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17 pages, 1731 KB  
Article
Comparative Analysis of Statistical and AI-Based Methods for Livestock Monitoring in Extensive Systems
by Marco Bonfanti, Dominga Mancuso, Giulia Castagnolo and Simona Maria Carmela Porto
Appl. Sci. 2025, 15(20), 11116; https://doi.org/10.3390/app152011116 - 16 Oct 2025
Abstract
In recent years, the research focusing on extensive farming systems has attracted considerable interest among experts in the field. Environmental sustainability and animal welfare are emerging as key elements, assuming a crucial role in global agriculture. In this context, monitoring animals is important [...] Read more.
In recent years, the research focusing on extensive farming systems has attracted considerable interest among experts in the field. Environmental sustainability and animal welfare are emerging as key elements, assuming a crucial role in global agriculture. In this context, monitoring animals is important not only to ensure their welfare, but also to preserve the balance of the land. Inadequate grazing management can in fact damage vegetation due to soil erosion. Therefore, monitoring the habits of animals during grazing is a challenging and crucial task for livestock management. Internet of Things (IoT) technologies, which allow for remote and real-time monitoring, may be a valid solution to these challenges in extensive farms where farmer-to-animal contact is not usual. In this regard, this paper examined three different methods to classify the behavioral activities of grazing cows, by using data collected with collars equipped with accelerometers. Three distinct approaches were compared: the former based on statistical methods, and the other on the use of Machine and Deep Learning techniques. From the comparison of the results obtained, strengths and weaknesses of each approach were examined, so to determine the most appropriate choice in relation to the characteristics of extensive livestock systems. In detail, Machine and Deep Learning-based approaches were found to be more accurate but highly energy-intensive. Therefore, in rural environments, the approach based on statistical methods, combined with LPWAN applications, was preferable due to its long range and low energy consumption. Ultimately, the statistical approach was found to be 64% accurate in classifying four behavioral classes. Full article
44 pages, 5323 KB  
Article
Secure Chaotic Cryptosystem for 3D Medical Images
by Antonios S. Andreatos and Apostolos P. Leros
Mathematics 2025, 13(20), 3310; https://doi.org/10.3390/math13203310 - 16 Oct 2025
Abstract
This study proposes a lightweight double-encryption cryptosystem for 3D medical images such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) scans, and Computed Tomography scans (CT). The first encryption process uses chaotic pseudo-random numbers produced by a Lorenz chaotic system while the [...] Read more.
This study proposes a lightweight double-encryption cryptosystem for 3D medical images such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) scans, and Computed Tomography scans (CT). The first encryption process uses chaotic pseudo-random numbers produced by a Lorenz chaotic system while the second applies Cipher Block Chaining (CBC) mode using outputs from a Pseudo-Random Number Generator (PRNG). To enhance diffusion and confusion, additional voxel shuffling and bit rotation operations are incorporated. Various sets of optimized parameters for the Lorenz system are calculated using either a genetic algorithm or a random walk. The master key of the cryptosystem is 672 bits long and consists of two components. The first component is the SHA-512 hash of the input image while the second component consists of the initial conditions of the Lorenz chaotic system and is 160 bits long. The master key is processed by a function that generates fourteen subkeys, which are then used in different stages of the algorithm. The cryptosystem exhibits excellent performance in terms of entropy, NPCR, UACI, key sensitivity, security, and speed, ensuring the confidentiality of personal medical data and resilience against advanced computational threats, and making it a good candidate for real-time 3D medical image encryption in healthcare systems. Full article
(This article belongs to the Special Issue Mathematical Computation for Pattern Recognition and Computer Vision)
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29 pages, 22311 KB  
Article
Comprehensive Optoelectronic Study of Copper Nitride: Dielectric Function and Bandgap Energies
by Manuel Ballester, Almudena P. Marquez, Eduardo Blanco, Jose M. Manuel, Maria I. Rodriguez-Tapiador, Susana M. Fernandez, Florian Willomitzer, Aggelos K. Katsaggelos and Emilio Marquez
Nanomaterials 2025, 15(20), 1577; https://doi.org/10.3390/nano15201577 - 16 Oct 2025
Abstract
Copper nitride (Cu3N) is gaining attention as an eco-friendly thin-film semiconductor in a myriad of applications, including storage devices, microelectronic components, photodetectors, and photovoltaic cells. This work presents a detailed optoelectronic study of Cu3N thin films grown by reactive [...] Read more.
Copper nitride (Cu3N) is gaining attention as an eco-friendly thin-film semiconductor in a myriad of applications, including storage devices, microelectronic components, photodetectors, and photovoltaic cells. This work presents a detailed optoelectronic study of Cu3N thin films grown by reactive RF-magnetron sputtering under pure N2. An overview of the state-of-the-art literature on this material and its potential applications is also provided. The studied films consist of Cu3N polycrystals with a cubic anti-ReO3 type structure exhibiting a preferential (100) orientation. Their optical properties across the UV-Vis-NIR spectral range were investigated using a combination of multi-angle spectroscopic ellipsometry, broadband transmission, and reflection measurements. Our model employs a stratified geometrical approach, primarily to capture the depth-dependent compositional variations of the Cu3N film while also accounting for surface roughness and the underlying glass substrate. The complex dielectric function of the film material is precisely determined through an advanced dispersion model that combines multiple oscillators. By integrating the Tauc–Lorentz, Gaussian, and Drude models, this approach captures the distinct electronic transitions of this polycrystal. This customized optical model allowed us to accurate extract both the indirect (1.83–1.85 eV) and direct (2.38–2.39 eV) bandgaps. Our multifaceted characterization provides one of the most extensive studies of Cu3N thin films to date, paving the way for optimized device applications and broader utilization of this promising binary semiconductor, and showing its particular potential for photovoltaic given its adequate bandgap energies for solar applications. Full article
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33 pages, 4308 KB  
Review
Review of Advances in Fire Extinguishing Based on Computer Vision Applications: Methods, Challenges, and Future Directions
by Valentyna Loboichenko, Grzegorz Wilk-Jakubowski, Lukasz Pawlik, Jacek Lukasz Wilk-Jakubowski, Roman Shevchenko, Olha Shevchenko, Radoslaw Harabin, Artur Kuchcinski, Valentyna Fedorchuk-Moroz, Anastasiia Khmyrova and Ivan Rushchak
Sensors 2025, 25(20), 6399; https://doi.org/10.3390/s25206399 (registering DOI) - 16 Oct 2025
Abstract
This paper examines the state-of-the-art in fire suppression technologies based on computer vision applications in the subject areas of computer science and engineering. The study involves a two-stage analysis of publications using keywords. This paper presents a bibliographic analysis of scientific literature from [...] Read more.
This paper examines the state-of-the-art in fire suppression technologies based on computer vision applications in the subject areas of computer science and engineering. The study involves a two-stage analysis of publications using keywords. This paper presents a bibliographic analysis of scientific literature from the Scopus database using VOSviewer software and the author’s methodological approach. General keywords were used for the initial analysis of the dataset, followed by a more detailed study with additional criteria and specific keywords. The categories considered in the article are as follows: Firefighting Robots, Fire Detection, Fire Suppression, Aerial Vehicles, and Computer Vision. It is shown that the research includes technical aspects of fire robots and systems, as well as the improvement of their software and hardware. The subsequent review highlights the important role of computer vision in improving the efficiency and effectiveness of fire suppression systems. It is noted that key advances include the development of sophisticated fire detection algorithms and the implementation of automated fire suppression systems. The study also discusses the challenges and future directions in this field, emphasizing the need for continuous innovation and interdisciplinary collaboration. This review provides valuable information for researchers, engineers, and practitioners in the field of fire safety by offering a comprehensive overview of state-of-the-art technologies and their applications in fire suppression. Full article
(This article belongs to the Section Sensing and Imaging)
32 pages, 25136 KB  
Article
Efficiency Evaluation of Sampling Density for Indoor Building LiDAR Point-Cloud Segmentation
by Yiquan Zou, Wenxuan Chen, Tianxiang Liang and Biao Xiong
Sensors 2025, 25(20), 6398; https://doi.org/10.3390/s25206398 (registering DOI) - 16 Oct 2025
Abstract
Prior studies on indoor LiDAR point-cloud semantic segmentation consistently report that sampling density strongly affects segmentation accuracy as well as runtime and memory, establishing an accuracy–efficiency trade-off. Nevertheless, in practice, the density is often chosen heuristically and reported under heterogeneous protocols, which limits [...] Read more.
Prior studies on indoor LiDAR point-cloud semantic segmentation consistently report that sampling density strongly affects segmentation accuracy as well as runtime and memory, establishing an accuracy–efficiency trade-off. Nevertheless, in practice, the density is often chosen heuristically and reported under heterogeneous protocols, which limits quantitative guidance. We present a unified evaluation framework that treats density as the sole independent variable. To control architectural variability, three representative backbones—PointNet, PointNet++, and DGCNN—are each augmented with an identical Point Transformer module, yielding PointNet-Trans, PointNet++-Trans, and DGCNN-Trans trained and tested under one standardized protocol. The framework couples isotropic voxel-guided uniform down-sampling with a decision rule integrating three signals: (i) accuracy sufficiency, (ii) the onset of diminishing efficiency, and (iii) the knee of the accuracy–density curve. Experiments on scan-derived indoor point clouds (with BIM-derived counterparts for contrast) quantify the accuracy–runtime trade-off and identify an engineering-feasible operating band of 1600–2900 points/m2, with a robust setting near 2400 points/m2. Planar components saturate at moderate densities, whereas beams are more sensitive to down-sampling. By isolating density effects and enforcing one protocol, the study provides reproducible, model-agnostic guidance for scan planning and compute budgeting in indoor mapping and Scan-to-BIM workflows. Full article
(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)
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29 pages, 8899 KB  
Article
Aerodynamic Performance of a Natural Laminar Flow Swept-Back Wing for Low-Speed UAVs Under Take Off/Landing Flight Conditions and Atmospheric Turbulence
by Nikolaos K. Lampropoulos, Ioannis E. Sarris, Spyridon Antoniou, Odysseas Ziogas, Pericles Panagiotou and Kyros Yakinthos
Aerospace 2025, 12(10), 934; https://doi.org/10.3390/aerospace12100934 (registering DOI) - 16 Oct 2025
Abstract
The topic of the present study is the aerodynamic performance of a Natural Laminar Flow (NLF) wing for UAVs at low speed. The basis is a thoroughly tested NLF airfoil in the wind tunnel of NASA which is well-customized for light aircrafts. The [...] Read more.
The topic of the present study is the aerodynamic performance of a Natural Laminar Flow (NLF) wing for UAVs at low speed. The basis is a thoroughly tested NLF airfoil in the wind tunnel of NASA which is well-customized for light aircrafts. The aim of this work is the numerical verification that a typical wing design (tapered with moderate aspect ratio and wash-out), being constructed out of aerodynamically highly efficient NLF airfoils during cruise, can deliver high aerodynamic loading under minimal freestream turbulence as well as realistic atmospheric conditions of intermediate turbulence. Thus, high mission flexibility is achieved, e.g., short take off/landing capabilities on the deck of ship where moderate air turbulence is prevalent. Special attention is paid to the effect of the Wing Tip Vortex (WTV) under minimal inflow turbulence regimes. The flight conditions are take off or landing at moderate Reynolds number, i.e., one to two millions. The numerical simulation is based on an open source CFD code and parallel processing on a High Performance Computing (HPC) platform. The aim is the identification of both mean flow and turbulent structures around the wing and subsequently the formation of the wing tip vortex. Due to the purely three-dimensional character of the flow, the turbulence is resolved with advanced modeling, i.e., the Improved Delayed Detached Eddy Simulation (IDDES) which is well-customized to switch modes between Delayed Detached Eddy Simulation (DDES) and Wall-Modeled Large Eddy Simulation (WMLES), thus increasing the accuracy in the shear layer regions, the tip vortex and the wake, while at the same time keeping the computational cost at reasonable levels. IDDES also has the capability to resolve the transition of the boundary layer from laminar to turbulent, at least with engineering accuracy; thus, it serves as a high-fidelity turbulence model in this work. The study comprises an initial benchmarking of the code against wind tunnel measurements of the airfoil and verifies the adequacy of mesh density that is used for the simulation around the wing. Subsequently, the wing is positioned at near-stall conditions so that the aerodynamic loading, the kinematics of the flow and the turbulence regime in the wing vicinity, the wake and far downstream can be estimated. In terms of the kinematics of the WTV, a thorough examination is attempted which comprises its inception, i.e., the detachment of the boundary layer on the cut-off wing tip, the roll-up of the shear layer to form the wake and the motion of the wake downstream. Moreover, the effect of inflow turbulence of moderate intensity is investigated that verifies the bibliography with regard to the performance degradation of static airfoils in a turbulent atmospheric regime. Full article
(This article belongs to the Section Aeronautics)
27 pages, 3065 KB  
Article
Chinese Financial News Analysis for Sentiment and Stock Prediction: A Comparative Framework with Language Models
by Hsiu-Min Chuang, Hsiang-Chih He and Ming-Che Hu
Big Data Cogn. Comput. 2025, 9(10), 263; https://doi.org/10.3390/bdcc9100263 - 16 Oct 2025
Abstract
Financial news has a significant impact on investor sentiment and short-term stock price trends. While many studies have applied natural language processing (NLP) techniques to financial forecasting, most have focused on single tasks or English corpora, with limited research in non-English language contexts [...] Read more.
Financial news has a significant impact on investor sentiment and short-term stock price trends. While many studies have applied natural language processing (NLP) techniques to financial forecasting, most have focused on single tasks or English corpora, with limited research in non-English language contexts such as Taiwan. This study develops a joint framework to perform sentiment classification and short-term stock price prediction using Chinese financial news from Taiwan’s top 50 listed companies. Five types of word embeddings—one-hot, TF-IDF, CBOW, skip-gram, and BERT—are systematically compared across 17 traditional, deep, and Transformer models, as well as a large language model (LLaMA3) fully fine-tuned on the Chinese financial texts. To ensure annotation quality, sentiment labels were manually assigned by annotators with finance backgrounds and validated through a double-checking process. Experimental results show that a CNN using skip-gram embeddings achieves the strongest performance among deep learning models, while LLaMA3 yields the highest overall F1-score for sentiment classification. For regression, LSTM consistently provides the most reliable predictive power across different volatility groups, with Bayesian Linear Regression remaining competitive for low-volatility firms. LLaMA3 is the only Transformer-based model to achieve a positive R2 under high-volatility conditions. Furthermore, forecasting accuracy is higher for the five-day horizon than for the fifteen-day horizon, underscoring the increasing difficulty of medium-term forecasting. These findings confirm that financial news provides valuable predictive signals for emerging markets and that short-term sentiment-informed forecasts enhance real-time investment decisions. Full article
(This article belongs to the Special Issue Natural Language Processing Applications in Big Data)
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33 pages, 2062 KB  
Article
Deterministic Parameter Control Methods for Genetic Algorithms: Benchmarking on Test Functions and Boost Converter Design Optimisation
by Cagatay Cebeci and Oğuzhan Timur
Appl. Sci. 2025, 15(20), 11093; https://doi.org/10.3390/app152011093 - 16 Oct 2025
Abstract
Genetic Algorithms (GAs) are pillars of evolutionary computing and one of the most well-known population-based metaheuristic optimisation techniques. They are widely used in engineering and applied optimisation problems for their capabilities in finding global solutions. Standard GAs (SGAs) determine probabilities of crossover and [...] Read more.
Genetic Algorithms (GAs) are pillars of evolutionary computing and one of the most well-known population-based metaheuristic optimisation techniques. They are widely used in engineering and applied optimisation problems for their capabilities in finding global solutions. Standard GAs (SGAs) determine probabilities of crossover and mutation by computationally expensive trials. Adaptive Genetic Algorithms (AGAs), on the other hand, improve this process by adjusting the parameters throughout generations. This study proposes three deterministic parameter control functions, ACM1, ACM2 and ACM3, for the regulation of crossover and mutation probabilities. Using advanced test functions, comparisons between four deterministic GAs, an SGA, two fixed-parameter GAs, and an AGA have been made. The fixed-parameter configurations are called FCM1 and FCM2. The AGA is called LTA, and four deterministic methods are called HAM and ACM1–3. Results show that the SGA is mostly inadequate for complex optimisation problems. The LTA performs inconsistently by failing on some functions and succeeding on others. The methods, ACM2, HAM, and FCM2, are highly robust and effective. Unexpectedly, the FCM2 performs the best for smaller population sizes. However, in higher-dimensional problems, the proposed method, ACM2, is superior and shows less variability in finding optimal solutions. The methods are also evaluated using a boost converter implementation. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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27 pages, 1553 KB  
Review
Engineering Bispecific Peptides for Precision Immunotherapy and Beyond
by Xumeng Ding and Yi Li
Int. J. Mol. Sci. 2025, 26(20), 10082; https://doi.org/10.3390/ijms262010082 - 16 Oct 2025
Abstract
Bispecific peptides represent an emerging therapeutic platform in immunotherapy, offering simultaneous engagement of two distinct molecular targets to enhance specificity, functional synergy, and immune modulation. Their compact structure and modular design enable precise interaction with protein–protein interfaces and shallow binding sites that are [...] Read more.
Bispecific peptides represent an emerging therapeutic platform in immunotherapy, offering simultaneous engagement of two distinct molecular targets to enhance specificity, functional synergy, and immune modulation. Their compact structure and modular design enable precise interaction with protein–protein interfaces and shallow binding sites that are otherwise difficult to target. This review summarizes current design strategies of bispecific peptides, including fused, linked, and self-assembled architectures, and elucidates their mechanisms in bridging tumor cells with immune effector cells and blocking immune checkpoint pathways. Recent developments highlight their potential applications not only in oncology but also in autoimmune and infectious diseases. Key translational challenges, including proteolytic stability, immunogenicity, delivery barriers, and manufacturing scalability, are discussed, along with emerging peptide engineering and computational design strategies to address these limitations. Bispecific peptides offer a versatile and adaptable platform poised to advance precision immunotherapy and expand therapeutic options across immune-mediated diseases. Full article
(This article belongs to the Section Molecular Immunology)
21 pages, 3303 KB  
Article
Research on Intelligent Early Warning System and Cloud Platform for Rockburst Monitoring
by Tianhui Ma, Yongle Duan, Wenshuo Duan, Hongqi Wang, Chun’an Tang, Kaikai Wang and Guanwen Cheng
Appl. Sci. 2025, 15(20), 11098; https://doi.org/10.3390/app152011098 - 16 Oct 2025
Abstract
Rockburst disasters in deep underground engineering present significant safety hazards due to complex geological conditions and high in situ stresses. To address the limitations of traditional microseismic (MS) monitoring methods—namely, vulnerability to noise interference, low recognition accuracy, and limited computational efficiency—this study proposes [...] Read more.
Rockburst disasters in deep underground engineering present significant safety hazards due to complex geological conditions and high in situ stresses. To address the limitations of traditional microseismic (MS) monitoring methods—namely, vulnerability to noise interference, low recognition accuracy, and limited computational efficiency—this study proposes an intelligent real-time monitoring and early warning framework that integrates deep learning, MS monitoring, and Internet of Things (IoT) technologies. The methodology includes db4 wavelet-based signal denoising for preprocessing, an improved Gaussian Mixture Model for automated waveform recognition, a U-Net-based neural network for P-wave arrival picking, and a particle swarm optimization algorithm with Lagrange multipliers for event localization. Furthermore, a cloud-based platform is developed to support automated data processing, three-dimensional visualization, real-time warning dissemination, and multi-user access. Field application in a deep-buried railway tunnel in Southwest China demonstrates the system’s effectiveness, achieving an early warning accuracy of 87.56% during 767 days of continuous monitoring. Comparative verification further indicates that the fine-tuned neural network outperforms manual approaches in waveform picking and event identification. Overall, the proposed system provides a robust, scalable, and intelligent solution for rockburst hazard mitigation in deep underground construction. Full article
60 pages, 1807 KB  
Review
Recent Advances of Artificial Intelligence Methods in PMSM Condition Monitoring and Fault Diagnosis in Elevator Systems
by Vasileios I. Vlachou, Theoklitos S. Karakatsanis and Dimitrios E. Efstathiou
Appl. Syst. Innov. 2025, 8(5), 154; https://doi.org/10.3390/asi8050154 - 16 Oct 2025
Abstract
Permanent magnet synchronous motors are the dominant technology in industrial applications such as elevator systems. Their unique advantages over induction motors give them higher energy efficiency and significant reduction in energy consumption. Accordingly, the elevator is one of the basic means of comfortable [...] Read more.
Permanent magnet synchronous motors are the dominant technology in industrial applications such as elevator systems. Their unique advantages over induction motors give them higher energy efficiency and significant reduction in energy consumption. Accordingly, the elevator is one of the basic means of comfortable and safe transportation. More generally, in elevator systems, electric motors are characterized by continuous use, increasing the risk of possible failure that may affect the operation of the system and the safety of passengers. The application of appropriate monitoring and artificial intelligence techniques contributes to the predictive maintenance of the motor and drive system. The main objective of this paper is a literature review on the application of modern monitoring methodologies using smart sensors and machine learning algorithms for early fault diagnosis and predictive maintenance generally. Thus, by exploiting the advantages and disadvantages of each method, a technique based on a multi-fault set is developed that can be integrated into an elevator control system offering desired results of immediate predictive maintenance. Full article
(This article belongs to the Section Industrial and Manufacturing Engineering)
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22 pages, 4406 KB  
Article
Activated Carbon and Diatomite as Filtration Materials for Nutrient Removal from Stormwater
by Agnieszka Grela, Justyna Pamuła, Karolina Łach, Izabela Godyń, Dagmara Malina and Damian Grela
Materials 2025, 18(20), 4742; https://doi.org/10.3390/ma18204742 (registering DOI) - 16 Oct 2025
Abstract
Activated carbon used as one of the layers of a rain garden may be a promising solution for removing nutrients (nitrogen and phosphorus compounds) from stormwater runoff. Progressive urbanization degrades the quality of stormwater that reaches water collectors. Rain gardens are a potential [...] Read more.
Activated carbon used as one of the layers of a rain garden may be a promising solution for removing nutrients (nitrogen and phosphorus compounds) from stormwater runoff. Progressive urbanization degrades the quality of stormwater that reaches water collectors. Rain gardens are a potential solution—nature-based systems that retain, infiltrate, and purify stormwater. The aim of this study was to evaluate the effectiveness of a model rain garden in the form of retention columns, depending on the composition of the filling material and the conditions of the simulation. The base column was filled with sand, gravel, and dolomite. The next two columns were enriched with diatomite, in a weight ratio to sand of 1:4 and 1:2, respectively. The experiment was based on four scenarios: (1) 30 min of heavy rain, (2) 2 h of rain after a drought, (3) during standard operation, and (4) with modification of the filtration material. This modification consisted of a uniform addition of granular activated carbon (GAC), which was intended to influence the column performance. The characteristics of the activated carbon were determined using XRD, SEM-EDS, and BET analysis. Pollutant concentrations were determined using a spectrophotometer and ion-selective electrodes. The analyses confirm the significant impact of the column filling materials on the efficiency of nutrient removal from stormwater, achieving even complete removal of phosphate ions, while nitrate ions were removed at a level of almost 40% and ammonium ions at >90%. Full article
(This article belongs to the Section Porous Materials)
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19 pages, 2117 KB  
Article
Point-Wise Full-Field Physics Neural Mapping Framework via Boundary Geometry Constrained for Large Thermoplastic Deformation
by Jue Wang, Xinyi Xu, Changxin Ye and Wei Huangfu
Algorithms 2025, 18(10), 651; https://doi.org/10.3390/a18100651 (registering DOI) - 16 Oct 2025
Abstract
Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches [...] Read more.
Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches between predicted and ground truth meshes. To overcome this limitation, we propose a novel boundary geometry-constrained neural framework that establishes direct point-wise mappings between spatial coordinates and full-field physical quantities within the deformed domain. The key contributions of this work are as follows: (1) a two-stage strategy that separates geometric prediction from physics-field resolution by constructing direct, point-wise mappings between coordinates and physical quantities, inherently avoiding errors from mesh misalignment; (2) a boundary-condition-aware encoding mechanism that ensures physical consistency under complex loading conditions; and (3) a fully mesh-free approach that operates on point clouds without structured discretization. Experimental results demonstrate that our method achieves a 36–98% improvement in prediction accuracy over deep learning baselines, offering a efficient alternative for high-fidelity simulation of large thermoplastic deformations. Full article
(This article belongs to the Special Issue AI Applications and Modern Industry)
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18 pages, 1828 KB  
Article
A Hybrid Global-Split WGAN-GP Framework for Addressing Class Imbalance in IDS Datasets
by Jisoo Jang, Taesu Kim, Hyoseng Park and Dongkyoo Shin
Electronics 2025, 14(20), 4068; https://doi.org/10.3390/electronics14204068 (registering DOI) - 16 Oct 2025
Abstract
The continuously evolving cyber threat landscape necessitates not only resilient defense mechanisms but also the sustained capacity development of security personnel. However, conventional training pipelines are predominantly dependent on static real-world datasets, which fail to adequately reflect the diversity and dynamics of emerging [...] Read more.
The continuously evolving cyber threat landscape necessitates not only resilient defense mechanisms but also the sustained capacity development of security personnel. However, conventional training pipelines are predominantly dependent on static real-world datasets, which fail to adequately reflect the diversity and dynamics of emerging attack tactics. To address these limitations, this study employs a Wasserstein GAN with Gradient Penalty (WGAN-GP) to synthesize realistic network traffic that preserves both temporal and statistical characteristics. Using the CIC-IDS-2017 dataset, which encompasses diverse attack scenarios including brute-force, Heartbleed, botnet, DoS/DDoS, web, and infiltration attacks, two training methodologies are proposed. The first trains a single conditional WGAN-GP on the entire dataset to capture the global distribution. The second employs multiple generators tailored to individual attack types, while sharing a discriminator pretrained on the complete traffic set, thereby ensuring consistent decision boundaries across classes. The quality of the generated traffic was evaluated using a Train on Synthetic, Test on Real (TSTR) protocol with LSTM and Random Forest classifiers, along with distribution similarity measures in the embedding space. The proposed approach achieved a classification accuracy of 97.88% and a Fréchet Inception Distance (FID) score of 3.05, surpassing baseline methods by more than one percentage point. These results demonstrate that the proposed synthetic traffic generation strategy provides advantages in scalability, diversity, and privacy, thereby enriching cyber range training scenarios and supporting the development of adaptive intrusion detection systems that generalize more effectively to evolving threats. Full article
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15 pages, 2271 KB  
Technical Note
Resource-Constrained 3D Volume Estimation of Lunar Regolith Particles from 2D Imagery for In Situ Dust Characterization in a Lunar Payload
by Filip Wylęgała and Tadeusz Uhl
Remote Sens. 2025, 17(20), 3450; https://doi.org/10.3390/rs17203450 - 16 Oct 2025
Abstract
Future lunar exploration will depend on a clearer understanding of regolith behavior, as underscored by adhesion issues observed during Apollo. The Lunaris Payload, a compact instrument developed in Poland, targets in situ assessment of lunar regolith adhesion to engineering materials using a resource-constrained [...] Read more.
Future lunar exploration will depend on a clearer understanding of regolith behavior, as underscored by adhesion issues observed during Apollo. The Lunaris Payload, a compact instrument developed in Poland, targets in situ assessment of lunar regolith adhesion to engineering materials using a resource-constrained optical approach. Here we introduce and validate six lightweight 2D-to-3D geometric models for estimating particle volume from planar images, benchmarked against the high-resolution micro-computed tomography (micro-CT) ground truth. The tested methods include spherical, cylindrical, fixed-aspect-ratio ellipsoid, adaptive ellipsoid, and Feret-based models and an empirically scaled voxel proxy. Using micro-CT scans of adhered simulant particles, we evaluate accuracy across >8000 particles segmented from 2D projections. Ellipsoid-based models consistently outperform the alternatives, with absolute percentage errors of 30–35%, while fixed-aspect-ratio variants offer strong accuracy–complexity trade-offs suitable for mass- and power-limited payloads. To our knowledge, this is the first comprehensive benchmarking of six 2D-to-3D volume models against micro-CT for bulk-adhered lunar regolith analogs. The results provide a validated, efficient framework for in situ dust characterization and reliable particle mass estimation, advancing Lunaris’ capability to quantify regolith adhesion and supporting broader goals in dust mitigation, ISRU, or habitat construction. Full article
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