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Zirconium-Containing Metal–Organic Frameworks (MOFs) as Catalysts for Biomass Conversion
Implementing Deep Neural Networks on ARM-Based Microcontrollers: Application for Ventricular Fibrillation Detection
Impact of Cross-Linking-Monomer Characteristics on Pore-Filling-Membrane Performance and Durability in Anion-Exchange Water Electrolysis
Innovative Antibacterial Polymer Coatings
Exploring Adsorption Performance of Functionalized Mesoporous Silicas with a Different Pore Structure as Strong Cation-Exchange Sorbents for Solid-Phase Extraction of Atropine and Scopolamine

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), Ei Compendex, Inspec, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q1 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.4 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2024).
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, JETA and AI in Medicine.

Impact Factor: 2.5 (2023); 5-Year Impact Factor: 2.7 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2076-3417

Latest Articles

16 pages, 4811 KiB

Open AccessArticle

Characteristics of the Damping Ratio of Undisturbed Offshore Silty Clay in Eastern Guangdong, China

by Peng Guo, Youhu Zhang and Qian Bi

Appl. Sci. 2025, 15(9), 4954; https://doi.org/10.3390/app15094954 (registering DOI) - 29 Apr 2025

Soil–pile interaction damping plays a crucial role in reducing wind turbine loads and fatigue damage in monopile foundations, thus aiding in the optimized design of offshore wind structures and lowering construction and installation costs. Investigating the damping properties at the element level is [...] Read more.

Soil–pile interaction damping plays a crucial role in reducing wind turbine loads and fatigue damage in monopile foundations, thus aiding in the optimized design of offshore wind structures and lowering construction and installation costs. Investigating the damping properties at the element level is essential for studying monopole–soil damping. Given the widespread distribution of silty clay in China’s seas, it is vital to conduct targeted studies on its damping characteristics. The damping ratio across the entire strain range is measured using a combination of resonant column and cyclic simple shear tests, with the results compared to predictions from widely used empirical models. The results indicate that the damping ratio–strain curve for silty clay remains “S”-shaped, with similar properties observed between overconsolidated and normally consolidated silty clay. While empirical models accurately predict the damping ratio at low strain levels, they tend to overestimate it at medium-to-high strain levels. This discrepancy should be considered when using empirical models in the absence of experimental data for engineering applications. The results in this study are significant for offshore wind earthquake engineering and structural optimization. Full article

(This article belongs to the Special Issue Seepage Problems in Geotechnical Engineering)

29 pages, 1505 KiB

Open AccessArticle

A Hybrid CNN-LSTM Approach for Muscle Artifact Removal from EEG Using Additional EMG Signal Recording

by Marcin Kołodziej, Marcin Jurczak, Andrzej Majkowski, Andrzej Rysz and Bartosz Świderski

Appl. Sci. 2025, 15(9), 4953; https://doi.org/10.3390/app15094953 (registering DOI) - 29 Apr 2025

Removing artifacts from electroencephalography (EEG) signals is a common technique. Although numerous algorithms have been proposed, most rely solely on EEG data. In this study, we introduce a novel approach utilizing a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture alongside simultaneous recording [...] Read more.

Removing artifacts from electroencephalography (EEG) signals is a common technique. Although numerous algorithms have been proposed, most rely solely on EEG data. In this study, we introduce a novel approach utilizing a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture alongside simultaneous recording of facial and neck EMG signals. This setup enables the precise elimination of artifacts from the EEG signal. To validate the method, we collected a dataset from 24 participants who were presented with a light-emitting diode (LED) stimulus that elicited steady-state visual evoked potentials (SSVEPs) while they performed strong jaw clenching, an action known to induce significant artifacts. We then assessed the algorithm’s ability to remove artifacts while preserving SSVEP responses. The results were compared against other commonly used algorithms, such as independent component analysis and linear regression. The findings demonstrate that the proposed method exhibits excellent performance, effectively removing artifacts while retaining the EEG signal’s useful components. Full article

(This article belongs to the Section Biomedical Engineering)

19 pages, 278 KiB

Open AccessArticle

Prosthesis Embodiment in Lower Extremity Limb Loss: A Narrative Review

by Tuyet Thao Nguyen, Bingjie Wang, Haddy Alas, Quincy Jones, Chase Clark, Sabrina Lazar, Shaddy Malik, Joshua Graham , Yasmeen Talaat, Chris Shin, Jonathon Schofield, Toran Macleod, Laduan Smedley, Clifford Pereira, Wilsaan Joiner, R. Lor Randall, Diana Farmer, Aijun Wang, Dake Hao, Spencer Greene, Ravi Sood, Danielle Brown, Rachel Russo, Kingsley Manoharan, Andrew Simpkins and Andrew Li Show full author list Hide full author list

Appl. Sci. 2025, 15(9), 4952; https://doi.org/10.3390/app15094952 (registering DOI) - 29 Apr 2025

Lower limb prosthesis abandonment is a significant challenge, leading to reliance on walking aids, such as wheelchairs, which frequently do not match the patient’s needs and lead to increased morbidity. Prosthesis abandonment is driven by a lack of embodiment, the latter defined as [...] Read more.

Lower limb prosthesis abandonment is a significant challenge, leading to reliance on walking aids, such as wheelchairs, which frequently do not match the patient’s needs and lead to increased morbidity. Prosthesis abandonment is driven by a lack of embodiment, the latter defined as the integration of a prosthetic device into one’s body schema. This review evaluates interventions enhancing embodiment through three dimensions: ownership, agency, and co-location. The aim of this narrative review is to ask what interventions are available to improve embodiment, and what dimensions of embodiment should be included in the standard of care for lower-limb amputation surgery and componentry development. This narrative is constructed through a thorough literature search on how the aforementioned dimensions of embodiment can be optimized. In the studies reviewed, standardization of embodiment metrics and longitudinal data are lacking, hindering clinical translation. Future work must prioritize patient-centered design, integrate multidimensional assessments, and address practical issues to expand eligibility for advanced interventions. Full article

(This article belongs to the Special Issue New Insight into the Biomechanics of Lower Limb: Prevention, Injury, Rehabilitation and Replacements)

19 pages, 1443 KiB

Open AccessArticle

A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems

by Dapeng Yan, Qingshu Guan, Bei Ou, Bowen Yan, Zheng Zhu and Hui Cao

Appl. Sci. 2025, 15(9), 4951; https://doi.org/10.3390/app15094951 (registering DOI) - 29 Apr 2025

In recent years, routing problems have attracted significant attention in the fields of operations research and computer science due to their fundamental importance in logistics and transportation. However, most existing learning-based methods employ simplistic context embeddings to represent the routing environment, which constrains [...] Read more.

In recent years, routing problems have attracted significant attention in the fields of operations research and computer science due to their fundamental importance in logistics and transportation. However, most existing learning-based methods employ simplistic context embeddings to represent the routing environment, which constrains their capacity to capture real-time visitation dynamics. To address this limitation, we propose a deep reinforcement learning-based decision-making framework (DRL-DM) built upon an encoder–decoder architecture. The encoder incorporates a batch normalization fronting mechanism and a gate-like threshold block to enhance the quality of node embeddings and improve convergence speed. The decoder constructs a dynamic-aware context embedding that integrates relational information among visited and unvisited nodes, along with the start and terminal locations, thereby enabling effective tracking of real-time state transitions and graph structure variations. Furthermore, the proposed approach exploits the intrinsic symmetry and circularity of routing solutions and adopts an actor–critic training paradigm with multiple parallel trajectories to improve exploration of the solution space. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate that DRL-DM consistently outperforms heuristic and learning-based baselines, achieving up to an 8.75% reduction in tour length. Moreover, the proposed method exhibits strong generalization capabilities, effectively scaling to larger problem instances and diverse node distributions, thereby highlighting its potential for solving complex, real-life routing tasks. Full article

38 pages, 28331 KiB

Open AccessArticle

Robustness Benchmark Evaluation and Optimization for Real-Time Vehicle Detection Under Multiple Adverse Conditions

by Jianming Cai, Yifan Gao and Jinjun Tang

Appl. Sci. 2025, 15(9), 4950; https://doi.org/10.3390/app15094950 (registering DOI) - 29 Apr 2025

This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and [...] Read more.

This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and optimizing the robustness of vehicle detection in real traffic scenarios remains challenging. When data distributions change, such as the impact of adverse weather or sensor damages, model reliability cannot be guaranteed. We first conducted a large-scale robustness benchmark evaluation for vehicle detection. Analysis revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. The impact of color changes and noise, while present, is relatively less pronounced. Moreover, the robustness of vehicle detection is closely linked to its baseline performance and model size. And as the severity of corruption intensifies, the performance of models experiences a sharp drop. When the data distribution of images changes, the features of the vehicles that the model focuses on are weakened, making the activation level of the targets significantly reduced. By evaluation, we provided guidance and direction for optimizing detection robustness. Based on these findings, we propose TDIRM, a traffic-degraded image restoration model based on stable diffusion, designed to efficiently restore degraded images in real traffic scenarios and thereby enhance the robustness of vehicle detection. The model introduces an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Through a triple condition control mechanism, TDIRM achieves restoration results with high fidelity and consistency. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration. Full article

(This article belongs to the Special Issue Advances in Autonomous Driving and Smart Transportation)

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31 pages, 1306 KiB

Open AccessArticle

Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method

by Jin Ran, Meiling Li, Jian Rong, Ding Zhao, Ahmetjan Kadir and Qiang Luo

Appl. Sci. 2025, 15(9), 4949; https://doi.org/10.3390/app15094949 (registering DOI) - 29 Apr 2025

With the rapid expansion of highway networks, the demand for timely and reliable road information has steadily increased. However, some guide signs on newly built or extended highways in China have not been updated or adjusted in time, resulting in incomplete information and [...] Read more.

With the rapid expansion of highway networks, the demand for timely and reliable road information has steadily increased. However, some guide signs on newly built or extended highways in China have not been updated or adjusted in time, resulting in incomplete information and non-standard setups. These issues not only affect drivers’ navigation experience but may also negatively impact road safety and traffic efficiency. Therefore, it is crucial to establish a scientifically sound evaluation system and a comprehensive assessment model for highway guide signs. This study selected a representative highway (G2 Expressway in China) as the research subject and combined questionnaire surveys with field investigations to identify common issues such as vague information and irregular placement of guide signs. Through an in-depth analysis of travel demand, the core requirements of drivers were summarized as safety, efficiency, and comfort. Based on these insights, the study proposes four key design principles for guide signs: standardization, readability, continuity, and consistency. A set of quantifiable evaluation indicators was developed through a comprehensive analysis of key factors affecting signage performance, and factor analysis was applied to verify the independence and rationality of the indicators. On this basis, an evaluation model was constructed using the technique for order preference by similarity to ideal solution (TOPSIS) to scientifically quantify the effectiveness of guide signs. The model was applied in a field study on the Hebei section of the G2 Expressway in China (with comprehensive traffic sign coverage, high traffic volume, and more traffic sign issues), with results demonstrating the feasibility and practicality of the proposed evaluation system and model. This research offers a systematic solution to enhance the service quality of highway guide signs and provides essential references for future highway planning and management practices. It aims to comprehensively improve drivers’ travel experiences and promote the development of sustainable and intelligent transportation networks, offering valuable insights for building integrated urban systems. Full article

(This article belongs to the Special Issue Intelligent Management and Application of Sustainable Transportation Systems)

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18 pages, 11518 KiB

Open AccessArticle

Use of Model-Based Weather Forecasting Systems for Validation of Areas for Marine Energy Deployment in Port Service Areas

by Raúl Cascajo, Rafael Molina-Sánchez and Gabriel Diaz-Hernandez

Appl. Sci. 2025, 15(9), 4948; https://doi.org/10.3390/app15094948 (registering DOI) - 29 Apr 2025

Ports function as logistical hubs through which approximately 80% of the world’s goods are transported annually. Recent regulatory frameworks from the International Maritime Organization (IMO) and the European Union require ships and ports to adopt measures aimed at minimizing the environmental impact of [...] Read more.

Ports function as logistical hubs through which approximately 80% of the world’s goods are transported annually. Recent regulatory frameworks from the International Maritime Organization (IMO) and the European Union require ships and ports to adopt measures aimed at minimizing the environmental impact of port activities and mitigate climate change. These measures include investing in renewable energy generation systems to transition from fossil fuel-based energy to renewable electricity. Consequently, to meet increasing energy demands, new energy infrastructure must be developed. However, due to spatial constraints in port environments, there is a growing interest in utilizing port service areas, inner docks, and exterior/adjacent water zones for the deployment of marine renewable energy generation systems. This study applies high-resolution meteorological and oceanographic modelling—incorporating validated wave agitation models—to assess the feasibility of integrating marine renewable energy generation within port service areas. Full article

(This article belongs to the Section Green Sustainable Science and Technology)

21 pages, 1049 KiB

Open AccessArticle

Efficient Implementation of Matrix-Based Image Processing Algorithms for IoT Applications

by Sorin Zoican and Roxana Zoican

Appl. Sci. 2025, 15(9), 4947; https://doi.org/10.3390/app15094947 - 29 Apr 2025

This paper analyzes implementation approaches of matrix-based image processing algorithms. As an example, an image processing algorithm that provides both image compression and image denoising using random sample consensus and discrete cosine transform is analyzed. Two implementations are illustrated: one using the Blackfin [...] Read more.

This paper analyzes implementation approaches of matrix-based image processing algorithms. As an example, an image processing algorithm that provides both image compression and image denoising using random sample consensus and discrete cosine transform is analyzed. Two implementations are illustrated: one using the Blackfin processor with 32-bit fixed-point representation and the second using the convolutional neural network (CNN) accelerator in the MAX78000 microcontroller. Implementation with Blackfin can be considered a classic approach, in C language, possible on all existing microcontrollers. This implementation is improved by using two cores. The proposed implementation with the CNN accelerator is a new approach that effectively uses the dedicated accelerator for convolutional neural networks, with better results than a classical implementation. The execution time of matrix-based image processing algorithms can be reduced by using CNN accelerators already integrated in some modern microcontrollers to implement artificial intelligence algorithms. The proposed method uses CNN in a different way, not for artificial intelligence algorithms, but for matrix calculations using CNN resources effectively while maintaining the accuracy of the calculations. A comparison of these two implementations and the validation using MATLAB with 64 bits floating point representation are conducted. The obtained performance is good both in terms of quality of reconstructed image and execution time, and the performance differences between the infinite precision implementation and the finite precision implementation are small. The CNN accelerator implementation, based on matrix multiplication implemented using CNN, has a better performance suitable for Internet of Things applications. Full article

22 pages, 5554 KiB

Open AccessArticle

A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region

by Qiang Zhang and Hsin Guan

Appl. Sci. 2025, 15(9), 4946; https://doi.org/10.3390/app15094946 (registering DOI) - 29 Apr 2025

Road traffic regulations usually require that a vehicle can only move one lane during one lane change and must turn on the turn signal before changing lanes. Under such constraints, if automated vehicles can plan multiple lane-change maneuvers at one time, then not [...] Read more.

Road traffic regulations usually require that a vehicle can only move one lane during one lane change and must turn on the turn signal before changing lanes. Under such constraints, if automated vehicles can plan multiple lane-change maneuvers at one time, then not only adjacent lanes but also farther lanes can be selected as target lanes when making decisions. This would help improve the driving performance in multi-lane scenarios. Many current lane-selection or lane-change methods focus on the surrounding region of the ego vehicle, usually only considering adjacent lanes as potential target lanes. This paper proposes a new tactical functional model that attempts to perform lane-level driving task planning and decision-making over a road area far beyond the surrounding region of the ego vehicle. We refer to this road area as the “outlook region”. In this functional model, the decision-making of lane-level driving tasks will take the overall performance within the outlook region as the goal, rather than pursuing the optimal single lane-change maneuver. The proposed method is implemented using a directed graph-based approach and simulation tests are conducted. The results show that the proposed method helps improve the driving performance of automated vehicles in multi-lane scenarios. Full article

(This article belongs to the Section Transportation and Future Mobility)

14 pages, 5277 KiB

Open AccessArticle

Analysis of the Applicability of the Yukawa Model and Chapman–Enskog Approach for Heated Beryllium at Metallic Density Using Quantum Molecular Dynamics

by Moldir Issanova, Nasriddin Djienbekov, Tlekkabul Ramazanov, Gaukhar Omiraliyeva, Sandugash Kodanova and Akmaral Kenzhebekova

Appl. Sci. 2025, 15(9), 4945; https://doi.org/10.3390/app15094945 - 29 Apr 2025

We conducted a comprehensive analysis of quantum molecular dynamics (QMD) simulation results for beryllium (Be) at metallic density and temperatures up to 32,000 K. Using the QMD results for the radial distribution function (RDF), velocity autocorrelation function (VACF), mean-squared displacement (MSD), and the [...] Read more.

We conducted a comprehensive analysis of quantum molecular dynamics (QMD) simulation results for beryllium (Be) at metallic density and temperatures up to 32,000 K. Using the QMD results for the radial distribution function (RDF), velocity autocorrelation function (VACF), mean-squared displacement (MSD), and the diffusion coefficient of ions, we confidently assess the effectiveness of the Yukawa one-component plasma model in describing ion structure and transport properties. Additionally, we analyzed the applicability and accuracy of the Chapman–Enskog method for calculating the diffusion coefficient. We found that Yukawa model-based molecular dynamics (MD) simulations accurately capture ion dynamics, as evidenced by the VACF and MSD, when the Yukawa potential parameters are correctly chosen. Through our comparative analysis of the QMD, Yukawa–MD, and Chapman–Enskog methods, we clearly identified the effective coupling parameter values at which the Chapman–Enskog method maintains its accuracy. Importantly, while a model that reproduces the RDF of ions may not guarantee precise transport properties, our findings underscore the necessity of benchmarking plasma models against QMD results from real materials to validate their applicability and efficacy. Full article

(This article belongs to the Section Applied Physics General)

16 pages, 16344 KiB

Open AccessArticle

Simulation-Guided Path Optimization for Resolving Interlocked Hook-Shaped Components

by Tomas Merva, Peter Jan Sincak, Robert Rakay, Martin Varga, Michal Kelemen and Ivan Virgala

Appl. Sci. 2025, 15(9), 4944; https://doi.org/10.3390/app15094944 - 29 Apr 2025

Manipulators performing pick-and-place tasks with objects of complex shapes must consider not only how to grasp the objects but also how to maneuver them out of a bin. In this paper, we explore the industrial challenge of picking hook-shaped components, whose interlocking nature [...] Read more.

Manipulators performing pick-and-place tasks with objects of complex shapes must consider not only how to grasp the objects but also how to maneuver them out of a bin. In this paper, we explore the industrial challenge of picking hook-shaped components, whose interlocking nature often leads to failed attempts at safely retrieving a single component at a time. Rather than explicitly modeling contact-rich interactions within optimization-based motion planners, we tackle this challenge by leveraging recent advances in sampling-based optimization and parallelizable physics simulators to predict the impact of motion on the separating subgoal, aimed at resolving interlocking. The proposed framework generates candidate trajectories initialized from a user-provided demonstration, which are then simulated and evaluated in a physics simulator to optimize robot trajectories in joint space while considering the entire planning horizon. We validate our approach through real-world experiments on a manipulator, demonstrating improved success rates in terms of separating interlocked objects compared to the industrial baseline. Full article

(This article belongs to the Special Issue Automation and Robotics: Latest Achievements, Challenges and Prospects, Volume 2)

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23 pages, 4769 KiB

Open AccessArticle

Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches

by Jiacheng Li, Shuyun Xie, Wenbing Yang, Weihang Zhou, Emmanuel John M. Carranza, Weiji Wen and Hongtao Shi

Appl. Sci. 2025, 15(9), 4943; https://doi.org/10.3390/app15094943 - 29 Apr 2025

Selenium-rich foods play a crucial role in human health and hold significant economic value for agricultural products. However, many regions in China are experiencing selenium deficiency, which has led to an increased demand for Se-rich agricultural products. This study focused on Nanzhang County, [...] Read more.

Selenium-rich foods play a crucial role in human health and hold significant economic value for agricultural products. However, many regions in China are experiencing selenium deficiency, which has led to an increased demand for Se-rich agricultural products. This study focused on Nanzhang County, a key area within the “Organic Valley” of Hubei Province, China. We employed fuzzy weights-of-evidence, backpropagation neural network, and support vector regression models to predict optimal planting zones for Selenium-rich crops. A comparative analysis indicated that the backpropagation neural network model provided the highest prediction accuracy (R²= 0.77), identifying Selenium-rich crop zones covering 68.87% of the aera, where Selenium-rich crops made up 86.67% of all samples. Notably, the backpropagation neural network yielded excellent performance for rice and rapeseed, with R² values of 0.95 and 0.99, respectively. The findings also indicate that the Selenium content in crops is closely linked to Selenium levels in the soil and is significantly influenced by synergistic and antagonistic interactions with other elements. This study provides scientific support for the cultivation of selenium-rich crops. It plays a positive role in promoting the development of the local selenium-rich industry and the sustainable utilization of soil selenium resources. Full article

(This article belongs to the Special Issue Recent Advances in Geochemistry)

12 pages, 656 KiB

Open AccessArticle

The Impact of the S−Adenosylmethionine Analogue Sinefungin on Viral Life Cycles

by Federica Dell’Annunziata, Nicoletta Capuano, Mariagrazia De Prisco, Sandra Rufolo, Veronica Folliero and Gianluigi Franci

Appl. Sci. 2025, 15(9), 4942; https://doi.org/10.3390/app15094942 - 29 Apr 2025

DNA and RNA methylation are essential epigenetic modifications that play a crucial role in regulating gene expression and cellular processes. Methylation is also significant in viral infections, influencing various stages of the viral life cycle and immune evasion. In this study, we investigated [...] Read more.

DNA and RNA methylation are essential epigenetic modifications that play a crucial role in regulating gene expression and cellular processes. Methylation is also significant in viral infections, influencing various stages of the viral life cycle and immune evasion. In this study, we investigated the antiviral potential of sinefungin, a potent methyltransferase inhibitor, against Herpes Simplex Virus 1 (HSV−1) and SARS−CoV−2. The cytotoxic effect of sinefungin was evaluated on VERO−76 cells by exposing them to concentrations ranging from 12.5 to 200 μg/mL for 24 h. The MTT assay results indicated that sinefungin reduced cell viability by approximately 21.7% at the highest concentration tested, with a CC₅₀ above 200 μg/mL. Our results demonstrated that sinefungin exhibited significant antiviral activity against both HSV−1 and SARS−CoV−2, with IC₅₀ values of 49.5 ± 0.31 μg/mL for HSV−1 and 100.1 ± 2.61 μg/mL for SARS−CoV−2. These results suggest that sinefungin may be a promising therapeutic candidate for treating viral infections, particularly those involving methylation−dependent processes. Full article

22 pages, 6086 KiB

Open AccessArticle

A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification

by A. M. Mutawa and Sai Sruthi

Appl. Sci. 2025, 15(9), 4941; https://doi.org/10.3390/app15094941 - 29 Apr 2025

Arabic poetry follows intricate rhythmic patterns known as ‘arūḍ’ (prosody), which makes its automated categorization particularly challenging. While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models—an area not extensively explored for [...] Read more.

Arabic poetry follows intricate rhythmic patterns known as ‘arūḍ’ (prosody), which makes its automated categorization particularly challenging. While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models—an area not extensively explored for this task. We investigate several pretrained transformer models, including Arabic Bidirectional Encoder Representations from Transformers (Arabic-BERT), BERT base Arabic (AraBERT), Arabic Efficiently Learning an Encoder that Classifies Token Replacements Accurately (AraELECTRA), Computational Approaches to Modeling Arabic BERT (CAMeLBERT), Multi-dialect Arabic BERT (MARBERT), and Modern Arabic BERT (ARBERT), alongside deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU). This study uses half-verse data across 14 m. The CAMeLBERT model achieved the highest performance, with an accuracy of 90.62% and an F1-score of 0.91, outperforming other models. We further analyze feature significance and model behavior using the Local Interpretable Model-Agnostic Explanations (LIME) interpretability technique. The LIME-based analysis highlights key linguistic features that most influence model predictions. These findings demonstrate the strengths and limitations of each method and pave the way for further advancements in Arabic poetry analysis using deep learning. Full article

(This article belongs to the Special Issue Neural Network Technologies in Natural Language Processing and Data Mining)

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28 pages, 10428 KiB

Open AccessArticle

Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning

by Rong Guo, Rou Dong, Ni Lu, Lin Yu, Chaoxian Chen, Yonglin Che, Jiajin Zhang and Jianke Yang

Appl. Sci. 2025, 15(9), 4940; https://doi.org/10.3390/app15094940 - 29 Apr 2025

With the rapid development of society and technology, the physical health of university students has become a critical concern, influencing both individual well-being and the national talent pool. This study employs an improved K-means algorithm integrated with machine learning models to analyze university [...] Read more.

With the rapid development of society and technology, the physical health of university students has become a critical concern, influencing both individual well-being and the national talent pool. This study employs an improved K-means algorithm integrated with machine learning models to analyze university students’ fitness data and develop personalized health intervention strategies. The enhanced K-means algorithm overcomes the limitations of traditional clustering approaches, leading to improved clustering accuracy and stability. Machine learning models—including Random Forest, decision trees, Gradient Boosting Trees, and logistic regression—were utilized to validate the clustering outcomes and to identify key health indicators associated with different student groups. Based on the clustering and model analysis, targeted intervention programs are proposed, such as strength training for groups with low muscular explosiveness, endurance training for those with stamina deficiencies, and flexibility exercises for groups exhibiting limited mobility. This integrated analytical framework provides a scientifically grounded tool for comprehensive health assessments and offers actionable, data-driven recommendations for student health management. Future research will focus on optimizing algorithmic performance, enhancing data diversity, and broadening the application scope to further improve the effectiveness and feasibility of health interventions. Full article

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29 pages, 2852 KiB

Open AccessArticle

Smart Buildings and Digital Twin to Monitoring the Efficiency and Wellness of Working Environments: A Case Study on IoT Integration and Data-Driven Management

by Giuseppe Piras, Sofia Agostinelli and Francesco Muzi

Appl. Sci. 2025, 15(9), 4939; https://doi.org/10.3390/app15094939 - 29 Apr 2025

Quality and efficiency of the work environment are essential to the well-being, health and productivity of employees. Despite the increasing focus on these aspects, many workplaces currently do not fully meet the needs and expectations of employees, with negative consequences for their well-being [...] Read more.

Quality and efficiency of the work environment are essential to the well-being, health and productivity of employees. Despite the increasing focus on these aspects, many workplaces currently do not fully meet the needs and expectations of employees, with negative consequences for their well-being and productivity. The research aims to develop a system based on the Smart Building and Digital Twin paradigm, focusing on the implementation of various IoT components, the creation of automation flows for energy-efficient lighting, HVAC and indoor air quality control systems, and decision support through real-time data visualization enabled by user interfaces and dashboards integrating the geometric and information model (BIM). The system also aims to provide a tool for both monitoring and simulation/planning/decision support through the processing and development of machine learning (ML) algorithms. In relation to emergency management, real-time data can be acquired, allowing information to be shared with users and building managers through the creation of dashboards and visual analysis. After defining the functional requirements and identifying all3 the monitorable quantities that can be translated into requirements, the system architecture is described, the implementation of the case study is illustrated and the preliminary results of the first data collection campaign and initial estimates of future forecasts are shown. Full article

32 pages, 4883 KiB

Open AccessArticle

Horizontal Control System for Maglev Ruler Based on Improved Active Disturbance Rejection Controller

by Gengyun Tian, Chunlin Tian, Jiyuan Sun and Shusen Diao

Appl. Sci. 2025, 15(9), 4938; https://doi.org/10.3390/app15094938 - 29 Apr 2025

This paper is centered around the autonomous displacement of the maglev ruler system, with the core objective of optimizing the performance of its horizontal control system. The horizontal positioning precision of the maglev ruler’s mover core plays a crucial role in determining the [...] Read more.

This paper is centered around the autonomous displacement of the maglev ruler system, with the core objective of optimizing the performance of its horizontal control system. The horizontal positioning precision of the maglev ruler’s mover core plays a crucial role in determining the survey accuracy. Moreover, its horizontal system exhibits distinct nonlinear and strongly coupled characteristics. Initially, a mathematical model is meticulously established based on the principles of magnetic circuits and dynamics. By analyzing the relationship between the Ampere force and the coil current, and constructing the dynamic equations, it is clearly demonstrated that this system belongs to a nonlinear and strongly coupled type with two inputs and two outputs. Subsequently, the active disturbance rejection control algorithm is employed to address the decoupling issue, and a novel differential tracker, SYSTD, is designed. SYSTD is constructed using specific elementary functions. Through rigorous theoretical derivation, its favorable stability is verified. The basis for parameter selection is obtained through phase-plane analysis, and a detailed tuning method is provided. Additionally, a sensor-less control technology is proposed, where survey coils are wound along the horizontal control coils to estimate the position of the mover core. The simulation and experimental results indicate that the improved system showcases excellent performance. In the simulation, the positioning accuracy can reach ±5 μm, while, in the experiment, the control accuracy can achieve ±2 μm. It can effectively realize decoupling control and possesses good dynamic, static characteristics, as well as remarkable robustness. This research paves the way for the practical application of the maglev ruler system in fields such as coordinate survey. Full article

14 pages, 557 KiB

Open AccessArticle

Home-Based vs. Clinic-Based Rehabilitation After Joint Arthroplasty: A Prospective Matched Cohort Study

by Erminia Cofano, Filippo Familiari, Tommaso Mori, Michele Mercurio, Andrea Vescio, Alessandro Giorgio, Giorgio Gasparini and Giuseppe Calafiore

Appl. Sci. 2025, 15(9), 4937; https://doi.org/10.3390/app15094937 - 29 Apr 2025

Background: Post-operative rehabilitation after total hip arthroplasty (THA) and total knee arthroplasty (TKA) is a crucial phase in the recovery process. The choice between clinic-based rehabilitation (CBR) and home-based rehabilitation (HBR) depends on the patient’s specific needs, available resources, and individual preferences. This [...] Read more.

Background: Post-operative rehabilitation after total hip arthroplasty (THA) and total knee arthroplasty (TKA) is a crucial phase in the recovery process. The choice between clinic-based rehabilitation (CBR) and home-based rehabilitation (HBR) depends on the patient’s specific needs, available resources, and individual preferences. This study aimed to compare CBR and HBR in terms of short-term post-operative functionality in patients who underwent THA and TKA. Methods: A prospective matched cohort study was performed on 120 patients who underwent primary THA and TKA; 60 patients underwent HBR, and 60 underwent CBR. Data gathered included instrumental activities of daily living (IADLs), as well as visual analogue scale (VAS), Vail Hip Score (VHS), and Western Ontario and McMaster Universities (WOMAC) questionnaire results. Results: Statistically significant recovery was found in terms of VAS, VHS, and WOMAC in the HBR and CBR groups (all p < 0.001) after THA and TKA. Multivariate regression analysis demonstrated that higher values of VHS and WOMAC at 1 month were associated with better values of VAS, VHS, and WOMAC preoperatively (r = 0.095, p = 0.021). Conclusion: HBR showed similar short-term postoperative outcomes when compared with CBR for patients who underwent total joint arthroplasty. Greater preoperative joint functionality, a lower level of pain, and a female gender predicted better functional outcomes at 1 month after surgery in both groups. Full article

(This article belongs to the Special Issue Orthopaedics and Joint Reconstruction: Latest Advances and Prospects)

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14 pages, 1611 KiB

Open AccessArticle

The Effects of FIFA 11+ and Harmoknee Warm-Up Protocols on Flexibility, Vertical Jump and Shooting Speed in Female Football Players: A Comparative Study

by Halit Şar, Gürkan Selim Çelgin, Cansel Arslanoğlu, Gizem Kızılörs, Erkal Arslanoğlu, Levent Ceylan and Hamza Küçük

Appl. Sci. 2025, 15(9), 4936; https://doi.org/10.3390/app15094936 - 29 Apr 2025

Background/Objectives: The aim of this study was to compare the acute effects of the FIFA 11+ and Harmoknee warm-up protocols in female football players on flexibility, vertical jump, and shooting speed performance. Methods: This study involved 17 female football players who volunteered to [...] Read more.

Background/Objectives: The aim of this study was to compare the acute effects of the FIFA 11+ and Harmoknee warm-up protocols in female football players on flexibility, vertical jump, and shooting speed performance. Methods: This study involved 17 female football players who volunteered to participate, had no history of medical conditions, maintained regular menstrual cycles (28 ± 2 days, range: 26–33 days) in the three months preceding the study, and consistently engaged in football training. The Harmoknee and FIFA 11+ neuromuscular warm-up protocols were applied to the study group on different days, 48 h apart. After the exercises, athletes were tested for flexibility, vertical jump, and shooting speed. Two trials were allowed for each test, with a 3 min break between trials. The Shapiro–Wilk test was used to check for normality, and an independent sample t-test was used to compare groups. The p-value was set at <0.05 to determine statistical significance. Results: When comparing warm-up protocols, it was established that the FIFA 11+ neuromuscular warm-up protocol positively affects athletes’ vertical jump performance compared to other protocols (p < 0.05). In conclusion, the FIFA 11+ warm-up protocol resulted in better vertical jump performance in female soccer players compared to the Harmoknee warm-up routine. The FIFA 11+ protocol activates leg muscles, and balance exercises improve the neuromuscular characteristics of the warm-up compared to the Harmoknee protocol. Full article

(This article belongs to the Special Issue Effects of Physical Training on Exercise Performance—2nd Edition)

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20 pages, 933 KiB

Open AccessArticle

Designing Innovative Digital Solutions in the Cultural Heritage and Tourism Industry: Best Practices for an Immersive User Experience

by Vito Del Vecchio, Mariangela Lazoi, Claudio Marche, Christos Mettouris, Mario Montagud, Giorgia Specchia and Mostafa Z. Ali

Appl. Sci. 2025, 15(9), 4935; https://doi.org/10.3390/app15094935 - 29 Apr 2025

Digital transformation is reshaping business strategies and driving innovation across various industries including Cultural Heritage (CH) and tourism. Digital technologies, such as eXtended Reality (XR) and the Internet of Things (IoT), are increasingly being adopted to enhance visitors’ experiences, foster interactive engagement, and [...] Read more.

Digital transformation is reshaping business strategies and driving innovation across various industries including Cultural Heritage (CH) and tourism. Digital technologies, such as eXtended Reality (XR) and the Internet of Things (IoT), are increasingly being adopted to enhance visitors’ experiences, foster interactive engagement, and promote cultural knowledge. Despite the growing number of digital solutions proposed in the CH sector, several challenges remain in differentiating digital products and services, including matching industry needs and user expectations. This aspect is of particular interest when dealing with small and medium enterprises (SMEs), which often suffer from limited resources. Therefore, to design an effective digital solution, like a cloud-based platform for tourism and heritage applications, it is essential to first identify the key requirements, expectations, and preferences of SMEs and customers. This study presents the findings of a survey-based analysis conducted among 122 CH and tourism professionals, focusing on the most relevant features, services, and functionalities that such platforms should integrate. Results indicate a strong demand for cloud-based solutions that incorporate XR, IoT, sensors, and smart devices to collect context data and deliver personalized, immersive, and context-aware experiences. These insights suggest valuable practices for the development of digital tools that effectively support cultural organizations in engaging visitors. Full article

(This article belongs to the Special Issue Virtual/Augmented Reality and Its Applications)

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