Applied Sciences

18 pages, 3027 KB

Open AccessArticle

Domain-Specialized Large Language Model for Corrosion Analysis: Construction and Evaluation of Corr-Lora-RAG

by Weitong Wu, Di Xu, Liangan Liu, Bingqin Wang, Yadi Zhao, Xuequn Cheng and Xiaogang Li

Appl. Sci. 2025, 15(16), 9226; https://doi.org/10.3390/app15169226 - 21 Aug 2025

Viewed by 298

This study proposes a large language model, Corr-Lora-RAG, designed to address the complexity and uncertainty inherent in corrosion data. A dedicated corrosion knowledge database (CKD) was constructed, and dataset generation code was provided to enhance the model’s reproducibility and adaptability. Based on the [...] Read more.

This study proposes a large language model, Corr-Lora-RAG, designed to address the complexity and uncertainty inherent in corrosion data. A dedicated corrosion knowledge database (CKD) was constructed, and dataset generation code was provided to enhance the model’s reproducibility and adaptability. Based on the Qwen2.5-7B model, the Corr-Lora model was developed by integrating prompt engineering and low-rank adaptation (LoRA) supervised fine-tuning (SFT) techniques, thereby improving the understanding and expression of domain-specific knowledge in the field of corrosion. Furthermore, the Corr-Lora-RAG model was built using retrieval-augmented generation (RAG) technology, enabling dynamic access to external knowledge. Experimental results demonstrate that the proposed model outperforms baseline models in terms of accuracy, completeness, and domain relevance, and exhibits knowledge generation capabilities comparable to those of large-scale language models under limited computational resources. This approach provides an intelligent solution for corrosion risk assessment, standards compliance analysis, and protective strategy formulation, and offers a valuable reference for the development of specialized language models in other engineering fields. Full article

(This article belongs to the Topic Advanced Development and Applications of AI-Generated Content (AIGC))

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14 pages, 17759 KB

Open AccessArticle

Influence of Thermally Treated Asbestos-Containing Materials on Cement Mortars Properties

by Robert Kusiorowski, Anna Gerle, Magdalena Kujawa and Andrzej Śliwa

Appl. Sci. 2025, 15(16), 9225; https://doi.org/10.3390/app15169225 - 21 Aug 2025

Viewed by 212

Abstract

This paper presents the potential use of calcined cement–asbestos waste as an additive in cement mortars. Due to its harmful asbestos content, cement–asbestos waste poses a significant environmental challenge. One method of disposal is high-temperature calcination, which degrades the structure of asbestos fibers [...] Read more.

This paper presents the potential use of calcined cement–asbestos waste as an additive in cement mortars. Due to its harmful asbestos content, cement–asbestos waste poses a significant environmental challenge. One method of disposal is high-temperature calcination, which degrades the structure of asbestos fibers and removes their carcinogenic properties. After appropriate thermal treatment, this material can be used as a mineral additive in cement mixtures. This study analyzed the physical and chemical properties of the calcined waste and its impact on the basic strength parameters of cement mortars. The results indicate that, with appropriate dosing, calcined cement–asbestos waste can serve as a useful additive or filler without significantly impairing—and in some cases even improving—the mechanical properties of the mortars. The developed solution aligns with the principles of the circular economy, enabling the safe and effective management of hazardous waste. Full article

(This article belongs to the Topic Solid Waste Recycling in Civil Engineering Materials)

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18 pages, 7102 KB

Open AccessArticle

Experimental Investigation on the Effects of POE Oil and Iron Powder on the Corrosion of TP2 Copper Tubes in Acetic Acid Vapors

by Jing Zhang, Changzheng Li, Yunlong Ou, Guofeng Su, Wenzhong Mi and Ming Fu

Appl. Sci. 2025, 15(16), 9224; https://doi.org/10.3390/app15169224 - 21 Aug 2025

Viewed by 233

Abstract

The incidence of fire accidents resulting from refrigerant leaking following the rupture of air conditioning condenser tubes has escalated in recent years. Corrosion from carboxylic acid is a primary cause in the rupture of copper tubes. The influence of lubricating oil and iron [...] Read more.

The incidence of fire accidents resulting from refrigerant leaking following the rupture of air conditioning condenser tubes has escalated in recent years. Corrosion from carboxylic acid is a primary cause in the rupture of copper tubes. The influence of lubricating oil and iron filings generated by the wear of air conditioning compressors on the corrosion of condenser copper tubes is rarely mentioned in the existing research. In order to simulate the environmental conditions inside the air conditioning unit, this study utilizes acetic acid vapor to corrode copper tubes and explores the effects of lubricating oil and iron powder on copper tube corrosion. The results demonstrate that copper corrosion follows a dendritic corrosion pattern, achieving a maximum depth of 51 μm after 28 days in 1% acetic acid vapor. A small amount of copper hydroxy acetate appears in the early stage. Copper hydroxy acetic and basic carbonate copper are converted into acetic acid copper hydrate as the acetic acid vapor increases over time. The ultimate products appear as turquoise-blue crystals. POE lubricant diminishes the corrosion rate by establishing an oil layer barrier that mitigates the volatilization of acetic acid. Iron powder preferentially reacts with acetic acid to initially protect the copper tube. The Fe³⁺ produced oxidizes the copper in acetic acid, hence the concentration of copper acetate rises, which facilitates the crystallization of copper acetate. Full article

(This article belongs to the Special Issue Advances in Fire Safety Engineering and Applications)

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19 pages, 1175 KB

Open AccessArticle

Empirical Evaluation of Prompting Strategies for Python Syntax Error Detection with LLMs

by Norah Aloufi and Abdulmajeed Aljuhani

Appl. Sci. 2025, 15(16), 9223; https://doi.org/10.3390/app15169223 - 21 Aug 2025

Viewed by 389

Abstract

As large language models (LLMs) are increasingly integrated into software development, there is a growing need to assess how effectively they address subtle programming errors in real-world environments. Accordingly, this study investigates the effectiveness of LLMs in identifying syntax errors within large Python [...] Read more.

As large language models (LLMs) are increasingly integrated into software development, there is a growing need to assess how effectively they address subtle programming errors in real-world environments. Accordingly, this study investigates the effectiveness of LLMs in identifying syntax errors within large Python code repositories. Building on the bug in the code stack (BICS) benchmark, this research expands the evaluation to include additional models, such as DeepSeek and Grok, while assessing their ability to detect errors across varying code lengths and depths. Two prompting strategies—two-shot and role-based prompting—were employed to compare the performance of models including DeepSeek-Chat, DeepSeek-Reasoner, DeepSeek-Coder, and Grok-2-Latest with GPT-4o serving as the baseline. The findings indicate that the DeepSeek models generally outperformed GPT-4o in terms of accuracy (Acc). Notably, DeepSeek-Reasoner exhibited the highest overall performance, achieving an Acc of 86.6% and surpassing all other models, particularly when integrated prompting strategies were used. Nevertheless, all models demonstrated decreased Acc with increasing input length and consistently struggled with certain types of errors, such as missing quotations (MQo). This work provides insight into the current strengths and weaknesses of LLMs within real-world debugging environments, thereby informing ongoing efforts to improve automated software tools. Full article

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17 pages, 5477 KB

Open AccessArticle

Optimisation of Supercritical CO₂ Extraction from Black (Ribes nigrum) and Red (Ribes rubrum) Currant Pomace

by Filip Herzyk and Małgorzata Korzeniowska

Appl. Sci. 2025, 15(16), 9222; https://doi.org/10.3390/app15169222 - 21 Aug 2025

Viewed by 260

Abstract

Fruit pomace, generated as a by-product of juice processing, is a valuable source of bioactive compounds but requires sustainable extraction approaches to enable its valorisation. Supercritical CO₂ extraction (SFE-CO₂) represents a promising green technology due to its efficiency, solvent-free character, [...] Read more.

Fruit pomace, generated as a by-product of juice processing, is a valuable source of bioactive compounds but requires sustainable extraction approaches to enable its valorisation. Supercritical CO₂ extraction (SFE-CO₂) represents a promising green technology due to its efficiency, solvent-free character, and tuneable selectivity. In this study, the response surface methodology (RSM) was applied to evaluate the effects of pressure, temperature, and time on the recovery of fat, protein, and total phenolic compounds (TPCs) from blackcurrant (Ribes nigrum) and redcurrant (Ribes rubrum) pomace subjected to conventional- and freeze-drying. The highest protein content (14.5%) was obtained in freeze-dried blackcurrant at 400 bar, 60 min, and 30 °C, while the maximum TPCs (24.60 mg GAE/g d.w.) was reached at 500 bar, 60 min, and 40 °C. The redcurrant samples consistently showed lower extractable values across all the responses. Pressure and time were identified as the most influential process variables, enhancing the solvent density and mass transfer during extraction. These results demonstrate that both the drying pre-treatment and raw material type significantly affect the SFE efficiency and confirm the potential of optimised SFE-CO₂ as a viable strategy for converting fruit pomace into functional ingredients for food, nutraceutical, and cosmetic applications. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

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13 pages, 417 KB

Open AccessArticle

The Effects of Non-Viable Probiotic Lactobacillus paracasei on the Biotechnological Properties of Saccharomyces cerevisiae

by Marina Pihurov, Mihaela Cotârleț, Daniela Borda and Gabriela Elena Bahrim

Appl. Sci. 2025, 15(16), 9221; https://doi.org/10.3390/app15169221 - 21 Aug 2025

Viewed by 218

Abstract

Due to the increasing interest in probiotic components to improve quality of life, this study aimed to investigate the bioactive potential of a paraprobiotic derived from a selected strain of probiotic lactic acid bacteria (Lacticaseibacillus paracasei MIUG BL80) on Saccharomyces cerevisiae MIUG [...] Read more.

Due to the increasing interest in probiotic components to improve quality of life, this study aimed to investigate the bioactive potential of a paraprobiotic derived from a selected strain of probiotic lactic acid bacteria (Lacticaseibacillus paracasei MIUG BL80) on Saccharomyces cerevisiae MIUG D129, used as a cellular model organism. The paraprobiotics (inactivated cells) were obtained through a combination of ultrasonic and conventional heat treatments. It was observed that adding more than 10 % of the paraprobiotic suspension to the cultivation medium of yeast had a positive influence on the metabolic activity of the starter culture (S. cerevisiae). The specific growth rate increased from 0.227 in the control sample to 0.507 in the sample with 15% paraprobiotic supplementation (S3), while the generation time decreased from 4.403 h to 1.972 h. This suggests that adding probiotics to the cultivation medium enhances the metabolic performance of S. cerevisiae cells. Additionally, an improvement in yeast cell viability during wet biomass storage (from 48 h to 14 days at 4 °C) was observed. Full article

(This article belongs to the Special Issue Current Trends in Food Microbiology: Food Fermentation, Safety, and Production)

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19 pages, 1510 KB

Open AccessReview

Functional Food as a Nutritional Countermeasure to Health Risks from Microgravity and Space Radiation in Long-Term Spaceflights: A Review

by Jesús Clemente-Villalba and Débora Cerdá-Bernad

Appl. Sci. 2025, 15(16), 9220; https://doi.org/10.3390/app15169220 - 21 Aug 2025

Viewed by 204

Abstract

(1) Background: Over the years, technology and space missions have advanced, although the development of potential functional food and food supplements must be improved for maintaining astronauts’ health and helping them overcome space-specific challenges during long missions. (2) Scope and approach: Using a [...] Read more.

(1) Background: Over the years, technology and space missions have advanced, although the development of potential functional food and food supplements must be improved for maintaining astronauts’ health and helping them overcome space-specific challenges during long missions. (2) Scope and approach: Using a review approach, this study aimed to investigate the potential of functional food to counteract radiation and microgravity spaceflight-related health problems. (3) Results: Microgravity and space radiation affect the body’s biochemical processes and increase levels of reactive oxygen species, which may lead to health problems, including musculoskeletal deconditioning, cardiovascular degeneration, disruptions in gastrointestinal health, ocular problems, alterations to the immune system, and hormonal imbalances, among others. In addition to medical care, functional food plays a key role as a countermeasure against space-induced physiological issues. Previous research showed that functional food rich in flavonoids, omega-3 fatty acids, vitamins, minerals, antioxidant compounds, proteins, probiotics, or prebiotics strengthens the immune system and reduces risks associated with long spaceflights, such as bone density loss, muscle atrophy, oxidative stress, and other health alterations. (4) Conclusions: Despite the fundamental role of functional food in spaceflights, the main challenges remain in preserving and packaging these foods to ensure their safety on long space missions. Future innovations include 3D food printing, space algae cultivation, and novel preservation technologies. Full article

(This article belongs to the Special Issue Functional Foods: Product Development, Technological Trends and Safety)

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25 pages, 336 KB

Open AccessReview

Modeling and Simulation Tools for Smart Local Energy Systems: A Review with a Focus on Emerging Closed Ecological Systems’ Application

by Andrzej Ożadowicz

Appl. Sci. 2025, 15(16), 9219; https://doi.org/10.3390/app15169219 - 21 Aug 2025

Viewed by 227

Abstract

The growing importance of microgrids—linking buildings with distributed energy resources and storage—is driving the evolution of Smart Local Energy Systems (SLESs). These systems require advanced modeling and simulations to address growing complexity, decentralization, and interoperability. This review presents an analysis of commonly used [...] Read more.

The growing importance of microgrids—linking buildings with distributed energy resources and storage—is driving the evolution of Smart Local Energy Systems (SLESs). These systems require advanced modeling and simulations to address growing complexity, decentralization, and interoperability. This review presents an analysis of commonly used environments and methods applied in the design and operation of SLESs. Particular emphasis is placed on their capabilities for multi-domain integration, predictive control, and smart automation. A novel contribution is the identification of Closed Ecological Systems (CES) and Life Support Systems (LSSs)—fully or semi-isolated environments designed to sustain human life through autonomous recycling of air, water, and other resources—as promising new application domains for SLES technologies. This review explores how concepts developed for building and energy systems, such as demand-side management, IoT-based monitoring, and edge computing, can be adapted to CES/LSS contexts, which demand isolation, autonomy, and high reliability. Challenges related to model integration, simulation scalability, and the bidirectional transfer of technologies and modeling between Earth-based and space systems are discussed. This paper concludes with a SWOT analysis and a roadmap for future research. This work lays the foundation for developing sustainable, intelligent, and autonomous energy infrastructures—both terrestrial and extraterrestrial. Full article

(This article belongs to the Special Issue Advanced Smart Grid Technologies, Applications and Challenges)

29 pages, 1620 KB

Open AccessArticle

A Multi-Layer Quantum-Resilient IoT Security Architecture Integrating Uncertainty Reasoning, Relativistic Blockchain, and Decentralised Storage

by Gerardo Iovane

Appl. Sci. 2025, 15(16), 9218; https://doi.org/10.3390/app15169218 - 21 Aug 2025

Viewed by 280

Abstract

The rapid development of the Internet of Things (IoT) has enabled the implementation of interconnected intelligent systems in extremely dynamic contexts with limited resources. However, traditional paradigms, such as those using ECC-based heuristics and centralised decision-making frameworks, cannot be modernised to ensure resilience, [...] Read more.

The rapid development of the Internet of Things (IoT) has enabled the implementation of interconnected intelligent systems in extremely dynamic contexts with limited resources. However, traditional paradigms, such as those using ECC-based heuristics and centralised decision-making frameworks, cannot be modernised to ensure resilience, scalability and security while taking quantum threats into account. In this case, we propose a modular architecture that integrates quantum-inspired cryptography (QI), epistemic uncertainty reasoning, the multiscale blockchain MuReQua, and the quantum-inspired decentralised storage engine (DeSSE) with fragmented entropy storage. Each component addresses specific cybersecurity weaknesses of IoT devices: quantum-resistant communication on epistemic agents that facilitate cognitive decision-making under uncertainty, lightweight adaptive consensus provided by MuReQua, and fragmented entropy storage provided by DeSSE. Tested through simulations and use case analyses in industrial, healthcare and automotive networks, the architecture shows exceptional latency, decision accuracy and fault tolerance compared to conventional solutions. Furthermore, its modular nature allows for incremental integration and domain-specific customisation. By adding reasoning, trust and quantum security, it is possible to design intelligent decentralised architectures for resilient IoT ecosystems, thereby strengthening system defences alongside architectures. In turn, this work offers a specific architectural response and a broader perspective on secure decentralised computing, even for the imminent advent of quantum computers. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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18 pages, 2908 KB

Open AccessArticle

Intelligent Fault Diagnosis for Rotating Machinery Utilizing Gramian Angular Field-Parallel Convolutional Neural Network and Gated Recurrent Unit Networks

by Yuxiang Hu, Shengyi Cheng and Xianjun Du

Appl. Sci. 2025, 15(16), 9217; https://doi.org/10.3390/app15169217 - 21 Aug 2025

Viewed by 162

Abstract

To address the limitations of traditional fault diagnosis methods for rotating machinery, which heavily rely on single-dimensional vibration data and fail to fully exploit the deep features of time-series data, this study proposes an innovative diagnostic model that integrates Gramian Angular Field-Parallel Convolutional [...] Read more.

To address the limitations of traditional fault diagnosis methods for rotating machinery, which heavily rely on single-dimensional vibration data and fail to fully exploit the deep features of time-series data, this study proposes an innovative diagnostic model that integrates Gramian Angular Field-Parallel Convolutional Neural Network (GAF-PCNN) with Gated Recurrent Units (GRU). Specifically, one-dimensional vibration signals are first transformed into Gramian angular and difference fields as image representations using Gramian Angular Field (GAF). These two types of images are then input into parallel-configured PCNN modules for feature learning. The features extracted by the two CNN branches are weighted and fused to construct a combined feature sequence. This sequence is subsequently fed into the GRU network to capture temporal dependencies and perform deep feature extraction. In this process, an integrated self-attention mechanism is applied to dynamically select key features. The proposed method is validated using two publicly available datasets, including comparative and noise interference experiments. The results demonstrate that the proposed model excels in diagnostic accuracy, model generalization, and robustness against noise interference. Full article

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22 pages, 2966 KB

Open AccessArticle

Reducing Water Resource Pressure and Determining Gross Nitrogen Balance of Agricultural Land in the European Union

by Wiktor Halecki, Konrad Kalarus, Agnieszka Kowalczyk, Tomasz Garbowski, Justyna Chudziak and Beata Grabowska-Polanowska

Appl. Sci. 2025, 15(16), 9216; https://doi.org/10.3390/app15169216 - 21 Aug 2025

Viewed by 322

Abstract

The evaluation of crop production that influences surface and groundwater quality is of growing importance in the context of agricultural sustainability in Europe. The primary aim of this study was to understand the relationship between gross nitrogen surplus in land and nitrate concentrations [...] Read more.

The evaluation of crop production that influences surface and groundwater quality is of growing importance in the context of agricultural sustainability in Europe. The primary aim of this study was to understand the relationship between gross nitrogen surplus in land and nitrate concentrations in surface and groundwater. The analysis was based on datasets collected from 2010 to 2021. Nitrate levels were categorized into three distinct quality classes based on the percentage of monitoring points, reflecting a spectrum from high quality, defined as nitrate levels below 25 mg/dm³, to poor quality, characterized by levels exceeding 50 mg/dm³. Redundancy analysis indicated that Gross Nitrogen Balance, a fertilizer use predictor, partially influences water quality, potentially due to long-term effects. Model selection for Gross Nitrogen Balance based on the AIC_c information criterion identified catch crops (or green cover), high-intensity agriculture, Natura 2000 sites, nitrogen-fixing plants, organic farming, fast-growing tree plantations, and EU27 states as predictors in the group of supported models. The best-fit model revealed differences between EU27 states for Gross Nitrogen Balance. Catch crops and Natura 2000 sites were also significant predictors, the former associated with a positive and the latter with a negative effect on nitrogen balance. In turn, WEI+ increased with nitrogen balance input but decreased with organic farming, indicating that promoting organic practices could help save water resources. Poland emerged as a country with relatively good water quality compared to several European counterparts, such as Denmark, Belgium, Malta, Czechia, Germany, and Lithuania. The implications of this research extend significantly to evaluation of the effects of the Common Agricultural Policy within the European Union. Full article

(This article belongs to the Special Issue Plant Management and Soil Improvement in Specialty Crop Production—2nd Edition)

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33 pages, 1931 KB

Open AccessReview

The Quality of Greek Islands’ Seawaters: A Scoping Review

by Ioannis Mozakis, Panagiotis Kalaitzoglou, Emmanouela Skoulikari, Theodoros Tsigkas, Anna Ofrydopoulou, Efstratios Davakis and Alexandros Tsoupras

Appl. Sci. 2025, 15(16), 9215; https://doi.org/10.3390/app15169215 - 21 Aug 2025

Viewed by 693

Abstract

Background: Greek islands face mounting pressures on their marine water resources due to tourism growth, agricultural runoff, climate change, and emerging pollutants. Safeguarding seawater quality is critical for ecosystem integrity, public health, and the sustainability of tourism-based economies. Objectives: This scoping review synthesizes [...] Read more.

Background: Greek islands face mounting pressures on their marine water resources due to tourism growth, agricultural runoff, climate change, and emerging pollutants. Safeguarding seawater quality is critical for ecosystem integrity, public health, and the sustainability of tourism-based economies. Objectives: This scoping review synthesizes and evaluates the existing research on seawater quality in the Greek islands, with emphasis on pollution sources, monitoring methodologies, and socio-environmental impacts, while highlighting the gaps in addressing emerging contaminants and aligning with sustainable development goals. Methods: A systematic literature search was conducted in Scopus, Google Scholar, ResearchGate, Web of Science, and PubMed for English- and Greek-language studies published over the last two to three decades. The search terms covered physical, chemical, and biological aspects of seawater quality, as well as emerging pollutants. The PRISMA-ScR guidelines were followed, resulting in the inclusion of 178 studies. The data were categorized by pollutant type, location, water quality indicators, monitoring methods, and environmental, health, and tourism implications. Results: This review identifies agricultural runoff, untreated wastewater, maritime traffic emissions, and microplastics as key pollution sources. Emerging contaminants such as pharmaceuticals, PFASs, and nanomaterials have been insufficiently studied. While monitoring technologies such as remote sensing, fuzzy logic, and Artificial Neural Networks (ANNs) are increasingly applied, these efforts remain fragmented and geographically uneven. Notable gaps exist in the quantification of socio-economic impact, source apportionment, and epidemiological assessments. Conclusions: The current monitoring and management strategies in the Greek islands have produced high bathing water quality in many areas, as reflected in the Blue Flag program, yet they do not fully address the spatial, temporal, and technological challenges posed by climate change and emerging pollutants. Achieving long-term sustainability requires integrated, region-specific water governance linked to the UN SDGs, with stronger emphasis on preventive measures, advanced monitoring, and cross-sector collaboration. Full article

(This article belongs to the Special Issue Assessment of Contaminants of Emerging Concern in the Environment: Occurrence, Detection, Removal and Toxicity)

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20 pages, 2434 KB

Open AccessArticle

Machine Learning-Based Prediction of Autism Spectrum Disorder and Discovery of Related Metagenomic Biomarkers with Explainable AI

by Mustafa Temiz, Burcu Bakir-Gungor, Nur Sebnem Ersoz and Malik Yousef

Appl. Sci. 2025, 15(16), 9214; https://doi.org/10.3390/app15169214 - 21 Aug 2025

Viewed by 205

Abstract

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Recent studies have suggested that gut microbiota may play a role in the pathophysiology of ASD. This study aims to develop a classification model for [...] Read more.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Recent studies have suggested that gut microbiota may play a role in the pathophysiology of ASD. This study aims to develop a classification model for ASD diagnosis and to identify ASD-associated biomarkers by analyzing metagenomic data at the taxonomic level. Methods: The performances of five different methods were tested in this study. These methods are (i) SVM-RCE, (ii) RCE-IFE, (iii) microBiomeGSM, (iv) different feature selection methods, and (v) a union method. The last method is based on creating a union feature set consisting of the features with importance scores greater than 0.5, identified using the best-performing feature selection methods. Results: In our 10-fold Monte Carlo cross-validation experiments on ASD-associated metagenomic data, the most effective performance metric (an AUC of 0.99) was obtained using the union feature set (17 features) and the AdaBoost classifier. In other words, we achieve superior machine learning performance with a few features. Additionally, the SHAP method, which is an explainable artificial intelligence method, is applied to the union feature set, and Prevotella sp. 109 is identified as the most important microorganism for ASD development. Conclusions: These findings suggest that the proposed method may be a promising approach for uncovering microbial patterns associated with ASD and may inform future research in this area. This study should be regarded as exploratory, based on preliminary findings and hypothesis generation. Full article

(This article belongs to the Special Issue Advances and Applications of Machine Learning for Bioinformatics)

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29 pages, 1124 KB

Open AccessReview

From Mathematical Modeling and Simulation to Digital Twins: Bridging Theory and Digital Realities in Industry and Emerging Technologies

by Antreas Kantaros, Theodore Ganetsos, Evangelos Pallis and Michail Papoutsidakis

Appl. Sci. 2025, 15(16), 9213; https://doi.org/10.3390/app15169213 - 21 Aug 2025

Viewed by 434

Abstract

Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within [...] Read more.

Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within the broader field of applied mathematics and computational simulation while highlighting the critical role of sound mathematical foundations, numerical methodologies, and advanced computational tools in creating data-informed virtual models of physical infrastructures and processes in real time. The discussion includes examples related to smart manufacturing, additive manufacturing technologies, and cyber–physical systems with a focus on the potential for collaboration between physics-informed simulations, data unification, and hybrid machine learning approaches. Central issues including a lack of scalability, measuring uncertainties, interoperability challenges, and ethical concerns are discussed along with rising opportunities for multi/macrodisciplinary research and innovation. This work argues in favor of the continued integration of advanced mathematical approaches with state-of-the-art technologies including artificial intelligence, edge computing, and fifth-generation communication networks with a focus on deploying self-regulating autonomous digital twins. Finally, defeating these challenges via effective collaboration between academia and industry will provide unprecedented society- and economy-wide benefits leading to resilient, optimized, and intelligent systems that mark the future of critical industries and services. Full article

(This article belongs to the Special Issue Feature Review Papers in Section Applied Industrial Technologies)

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21 pages, 9510 KB

Open AccessArticle

A Space Discretization Method for Smooth Trajectory Planning of a 5PUS-RPUR Parallel Robot

by Yiqin Luo, Sheng Li, Jian Ruan and Jiping Bai

Appl. Sci. 2025, 15(16), 9212; https://doi.org/10.3390/app15169212 - 21 Aug 2025

Viewed by 200

Abstract

To improve the dynamic performance of parallel robots in multi-dimensional space, a novel trajectory planning method of space discretization for parallel robots is proposed. First, the kinematic model of the 5PUS-RPUR parallel robot is established. Then, the normalized Jacobian condition number is obtained [...] Read more.

To improve the dynamic performance of parallel robots in multi-dimensional space, a novel trajectory planning method of space discretization for parallel robots is proposed. First, the kinematic model of the 5PUS-RPUR parallel robot is established. Then, the normalized Jacobian condition number is obtained via the variable weighting matrix method, and is used as the performance metric of path optimization. The weighted sum method is utilized to construct a composite objective function for the trajectory that incorporates travel time and acceleration fluctuations. Next, the position space between the start and end points is discretized, and the robot pose space based on the position points is analyzed via the search method. The discrete pose point weights are assigned according to the condition number. Dijkstra’s algorithm is used to find the path with the minimum condition number. The trajectory optimization model is established by fitting the discrete path with a B-spline curve and optimized via genetic algorithm. Finally, comparative numerical simulations validate the proposed method, which reduces actuator RMS displacement difference by up to 32.9% and acceleration fluctuation by up to 25.6% against state-of-the-art techniques, yielding superior motion smoothness and dynamic stability. Full article

(This article belongs to the Section Robotics and Automation)

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24 pages, 1496 KB

Open AccessArticle

The Gradual Cyclical Process in Adaptive Gamified Learning: Generative Mechanisms for Motivational Transformation, Cognitive Advancement, and Knowledge Construction Strategy

by Liwei Ding and Hongfeng Zhang

Appl. Sci. 2025, 15(16), 9211; https://doi.org/10.3390/app15169211 - 21 Aug 2025

Viewed by 289

Abstract

The integration of gamification into digital learning environments is reshaping educational models, advancing towards more adaptive and personalized teaching evolution. However, within large Chinese corpora, the transition mechanism from passive participation to adaptive gamified learning remains underexplored in a systematic manner. This study [...] Read more.

The integration of gamification into digital learning environments is reshaping educational models, advancing towards more adaptive and personalized teaching evolution. However, within large Chinese corpora, the transition mechanism from passive participation to adaptive gamified learning remains underexplored in a systematic manner. This study fills this gap by utilizing LDA topic modeling and sentiment analysis techniques to delve into user comment data on the Bilibili platform. The results extract five major themes, which include multilingual task-driven learning, early-age programming thinking cultivation, modular English competency certification, cross-domain cognitive integration and psychological safety, as well as ubiquitous intelligent educational environments. The analysis reveals that most themes exhibit highly positive emotions, particularly in applications for early childhood education, while learning models that involve certification mechanisms and technological dependencies tend to provoke emotional fluctuations. Nevertheless, learners still experience certain challenges and pressures when faced with frequent cognitive tasks. In an innovative manner, this study proposes a theoretical framework based on Self-Determination Theory and Connectivism to analyze how motivation satisfaction drives cognitive restructuring, thereby facilitating the process of adaptive learning. This model demonstrates the evolutionary logic of learners’ cross-disciplinary knowledge integration and metacognitive strategy optimization, providing empirical support for the gamification learning transformation mechanism in China’s digital education sector and extending the research framework for personalized teaching and self-regulation in educational technology. Full article

(This article belongs to the Special Issue Adaptive E-Learning Technologies and Experiences)

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21 pages, 5121 KB

Open AccessArticle

Research on Cracking Mechanism and Crack Extension of Diversion Tunnel Lining Structure

by Hui Xie, Haoran Wang, Xingtong Zou, Yongcan Chen, Zhaowei Liu, Liyi Yang and Kang Liu

Appl. Sci. 2025, 15(16), 9210; https://doi.org/10.3390/app15169210 - 21 Aug 2025

Viewed by 291

Abstract

Tunnel systems are often confronted with issues such as cracks, water seepage, and exposed tendons, all of which compromise their structural integrity. This study utilizes an advanced robotic system equipped with a 3D laser scanner to capture data on visible lining defects. By [...] Read more.

Tunnel systems are often confronted with issues such as cracks, water seepage, and exposed tendons, all of which compromise their structural integrity. This study utilizes an advanced robotic system equipped with a 3D laser scanner to capture data on visible lining defects. By analyzing the distribution of defects across various tunnel segments, we explore the mechanisms underlying structural cracks. Finite element software is employed to assess stress, deformation, and crack progression within the tunnel linings. The result found that the diversion tunnel’s segments exhibit notable variations: 66.0% of the defects are concentrated in the upper flat section, while 34.0% are found in the inclined shaft segment. Cracks, primarily located in the vault area, characterize these defects. Under water pressure, stress deformation in the intact lining follows a linear escalation pattern. Specifically, after the formation of cracks measuring 0.1 m, 0.2 m, and 0.3 m, circumferential stresses increase by approximately 4.50%, 9.10%, and 15.10%, respectively. Numerical simulations reveal significant stress concentration near the cave entrance at the upper flat break. Crack propagation at the arch crown is found to pose a greater risk than at the sides of the arch waist. These findings offer valuable scientific insights and practical implications for improving safety and enabling intelligent monitoring of power station tunnels. Full article

(This article belongs to the Special Issue Civil Structural Health Monitoring: Techniques, Systems and Applications)

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18 pages, 3632 KB

Open AccessArticle

Multilingual Mobility: Audio-Based Language ID for Automotive Systems

by Joowon Oh and Jeaho Lee

Appl. Sci. 2025, 15(16), 9209; https://doi.org/10.3390/app15169209 - 21 Aug 2025

Viewed by 303

Abstract

With the growing demand for natural and intelligent human–machine interaction in multilingual environments, automatic language identification (LID) has emerged as a crucial component in voice-enabled systems, particularly in the automotive domain. This study proposes an audio-based LID model that identifies the spoken language [...] Read more.

With the growing demand for natural and intelligent human–machine interaction in multilingual environments, automatic language identification (LID) has emerged as a crucial component in voice-enabled systems, particularly in the automotive domain. This study proposes an audio-based LID model that identifies the spoken language directly from voice input without requiring manual language selection. The model architecture leverages two types of feature extraction pipelines: a Variational Autoencoder (VAE) and a pre-trained Wav2Vec model, both used to obtain latent speech representations. These embeddings are then fed into a multi-layer perceptron (MLP)-based classifier to determine the speaker’s language among five target languages: Korean, Japanese, Chinese, Spanish, and French. The model is trained and evaluated using a dataset preprocessed into Mel-Frequency Cepstral Coefficients (MFCCs) and raw waveform inputs. Experimental results demonstrate the effectiveness of the proposed approach in achieving accurate and real-time language detection, with potential applications in in-vehicle systems, speech translation platforms, and multilingual voice assistants. By eliminating the need for predefined language settings, this work contributes to more seamless and user-friendly multilingual voice interaction systems. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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15 pages, 2044 KB

Open AccessArticle

Degradation Modeling and Telemetry-Based Analysis of Solar Cells in LEO for Nano- and Pico-Satellites

by Angsagan Kenzhegarayeva, Kuanysh Alipbayev and Algazy Zhauyt

Appl. Sci. 2025, 15(16), 9208; https://doi.org/10.3390/app15169208 - 21 Aug 2025

Viewed by 314

Abstract

In the last decades, small satellites such as CubeSats and PocketQubes have become popular platforms for scientific and applied missions in low Earth orbit (LEO). However, prolonged exposure to atomic oxygen, ultraviolet radiation, and thermal cycling in LEO leads to gradual degradation of [...] Read more.

In the last decades, small satellites such as CubeSats and PocketQubes have become popular platforms for scientific and applied missions in low Earth orbit (LEO). However, prolonged exposure to atomic oxygen, ultraviolet radiation, and thermal cycling in LEO leads to gradual degradation of onboard solar panels, reducing mission lifetime and performance. This study addresses the need to quantify and compare the degradation behavior of different solar cell technologies and protective coatings used in nanosatellites and pico-satellites. The aim is to evaluate the in-orbit performance of monocrystalline silicon (Si), gallium arsenide (GaAs), triple-junction (TJ) structures, and copper indium gallium selenide (CIGS) cells under varying orbital and satellite parameters. Telemetry data from recent small satellite missions launched after 2020, combined with numerical modeling in GNU Octave, were used to assess degradation trends. The models were validated using empirical mission data, and statistical goodness-of-fit metrics (RMSE, R²) were applied to evaluate linear and exponential degradation patterns. Results show that TJ cells exhibit the highest resistance to LEO-induced degradation, while Si-based panels experience more pronounced power loss, especially in orbits below 500 km. Furthermore, smaller satellites (<10 kg) display higher degradation rates due to lower thermal inertia and limited shielding. These findings provide practical guidance for the selection of solar cell technologies, anti-degradation coatings, and protective strategies for long-duration CubeSat missions in diverse LEO environments. Full article

(This article belongs to the Section Aerospace Science and Engineering)

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25 pages, 20149 KB

Open AccessArticle

Bio-Inspired Visual Network for Detecting Small Moving Targets in Low-Light Dynamic Complex Environments Based on Target Gradient Temporal Features

by Jun Ling, Hecheng Meng and Deming Gong

Appl. Sci. 2025, 15(16), 9207; https://doi.org/10.3390/app15169207 - 21 Aug 2025

Viewed by 280

Abstract

Monitoring and tracking small moving objects in cluttered environments remain a major challenge for artificial-intelligence-based motion vision systems. This difficulty is not only due to the limited features presented by small objects themselves but also because of the numerous fake features present in [...] Read more.

Monitoring and tracking small moving objects in cluttered environments remain a major challenge for artificial-intelligence-based motion vision systems. This difficulty is not only due to the limited features presented by small objects themselves but also because of the numerous fake features present in complex dynamic environments. Drawing inspiration from the efficient small target motion detection mechanisms in insects’ brains, researchers have developed various visual networks for detecting tiny moving objects within complex natural environments. Although these networks perform well in detecting small-object motion by leveraging motion information, their ability to distinguish true targets from background noise remains severely limited under low-light conditions, where the contrast of small targets drops sharply and they are more easily overwhelmed by false motion in the background. To resolve the aforementioned limitation, this research proposes a new visual neural network. The network achieves effective discrimination between small moving targets and false targets in the background in low-light environments by leveraging the motion information for the targets and the differences in the response gradients between real moving targets and fake objects from the background. The designed network is composed of two main components: a motion perception module and a response gradient analysis module. The motion information perception module is responsible for acquiring the motion and position information for small targets, while the response gradient detection module extracts the response gradients between a tiny object and a background object and integrates the motion information, thereby effectively distinguishing small targets from fake background objects. The experimental results demonstrate that the proposed model can effectively distinguish small targets and suppress background false alarms in low-light environments. Comparisons of the experimental performance show that under a fixed false alarm rate, our model achieved a detection rate of 0.8. In addition, the proposed method recorded an average precision of 0.1 and an average F1-score of 0.1888. In contrast, the highest average precision achieved by the other methods was only 0.0075, and the highest F1-score was 0.0151. These results clearly indicate that our method substantially outperforms previous approaches in both its average precision and F1-score. These results collectively validate the effectiveness and competitiveness of the proposed model in small target detection tasks under low-light conditions. Full article

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45 pages, 2283 KB

Open AccessReview

Agricultural Image Processing: Challenges, Advances, and Future Trends

by Xuehua Song, Letian Yan, Sihan Liu, Tong Gao, Li Han, Xiaoming Jiang, Hua Jin and Yi Zhu

Appl. Sci. 2025, 15(16), 9206; https://doi.org/10.3390/app15169206 - 21 Aug 2025

Viewed by 259

Abstract

Agricultural image processing technology plays a critical role in enabling precise disease detection, accurate yield prediction, and various smart agriculture applications. However, its practical implementation faces key challenges, including environmental interference, data scarcity and imbalance datasets, and the difficulty of deploying models on [...] Read more.

Agricultural image processing technology plays a critical role in enabling precise disease detection, accurate yield prediction, and various smart agriculture applications. However, its practical implementation faces key challenges, including environmental interference, data scarcity and imbalance datasets, and the difficulty of deploying models on resource-constrained edge devices. This paper presents a systematic review of recent advances in addressing these challenges, with a focus on three core aspects: environmental robustness, data efficiency, and model deployment. The study identifies that attention mechanisms, Transformers, multi-scale feature fusion, and domain adaptation can enhance model robustness under complex conditions. Self-supervised learning, transfer learning, GAN-based data augmentation, SMOTE improvements, and Focal loss optimization effectively alleviate data limitations. Furthermore, model compression techniques such as pruning, quantization, and knowledge distillation facilitate efficient deployment. Future research should emphasize multi-modal fusion, causal reasoning, edge–cloud collaboration, and dedicated hardware acceleration. Integrating agricultural expertise with AI is essential for promoting large-scale adoption, as well as achieving intelligent, sustainable agricultural systems. Full article

(This article belongs to the Special Issue Pattern Recognition Applications of Neural Networks and Deep Learning)

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24 pages, 3568 KB

Open AccessArticle

Employing AI for Better Access to Justice: An Automatic Text-to-Video Linking Tool for UK Supreme Court Hearings

by Hadeel Saadany, Constantin Orăsan, Catherine Breslin, Mikolaj Barczentewicz and Sophie Walker

Appl. Sci. 2025, 15(16), 9205; https://doi.org/10.3390/app15169205 - 21 Aug 2025

Viewed by 450

Abstract

The increasing adoption of artificial intelligence across domains presents new opportunities to enhance access to justice. In this paper, we introduce a human-centric AI tool that utilises advances in Automatic Speech Recognition (ASR) and Large Language Models (LLMs) to facilitate semantic linking between [...] Read more.

The increasing adoption of artificial intelligence across domains presents new opportunities to enhance access to justice. In this paper, we introduce a human-centric AI tool that utilises advances in Automatic Speech Recognition (ASR) and Large Language Models (LLMs) to facilitate semantic linking between written UK Supreme Court (SC) judgements and their corresponding hearing videos. The motivation stems from the critical role UK SC hearings play in shaping landmark legal decisions, which often span several hours and remain difficult to navigate manually. Our approach involves two key components: (1) a customised ASR system fine-tuned on 139 h of manually edited SC hearing transcripts and legal documents and (2) a semantic linking module powered by GPT-based text embeddings adapted to the legal domain. The ASR system addresses domain-specific transcription challenges by incorporating a custom language model and legal phrase extraction techniques. The semantic linking module uses fine-tuned embeddings to match judgement paragraphs with relevant spans in the hearing transcripts. Quantitative evaluation shows that our customised ASR system improves transcription accuracy by 9% compared to generic ASR baselines. Furthermore, our adapted GPT embeddings achieve an F1 score of 0.85 in classifying relevant links between judgement text and hearing transcript segments. These results demonstrate the effectiveness of our system in streamlining access to critical legal information and supporting legal professionals in interpreting complex judicial decisions. Full article

(This article belongs to the Special Issue Computational Linguistics: From Text to Speech Technologies)

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17 pages, 1733 KB

Open AccessArticle

Synergistic Remote Sensing and In Situ Observations for Rapid Ocean Temperature Profile Forecasting on Edge Devices

by Jingpeng Shi, Yang Zhao and Fangjie Yu

Appl. Sci. 2025, 15(16), 9204; https://doi.org/10.3390/app15169204 - 21 Aug 2025

Viewed by 246

Abstract

Regional rapid forecasting of vertical ocean temperature profiles is increasingly important for marine aquaculture, as these profiles directly affect habitat management and the physiological responses of farmed species. However, observational temperature profile data with sufficient temporal resolution are often unavailable, limiting their use [...] Read more.

Regional rapid forecasting of vertical ocean temperature profiles is increasingly important for marine aquaculture, as these profiles directly affect habitat management and the physiological responses of farmed species. However, observational temperature profile data with sufficient temporal resolution are often unavailable, limiting their use in regional rapid forecasting. In addition, traditional numerical ocean models suffer from intensive computational demands and limited operational flexibility, making them unsuitable for regional rapid forecasting applications. To address this gap, we propose PICA-Net (Physics-Inspired CNN–Attention–BiLSTM Network), a hybrid deep learning model that coordinates large-scale satellite observations with local-scale, continuous in situ data to enhance predictive fidelity. The model also incorporates weak physical constraints during training that enforce temporal–spatial diffusion consistency, mixed-layer homogeneity, and surface heat flux consistency, enhancing physical consistency and interpretability. The model uses hourly historical inputs to predict temperature profiles at 6 h intervals over a period of 24 h, incorporating features such as sea surface temperature, sea surface height anomalies, wind fields, salinity, ocean currents, and net heat flux. Experimental results demonstrate that PICA-Net outperforms baseline models in terms of accuracy and generalization. Additionally, its lightweight design enables real-time deployment on edge devices, offering a viable solution for localized, on-site forecasting in smart aquaculture. Full article

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15 pages, 3290 KB

Open AccessArticle

Dynamic Modelling of Building Thermostatically Controlled Loads as a Stochastic Battery for Grid Stability in Wind-Integrated Power Systems

by Zahid Ullah, Giambattista Gruosso, Kaleem Ullah and Alda Scacciante

Appl. Sci. 2025, 15(16), 9203; https://doi.org/10.3390/app15169203 - 21 Aug 2025

Viewed by 354

Abstract

Integrating renewable energy, particularly wind power, into modern power systems introduces challenges concerning stability and reliability. These issues require enhanced regulation to balance power supply with load demand. Flexible loads and energy storage provide viable solutions to stabilize the grid without relying on [...] Read more.

Integrating renewable energy, particularly wind power, into modern power systems introduces challenges concerning stability and reliability. These issues require enhanced regulation to balance power supply with load demand. Flexible loads and energy storage provide viable solutions to stabilize the grid without relying on new resources. This paper proposes building thermostatically controlled loads (BTLs), such as heating, ventilation, and air conditioning (HVAC) systems, as flexible demand-side management tools to address the challenges of intermittent energy sources. A new concept is introduced, portraying BTLs as a stochastic battery with losses, offering a compact representation of their dynamics. BTLs’ thermal characteristics, user-defined set points, and ambient temperature changes determine the power limits and energy capacity of this stochastic battery. The model is simulated using DIgSILENT Power Factory, which includes thermal power plants, gas turbines, wind power plants, and BTLs. A dynamic dispatch strategy optimizes power generation while utilizing BTLs to balance grid fluctuations caused by variable wind energy. Performance analysis shows that integrating BTLs with conventional thermal plants can reduce variability and improve grid stability. The study highlights the dual role of simulating overall flexibility and applying dynamic dispatch strategies to enhance power systems with high renewable energy integration. Full article

(This article belongs to the Section Energy Science and Technology)

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29 pages, 2212 KB

Open AccessArticle

Predicting Student Dropout from Day One: XGBoost-Based Early Warning System Using Pre-Enrollment Data

by Blanca Carballo-Mendívil, Alejandro Arellano-González, Nidia Josefina Ríos-Vázquez and María del Pilar Lizardi-Duarte

Appl. Sci. 2025, 15(16), 9202; https://doi.org/10.3390/app15169202 - 21 Aug 2025

Viewed by 450

Abstract

Student dropout remains a critical challenge in higher education, especially within public universities that serve diverse and vulnerable populations. This research presents the design and evaluation of an early warning system based on an XGBoost classifier, trained exclusively on data collected at the [...] Read more.

Student dropout remains a critical challenge in higher education, especially within public universities that serve diverse and vulnerable populations. This research presents the design and evaluation of an early warning system based on an XGBoost classifier, trained exclusively on data collected at the time of student enrollment. Using a retrospective dataset of nearly 40,000 first-year students (2014–2024) from a Mexican public university, the model incorporated academic, socioeconomic, demographic, and perceptual variables. The final XGBoost model achieved an AUC-ROC of 0.6902 and an F1-score of 0.6946 for the dropout class, with a sensitivity of 88%. XGBoost was chosen over Random Forest due to its superior ability to detect students at risk, a critical requirement for early intervention. The model flagged 59% of incoming students as high-risk, with considerable variability across academic programs. The most influential predictors included age, high school GPA, conditioned admission, and other family responsibilities and economic constraints. This research demonstrates that early warning systems can transform enrollment data into timely and actionable insights, enabling universities to identify vulnerable students earlier and respond more effectively, allocate support more efficiently, and enhance their efforts to reduce dropout rates and improve student retention. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Educational Data Mining and Learning Analytics)

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17 pages, 2028 KB

Open AccessReview

CMOS-Compatible Ultrasonic 3D Beamforming Sensor System for Automotive Applications

by Khurshid Hussain, Wanhae Jeon, Yongmin Lee, In-Hyouk Song and Inn-Yeal Oh

Appl. Sci. 2025, 15(16), 9201; https://doi.org/10.3390/app15169201 - 21 Aug 2025

Viewed by 391

Abstract

This paper presents a fully electronic, CMOS-compatible ultrasonic sensing system integrated into a 3D beamforming architecture for advanced automotive applications. The proposed system eliminates mechanical scanning by implementing a dual-path beamforming structure comprising programmable transmit (TX) and receive (RX) paths. The TX beamformer [...] Read more.

This paper presents a fully electronic, CMOS-compatible ultrasonic sensing system integrated into a 3D beamforming architecture for advanced automotive applications. The proposed system eliminates mechanical scanning by implementing a dual-path beamforming structure comprising programmable transmit (TX) and receive (RX) paths. The TX beamformer introduces per-element time delays derived from steering angles to control the direction of ultrasonic wave propagation, while the RX beamformer aligns echo signals for spatial focusing. Electrostatic actuation governs the CMOS-compatible ultrasonic transmission mechanism, whereas dynamic modulation under acoustic pressure forms the reception mechanism. The system architecture supports full horizontal and vertical angular coverage, leveraging delay-and-sum processing to achieve electronically steerable beams. The system enables low-power, compact, and high-resolution sensing modules by integrating signal generation, beam control, and delay logic within a CMOS framework. Theoretical modeling demonstrates its capability to support fine spatial resolution and fast response, making it suitable for integration into autonomous vehicle platforms and driver-assistance systems. Full article

(This article belongs to the Special Issue Ultrasonic Transducers in Next-Generation Application)

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15 pages, 2793 KB

Open AccessArticle

Vibration Analysis of Variable-Thickness Multi-Layered Graphene Sheets

by Yunus Onur Yildiz, Murat Sen, Osman Yigid, Mesut Huseyinoglu and Sertac Emre Kara

Appl. Sci. 2025, 15(16), 9200; https://doi.org/10.3390/app15169200 - 21 Aug 2025

Viewed by 257

Abstract

This study investigates the vibrational characteristics of multi-layered graphene sheets with variable thickness (VTGSs) by using molecular dynamics (MD) simulations. It is aimed to determine how the natural frequencies and vibration damping ratios of variable-thickness graphene change with respect to temperature. Atomistic models [...] Read more.

This study investigates the vibrational characteristics of multi-layered graphene sheets with variable thickness (VTGSs) by using molecular dynamics (MD) simulations. It is aimed to determine how the natural frequencies and vibration damping ratios of variable-thickness graphene change with respect to temperature. Atomistic models for six distinct geometries (1L, 3LT, 3LTB, 5LT, 5LTB, and 9LTB) were generated to analyze the influence of structural design and temperature on their natural frequencies. The simulations were performed using the Large-Scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) with an AIREBO potential to represent interatomic carbon interactions. Natural frequencies of all atomistic models were extracted by applying the Fast Fourier Transform (FFT) method to the Velocity Autocorrelation Function (VACF) data obtained from the simulations. In addition, the analysis was conducted at three different temperatures: 250 K, 300 K, and 350 K. Key findings reveal that an increase in the number of graphene layers results in a decrease in the fundamental natural frequency due to the increased mass of the structure. Moreover, it was noted that natural frequencies decrease with increasing temperature. It is attributed to the reduction in structural rigidity at higher thermal energies. These results provide critical insights into how geometric and thermal variations affect the dynamic behavior of complex multi-layered graphene structures. Full article

(This article belongs to the Special Issue Advanced Functional Thin Film Materials: Simulations, Design, Preparation and Applications)

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28 pages, 5495 KB

Open AccessArticle

Model Comparison and Parameter Estimation for Gompertz Distributions Under Constant Stress Accelerated Lifetime Tests

by Shuyu Du and Wenhao Gui

Appl. Sci. 2025, 15(16), 9199; https://doi.org/10.3390/app15169199 - 21 Aug 2025

Viewed by 231

Abstract

The accelerated lifetime test is a widely used and effective approach in reliability analysis because of its shorter testing duration. In this study, product lifetimes are assumed to follow the Gompertz distribution. This article primarily focuses on performance comparisons between the linear model [...] Read more.

The accelerated lifetime test is a widely used and effective approach in reliability analysis because of its shorter testing duration. In this study, product lifetimes are assumed to follow the Gompertz distribution. This article primarily focuses on performance comparisons between the linear model and the inverse power-law model, both of which are utilized to characterize the relationship between the shape parameter and stress levels. To test model robustness, we also generate data from the Sine-Modified Power Gompertz distribution, a more flexible alternative. We conduct Monte Carlo simulations using four estimation methods: the maximum likelihood method, the least squares method, the maximum product of spacing method, and the Cramér-von Mises method, for small, medium, and large sample sizes. The comparison of mean squared error serves as a critical indicator for evaluating the performance of different methods and models. Additionally, the shape parameter and reliability function are obtained based on the estimation results. Finally, a real dataset is analyzed to demonstrate the most suitable accelerated life model, and the Akaike Information Criterion is used to further assess model fit. Furthermore, we employ leave-one-out cross-validation (LOOCV) to prove this model’s generalizability. Full article

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34 pages, 2795 KB

Open AccessArticle

Development of a Decision Support System for Biomaterial Selection Based on MCDM Methods

by Dušan Lj. Petković, Miloš J. Madić and Milan M. Mitković

Appl. Sci. 2025, 15(16), 9198; https://doi.org/10.3390/app15169198 - 21 Aug 2025

Viewed by 298

Abstract

The material selection process can be viewed as a multi-criteria decision-making (MCDM) problem with multiple objectives, which are often conflicting and of different importance. The selection of the most suitable biomaterial is considered as a very complex, important, and responsible task that is [...] Read more.

The material selection process can be viewed as a multi-criteria decision-making (MCDM) problem with multiple objectives, which are often conflicting and of different importance. The selection of the most suitable biomaterial is considered as a very complex, important, and responsible task that is influenced by many factors. In this paper, a procedure for biomaterial selection based on MCDM is proposed by using a developed decision support system (DSS) named MCDM Solver. Within the framework of the developed DSS, the complete procedure for selecting the criteria weights was developed. Also, in addition to the adapted standard MCDM methods such as TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), an extended WASPAS (Weighted Aggregated Sum Product Assessment) method was developed, enabling its application for considering target-based criteria in solving biomaterial selection problems. The proposed MCDM Solver enables a structured decision-making process helping decision-makers rank biomaterials with respect to multiple conflicting criteria and make rational and justifiable decisions. For the validation of the developed DSS, two case studies, i.e., the selection of a plate for internal bone fixation and a hip prosthesis, were presented. Finally, lists of potential biomaterials (alternatives) in the considered case studies were ranked based on the selected criteria, where the best-ranked one presents the most suitable choice for the specific biomedical application. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Biomedical Engineering)

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25 pages, 4997 KB

Open AccessArticle

Application of Game Theory Weighting in Roof Water Inrush Risk Assessment: A Case Study of the Banji Coal Mine, China

by Yinghao Cheng, Xingshuo Xu, Peng Li, Xiaoshuai Guo, Wanghua Sui and Gailing Zhang

Appl. Sci. 2025, 15(16), 9197; https://doi.org/10.3390/app15169197 - 21 Aug 2025

Viewed by 171

Abstract

Mine roof water inrush represents a prevalent hazard in mining operations, characterized by its concealed onset, abrupt occurrence, and high destructiveness. Since mine water inrush is controlled by multiple factors, rigorous risk assessment in hydrogeologically complex coal mines is critically important for operational [...] Read more.

Mine roof water inrush represents a prevalent hazard in mining operations, characterized by its concealed onset, abrupt occurrence, and high destructiveness. Since mine water inrush is controlled by multiple factors, rigorous risk assessment in hydrogeologically complex coal mines is critically important for operational safety. This study focuses on the roof water inrush hazard in coal seams of the Banji coal mine, China. The conventional water-conducting fracture zone height estimation formula was calibrated through comparative analysis of empirical models and analogous field measurements. Eight principal controlling factors were systematically selected, with subjective and objective weights assigned using AHP and EWM, respectively. Game theory was subsequently implemented to compute optimal combined weights. Based on this, the vulnerability index model and fuzzy comprehensive evaluation model were constructed to assess the roof water inrush risk in the coal seams. The risk in the study area was classified into five levels: safe zone, relatively safe zone, transition zone, relatively hazardous zone, and hazardous zone. A zoning map of water inrush risk was generated using Geographic Information System (GIS) technology. The results show that the safe zone is located in the western part of the study area, while the hazardous and relatively hazardous zones are situated in the eastern part. Among the two models, the fuzzy comprehensive evaluation model aligns more closely with actual engineering practices and demonstrates better predictive performance. It provides a reliable evaluation and prediction model for addressing roof water hazards in the Banji coal seam. Full article

(This article belongs to the Special Issue Hydrogeology and Regional Groundwater Flow)

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