editor: Petra SurlinDeadline: 25 May 2024
Special Issue in
Applied Sciences
Functional Fermented Food Products II
Guest Editor: Pawel GlibowskiDeadline: 30 May 2024
Topical Collections
Topical Collection in
Applied Sciences
Structural Dynamics and Aeroelasticity
Collection Editors: Sergio Ricci, Paolo Mantegazza, Alessandro De Gaspari, Jonathan E. Cooper, Afzal Suleman, Hector Climent
Topical Collection in
Applied Sciences
Distributed Energy Systems
Collection Editor: Rodolfo Dufo-López
Topical Collection in
Applied Sciences
Intelligent Transportation Systems II: Beyond Intelligent Vehicles
Collection Editors: Javier Alonso Ruiz, Jeroen Ploeg, Angel Llamazares, Carlota Salinas, Rubén Izquierdo, Noelia Hernández Parra
Topical Collection in
Applied Sciences
Optical Design and Engineering
Collection Editors: Zhi-Ting Ye, Pin Han, Chun Hung Lai, Yi Chin Fang