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
Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model
Appl. Sci. 2024, 14(9), 3742; https://doi.org/10.3390/app14093742 (registering DOI) - 27 Apr 2024
Abstract
The delayed surface subsidence caused by coal seam mining is a problem that cannot be ignored, while accurate prediction of the surface subsidence provides a guarantee of the safety and stability of the relevant areas. However, the traditional Knothe model has limitations in
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The delayed surface subsidence caused by coal seam mining is a problem that cannot be ignored, while accurate prediction of the surface subsidence provides a guarantee of the safety and stability of the relevant areas. However, the traditional Knothe model has limitations in considering delayed surface subsidence. Because of this, the Knothe-n time function model is segmented and improved by using the data of the subsidence area obtained from a FLAC3D-based numerical model, and the maximum delayed surface subsidence in different periods is calculated. The analytical results are compared with the numerical results to validate the effectiveness of the improved segmented time function model in predicting delayed surface subsidence. The improved model is applied to predict the surface subsidence in the Yutianbao subsidence area. The root-mean-square error between the predicted and measured values for the maximum subsidence monitoring point is 1.12, and the root-mean-square error between the average predicted and measured values for the surface monitoring points is 0.37, which verifies the accuracy of the improved model. The prediction model provides a scientific basis for environmental protection and safety management after coal seam mining.
Full article
(This article belongs to the Special Issue Advances in Disaster Prevention and Reduction for Geotechnical Engineering)
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Open AccessArticle
Study on Ring Deformation and Contact Characteristics of Thin-Walled Bearing for RV Reducer
by
Yanshuang Wang and Fangzheng Liu
Appl. Sci. 2024, 14(9), 3741; https://doi.org/10.3390/app14093741 (registering DOI) - 27 Apr 2024
Abstract
The thin-walled rings of the RV reducer main bearings are prone to structural elastic deformation, which can significantly change the bearing mechanical characteristics. According to the actual assembly state of the RV reducer, the simulation model of the planetary frame–main bearings–pin gear housing
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The thin-walled rings of the RV reducer main bearings are prone to structural elastic deformation, which can significantly change the bearing mechanical characteristics. According to the actual assembly state of the RV reducer, the simulation model of the planetary frame–main bearings–pin gear housing is established considering the ring deformation. The model was used to calculate and comparatively analyze the ring deformation and contact characteristics of thin-walled bearings under rigid and flexible conditions, on the basis of which the mechanism of ring deformation was described, and the effects of load conditions, ring thickness and radial clearance on ring deformation, flexible contact characteristics, and ultimate carrying capacity were analyzed. The results show that the distribution of contact loads is the main factor affecting the ring deformation. The ring deformation can optimize the bearing contact characteristics, and the greater the deformation, the more pronounced the optimization effect. However, excessive ring deformation makes the contact ellipse more susceptible to truncation, which, in turn, reduces the ultimate carrying capacity. This study indicates a 38.2% decrease in the carrying capacity of the flexible ring model compared to that of the rigid ring model. In this paper, the effect of ring deformation on bearing mechanical characteristics is deeply discussed. The research results have important guiding significance for the structural optimization design of thin-walled bearings.
Full article
(This article belongs to the Special Issue Advances and Challenges in Reliability and Maintenance Engineering)
Open AccessArticle
Exploration of Fragmentation Mechanisms of Yellow Split Peas during Grinding Using a Multimodal Approach
by
Laurène Koëgel, Reine Barbar, Adrien Réau and Bernard Cuq
Appl. Sci. 2024, 14(9), 3740; https://doi.org/10.3390/app14093740 (registering DOI) - 27 Apr 2024
Abstract
In the context of food and agro-ecological transitions, the development of food applications based on legume flours and plant proteins requires a mastery of grain milling. While wheat grain milling has been extensively studied and is well-mastered, legume grinding and its underlying mechanisms
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In the context of food and agro-ecological transitions, the development of food applications based on legume flours and plant proteins requires a mastery of grain milling. While wheat grain milling has been extensively studied and is well-mastered, legume grinding and its underlying mechanisms are still poorly understood. The aim of this work is to contribute to the study of the fragmentation mechanisms of pea grains during grinding. Experiments were carried out on hulled yellow pea grains (Kameleon variety) ground under different conditions using a ball mill (MM400, Retsch®, Haan, Germany) or a micro-cylinder mill. The results showed that the grinding of pea grains, regardless of the type of mill, produced powders characterized by particle size distribution curves with a multimodal shape. The curve analysis was performed according to a simplified deconvolution approach, taking into account different particle populations without diameter overlap. Four particle populations of different sizes were identified and correlated with specific mechanisms governing the grinding of yellow split peas. The physical and biochemical properties of the resulting powders were determined. Taking into account the proportions of the four populations within the powders, the results showed a positive correlation between the volume proportions of very fine (0–10 µm) and fine (10–55 µm) particles within the powders and the starch damage rate and the specific surface area developed, irrespective of the type of mill.
Full article
(This article belongs to the Special Issue Food Processing Technology of Pulses and Cereals)
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Open AccessArticle
Design and Analysis of a Curved Composite Bracket
by
Hyunbum Park
Appl. Sci. 2024, 14(9), 3739; https://doi.org/10.3390/app14093739 (registering DOI) - 27 Apr 2024
Abstract
The structural design of a composite bracket applied to an aircraft propulsion system was carried out in this study. Aircraft engine intakes are fitted with various components in order for the engine to operate. The thickness of the composite laminate was determined through
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The structural design of a composite bracket applied to an aircraft propulsion system was carried out in this study. Aircraft engine intakes are fitted with various components in order for the engine to operate. The thickness of the composite laminate was determined through classical laminate theory. The mechanical properties of the manufactured specimen were analyzed and reflected in the conceptual design. The material for the design and analysis was a composite material consisting of carbon fiber and epoxy resin. The results of the designed composite structures were compared with those of aluminum alloy structures, and the structural integrity was investigated via the structural analysis of the designed bracket. The commercial FEM code Nastran 2022 and ANSYS 2023 software were used for numerical analysis. A stress and deformation analysis was carried out, and the buckling stability was also evaluated due to the characteristics of the composite structure. The bracket was shown to be sufficiently safe through structural analysis.
Full article
(This article belongs to the Special Issue Multifunctional Composites and Structures for Aerospace and Mechanical Applications)
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Open AccessArticle
Precision Agriculture: Assessment of Ergonomic Risks of Assisted Driving System
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Ermanno Vitale, Francesca Vella, Serena Matera, Giuseppe Christian Rizzo, Lucia Rapisarda, Federico Roggio, Giuseppe Musumeci, Venerando Rapisarda, Elio Romano and Veronica Filetti
Appl. Sci. 2024, 14(9), 3738; https://doi.org/10.3390/app14093738 (registering DOI) - 27 Apr 2024
Abstract
Background: the precision agriculture field optimizes resource use, enhancing performance. However, this leads to exposure to ergonomic risks for operators, in particular, tractor drivers, potentially causing musculoskeletal disorders (MSDs). This study investigates how the display position in a semi-automatic tractor system influences operator
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Background: the precision agriculture field optimizes resource use, enhancing performance. However, this leads to exposure to ergonomic risks for operators, in particular, tractor drivers, potentially causing musculoskeletal disorders (MSDs). This study investigates how the display position in a semi-automatic tractor system influences operator comfort and muscle activation during harrowing operations. Methods: the assessment of muscular strain involved the use of surface electromyographic devices, while posture was evaluated throughout the analysis of the distribution of pressure exerted by the operator’s body on the seat, which was observed using two barometric pads, each positioned on the backrest and base of the seat. Finally, infrared thermography (IRT), a non-invasive tool to assess muscle activation, was used to measure the surface temperature of the driver’s back. The results showed a significantly greater muscular activation display for the position of display in semi-automatic driving at 50° and 80°. Conclusions: this study showed how the position of the display on the vehicle negatively influences posture, exposing workers to the risk of developing fatigue and, therefore, discomfort, with the potential onset of MSDs. The combined use of sEMG and IRT allowed for a non-invasive, cheap, and repeatable mechanical and functionality analysis.
Full article
(This article belongs to the Section Applied Biosciences and Bioengineering)
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Open AccessArticle
Analyzing Delay and CO Emissions: A Simulation Study of the Median U-Turn Method at Intersections
by
Ziyan Zhao, Caixia Tian, Baohua Guo, Chengming Zhu and Qingwen Guo
Appl. Sci. 2024, 14(9), 3737; https://doi.org/10.3390/app14093737 (registering DOI) - 27 Apr 2024
Abstract
To improve traffic efficiency and reduce pollutant emissions at urban road intersections, VISSIM software was used to simulate traffic states to compare the median U-turn method with the direct left-turn method under various traffic volumes and left-turn ratios. Based on the average delay
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To improve traffic efficiency and reduce pollutant emissions at urban road intersections, VISSIM software was used to simulate traffic states to compare the median U-turn method with the direct left-turn method under various traffic volumes and left-turn ratios. Based on the average delay and CO emissions, suitable conditions were identified for using the median U-turn method at intersections. The results show that there are three critical left-turn ratio boundary curves named ,, and based on the relatively smaller average delay and there is a critical left-turn ratio boundary curve based on the lower average CO emissions at the intersection when the through traffic volume is in the range of 0–3000 veh/h and the left-turn ratio is in the range of 0–4. The median U-turn method is considered applicable at the intersection when the through traffic volumes are in the range of 0–87 veh/h, 87–400 veh/h, 400–416 veh/h, 416– veh/h, and 934–3000 veh/h, respectively, and, accordingly, the left-turn ratios are in the range of 0–, 0– or –4, 0–4, 0–, and . These findings can provide a reference for traffic managers to organize the left-turn traffic at an intersection reasonably.
Full article
(This article belongs to the Special Issue Transportation Planning, Management and Optimization)
Open AccessArticle
New Method to Estimate Rock Mass Deformation Modulus Based on BQ System
by
Huishi Xue, Yanhui Song, Man Feng and Guanghong Ju
Appl. Sci. 2024, 14(9), 3736; https://doi.org/10.3390/app14093736 (registering DOI) - 27 Apr 2024
Abstract
The rock mass deformation modulus is one of the most important design parameters in a range of rock engineering applications. Its value is usually obtained directly through in situ testing or estimated indirectly on the basis of a rock mass quality classification system.
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The rock mass deformation modulus is one of the most important design parameters in a range of rock engineering applications. Its value is usually obtained directly through in situ testing or estimated indirectly on the basis of a rock mass quality classification system. Because in situ testing is generally costly, time-consuming, and presents operational difficulties, it cannot be carried out extensively, and many researchers have concentrated on developing indirect procedures to obtain information on the modulus of deformation, such as the RMR method, Q method, and GSI method. The purpose of this paper is to present a new system for estimating the rock mass deformation modulus called the BQ method, which is based on the BQ (basic quality) system. In this paper, the BQ system is first briefly reviewed, and then more than 60 in situ measurements from three large hydropower stations in China are used to develop a new relationship between BQ and the deformation modulus, based on a power function relationship. The paper also derives correlations based on the existing estimation formula and the relationship between BQ and other classification schemes, resulting in several recommended formulas for estimating the deformation modulus of a rock mass using the BQ method.
Full article
(This article belongs to the Special Issue Rock Mass Characterization: Failure and Mechanical Behavior)
Open AccessEditorial
Advances in Vehicle Dynamics and Road Safety: Technologies, Simulations, and Applications
by
Edgar Sokolovskij and Vidas Žuraulis
Appl. Sci. 2024, 14(9), 3735; https://doi.org/10.3390/app14093735 (registering DOI) - 27 Apr 2024
Abstract
Alongside various road safety aspects, vehicle dynamics play a crucial role in enhancing the quality of life in modern society within a holistic traffic safety framework [...]
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(This article belongs to the Special Issue Advances in Vehicle Dynamics and Road Safety: Technologies, Simulations and Applications)
Open AccessArticle
Research on the Improvement of Granite Residual Soil Caused by Fly Ash and Its Slope Stability under Rainfall Conditions
by
Bowen Hu, Qizhi Hu, Yiming Liu and Gaoliang Tao
Appl. Sci. 2024, 14(9), 3734; https://doi.org/10.3390/app14093734 (registering DOI) - 27 Apr 2024
Abstract
Granite residual soil has distinctive engineering characteristics due to its unique properties, and the resulting slopes are less stable and less resistant to rain erosion. The granite residual soil was improved by the addition of 5%, 10%, 15% and 20% fly ash, and
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Granite residual soil has distinctive engineering characteristics due to its unique properties, and the resulting slopes are less stable and less resistant to rain erosion. The granite residual soil was improved by the addition of 5%, 10%, 15% and 20% fly ash, and the effects of fly ash on the intensity index and penetration of granite residual soil were investigated by triaxial strength tests and permeability tests. In combination with scanning electron microscopy measurements, a study of the stability of fly ash-modified granite residual soil slopes by modeling rainfall using the finite element software ABAQUS revealed the following: (1) the permeability coefficients of the residual granitic soils decreased by one order of magnitude when fly ash was added; (2) the improvement in the triaxial strength index of the improved soil was most pronounced when the dosage of fly ash was 15%, so that a dosage of 15% was considered optimal; and (3) numerical simulations concluded that the stability of the slope formed by 15% fly ash-improved soil fill improved significantly relative to the original slope, with the coefficient of safety increasing from 1.06 to 1.42, and the resistance to water seepage also significantly improved.
Full article
(This article belongs to the Topic Slope Erosion Monitoring and Anti-erosion)
Open AccessReview
Current Status and Economic Prospects of Alternative Protein Sources for the Food Industry
by
Fábio Medeiros, Ricardo S. Aleman, Lucia Gabríny, Seung Woon You, Roberta Targino Hoskin and Marvin Moncada
Appl. Sci. 2024, 14(9), 3733; https://doi.org/10.3390/app14093733 (registering DOI) - 27 Apr 2024
Abstract
The rising demand for novel and alternative protein (AP) sources has transformed both the marketplace and the food industry. This solid trend is driven by social awareness about environmental sustainability, fair food production practices, affordability, and pursuit of high-quality nutritional sources. This short
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The rising demand for novel and alternative protein (AP) sources has transformed both the marketplace and the food industry. This solid trend is driven by social awareness about environmental sustainability, fair food production practices, affordability, and pursuit of high-quality nutritional sources. This short review provides an overview of key aspects of promising AP sources (plants, algae, insects, fungi and cultured protein) as well as the economic potential, prospects, and operational challenges of this market. The low environmental performance of livestock production, associated with high GHG emissions and land use, can be overcome by less resource-intensive AP production. However, despite the forecasted expansion and improved economic viability, key challenges such as regulatory concerns, consumer acceptance and product functionality still need to be addressed. While the consumption and production of plant-based products are relatively well established, research and development efforts are needed to remediate the main commercialization and manufacturing issues of unprecedented protein sources such as cultured protein and the emerging edible insects sector.
Full article
(This article belongs to the Special Issue Spectroscopy Applications in Plant and Plant-Based Foods)
Open AccessArticle
Techniques for Enhancing Thermal Conductivity and Heat Transfer in Phase Change Materials in Hybrid Phase Change Material–Water Storage Tanks
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Dmytro Shmyhol, Miroslav Rimár, Marcel Fedak, Tibor Krenický, Martin Lopušniak and Nikolas Polivka
Appl. Sci. 2024, 14(9), 3732; https://doi.org/10.3390/app14093732 (registering DOI) - 27 Apr 2024
Abstract
In recent years, extensive research has been dedicated to enhancing energy efficiency and promoting environmental sustainability in heating and cooling systems. Among the promising solutions, phase change materials (PCM) technology stands out as a key area of exploration. This study focuses on improving
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In recent years, extensive research has been dedicated to enhancing energy efficiency and promoting environmental sustainability in heating and cooling systems. Among the promising solutions, phase change materials (PCM) technology stands out as a key area of exploration. This study focuses on improving the thermal performance of PCM–water hybrid tanks by investigating methods to enhance thermal conductivity and heat transfer. Through experimental testing using techniques such as copper matrices, steel twisted matrices, and copper spirals, this study demonstrates significant improvements in thermal conductivity, particularly with the use of copper matrices. The integration of a copper matrix placed in the PCM reservoir increased the heat transfer coefficient and thermal conductivity of the PCM, and thus, the total phase transformation time for solidification was reduced by 79.19% and for melting by 54.7%. Our experimental results demonstrate that the integration of a copper matrix can increase latent heat transfer from 55,677.6 J up to 125,274.6 J, marking a 125% enhancement over the experiment with pure PCM. Additionally, comparisons of the energy storage potentials for different PCMs underscore the benefits of integrating PCMs into hybrid storage tanks. These findings highlight the immense potential of PCM technology to increase energy storage efficiency in heating and cooling applications.
Full article
(This article belongs to the Special Issue Applications of Phase Change Materials in Heat Transport Systems)
Open AccessArticle
Prediction and Optimization of Heat Transfer Performance of Premixed Methane Impinging Flame Jet Using the Kriging Model and Genetic Algorithm
by
Xiang-Xin Chen, Ray-Bing Chen and Chih-Yung Wu
Appl. Sci. 2024, 14(9), 3731; https://doi.org/10.3390/app14093731 (registering DOI) - 27 Apr 2024
Abstract
In practical applications, rapid prediction and optimization of heat transfer performance are essential for premixed methane impinging flame jets (PMIFJs). This study uses computational fluid dynamics (CFD) combined with a methane detailed chemical reaction mechanism (GRI–Mech 3.0) to study the equivalence ratio (),
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In practical applications, rapid prediction and optimization of heat transfer performance are essential for premixed methane impinging flame jets (PMIFJs). This study uses computational fluid dynamics (CFD) combined with a methane detailed chemical reaction mechanism (GRI–Mech 3.0) to study the equivalence ratio (), Reynolds number () of the mixture, and the normalized nozzle–to–plate distance () on the heat transfer performance of PMIFJs. Moreover, the Kriging model (KM) was used to construct a prediction model of PMIFJ heat transfer performance. A genetic algorithm (GA) was used to determine the maximum likelihood function (MLE) of the model parameters for constructing KM and identify the points with the maximum root mean square error (RMSE) as the new infilled points for surrogate–based optimization (SBO). Combining these methods to analyze the simulation results, the results show that the global heat transfer performance of PMIFJs is enhanced with the increase in , the increase in , and the decrease in . Sensitivity analysis points out that and significantly affect enhanced heat transfer, while has a relatively small effect. In addition, GA was also used to search for the optimal heat transfer performance, and the global heat transfer performance at specific conditions was significantly enhanced. This study deepens the understanding of the heat transfer mechanism of impinging flame jets and provides an efficient method framework for practical applications.
Full article
(This article belongs to the Topic Applied Heat Transfer)
Open AccessArticle
A Static Security Region Analysis of New Power Systems Based on Improved Stochastic–Batch Gradient Pile Descent
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Jiahui Wu, Yide Zhou, Haiyun Wang and Weiqing Wang
Appl. Sci. 2024, 14(9), 3730; https://doi.org/10.3390/app14093730 (registering DOI) - 27 Apr 2024
Abstract
The uncertainty in the new power system has increased, leading to limitations in traditional stability analysis methods. Therefore, considering the perspective of the three-dimensional static security region (SSR), we propose a novel approach for system static stability analysis. To address the slow training
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The uncertainty in the new power system has increased, leading to limitations in traditional stability analysis methods. Therefore, considering the perspective of the three-dimensional static security region (SSR), we propose a novel approach for system static stability analysis. To address the slow training speed of traditional deep learning algorithms using batch gradient descent (BGD), we introduce an improved stochastic–batch gradient descent (S-BGD) search method that combines the advantages of stochastic gradient descent (SGD) in fast training. This method ensures both speed and precision in parameter training. Moreover, to tackle the problem of getting trapped in local optima and saddle points during parameter training, we draw inspiration from kinematic theory and propose a gradient pile (GP) training method. By utilizing accumulated gradients as parameter corrections, this method effectively avoids getting stuck in local optima and saddle points, thereby enhancing precision. Finally, we apply the proposed methods to construct the static security region for the IEEE-118 new power system using its data as samples, demonstrating the effectiveness of our approach.
Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
Open AccessArticle
Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
by
Haoyue Zhang, Chunmei Zhao and Zhengbin He
Appl. Sci. 2024, 14(9), 3729; https://doi.org/10.3390/app14093729 (registering DOI) - 27 Apr 2024
Abstract
The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian
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The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian Mixture Model (MGMM) is introduced to consider the interdependencies among various indicators. Additionally, a Multi-strategy Genetic Algorithm (MGA) is employed to adjust the complexity of the MGMM, allowing it to accurately learn the actual distribution of TLE data. Initially, the proposed method applies probabilistic fits to the predicted error rate changes for both the TLE semi-major axis and the orbital inclination. Chaos initialization, a posterior probability penalty, and local optimization iterations are subsequently integrated into the genetic algorithm. These enhancements aim to estimate the MGMM parameters, addressing issues related to poor robustness and the susceptibility of the MGMM to converge to local optima. The algorithm’s effectiveness is validated using TLE data from typical space targets. The results demonstrate that the optimized algorithm can efficiently detect outliers and maneuver events within complex TLE data. Notably, the comprehensive detection performance index, measured, using the F1 score, improved by 15.9% compared to the Gaussian mixture model. This significant improvement underscores the importance of the proposed method in bolstering the security of complex space environments.
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(This article belongs to the Section Earth Sciences)
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Open AccessReview
A Review on the Potential Use of Medicinal Plants from the Apiaceae and the Rosaceae Families in Cardiovascular Diseases—Experimental Evidence and Traditional Applications
by
Rafał Celiński, Barbara Krzemińska, Anna Grzywa-Celińska, Gabriela Szewczyk and Katarzyna Dos Santos Szewczyk
Appl. Sci. 2024, 14(9), 3728; https://doi.org/10.3390/app14093728 (registering DOI) - 27 Apr 2024
Abstract
Cardiovascular diseases are the leading cause of mortality worldwide. The World Health Organization has presented alarming data stating that in 2019, 17.9 million people globally died due to cardiovascular diseases, constituting 32% of all deaths. Despite increasingly advanced pharmacological and procedural treatment methods
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Cardiovascular diseases are the leading cause of mortality worldwide. The World Health Organization has presented alarming data stating that in 2019, 17.9 million people globally died due to cardiovascular diseases, constituting 32% of all deaths. Despite increasingly advanced pharmacological and procedural treatment methods for these diseases, there is still a quest for new therapeutic possibilities that promise even greater efficacy and safety. The overriding purpose of this study is to provide an insight into the traditional uses of species from the Apiaceae and Rosaceae families as well as to systematize knowledge regarding their scientifically proven cardiovascular activities (animal studies and clinical trials). The review is intended to indicate knowledge gaps for future studies concerning plants used in traditional medicine but without scientific research. As a result, various plant species from both Apiaceae and Rosaceae family have been collected and described based on their study that has proven their effectiveness and uses in cardiovascular diseases. Most of these plants have a hypotensive effect, followed by anti-hyperlipidemic, vasorelaxant, antithrombotic, and diuretic activity. These are the mechanisms that contribute to various cardiovascular diseases, such as heart attack, coronary heart disease, hypertension, and stroke.
Full article
(This article belongs to the Special Issue Biological Activity, Chemical Characterization and Contaminants of Plants and Waste)
Open AccessTechnical Note
Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
by
Jing Wang, Qiufeng Wu, Tianci Liu, Yuqi Wang, Pengxian Li, Tianhao Yuan and Ziyang Ji
Appl. Sci. 2024, 14(9), 3727; https://doi.org/10.3390/app14093727 (registering DOI) - 27 Apr 2024
Abstract
With the application of computer vision in the field of agricultural disease recognition, the convolutional neural network is widely used in grape leaf disease recognition and has achieved remarkable results. However, most of the grape leaf disease recognition models have the problem of
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With the application of computer vision in the field of agricultural disease recognition, the convolutional neural network is widely used in grape leaf disease recognition and has achieved remarkable results. However, most of the grape leaf disease recognition models have the problem of weak generalization ability. In order to overcome this challenge, this paper proposes an image identification method for grape leaf diseases in different domains based on Fourier domain adaptation. Firstly, Fourier domain adaptation is performed on the labeled source domain data and the unlabeled target domain data. To decrease the gap in distribution between the source domain data and the target domain data, the low-frequency spectrum of the source domain data and the target domain data is swapped. Then, three convolutional neural networks (AlexNet, VGG13, and ResNet101) were used to train the images after style changes and the unlabeled target domain images were classified. The highest accuracy of the three networks can reach 94.6%, 96.7%, and 91.8%, respectively, higher than that of the model without Fourier transform image training. In order to reduce the impact of randomness, when selecting the transformed image, we propose using farthest point sampling to select the image with low feature correlation for the Fourier transform. The final identification result is also higher than the accuracy of the network model trained without transformation. Experimental results showed that Fourier domain adaptation can improve the generalization ability of the model and obtain a more accurate grape leaf disease recognition model.
Full article
Open AccessArticle
Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net
by
Yanhui Shi, Xiaojian Hao, Xiaodong Huang, Pan Pei, Shuaijun Li and Tong Wei
Appl. Sci. 2024, 14(9), 3726; https://doi.org/10.3390/app14093726 (registering DOI) - 27 Apr 2024
Abstract
Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to
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Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion.
Full article
(This article belongs to the Section Applied Physics General)
Open AccessArticle
Integrating Machine Learning and MLOps for Wind Energy Forecasting: A Comparative Analysis and Optimization Study on Türkiye’s Wind Data
by
Saadin Oyucu and Ahmet Aksöz
Appl. Sci. 2024, 14(9), 3725; https://doi.org/10.3390/app14093725 (registering DOI) - 27 Apr 2024
Abstract
This study conducted a detailed comparative analysis of various machine learning models to enhance wind energy forecasts, including linear regression, decision tree, random forest, gradient boosting machine, XGBoost, LightGBM, and CatBoost. Furthermore, it developed an end-to-end MLOps pipeline leveraging SCADA data from a
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This study conducted a detailed comparative analysis of various machine learning models to enhance wind energy forecasts, including linear regression, decision tree, random forest, gradient boosting machine, XGBoost, LightGBM, and CatBoost. Furthermore, it developed an end-to-end MLOps pipeline leveraging SCADA data from a wind turbine in Türkiye. This research not only compared models using the RMSE metric for selection and optimization but also explored in detail the impact of integrating machine learning with MLOps on the precision of energy production forecasts. It investigated the suitability and efficiency of ML models in predicting wind energy with MLOps integration. The study explored ways to improve LightGBM algorithm performance through hyperparameter tuning and Docker utilization. It also highlighted challenges in speeding up MLOps development and deployment processes. Model performance was assessed using the RMSE metric, conducting a comparative evaluation across different models. The findings revealed that the RMSE values among the regression models ranged from 460 kW to 192 kW. Focusing on enhancing LightGBM, the research decreased the RMSE value to 190.34 kW. Despite facing technical and operational hurdles, the implementation of MLOps was proven to enhance the speed (latency of 9 ms), reliability (through Docker encapsulation), and scalability (using Docker swarm) of machine learning endeavors.
Full article
(This article belongs to the Special Issue AutoML: Advances and Applications)
Open AccessArticle
Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
by
Hongyu Ren, Tong Guo, Zhongxiang Liu, Guoliang Zhi and Xiangyang Xu
Appl. Sci. 2024, 14(9), 3724; https://doi.org/10.3390/app14093724 (registering DOI) - 27 Apr 2024
Abstract
Submerged floating tunnels (SFTs), also known as the Archimedes Bridge, are new transportation structures designed for crossing deep waters. Compared with cross-sea bridges and subsea tunnels, SFTs offer superior environmental adaptability, reduced construction costs, and an enhanced spanning capacity, highlighting their significant development
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Submerged floating tunnels (SFTs), also known as the Archimedes Bridge, are new transportation structures designed for crossing deep waters. Compared with cross-sea bridges and subsea tunnels, SFTs offer superior environmental adaptability, reduced construction costs, and an enhanced spanning capacity, highlighting their significant development potential and research value. This paper introduces a new type of SFT scale model for hydrodynamic experiments, adhering to the criteria for geometric similarity, motion similarity, and dynamic similarity principles, including the Froude and Cauchy similarity principles. This model enables the accurate simulation of the elastic deformation of the tunnel body and complex hydrodynamic phenomena, such as fluid–structure interactions and vortex–induced vibrations. Moreover, this paper details the design methodology, fabrication process, and method for similarity evaluation, covering the mass, deflection under load, natural frequency in air, and the natural frequency of the various underwater motion freedoms of the model. The results of our experiments and numerical simulations demonstrate a close alignment, proving the reliability of the new SFT scale model. The frequency distribution observed in the white noise wave tests indicates that the SFT equipped with inclined mooring cables experiences a coupled interaction between horizontal motion, vertical motion, and rotation. Furthermore, the design methodology of this model can be applied to other types of SFTs, potentially advancing technical progress in scale modeling of SFTs and enhancing the depth of SFT research through hydrodynamic experiments.
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(This article belongs to the Special Issue Advances in Intelligent Bridge: Maintenance and Monitoring)
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Tribological Performance Study of Low-Friction PEEK Composites under Different Lubrication Conditions
by
Shibo Wu, Zhijun Yan, Haocheng Sun, Ze Liu, Lixia Xue and Tao Sun
Appl. Sci. 2024, 14(9), 3723; https://doi.org/10.3390/app14093723 (registering DOI) - 27 Apr 2024
Abstract
This study introduces a low-friction composite based on PEEK to improve its friction and wear properties. The composite incorporates PTFE as a solid lubricant and utilizes PPTA as a reinforcing material within the PEEK matrix. These components were prepared utilizing a compression molding
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This study introduces a low-friction composite based on PEEK to improve its friction and wear properties. The composite incorporates PTFE as a solid lubricant and utilizes PPTA as a reinforcing material within the PEEK matrix. These components were prepared utilizing a compression molding method, followed by a series of exploratory experiments to identify the optimal preparation conditions for PEEK. This research assesses how the PTFE/PPTA/PEEK composites perform in terms of friction and wear under dry and oil-lubricated conditions. By examining wear tracks using scanning electron microscopy and white light interference microscopy, this study aims to uncover the wear mechanisms of PEEK and its composites under different lubrication scenarios. Results show that the main wear mechanisms for the PTFE/PPTA/PEEK composites and bearing steel are ploughing and adhesive wear. The presence of PPTA helps reduce wear by leveraging its strong fibers and thermal stability, while the coefficient of friction decreases as PTFE creates a smooth, solid lubricating film on the surface. Notably, PEEK composites containing 25 wt% PTFE and 6 wt% PPTA demonstrate the lowest wear rates and reduced coefficient of friction in both dry and oil-lubricated conditions.
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(This article belongs to the Special Issue Advanced Composites and Hybrid Materials)
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