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
Dynamic Grouping within Minimax Optimal Strategy for Stochastic Multi-ArmedBandits in Reinforcement Learning Recommendation
Appl. Sci. 2024, 14(8), 3441; https://doi.org/10.3390/app14083441 (registering DOI) - 18 Apr 2024
Abstract
The multi-armed bandit (MAB) problem is a typical problem of exploration and exploitation. As a classical MAB problem, the stochastic multi-armed bandit (SMAB) is the basis of reinforcement learning recommendation. However, most existing SMAB and MAB algorithms have two limitations: (1)they do not
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The multi-armed bandit (MAB) problem is a typical problem of exploration and exploitation. As a classical MAB problem, the stochastic multi-armed bandit (SMAB) is the basis of reinforcement learning recommendation. However, most existing SMAB and MAB algorithms have two limitations: (1)they do not make full use of feedback from the environment or agent, such as the number of arms and rewards contained in user feedback; (2) they overlook the utilization of different action selections, which can affect the exploration and exploitation of the algorithm. These limitations motivate us to propose a novel dynamic grouping within the minimax optimal strategy in the stochastic case (DG-MOSS) algorithm for reinforcement learning recommendation for small and medium-sized data scenarios. DG-MOSS does not require additional contextual data and can be used for recommendation of various types of data. Specifically, we designed a new exploration calculation method based on dynamic grouping which uses the feedback information automatically in the selection process and adopts different action selections. During the thorough training of the algorithm, we designed an adaptive episode length to effectively improve the training efficiency. We also analyzed and proved the upper bound of DG-MOSS’s regret. Our experimental results for different scales, densities, and field datasets show that DG-MOSS can yield greater rewards than nine baselines with sufficiently trained recommendation and demonstrate that it has better robustness.
Full article
(This article belongs to the Section Computing and Artificial Intelligence)
Open AccessArticle
A Year in the Life of Sea Fennel: Annual Phytochemical Variations of Major Bioactive Secondary Metabolites
by
Marijana Popović, Sanja Radman, Ivana Generalić Mekinić, Tonka Ninčević Runjić, Branimir Urlić and Maja Veršić Bratinčević
Appl. Sci. 2024, 14(8), 3440; https://doi.org/10.3390/app14083440 - 18 Apr 2024
Abstract
Sea fennel (Crithmum maritimum L.) is one of the most abundant and widespread Mediterranean halophytes, traditionally harvested and used in the summer months. As the plant bioactive metabolites are strongly influenced by the plant vegetation period and environmental conditions, we investigated some
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Sea fennel (Crithmum maritimum L.) is one of the most abundant and widespread Mediterranean halophytes, traditionally harvested and used in the summer months. As the plant bioactive metabolites are strongly influenced by the plant vegetation period and environmental conditions, we investigated some of the main bioactive compounds from sea fennel leaves over a one-year period to gain a deeper insight into their annual changes. A comprehensive phytochemical analysis of the essential oils using GC-MS, as well as the major phenolic and carotenoid compounds using HPLC, was performed. The results showed a high positive correlation between temperature and all major bioactive compounds, especially phenolic acids, cryptochlorogenic acid, and chlorogenic acid (r = 0.887, p = 0.0001 and r = 0.794, p = 0.002, respectively), as well as the limonene content in the essential oil (r = 0.694, p = 0.012). PCA analysis clearly distinguishes the period from February to April from the rest of the year, which contained the least bioactive metabolites overall. The overall data analyzed show great variations in sea fennel phytochemicals during the period of a year, with β-carotene content being the least effected. Therefore, it can be concluded that the plant can be used as a functional food or in other industries, such as the cosmetic and/or pharmaceutic industries, beyond its typical harvest period (early to midsummer).
Full article
(This article belongs to the Special Issue Biological Activity, Chemical Characterization and Contaminants of Plants and Waste)
Open AccessArticle
The Synthesis and Characterization of Geopolymers Based on Metakaolin and on Automotive Glass Waste
by
Ivana Perná, Martina Havelcová, Monika Šupová, Margit Žaloudková and Olga Bičáková
Appl. Sci. 2024, 14(8), 3439; https://doi.org/10.3390/app14083439 - 18 Apr 2024
Abstract
The presented article studies a metakaolin-based geopolymer matrix for which two types of non-recyclable automotive glass waste (AGW) have been used as an alternative aggregate. Their composition and character, as well as their influence on the properties and structure of geopolymer composites (AGW-Gs),
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The presented article studies a metakaolin-based geopolymer matrix for which two types of non-recyclable automotive glass waste (AGW) have been used as an alternative aggregate. Their composition and character, as well as their influence on the properties and structure of geopolymer composites (AGW-Gs), have been investigated by means of X-ray fluorescence and X-ray diffraction analyses, scanning electron microscopy, Fourier transform infrared spectrometry and gas chromatography/mass spectrometry. Infrared analysis has proven that the use of AGW does not affect the formation of geopolymer bonds. GC/MS analysis has revealed the presence of triethylene glycol bis(2-ethylhexanoate) in AGW and geopolymers, whose concentration varied according to the size of the fractions used. Preliminary compressive-strength tests have shown the promising potential of AGW-Gs. From the presented results, based on the study of two types of automotive glass waste, it is possible to assume that automotive glass will generally behave in the same or a similar manner in metakaolin-based geopolymer matrices and can be considered as potential alternative aggregates. The result is promising for the current search for new sources of raw materials, for ensuring resource security, for the promotion of sustainability and innovation and for meeting the needs of the growing world population while reducing dependence on limited resources.
Full article
(This article belongs to the Special Issue Development, Characterization, Application and Recycling of Novel Construction Materials)
Open AccessArticle
Research on a Highway Passenger Volume Prediction Model Based on a Multilayer Perceptron Neural Network
by
He Lu, Baohua Guo, Zhezhe Zhang and Weifan Gu
Appl. Sci. 2024, 14(8), 3438; https://doi.org/10.3390/app14083438 - 18 Apr 2024
Abstract
The accurate prediction of highway passenger volume is very important for China’s transportation planning and economic development. Based on a neural network, this paper establishes a prediction model by using historical road passenger traffic and related influencing factor data, aiming to provide an
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The accurate prediction of highway passenger volume is very important for China’s transportation planning and economic development. Based on a neural network, this paper establishes a prediction model by using historical road passenger traffic and related influencing factor data, aiming to provide an accurate road passenger traffic prediction. Firstly, the historical highway passenger volume data and the factor data affecting passenger volume are collected. Then, a multilayer perceptron neural network is established by using SPSS software (PASW Statistics 18) to analyze the significant relationship between highway passenger volume and influencing factors. Then, through the training and verification of the model by MATLAB software (R2021a), the reliability of the prediction model is proved. Finally, the model is used to predict the passenger traffic volume in 2020–2022, and the actual passenger traffic volume is compared and analyzed. It is concluded that the highway passenger traffic volume decreased significantly in 2020–2022 due to various factors such as the epidemic situation and policies, which have had an impact on China’s economic development.
Full article
(This article belongs to the Section Transportation and Future Mobility)
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Open AccessArticle
Evaluation of the Dark Fermentation Process as an Alternative for the Energy Valorization of the Organic Fraction of Municipal Solid Waste (OFMSW) for Bogotá, Colombia
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Ana-Paola Becerra-Quiroz, Santiago-Andrés Rodríguez-Morón, Paola-Andrea Acevedo-Pabón, Javier Rodrigo-Ilarri and María-Elena Rodrigo-Clavero
Appl. Sci. 2024, 14(8), 3437; https://doi.org/10.3390/app14083437 - 18 Apr 2024
Abstract
In the context of valorizing the organic fraction of urban solid waste (OFMSW) in megacities, dark fermentation emerges as a central strategy alongside composting and anaerobic digestion. This article focuses on assessing the environmental, technical, and energy viability of dark fermentation using life
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In the context of valorizing the organic fraction of urban solid waste (OFMSW) in megacities, dark fermentation emerges as a central strategy alongside composting and anaerobic digestion. This article focuses on assessing the environmental, technical, and energy viability of dark fermentation using life cycle assessment (LCA) and circular economy principles. Dark fermentation for biohydrogen production is an active and promising research field in the quest for sustainable biofuels. In this context, defining operational parameters such as organic loading and the substrate-inoculum ratio is relevant for achieving better production yields. Laboratory tests were conducted using organic loading values of 5, 10, and 15 g of volatile solids per liter (gVS/L) and with substrate-inoculum ratios (s/x) of 1, 0.75, and 0.5 g of volatile solids of substrate per gram of volatile solids of inoculum (gVSs/gVSi). The combination with the best performance turned out to be an initial organic loading of 10 gVS/L and an s/x of 1 gVSs/gVSi. From this result, it was determined that the s/x had a greater impact on production. Finally, a valorization plant was dimensioned with the scaled-up process, starting from the municipal solid waste generated by Bogotá projected for 2042. The scaling was demonstrated to be energetically sustainable, producing a power of 2,368,358.72 kWh per day.
Full article
(This article belongs to the Special Issue Application of Municipal/Industrial Solid and Liquid Waste in Energy Area, 2nd Edition)
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Open AccessArticle
Analyzing Data Reduction Techniques: An Experimental Perspective
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Vítor Fernandes, Gonçalo Carvalho, Vasco Pereira and Jorge Bernardino
Appl. Sci. 2024, 14(8), 3436; https://doi.org/10.3390/app14083436 - 18 Apr 2024
Abstract
The exponential growth in data generation has become a ubiquitous phenomenon in today’s rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures,
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The exponential growth in data generation has become a ubiquitous phenomenon in today’s rapidly growing digital technology. Technological advances and the number of connected devices are the main drivers of this expansion. However, the exponential growth of data presents challenges across different architectures, particularly in terms of inefficient energy consumption, suboptimal bandwidth utilization, and the rapid increase in data stored in cloud environments. Therefore, data reduction techniques are crucial to reduce the amount of data transferred and stored. This paper provides a comprehensive review of various data reduction techniques and introduces a taxonomy to classify these methods based on the type of data loss. The experiments conducted in this study include distinct data types, assessing the performance and applicability of these techniques across different datasets.
Full article
(This article belongs to the Special Issue Knowledge and Data Engineering)
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Open AccessArticle
Earthquake-Induced Landslides in Italy: Evaluation of the Triggering Potential Based on Seismic Hazard
by
Sina Azhideh, Simone Barani, Gabriele Ferretti and Davide Scafidi
Appl. Sci. 2024, 14(8), 3435; https://doi.org/10.3390/app14083435 - 18 Apr 2024
Abstract
In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s,
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In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s, 0.5 s, and 1.0 s) and three return periods (475, 975, and 2475 years). First, joint distributions of magnitude (M) and distance (R) from hazard disaggregation were analyzed by means of an innovative approach based on image processing techniques to find all modal scenarios contributing to the hazard. In order to obtain the M-R scenarios controlling the triggering of earthquake-induced landslides at any computation node, mean and modal M-R pairs were compared to empirical curves defining the M-R bounds associated with landslide triggering. Three types of landslides were considered (i.e., disrupted slides and falls, coherent slides, and lateral spreads and flows). As a result, screening maps for all of Italy showing the potential for triggering landslides based on the level of seismic hazard were obtained. The maps and the related data are freely accessible.
Full article
(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)
Open AccessArticle
A Variable-Scale Attention Mechanism Guided Time-Frequency Feature Fusion Transfer Learning Method for Bearing Fault Diagnosis in an Annealing Kiln Roller System
by
Yu Xin, Kangqu Zhou, Songlin Liu and Tianchuang Liu
Appl. Sci. 2024, 14(8), 3434; https://doi.org/10.3390/app14083434 - 18 Apr 2024
Abstract
Effective real-time health condition monitoring of the roller table and through shaft bearings in the annealing kiln roller system of glass production lines is crucial for maintaining their operational safety and stability for the quality and production efficiency of glass products. However, the
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Effective real-time health condition monitoring of the roller table and through shaft bearings in the annealing kiln roller system of glass production lines is crucial for maintaining their operational safety and stability for the quality and production efficiency of glass products. However, the collected vibration signal of the roller bearing system is affected by the low rotating frequency and strong mechanical background noise, which shows the width impact interval and non-stationary multi-component characteristics. Moreover, the distribution characteristics of monitoring data and probability of fault occurrence of the roller bearing and through shaft bearing improve the difficulty of the fault diagnosis and condition monitoring of the annealing kiln roller system, as well as the reliance on professional experience and prior knowledge. Therefore, this paper proposes a variable-scale attention mechanism guided time-frequency feature fusion transfer learning method for a bearing fault diagnosis at different installation positions in an annealing kiln roller system. Firstly, the instinct time decomposition method and the Gini–Kurtosis composed index are used to decompose and reconstruct the signal for noise reduction, wavelet transform with the Morlet basic function is used to extract the time-frequency features, and histogram equalization is introduced to reform the time-frequency map for the blur and implicit time-frequency features. Secondly, a variable-scale attention mechanism guided time-frequency feature fusion framework is established to extract multiscale time-dependency features from the time-frequency representation for the distinguished fault diagnosis of roller table bearings. Then, for through shaft bearings, the vibration signal of the roller table bearing is used as the source domain and the signal of the through shaft bearing is used as the target domain, based on the feature fusion framework and the multi-kernel maximum mean differences metric function, and the transfer diagnosis method is proposed to reduce the distribution differences and extract the across-domain invariant feature to diagnose the through shaft bearing fault speed under different working conditions, using a small sample. Finally, the effectiveness of the proposed method is verified based on the vibration signal from the experimental platform and the roller bearing system of the glass production line. Results show that the proposed method can effectively diagnose roller table and through shaft bearings’ fault information in the annealing kiln roller system.
Full article
(This article belongs to the Section Applied Industrial Technologies)
Open AccessArticle
Salient Object Detection via Fusion of Multi-Visual Perception
by
Wenjun Zhou, Tianfei Wang, Xiaoqin Wu, Chenglin Zuo, Yifan Wang, Quan Zhang and Bo Peng
Appl. Sci. 2024, 14(8), 3433; https://doi.org/10.3390/app14083433 - 18 Apr 2024
Abstract
Salient object detection aims to distinguish the most visually conspicuous regions, playing an important role in computer vision tasks. However, complex natural scenarios can challenge salient object detection, hindering accurate extraction of objects with rich morphological diversity. This paper proposes a novel method
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Salient object detection aims to distinguish the most visually conspicuous regions, playing an important role in computer vision tasks. However, complex natural scenarios can challenge salient object detection, hindering accurate extraction of objects with rich morphological diversity. This paper proposes a novel method for salient object detection leveraging multi-visual perception, mirroring the human visual system’s rapid identification, and focusing on impressive objects/regions within complex scenes. First, a feature map is derived from the original image. Then, salient object detection results are obtained for each perception feature and combined via a feature fusion strategy to produce a saliency map. Finally, superpixel segmentation is employed for precise salient object extraction, removing interference areas. This multi-feature approach for salient object detection harnesses complementary features to adapt to complex scenarios. Competitive experiments on the MSRA10K and ECSSD datasets place our method in the first tier, achieving 0.1302 MAE and 0.9382 F-measure for the MSRA10K dataset and 0.0783 MAE and and 0.9635 F-measure for the ECSSD dataset, demonstrating superior salient object detection performance in complex natural scenarios.
Full article
(This article belongs to the Topic Computer Vision and Image Processing, 2nd Edition)
Open AccessArticle
Research on the Vehicle-Behavior Boundary of Intersection Traffic Based on Naturalistic Driving Data Study
by
Biao Wu, Zhixiong Ma, Xichan Zhu and Yu Lin
Appl. Sci. 2024, 14(8), 3432; https://doi.org/10.3390/app14083432 - 18 Apr 2024
Abstract
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With the development and application of vehicle-infrastructure cooperative technology, the traffic regional safety related to intelligent connected vehicles (ICVs) has become the hotspot of the intelligent transportation system (ITS), and the integration of mixed autonomous and non-autonomous vehicles that are not cooperative in
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With the development and application of vehicle-infrastructure cooperative technology, the traffic regional safety related to intelligent connected vehicles (ICVs) has become the hotspot of the intelligent transportation system (ITS), and the integration of mixed autonomous and non-autonomous vehicles that are not cooperative in intersection areas has become a significant challenge due to the rapid advancement of autonomous vehicle technology. Autonomous vehicles in intersections with strong-structure and weak-rule characteristics pose a potential hazard in complex traffic situations. Studying the driving behavior of vehicles in intersections is of great significance due to the complex traffic environment, frequent traffic signals, and traffic violations, which can optimize the vehicle driving behavior and improve the safety and efficiency of intersection traffic. By using naturalistic driving data from the DAIR V2X-Seq dataset and general vehicle dynamic parameters, it is possible to obtain the joint-probability-density distribution of the bivariate dynamic parameters of a vehicle. This distribution represents the driving characteristics of vehicles in intersection traffic. The three vehicle dynamic parameters that have an impact on vehicles driving through the intersection area are velocity, angular velocity, and acceleration. The driving behavior characteristics of human-driven vehicles (HVs) and autonomous vehicles (AVs) were analyzed using the multivariate kernel density estimation (MKDE) method to establish the vehicle-behavior boundary. The assessment of the boundary model showed that it accurately characterizes the driving characteristics of HVs and AVs. This boundary can be used to improve the safety detection of intersection areas, enhancing the performance of autonomous vehicles and optimizing intersection traffic.
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Open AccessArticle
Research on Settlement and Section Optimization of Cemented Sand and Gravel (CSG) Dam Based on BP Neural Network
by
Shuyan Wang, Haixia Yang and Zhanghuan Lin
Appl. Sci. 2024, 14(8), 3431; https://doi.org/10.3390/app14083431 - 18 Apr 2024
Abstract
In order to predict the settlement and compressive stress of the cemented sand and gravel (CSG) dam, and optimize its section design, relying on a CSG dam in the design phase, using finite element software ANSYS, the influence of the dam’s own geometric
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In order to predict the settlement and compressive stress of the cemented sand and gravel (CSG) dam, and optimize its section design, relying on a CSG dam in the design phase, using finite element software ANSYS, the influence of the dam’s own geometric dimensions and the material parameters of the overburden, including upstream and downstream slope coefficients of the first and the second stage of the dam body, the elastic modulus and the Poisson’s ratio of the overburden on the dam’s settlement and compressive stress are studied. An orthogonal experiment with six factors and three levels is conducted for a grey relational analysis of the dam’s maximum settlement and maximum compressive stress separately on these six parameters. Based on the BP neural network, the six selected factors are used as input layers for the neural network prediction model, and the maximum settlement and compressive stress of the dam are taken as the result to be output. The mapping relationship between the geometric dimensions of the dam body and the maximum settlement and the maximum compressive stress in the trained prediction model is combined with the global optimization tool Pattern Search in the MATLAB toolbox to optimize the section design of the dam. The results reveal that the six selected factors have a high correlation degree with the dam’s maximum settlement and maximum compressive stress. In dimension parameters, the downstream slope coefficient of the second stage of the dam has the greatest impact on the maximum settlement, with a grey correlation degree of 0.7367, and the upstream slope coefficient of the second stage of the dam has the greatest impact on the maximum compressive stress, with a grey correlation degree of 0.7012. The influence of the elastic modulus of the overburden on the maximum settlement and maximum compressive stress of the dam body is greater than its Poisson’s ratio. The BP neural network is applicable for predicting the dam’s settlement based on geometric dimension parameters of the dam and material parameters of the surrounding environment, with R2 reaching 0.9996 and RMSE only 0.0109 cm. Based on the optimization method combined with BP neural network, the material consumption is saved by 11.83%, the maximum settlement is reduced by 2.6%, the maximum compressive stress is reduced by 37.35%, and the optimization time is shortened by 40.92%, compared to the traditional method. The findings have certain reference value for site selection, dimension design, overburden treatment, and design optimization of CSG dams.
Full article
(This article belongs to the Section Civil Engineering)
Open AccessArticle
Investigation of Steep Waste Dump Slope Stability of Iron Ore Mine—A Case Study
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Zhongao Yang, Xin Liu, Weimin Qian, Xiaohua Ding, Zhongchen Ao, Zhiyuan Zhang, Izhar Mithal Jiskani, Ya Tian, Bokang Xing and Abdoul Wahab
Appl. Sci. 2024, 14(8), 3430; https://doi.org/10.3390/app14083430 - 18 Apr 2024
Abstract
Using a combination of experimental and numerical methods, this study examines the stability of the slope of Waste Dump#1 in Ziluoyi Iron Mine. We conducted direct shear tests on soil samples taken from the waste dump, which provided important insights into slope stability.
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Using a combination of experimental and numerical methods, this study examines the stability of the slope of Waste Dump#1 in Ziluoyi Iron Mine. We conducted direct shear tests on soil samples taken from the waste dump, which provided important insights into slope stability. The tests identified key mechanical parameters, including an average cohesion of 4.80 kPa and an internal friction angle of 25.63°. By implementing GEO-SLOPE software, we could determine that the slope stability factor is 1.047, which is far from the required safety standards. To address this issue, we proposed an appropriate rectification strategy including the construction of safety platforms and reconfiguration of the slope structure. This approach effectively improved the slope stability factor to 1.219 and met the safety criteria. In addition, particle flow code (PFC) simulations were methodically performed to model the slope morphology and particle displacement before and after rectification. The obtained results revealed a remarkable reduction in sliding areas and particle displacement post-rectification, enhancing mine safety and efficiency. Our findings provide valuable insights into the application of combined experimental and numerical methods to assess and improve slope stability in open-pit mines, which will substantially contribute to the field of geotechnical engineering and mining safety.
Full article
(This article belongs to the Special Issue Advances in Rock Fracture Mechanics: From Microscale Interactions to Macroscopic Failure)
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Open AccessArticle
Exploring Deep Neural Networks in Simulating Human Vision through Five Optical Illusions
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Hongtao Zhang and Shinichi Yoshida
Appl. Sci. 2024, 14(8), 3429; https://doi.org/10.3390/app14083429 - 18 Apr 2024
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Recent research has delved into the biological parallels between deep neural networks (DNNs) in vision and human perception through the study of visual illusions. However, the bulk of this research is currently constrained to the investigation of visual illusions within a single model
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Recent research has delved into the biological parallels between deep neural networks (DNNs) in vision and human perception through the study of visual illusions. However, the bulk of this research is currently constrained to the investigation of visual illusions within a single model focusing on a singular type of illusion. There exists a need for a more comprehensive explanation of visual illusions in DNNs, as well as an expansion in the variety of illusions studied. This study is pioneering in its application of representational dissimilarity matrices and feature activation visualization techniques for a detailed examination of how five classic visual illusions are processed by DNNs. Our findings uncover the potential of DNNs to mimic human visual illusions, particularly highlighting notable differences in how these networks process illusions pertaining to color, contrast, length, angle, and spatial positioning. Although there are instances of consistency between DNNs and human perception in certain illusions, the performance distribution and focal points of interest within the models diverge from those of human observers. This study significantly advances our comprehension of DNNs’ capabilities in handling complex visual tasks and their potential to emulate the human biological visual system. It also underscores the existing gaps in our understanding and processing of intricate visual information. While DNNs have shown progress in simulating human vision, their grasp of the nuance and intricacy of complex visual data still requires substantial improvement.
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Open AccessArticle
Ensemble Empirical Mode Decomposition Granger Causality Test Dynamic Graph Attention Transformer Network: Integrating Transformer and Graph Neural Network Models for Multi-Sensor Cross-Temporal Granularity Water Demand Forecasting
by
Wenhong Wu and Yunkai Kang
Appl. Sci. 2024, 14(8), 3428; https://doi.org/10.3390/app14083428 - 18 Apr 2024
Abstract
Accurate water demand forecasting is crucial for optimizing the strategies across multiple water sources. This paper proposes the Ensemble Empirical Mode Decomposition Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) for multi-sensor cross-temporal granularity water demand forecasting, which combines the Transformer and
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Accurate water demand forecasting is crucial for optimizing the strategies across multiple water sources. This paper proposes the Ensemble Empirical Mode Decomposition Granger causality test Dynamic Graph Attention Transformer Network (EG-DGATN) for multi-sensor cross-temporal granularity water demand forecasting, which combines the Transformer and Graph Neural Networks. It employs the EEMD‒Granger test to delineate the interconnections among sensors and extracts the spatiotemporal features within the causal domain by stacking dynamical graph spatiotemporal attention layers. The experimental results demonstrate that compared to baseline models, the EG-DGATN improves the MAPE metrics by 2.12%, 4.33%, and 6.32% in forecasting intervals of 15 min, 45 min, and 90 min, respectively. The model achieves an R2 score of 0.97, indicating outstanding predictive accuracy and exceptional explanatory power for the target variable. This research highlights significant potential applications in predictive tasks within smart water management systems.
Full article
(This article belongs to the Special Issue Innovative Applications of Artificial Intelligence in Multidisciplinary Sciences: Latest Advances and Prospects)
Open AccessArticle
Variability in Mechanical Properties and Cracking Behavior of Frozen Sandstone Containing En Echelon Flaws under Compression
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Weimin Liu, Li Han, Di Wu, Hailiang Jia and Liyun Tang
Appl. Sci. 2024, 14(8), 3427; https://doi.org/10.3390/app14083427 - 18 Apr 2024
Abstract
The mechanical properties of frozen fissured rock masses are crucial considerations for engineering in frozen earth. However, there has been little research on the mechanical properties of frozen fissured sandstone, including its strength, deformation, and geometric parameters. In this study, sandstone samples with
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The mechanical properties of frozen fissured rock masses are crucial considerations for engineering in frozen earth. However, there has been little research on the mechanical properties of frozen fissured sandstone, including its strength, deformation, and geometric parameters. In this study, sandstone samples with three open en echelon fissures were observed using high-speed photography and acoustic emissions during uniaxial compression tests. The aim was to investigate sandstone’s strength, deformability, and failure process in order to elucidate the effects of freezing on its mechanical properties. In the frozen-saturated and dried states, the uniaxial compression strength (UCS) initially decreases and then increases with an increase in fissure inclination angle. Conversely, the UCS of samples in the saturated state continuously increases. The UCS follows a decreasing trend, as follows: frozen-saturated state > dried state > saturated state. The initial crack angle decreases as the fissure inclination increases in all states, irrespective of temperature and moisture conditions. However, the initial crack stress and time show an increasing trend. The uniaxial compression strength (UCS) of frozen fissured sandstone is influenced by four mechanisms: (1) ice provides support to the rock under compression, (2) ice fills microcracks, (3) unfrozen water films act as a cementing agent under tension or shearing loads, and (4) frost damage leads to softening of the rock.
Full article
(This article belongs to the Special Issue Geo-Environmental Problems Caused by Underground Construction, 2nd Edition)
Open AccessArticle
A Metaverse Platform for Preserving and Promoting Intangible Cultural Heritage
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Chiara Innocente, Francesca Nonis, Antonio Lo Faro, Rossella Ruggieri, Luca Ulrich and Enrico Vezzetti
Appl. Sci. 2024, 14(8), 3426; https://doi.org/10.3390/app14083426 - 18 Apr 2024
Abstract
The metaverse, powered by XR technologies, enables human augmentation by enhancing physical, cognitive, and sensory capabilities. Cultural heritage sees the metaverse as a vehicle for expression and exploration, providing new methods for heritage fruition and preservation. This article proposes a metaverse application, inspired
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The metaverse, powered by XR technologies, enables human augmentation by enhancing physical, cognitive, and sensory capabilities. Cultural heritage sees the metaverse as a vehicle for expression and exploration, providing new methods for heritage fruition and preservation. This article proposes a metaverse application, inspired by the events of the Italian Resistance, promoting interactions between multiple users in an immersive VR experience while safeguarding intangible cultural assets according to an edutainment approach. The virtual environment, based on Ivrea’s town hall square, provides in-depth information about the partisan’s life and the historical value of its actions for the city. Furthermore, the application allows users to meet in the same virtual place and engage with one another in real time through the Spatial SDK. Before the public presentation, a heterogeneous group of thirty users underwent usability and engagement tests to assess the experience on both VR headsets and smartphones. Tests revealed statistically significant evidence that there is a genuine difference in users’ perceptions of usability and engagement with different devices and types of interaction. This study highlights the effectiveness of adopting XR as a supporting technology to complement the real experience of cultural heritage valorization.
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(This article belongs to the Special Issue Advanced Technologies Applied to Cultural Heritage)
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An Enhanced Deep Knowledge Tracing Model via Multiband Attention and Quantized Question Embedding
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Jiazhen Xu and Wanting Hu
Appl. Sci. 2024, 14(8), 3425; https://doi.org/10.3390/app14083425 - 18 Apr 2024
Abstract
Knowledge tracing plays a crucial role in effectively representing learners’ understanding and predicting their future learning progress. However, existing deep knowledge tracing methods, reliant on the forgetting model and Rasch model, often fail to account for the varying rates at which learners forget
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Knowledge tracing plays a crucial role in effectively representing learners’ understanding and predicting their future learning progress. However, existing deep knowledge tracing methods, reliant on the forgetting model and Rasch model, often fail to account for the varying rates at which learners forget different knowledge concepts and the variations in question embedding covering the same concept. To address these limitations, this paper introduces an enhanced deep knowledge tracing model that combines the transformer network model with two innovative components. The first component is a multiband attention mechanism, which comprehensively summarizes a learner’s past response history across various temporal scales. By computing attention weights using different decay rates, this mechanism adaptively captures both long-term and short-term interactions for different knowledge concepts. The second component utilizes a quantized question embedding module to effectively capture variations among questions addressing the same knowledge concept. This module represents these differences in a rich embedding space, avoiding overparameterization or overfitting issues. The proposed model is evaluated on popular benchmark datasets, demonstrating its superiority over existing knowledge tracing methods in accuracy. This enhancement holds potential for improving personalized learning systems by providing more precise insights into learners’ progress.
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(This article belongs to the Section Computing and Artificial Intelligence)
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Open AccessArticle
Advancing Brain Tumor Segmentation with Spectral–Spatial Graph Neural Networks
by
Sina Mohammadi and Mohamed Allali
Appl. Sci. 2024, 14(8), 3424; https://doi.org/10.3390/app14083424 - 18 Apr 2024
Abstract
In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial
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In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and global information. Our Spectral GNN implementation employs the Laplacian matrix to efficiently map tumor tissue connectivity by capturing the graph’s global structure. Consequently, this enhances the model’s precision in classifying brain tumors into distinct types: necrosis, edema, and enhancing tumor. This model underwent extensive hyper-parameter tuning to ascertain the most effective configuration for optimal segmentation performance. Our Spectral–Spatial GNN model surpasses traditional segmentation methods in accuracy for both whole tumor and sub-regions, validated by metrics such as the dice coefficient and accuracy. For the necrotic core, the Spectral–Spatial GNN model showed a 10.6% improvement over the Spatial GNN and 8% over the Spectral GNN. Enhancing tumor gains were 9.5% and 6.4%, respectively. For edema, improvements were 12.8% over the Spatial GNN and 7.3% over the Spectral GNN, highlighting its segmentation accuracy for each tumor sub-region. This superiority underscores the model’s potential in improving brain tumor segmentation accuracy, precision, and computational efficiency.
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(This article belongs to the Special Issue Graph-Based Methods in Artificial Intelligence and Machine Learning)
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Open AccessArticle
Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
by
Jie Duan, Jiyu Li, Yundong Zhu, Hongtao Zhang, Yuting Liu and Yanan Zhao
Appl. Sci. 2024, 14(8), 3423; https://doi.org/10.3390/app14083423 - 18 Apr 2024
Abstract
Addressing the prevalent issues of low accuracy, low efficiency, and poor image quality in online diameter measurement of large-sized stepped shafts, this study introduces a novel method based on a symmetrical dual-telecentric optical path utilizing dual CCDs, specifically designed for step shafts with
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Addressing the prevalent issues of low accuracy, low efficiency, and poor image quality in online diameter measurement of large-sized stepped shafts, this study introduces a novel method based on a symmetrical dual-telecentric optical path utilizing dual CCDs, specifically designed for step shafts with diameters ranging from 600 mm to 800 mm. By developing and optimizing an imaging system grounded in the object-image dual-telecentric optical path principle and employing Zemax software for comprehensive analysis and optimization, this research achieves significant findings. The system’s Airy disk radius is calculated at 3.204 μm; the modulation transfer function (MTF) remains above 0.6 across various fields of view at a spatial cutoff frequency of 71.4 lp/mm, with smooth MTF curves; the field curvature is confined within 0.1 μm; and the distortion is maintained below 0.1%, fulfilling high-quality imaging requirements. Additionally, a tolerance analysis is conducted to ensure the system’s stability and reliability. Applied to an experimental setup for measuring the diameter of large-sized step shafts, the system demonstrates an improved measurement precision of 0.02 mm. This research offers a robust technical solution for the high-precision online measurement of large stepped shaft diameters, presenting significant practical implications for enhancing productivity and product quality.
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(This article belongs to the Collection Optical Design and Engineering)
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Open AccessArticle
Multilevel Distributed Linear State Estimation Integrated with Transmission Network Topology Processing
by
Dulip Madurasinghe and Ganesh Kumar Venayagamoorthy
Appl. Sci. 2024, 14(8), 3422; https://doi.org/10.3390/app14083422 - 18 Apr 2024
Abstract
State estimation (SE) is an important energy management system application for power system operations. Linear state estimation (LSE) is a variant of SE based on linear relationships between state variables and measurements. LSE estimates system state variables, including bus voltage magnitudes and angles
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State estimation (SE) is an important energy management system application for power system operations. Linear state estimation (LSE) is a variant of SE based on linear relationships between state variables and measurements. LSE estimates system state variables, including bus voltage magnitudes and angles in an electric power transmission network, using a network model derived from the topology processor and measurements. Phasor measurement units (PMUs) enable the implementation of LSE by providing synchronized high-speed measurements. However, as the size of the power system increases, the computational overhead of the state-of-the-art (SOTA) LSE grows exponentially, where the practical implementation of LSE is challenged. This paper presents a distributed linear state estimation (D-LSE) at the substation and area levels using a hierarchical transmission network topology processor (H-TNTP). The proposed substation-level and area-level D-LSE can efficiently and accurately estimate system state variables at the PMU rate, thus enhancing the estimation reliability and efficiency of modern power systems. Network-level LSE has been integrated with H-TNTP based on PMU measurements, thus enhancing the SOTA LSE and providing redundancy to substation-level and area-level D-LSE. The implementations of D-LSE and enhanced LSE have been investigated for two benchmark power systems, a modified two-area four-machine power system and the IEEE 68 bus power system, on a real-time digital simulator. The typical results indicate that the proposed multilevel D-LSE is efficient, resilient, and robust for topology changes, bad data, and noisy measurements compared to the SOTA LSE.
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(This article belongs to the Special Issue New Insights into Power System Resilience)
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