Applied Sciences

21 pages, 3446 KiB

Open AccessArticle

Towards a Digital Twin for Open-Frame Underwater Vehicles Using Evolutionary Algorithms

by Félix Orjales, Julián Rodríguez-Cortegoso, Enrique Fernández-Pérez, Alejandro Romero and Vicente Diaz-Casas

Appl. Sci. 2025, 15(13), 7085; https://doi.org/10.3390/app15137085 (registering DOI) - 24 Jun 2025

Abstract

Hydrodynamic coefficients determine the behavior of all simulated underwater vehicles. Therefore, it is essential to precisely define their values when aiming to replicate a real vehicle. Generally established procedures for obtaining them tend to have limitations, especially in transient responses. To address these [...] Read more.

Hydrodynamic coefficients determine the behavior of all simulated underwater vehicles. Therefore, it is essential to precisely define their values when aiming to replicate a real vehicle. Generally established procedures for obtaining them tend to have limitations, especially in transient responses. To address these issues, this paper proposes a comprehensive methodology for obtaining the hydrodynamic coefficients of an underwater vehicle. The main novelty is the combination of empirical measurements as a first step and evolutionary algorithms as a final step for optimizing the coefficients. The proposed methodology is described and applied to a commercially available remotely operated vehicle (ROV) BlueROV2, followed by analyzing the results in detail and including several tests that compare it to the real vehicle to validate its adequacy. Full article

(This article belongs to the Special Issue Advances in Robotics and Autonomous Systems)

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39 pages, 2733 KiB

Open AccessReview

From Dysbiosis to Cardiovascular Disease: The Impact of Gut Microbiota on Atherosclerosis and Emerging Therapies

by Tiago Lima, Verónica Costa, Carla Nunes, Gabriela Jorge da Silva and Sara Domingues

Appl. Sci. 2025, 15(13), 7084; https://doi.org/10.3390/app15137084 (registering DOI) - 24 Jun 2025

Abstract

The gut microbiota consists of trillions of microorganisms, mostly bacteria, which establish a symbiotic relationship with the host. The host provides a favourable environment and the essential nutrients for their proliferation, while the gut microbiota plays a key role in maintaining the host’s [...] Read more.

The gut microbiota consists of trillions of microorganisms, mostly bacteria, which establish a symbiotic relationship with the host. The host provides a favourable environment and the essential nutrients for their proliferation, while the gut microbiota plays a key role in maintaining the host’s health. Therefore, imbalances in its composition, a state known as dysbiosis, can contribute to the onset or progression of various pathological conditions, including atherosclerosis. Atherosclerosis is a chronic, slow-progressing inflammatory disease characterised by the formation and potential rupture of atheromatous plaques in medium- and large-calibre arteries. It underlies major cardiovascular events, such as stroke and myocardial infarction, and remains a leading cause of global morbidity and mortality. The modulation of the gut microbiota using prebiotics, probiotics, and faecal microbiota transplantation (FMT) has emerged as a promising approach for preventing and managing atherosclerosis. Although numerous studies have explored these strategies, further research is needed to establish their efficacy and mechanisms. This review explores the pathophysiology of atherosclerosis, its main risk factors, and the interplay between the gut microbiota and atherosclerosis, with a particular focus on the mechanisms by which microbiota-targeted interventions, including prebiotics, probiotics, and FMT, may serve as therapeutic adjuvants in the prevention and treatment of atherosclerosis. Full article

(This article belongs to the Special Issue Advances in Microbiota in Human Health and Diseases)

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21 pages, 9473 KiB

Open AccessArticle

Design Guidelines for Combining Digital Human Modeling and Virtual Reality to Foresee Workplaces Ergonomics Issues During Product Development

by Adailton G. Silva, Rafael Vieira Miguez, Lucas G. G. de Almeida, Márcio F. Catapan, Carina S. Silveira, Marcelo da S. Hounsell, Marcus V. M. Gomes and Ingrid Winkler

Appl. Sci. 2025, 15(13), 7083; https://doi.org/10.3390/app15137083 (registering DOI) - 24 Jun 2025

Abstract

A product development process establishes requirements not just for the new product’s quality and performance, but also for its manufacturing process, to guarantee that the item is manufactured with minimal impact. This is because, if an issue is discovered after the product has [...] Read more.

A product development process establishes requirements not just for the new product’s quality and performance, but also for its manufacturing process, to guarantee that the item is manufactured with minimal impact. This is because, if an issue is discovered after the product has been released, the implications go beyond the expensive cost of the repair; the physical ergonomics problem can affect the worker’s comfort, productivity, and product quality. Virtual reality and digital human modeling are often employed in Industry 4.0 to evaluate ergonomics, but they are rarely used to examine physical ergonomics throughout the product development phases. Our study presents design guidelines to combine virtual reality and digital human modeling to anticipate the physical ergonomics evaluations of the assembly process while the product is still in development. Based on physical observations of body-posture angles and total effort classification, our proof of concept performed comparably to conventional methods. We also observed comparable results when we analyzed attributive factors such as hand clearance and strength. In contrast, our proof of concept has been shown to be limited for occupations involving extra ergonomic physical risk factors, such as touch perception, temperature fluctuations, or size changes. Full article

(This article belongs to the Special Issue Integration of Digital Simulation Models in Smart Manufacturing)

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13 pages, 2264 KiB

Open AccessArticle

Biflavonoid Profiling of Juniperus Species: The Influence of Plant Part and Growing Location

by Barbara Medvedec, Iva Jurčević Šangut, Armin Macanović, Erna Karalija and Dunja Šamec

Appl. Sci. 2025, 15(13), 7082; https://doi.org/10.3390/app15137082 (registering DOI) - 24 Jun 2025

Abstract

Biflavonoids are an important group of flavonoids found in Juniperus species, yet their distribution and accumulation patterns remain insufficiently explored. In this study, we applied a method for the simultaneous quantification of seven biflavonoids to analyze different plant parts of J. communis, [...] Read more.

Biflavonoids are an important group of flavonoids found in Juniperus species, yet their distribution and accumulation patterns remain insufficiently explored. In this study, we applied a method for the simultaneous quantification of seven biflavonoids to analyze different plant parts of J. communis, J. communis subsp. nana, and J. oxycedrus. In order to determinate the influence of growing location, we also analyzed J. communis samples collected from different locations. Four biflavonoids—cupressuflavone, amentoflavone, bilobetin, and hinokiflavone—were detected. In both analyzed J. communis varieties, amentoflavone was the predominant biflavonoid in cones and needles, while in J. oxycedrus, cupressuflavone was the most abundant in cones, with amentoflavone dominating in needles. Overall, biflavonoid content was significantly higher in needles than in cones, with total biflavonoid levels in needles exceeding 5 mg/g dw, highlighting the tissue-specific nature of biflavonoid biosynthesis within Juniperus species. Additionally, our results suggest that in J. communis, biflavonoid accumulation is significantly influenced by growing location. Full article

(This article belongs to the Special Issue Advances in Phytochemicals: Extraction, Isolation, and Identification)

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18 pages, 1159 KiB

Open AccessArticle

Application of Machine Learning Models for Baseball Outcome Prediction

by Tzu-Chien Lo, Chen-Yin Lee, Chien-Lin Chen, Tsung-Yu Hsieh, Che-Hsiu Chen and Yen-Kuang Lin

Appl. Sci. 2025, 15(13), 7081; https://doi.org/10.3390/app15137081 (registering DOI) - 24 Jun 2025

Abstract

Data science has become an essential component in professional sports, particularly for predicting team performance and outcomes. This study aims to develop and evaluate machine learning models that accurately predict game outcomes in the Chinese Professional Baseball League (CPBL). Method: A total of [...] Read more.

Data science has become an essential component in professional sports, particularly for predicting team performance and outcomes. This study aims to develop and evaluate machine learning models that accurately predict game outcomes in the Chinese Professional Baseball League (CPBL). Method: A total of 859 games from the 2021 to 2023 regular seasons were analyzed, using both traditional baseball statistics and advanced sabermetric indicators such as the Weighted Runs Created Plus (wRC+), Weighted Runs Above Average (wRAA), and Percentage of Leadoff Batters on Base (PLOB%). Five machine learning models—decision tree, logistic regression, Neural Network, Random Forest, and XGBoost—were constructed and assessed through a five-fold cross-validation. Evaluation metrics included accuracy, F1 scores, sensitivity, specificity, and the AUC-ROC. Results: Among the models, logistic regression and XGBoost achieved the highest performance, with an accuracy ranging from 0.89 to 0.93 and an AUC-ROC from 0.97 to 0.98. The feature importance and SHapley Additive exPlanations (SHAP) analysis revealed that the wRC+ and PLOB% were the most influential predictors, reflecting the offensive efficiency and pitching control. Conclusion: The results suggest that combining interpretable machine learning with sabermetrics provides valuable insights for coaches and analysts in professional baseball. Furthermore, incorporating performance weighting based on game context may further enhance model accuracy. This research demonstrates the potential of data-driven strategies in sports analytics and decision-making. Full article

(This article belongs to the Special Issue Exercise, Fitness, Human Performance and Health: 2nd Edition)

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40 pages, 10781 KiB

Open AccessReview

Recent Developments in Additively Manufactured Crash Boxes: Geometric Design Innovations, Material Behavior, and Manufacturing Techniques

by Ahmed Saber, A. M. Amer, A. I. Shehata, H. A. El-Gamal and A. Abd_Elsalam

Appl. Sci. 2025, 15(13), 7080; https://doi.org/10.3390/app15137080 (registering DOI) - 24 Jun 2025

Abstract

Crash boxes play a vital role in improving vehicle safety by absorbing collision energy and reducing the forces transmitted to occupants. Additive manufacturing (AM) has become a powerful method for developing advanced crash boxes by enabling complex geometries. This review provides a comprehensive [...] Read more.

Crash boxes play a vital role in improving vehicle safety by absorbing collision energy and reducing the forces transmitted to occupants. Additive manufacturing (AM) has become a powerful method for developing advanced crash boxes by enabling complex geometries. This review provides a comprehensive examination of recent progress in AM crash boxes, with a focus on three key aspects: geometric design innovations, material behavior, and manufacturing techniques. The review investigates the influence of various AM-enabled structural configurations, including tubular, origami-inspired, lattice, and bio-inspired designs, on crashworthiness performance. Among these, bio-inspired structures exhibit superior energy absorption characteristics, achieving a mean specific energy absorption

(S E A)

of 21.51 J/g. Material selection is also explored, covering polymers, fiber-reinforced polymers, metals, and multi-material structures. Metallic AM crash boxes demonstrate the highest energy absorption capacity, with a mean

S E A

of 28.65 J/g. In addition, the performance of different AM technologies is evaluated, including Stereolithography (SLA), Material Jetting (MJT), Selective Laser Melting (SLM), Selective Laser Sintering (SLS), Fused Deposition Modeling (FDM), and hybrid manufacturing techniques. Among these, crash boxes produced by SLM show the most favorable energy absorption performance, with a mean

S E A

of 16.50 J/g. The findings presented in this review offer critical insights to guide future research and development in the design and manufacturing of next-generation AM crash boxes intended to enhance vehicle safety. Full article

(This article belongs to the Section Additive Manufacturing Technologies)

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21 pages, 1387 KiB

Open AccessArticle

Methodology for Identification of Occupational Hazards Using Their Characteristic Features in Hard Coal Mining

by Zbigniew Burtan, Dagmara Nowak-Senderowska and Paweł Szczepański

Appl. Sci. 2025, 15(13), 7079; https://doi.org/10.3390/app15137079 (registering DOI) - 23 Jun 2025

Abstract

Ensuring employee safety is a top priority for every enterprise, and it is especially critical in high-risk industries like coal mining. To achieve this goal, it is essential to focus efforts on identifying existing hazards and thoroughly assessing the associated risks. Accurate identification [...] Read more.

Ensuring employee safety is a top priority for every enterprise, and it is especially critical in high-risk industries like coal mining. To achieve this goal, it is essential to focus efforts on identifying existing hazards and thoroughly assessing the associated risks. Accurate identification and detailed characterization of occupational hazards play a pivotal role in the occupational risk assessment process, providing the foundation for effective safety strategies. This article presents an analysis of the process of identifying occupational hazards in hard coal mining, based on applicable legal regulations and a review of the relevant literature. The analysis reveals, on the one hand, a diversity of approaches to hazard classification, and on the other, a limited use of the characteristic features of hazards in classification processes. The findings of this review form the basis for proposing a systematic classification of occupational hazards in hard coal mining, taking into account the specific features of hazards in relation to their sources and potential consequences. The proposed classification not only categorizes hazards but also describes the specifics of hazard sources, such as environmental conditions, machinery, chemicals, and human factors, as well as the possible outcomes of these hazards, including physical injury, health impacts, and even fatalities. The aim of this article is to present a proposed classification of occupational hazards in hard coal mining and to provide a detailed characterization of these hazards based on the description of their sources and potential consequences. The proposed approach, grounded in the identification of characteristic features of hazards, facilitates the effective selection of preventive measures that can be implemented to reduce risk and improve workplace safety. Due to the presence of the full spectrum of natural hazards in Polish hard coal mining, the analysis draws on available statistical data, focusing on those hazards that contribute most significantly to fatal accidents and serious injuries. In conclusion, the article emphasizes the importance of a structured and systematic approach to identifying and assessing occupational hazards in the coal mining industry. By drawing on legal and literature-based insights, it aims to contribute to the development of more effective safety practices that protect workers and minimize the occurrence of workplace accidents and illnesses. Full article

20 pages, 1710 KiB

Open AccessArticle

The Study of the Synthesis of SiC by the Carbonization of Si(111) Substrates: The Role of Native Silicon Oxide

by Teodor Milenov, Vladimir Mehandzhiev, Peter Rafailov, Ivalina Avramova, Ivan Zahariev, Georgi Avdeev, Daniela Karashanova, Biliana Georgieva, Penka Terziyska, Kiril Kirilov, Blagoy Blagoev, Stefan Kolev, Dimitar Dimov, Dobromir Kalchevski, Desislava Karaivanova and Valentin Popov

Appl. Sci. 2025, 15(13), 7078; https://doi.org/10.3390/app15137078 (registering DOI) - 23 Jun 2025

Abstract

We present the results of silicon carbide (SiC) thin film synthesis on Si(111) substrates using chemical vapor deposition by decomposing CH₄ in H₂ at 1135 °C. The experiments were conducted in an Oxford Nanofab Plasmalab System 100 for carbon phase deposition [...] Read more.

We present the results of silicon carbide (SiC) thin film synthesis on Si(111) substrates using chemical vapor deposition by decomposing CH₄ in H₂ at 1135 °C. The experiments were conducted in an Oxford Nanofab Plasmalab System 100 for carbon phase deposition times of 3, 5, 20, 60, and 90 min on Si(111) with or without native oxide, following established protocols. Our studies show that either predominantly crystalline SiC or a mixture of SiC and Si–O/Si–O–C glass forms on Si substrates significantly doped with carbon and oxygen, depending on the presence or absence of native oxide. The thickness of the SiC film ranges from approximately 5–6 nm for films synthesized in 3 min to over 15 nm for those synthesized in 90 min, while the size of the crystal grains varies from a few to 110 nm depending on the synthesis duration. The findings suggest that the complex composition of the thin films and the region beneath them can more effectively compensate for the differences in lattice parameters and thermal expansion coefficients between the SiC film and the Si substrate; thus, this method is promising for depositing intermediate thin films of SiC on Si substrates. Full article

(This article belongs to the Section Surface Sciences and Technology)

31 pages, 2159 KiB

Open AccessArticle

Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages

by Shimon Fridkin, Sigal Kordova and Ron S. Hirschprung

Appl. Sci. 2025, 15(13), 7077; https://doi.org/10.3390/app15137077 (registering DOI) - 23 Jun 2025

Abstract

Management in the current era is strongly supported by a range of advanced technologies that enhance organizational efficiency and effectiveness. One particularly promising technology is Virtual Reality (VR). This exploratory study investigates the perceived practicality of VR across the systems engineering project lifecycle, [...] Read more.

Management in the current era is strongly supported by a range of advanced technologies that enhance organizational efficiency and effectiveness. One particularly promising technology is Virtual Reality (VR). This exploratory study investigates the perceived practicality of VR across the systems engineering project lifecycle, providing quantitative insights into how VR is valued at different stages and by various user profiles. Drawing on data from n = 56 professionals, the study employs advanced analytical methods like GLM, CFA, SEM, CATPCA, and CHAID classification trees to extract insights from the dataset. Results reveal that the perceived utility of VR varies significantly across project stages, with the training and proposal phases receiving the highest ratings, and early planning stages such as specification and requirement definition rated the lowest. A unidimensional structure of VR practicality was validated, with strong factor loadings for stages involving prototyping and testing. Key predictors of perceived practicality included willingness to adopt VR (positive effect), familiarity with VR (mixed effects), and education level (negative effect). While traditional demographic variables had limited explanatory power, experiential and psychological factors—such as hands-on VR exposure and openness to innovation—emerged as critical determinants of perceived value. These findings offer an empirically grounded framework for the strategic implementation of VR in systems engineering. Practically, this research guides decisions on resource allocation by identifying the specific project stages (e.g., training, proposal development) and user profiles (e.g., those open to adoption, those with specific experiential backgrounds) where VR is likely to provide the most significant benefit and return on investment. Full article

(This article belongs to the Special Issue Applications of Automated Management System)

15 pages, 1326 KiB

Open AccessArticle

Experimental Study of Two-Stage Anaerobic Co-Digestion of Corn Steep Liquor and Agricultural Wastes for Hydrogen and Methane Production Including Metagenomics

by Elena Chorukova, Galina Stoyancheva and Lyudmila Kabaivanova

Appl. Sci. 2025, 15(13), 7076; https://doi.org/10.3390/app15137076 (registering DOI) - 23 Jun 2025

Abstract

This study investigated the dynamics and composition of microbial communities within the bioreactors of a two-stage anaerobic system employed for the bioconversion of corn steep liquor, a food processing byproduct, into hydrogen and methane. The high organic matter content of such wastes positions [...] Read more.

This study investigated the dynamics and composition of microbial communities within the bioreactors of a two-stage anaerobic system employed for the bioconversion of corn steep liquor, a food processing byproduct, into hydrogen and methane. The high organic matter content of such wastes positions them as valuable substrates for biotechnological applications. The two-stage anaerobic digestion (AD) process was compartmentalized into a hydrogen-producing bioreactor (3 dm³) and a methane-producing bioreactor (15 dm³), each harboring distinct microbial consortia. The system yielded a maximal hydrogen production of 1.02 L/day and a peak methane production of 24.1 L/day with substrate corn steep liquor and cattle manure in a ratio 1:1. Microbial consortia were recognized as critical drivers of AD performance and biofuel yield. This research demonstrated the efficacy of a two-stage approach, segregating the hydrogenic (hydrolysis and acidogenesis) and methanogenic (acetogenesis and methanogenesis) phases, for optimized energy recovery from the co-digestion of corn steep liquor and cattle manure under controlled conditions. Metagenomic sequencing and a subsequent bioinformatics analysis were utilized to characterize the microbial diversity within each bioreactors. These findings contribute to a deeper understanding of the microbial ecology of AD and hold the potential for broader applications in waste-to-energy bioconversion. Full article

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27 pages, 2659 KiB

Open AccessArticle

A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO

by Chetanpal Singh, Santoso Wibowo and Srimannarayana Grandhi

Appl. Sci. 2025, 15(13), 7075; https://doi.org/10.3390/app15137075 (registering DOI) - 23 Jun 2025

Abstract

Cotton is one of the most valuable non-food agricultural products in the world. However, cotton production is often hampered by the invasion of disease. In most cases, these plant diseases are a result of insect or pest infestations, which can have a significant [...] Read more.

Cotton is one of the most valuable non-food agricultural products in the world. However, cotton production is often hampered by the invasion of disease. In most cases, these plant diseases are a result of insect or pest infestations, which can have a significant impact on production if not addressed promptly. It is, therefore, crucial to accurately identify leaf diseases in cotton plants to prevent any negative effects on yield. This paper presents a hybrid deep learning approach based on Bidirectional Encoder Representations from Transformers with Residual network and particle swarm optimization (BERT-ResNet-PSO) for detecting cotton plant diseases. This approach starts with image pre-processing, which they pass to a BERT-like encoder after linearly embedding the image patches. It results in segregating disease regions. Then, the output of the encoded feature is passed to ResNet-based architecture for feature extraction and further optimized by PSO to increase the classification accuracy. The approach is tested on a cotton dataset from the Plant Village dataset, where the experimental results show the effectiveness of this hybrid deep learning approach, achieving an accuracy of 98.5%, precision of 98.2% and recall of 98.7% compared to the existing deep learning approaches such as ResNet50, VGG19, InceptionV3, and ResNet152V2. This study shows that the hybrid deep learning approach is capable of dealing with the cotton plant disease detection problem effectively. This study suggests that the proposed approach is beneficial to help avoid crop losses on a large scale and support effective farming management practices. Full article

19 pages, 1034 KiB

Open AccessArticle

Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data

by Theodoros Petropoulos, Lefteris Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis and Dionysis Bochtis

Appl. Sci. 2025, 15(13), 7074; https://doi.org/10.3390/app15137074 (registering DOI) - 23 Jun 2025

Abstract

Accurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. To bridge this literature gap, [...] Read more.

Accurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. To bridge this literature gap, an interpretable ML framework was developed for predicting lupin yield using Sentinel-2 remote sensing data integrated with georeferenced yield measurements. Data preprocessing involved computing vegetation indices, removing outliers, addressing multicollinearity, normalizing feature scales, and applying data augmentation techniques to correct target imbalance. Subsequently, six ML models were evaluated representing different algorithmic strategies. Among them, XGBoost showed the best performance (R² = 0.8756) and low error values across MAE, MSE, and RMSE metrics. To enhance model transparency, SHapley Additive exPlanations (SHAP) values were applied to interpret the feature contributions of the XGBoost model. The Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) were found to be key predictors of crop yield, both showing a positive correlation with higher values reflecting greater vegetation vigor and corresponding to increased yield. These were followed by B03 (green) and B12 (short-wave infrared), which captured key reflectance properties associated with chlorophyll activity and water content, respectively. Both of them substantially influence photosynthetic efficiency and plant health, ultimately affecting yield potential. Full article

29 pages, 4899 KiB

Open AccessArticle

PcBD: A Novel Point Cloud Processing Flow for Boundary Detecting and De-Noising

by Shuyu Sun, Jianqiang Huang, Shuai Zhao and Tengchao Huang

Appl. Sci. 2025, 15(13), 7073; https://doi.org/10.3390/app15137073 (registering DOI) - 23 Jun 2025

Abstract

In target detection tasks equipped with depth sensors, it is crucial to adopt the point cloud pretreatment process, which is directly related to the quality of the obtained three-dimensional model of the target. However, there are few methods that can be combined with [...] Read more.

In target detection tasks equipped with depth sensors, it is crucial to adopt the point cloud pretreatment process, which is directly related to the quality of the obtained three-dimensional model of the target. However, there are few methods that can be combined with common preprocessing methods to quickly process ToF camera output. In real-life experiments, the common method is to adopt multiple types of preprocessing methods and adjust parameters separately. We proposed PcBD, a method that integrates outlier removal, boundary detection, and smooth sliders. PcBD does not limit the number of input points, and can remove outliers and predict smooth projection boundaries at one time while ensuring that the total number of points remains unchanged. We also introduced Bound57, a benchmark dataset that contains point clouds with synthetic noise, outliers, and projected boundary labels. Experimental results show that PcBD performs significantly better than state-of-the-art methods in various de-noising and boundary detection tasks. Full article

(This article belongs to the Section Optics and Lasers)

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29 pages, 1243 KiB

Open AccessArticle

Development of an Intelligent Method for Target Tracking in Radar Systems at the Initial Stage of Operation Under Intentional Jamming Conditions

by Serhii Semenov, Olga Wasiuta, Alla Jammine, Justyna Golec, Magdalena Krupska-Klimczak, Yevhen Tarasenko, Vitalii Voronets, Vitalii Breslavets, Serhii Lvov and Artem Moskalenko

Appl. Sci. 2025, 15(13), 7072; https://doi.org/10.3390/app15137072 (registering DOI) - 23 Jun 2025

Abstract

The object of this research is the process of tracking air targets at the initial stage of radar system operation. The problem lies in the lack of a comprehensive approach to tracking air targets in difficult conditions that is able to dynamically adapt [...] Read more.

The object of this research is the process of tracking air targets at the initial stage of radar system operation. The problem lies in the lack of a comprehensive approach to tracking air targets in difficult conditions that is able to dynamically adapt filtering parameters, predict signal reliability, and change the processing mode depending on the level of interference. In conditions of signal loss, noise, and unstable measurement reliability, traditional methods do not provide stable and accurate tracking, especially at the initial stages of radar operation. To address this issue, an intelligent method is proposed that integrates a probabilistic graphical evaluation and review technique (GERT) model, a recursive Kalman filter, and a measurement reliability prediction module based on a long short-term memory (LSTM) neural network. The proposed approach allows for the real-time adaptation of filtering parameters, fusion of local and global trajectory estimates, and dynamic switching between tracking modes depending on the environmental conditions. The dynamic weighting algorithm between model estimates ensures a balance between accuracy and robustness. Simulation experiments confirmed the effectiveness of the method: the root mean square error (RMSE) of coordinate estimation was reduced by 25%; the probability of tracking loss decreased by half (from 0.2 to 0.1); and the accuracy of loss prediction exceeded 85%. The novelty of the approach lies in integrating stochastic modeling, machine learning, and classical filtering into a unified adaptive loop. The proposed system can be adapted to various types of radar and easily scaled to multi-sensor architectures. This makes it suitable for practical implementation in both defense and civilian air object detection systems operating under complex conditions. Full article

(This article belongs to the Special Issue Information Security and Privacy Protection in Networks and Communication)

18 pages, 1568 KiB

Open AccessArticle

Improving Multi-Class Classification for Recognition of the Prioritized Classes Using the Analytic Hierarchy Process

by Algimantas Venčkauskas, Jevgenijus Toldinas and Nerijus Morkevičius

Appl. Sci. 2025, 15(13), 7071; https://doi.org/10.3390/app15137071 (registering DOI) - 23 Jun 2025

Abstract

Machine learning (ML) algorithms are widely used in various fields, including cyber threat intelligence (CTI), financial technology (Fintech), and intrusion detection systems (IDSs). They automate security alert data analysis, enhancing attack detection, incident response, and threat mitigation. Fintech is particularly vulnerable to cyber-attacks [...] Read more.

Machine learning (ML) algorithms are widely used in various fields, including cyber threat intelligence (CTI), financial technology (Fintech), and intrusion detection systems (IDSs). They automate security alert data analysis, enhancing attack detection, incident response, and threat mitigation. Fintech is particularly vulnerable to cyber-attacks and cyber espionage due to its data-centric nature. Because of this, it is essential to give priority to the classification of cyber-attacks to accomplish the most crucial attack detection. Improving ML models for superior prioritized recognition requires a comprehensive strategy that includes data preprocessing, enhancement, algorithm refinement, and customized assessment. To improve cyber-attack detection in the Fintech, CTI, and IDS sectors, it is necessary to develop an ML model that better recognizes the prioritized classes, thereby enhancing security against important types of threats. This research introduces adaptive incremental learning, which enables ML models to keep learning new information by looking at changing data from a data stream, improving their ability to accurately identify types of cyber-attacks with high priority. The Analytical Hierarchy Process (AHP) is suggested to help make the best decision by evaluating model performance based on prioritized classes using real multi-class datasets instead of artificially improved ones. The findings demonstrate that the ML model improved its ability to identify prioritized classes of cyber-attacks utilizing the ToN_IoT network dataset. The recall value for the “injection” class rose from 59.5% to 61.8%, the recall for the “password” class increased from 86.7% to 88.6%, and the recall for the “ransomware” class improved from 0% to 23.6%. Full article

(This article belongs to the Special Issue Artificial Intelligence, Security and Safety in Information Systems and Networks)

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22 pages, 1495 KiB

Open AccessArticle

Investigation of the Antioxidant and Antimicrobial Properties of Ultrasound-Assisted Extracted Phenolics from Aronia melanocarpa Pomace

by Iuliana Aprodu, Carmen Lidia Chitescu, Leontina Grigore-Gurgu and Loredana Dumitrașcu

Appl. Sci. 2025, 15(13), 7070; https://doi.org/10.3390/app15137070 (registering DOI) - 23 Jun 2025

Abstract

Black chokeberry (Aronia melanocarpa; BC) pomace represents an excellent source of compounds with health-promoting properties. This study investigated the contribution of ultrasound treatment to the recovery of phenolic compounds in comparison with conventional extraction, using water and ethanol solvents. The ultrasound [...] Read more.

Black chokeberry (Aronia melanocarpa; BC) pomace represents an excellent source of compounds with health-promoting properties. This study investigated the contribution of ultrasound treatment to the recovery of phenolic compounds in comparison with conventional extraction, using water and ethanol solvents. The ultrasound amplitude was tested between 20% and 60%, for 10 min, with the highest concentrations of total polyphenols and antioxidant activity being measured at a 30% amplitude. Ultrasound treatment was able to reduce the extraction time for the efficient recovery of antioxidants, from 24 h as required in conventional extraction to several minutes while using lower amplitudes. Regardless of the ultrasound extraction conditions, the ethanolic extracts provided higher content of antioxidants compared to water extracts. The chromatographic analysis highlighted the presence of 48 bioactive compounds, including phenolic acids, isoflavones, flavones, flavanones, proanthocyanidins, flavonols and terpenes. BC extracts showed potential to inhibit the growth of Escherichia coli and Staphylococcus aureus. In addition, the potential mechanism associated with the antibacterial activity was revealed after performing molecular docking tests involving, as receptors, essential proteins for the survival and colonization functions of E. coli and S. aureus. Full article

(This article belongs to the Special Issue Application of Natural Components in Food Production)

25 pages, 6047 KiB

Open AccessReview

Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil

by Anqi Wang, Zhiwen Dang, Yibo Wang, Hui Fan and Shiding Miao

Appl. Sci. 2025, 15(13), 7069; https://doi.org/10.3390/app15137069 (registering DOI) - 23 Jun 2025

Abstract

Chromium and arsenic, as prevalent heavy metal contaminants in water environments, pose significant threats to ecological systems and public health, necessitating urgent remediation measures. Conventional remediation techniques face challenges including high costs, prolonged remediation cycles, limited durability, and secondary contamination risks. While stabilization [...] Read more.

Chromium and arsenic, as prevalent heavy metal contaminants in water environments, pose significant threats to ecological systems and public health, necessitating urgent remediation measures. Conventional remediation techniques face challenges including high costs, prolonged remediation cycles, limited durability, and secondary contamination risks. While stabilization materials have emerged as promising solutions, the complex stabilization mechanisms for chromium and arsenic remain diverse and have not yet been fully elucidated. With reference to previous research, this paper systematically reviews inorganic stabilization materials for chromium and arsenic contamination remediation, with particular emphasis on elucidating their stabilization mechanisms and influencing factors. This review extensively evaluates various material types to inform practical applications, while highlighting investigations into novel composite materials, which advance technological innovation in water environmental remediation. It offers novel perspectives for addressing chromium and arsenic pollution challenges, potentially driving the development of more sustainable remediation strategies. Full article

(This article belongs to the Topic The Hydrosphere in Crisis: Human Impact, Climate Change, and Pathways to Resilience, 2nd Edition)

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27 pages, 4737 KiB

Open AccessArticle

Context-Aware Multimodal Fusion with Sensor-Augmented Cross-Modal Learning: The BLAF Architecture for Robust Chinese Homophone Disambiguation in Dynamic Environments

by Yu Sun, Yihang Qin, Wenhao Chen, Xuan Li and Chunlian Li

Appl. Sci. 2025, 15(13), 7068; https://doi.org/10.3390/app15137068 (registering DOI) - 23 Jun 2025

Abstract

Chinese, a tonal language with inherent homophonic ambiguity, poses significant challenges for semantic disambiguation in natural language processing (NLP), hindering applications like speech recognition, dialog systems, and assistive technologies. Traditional static disambiguation methods suffer from poor adaptability in dynamic environments and low-frequency scenarios, [...] Read more.

Chinese, a tonal language with inherent homophonic ambiguity, poses significant challenges for semantic disambiguation in natural language processing (NLP), hindering applications like speech recognition, dialog systems, and assistive technologies. Traditional static disambiguation methods suffer from poor adaptability in dynamic environments and low-frequency scenarios, limiting their real-world utility. To address these limitations, we propose BLAF—a novel MacBERT-BiLSTM Hybrid Architecture—that synergizes global semantic understanding with local sequential dependencies through dynamic multimodal feature fusion. This framework incorporates innovative mechanisms for the principled weighting of heterogeneous features, effective alignment of representations, and sensor-augmented cross-modal learning to enhance robustness, particularly in noisy environments. Employing a staged optimization strategy, BLAF achieves state-of-the-art performance on the SIGHAN 2015 (data fine-tuning and supplementation): 93.37% accuracy and 93.25% F1 score, surpassing pure BERT by 15.74% in accuracy. Ablation studies confirm the critical contributions of the integrated components. Furthermore, the sensor-augmented module significantly improves robustness under noise (speech SNR to 18.6 dB at 75 dB noise, 12.7% reduction in word error rates). By bridging gaps among tonal phonetics, contextual semantics, and computational efficiency, BLAF establishes a scalable paradigm for robust Chinese homophone disambiguation in industrial NLP applications. This work advances cognitive intelligence in Chinese NLP and provides a blueprint for adaptive disambiguation in resource-constrained and dynamic scenarios. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications—2nd Edition)

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13 pages, 797 KiB

Open AccessArticle

Radiation Hardness of Oxide Thin Films Prepared by Magnetron Sputtering Deposition

by Marko Škrabić, Marija Majer, Zdravko Siketić, Maja Mičetić, Željka Knežević and Marko Karlušić

Appl. Sci. 2025, 15(13), 7067; https://doi.org/10.3390/app15137067 (registering DOI) - 23 Jun 2025

Abstract

Thin amorphous oxide films (a-SiO₂, a-Al₂O₃, a-MgO) were prepared by magnetron sputtering deposition. Their response to high-energy heavy ion beams (23 MeV I, 18 MeV Cu, 2.5 MeV Cu) and gamma-ray (1.25 MeV) irradiation was studied by [...] Read more.

Thin amorphous oxide films (a-SiO₂, a-Al₂O₃, a-MgO) were prepared by magnetron sputtering deposition. Their response to high-energy heavy ion beams (23 MeV I, 18 MeV Cu, 2.5 MeV Cu) and gamma-ray (1.25 MeV) irradiation was studied by elastic recoil detection analysis and infrared spectroscopy. It was established that their high radiation hardness is due to a high level of disorder, already present in as-prepared samples, so the high-energy heavy ion irradiation cannot change their structure much. In the case of a-SiO₂, this resulted in a completely different response to high-energy heavy ion irradiation found previously in thermally grown a-SiO₂. In the case of a-MgO, only gamma-ray irradiation was found to induce significant changes. Full article

(This article belongs to the Special Issue Nuclear Materials: Alloy Design, Processing, Microstructure, Properties, and Qualification)

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