Search Results (19,798)

With the rapid growth of data transmission and visual encryption technologies, Visual Secret Sharing (VSS) has become an important technique for image-based information protection. However, many existing VSS schemes remain vulnerable to dishonest participants who attempt to recover secret images through unauthorized stacking or manipulation of shares. To address this issue, this paper proposes a dishonest-participant-resistant VSS scheme based on linearly polarized shares and Probability Distribution Trees (PDTs). The proposed method embeds both secret and fake images into polarized shares, such that any unauthorized stacking of ordinary shares produces a visually plausible fake image or random noise, while only stacking that includes the master share under a predefined optical ordering reveals the true secret image. Binary image binarization and probability-guided polarization assignment are employed to improve computational efficiency and increase uncertainty against adaptive attacks. In addition to visual inspection and contrast analysis, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and visual information fidelity (VIF) are used as complementary metrics to distinguish authorized reconstructions from unauthorized and partial ones. Experimental results show that authorized reconstructions achieve high visual fidelity and perceptual recognizability, whereas unauthorized and partial reconstructions yield significantly degraded or misleading outputs, demonstrating effective suppression of information leakage and strong resistance against dishonest behavior. Consequently, the proposed scheme enhances security and practical usability compared with existing polarization-based VSS approaches. Full article

In this study, a finite-volume computational fluid dynamics (CFD) technique for application on skewed meshes using staggered pressure nodes is proposed. The method is based on the derivation of a momentum equation for the cell face velocities from appropriately discretised momentum equations in the two cells surrounding the cell face with the driving pressure difference pertaining to the staggered adjacent nodes. In this way, a staggered mesh-like method is obtained that would prevent the occurrence of oscillatory behaviour in pressure or velocity fields. The cell-face velocities are then forced to obey continuity via an equation for pressure akin to other standard CFD schemes. This article describes the formulation of the cell-face momentum equation as well as the way the nodal velocity is reconstructed from the surrounding cell-face velocities. The method is demonstrated to recover the advantages of the PISO solution algorithm that were diminished in implementations in collocated schemes. It is also validated on a reference two-dimensional, steady viscous flow case on both rectangular and skewed meshes to verify its accuracy. It is then applied to the case of an unsteady vortex-shedding flow past a square obstacle, on both rectangular and skewed meshes, and the results are compared with a solution obtained from a collocated method as well as with an experimental value of the Strouhal number. Full article

With the continuous advancement of the “dual-carbon” strategy, the penetration of renewable energy sources such as wind and photovoltaic (PV) power has steadily increased, imposing more stringent requirements on the safe and stable operation of modern power systems. As the core components of these systems, critical electrical devices operate under harsh conditions characterized by high voltage, strong electromagnetic interference (EMI), and confined high-temperature environments. Their operating status directly affects the reliability of the power supply, and any fault may trigger cascading failures, resulting in significant economic losses. To address the issues of low inspection efficiency, limited fault-identification accuracy, and unstable data transmission in strong-EMI environments, this study proposes an intelligent inspection system for power equipment based on multi-technology integration. The system incorporates a redundant dual-mode wireless transmission architecture combining Wireless Fidelity (Wi-Fi) and Fourth Generation (4G) cellular communication, ensuring reliable data transfer through adaptive link switching and anti-interference optimization. A You Only Look Once version 8 (YOLOv8) object-detection algorithm integrated with Open Source Computer Vision (OpenCV) techniques enables precise visual fault identification. Furthermore, a multi-source data-fusion strategy enhances diagnostic accuracy, while a dedicated monitoring scheme is developed for the water-cooling subsystem to simultaneously assess cooling performance and fault conditions. Experimental validation demonstrates that the proposed system achieves a fault-diagnosis accuracy exceeding 95.5%, effectively meeting the requirements of intelligent inspection in modern power systems and providing robust technical support for the operation and maintenance of critical electrical equipment. Full article

In elite sports, discovering interdisciplinary causal relationships from public data is critical for gaining a competitive edge. However, the causal knowledge required for these practices is difficult to obtain through either existing intervention-based sports science methods or computational techniques focused on statistical association. This paper formalizes a multi-domain collaborative framework, which involves three roles: (1) the elite sports team; (2) the sport science expert; and (3) the causal inference expert. Our nine-step workflow, which processes three core elements of problem, data, and computing, guides these experts through a cycle that systematically transforms practical problems into computational models and, crucially, translates complex analytical outputs back into actionable strategies. The framework also introduces a dual-dimensional “field evaluation” method, encompassing both process and outcome, to quantify the trustworthiness of knowledge in practical settings where a “gold standard” is absent. This framework was applied in an illustrative case study prior to the Paris 2024 Olympics, providing one additional evidence-informed input for the national team. The success was observed and interpreted as contextual consistency rather than causal validation. This framework ensures the practical application of causal discovery in elite sports, offering a repeatable and explainable pathway for generating credible, evidence-based insights from public data for elite sports decision-making. Full article

Cone-beam computed tomography (CBCT) is widely utilized in endodontics for evaluating root canal shaping outcomes, offering critical three-dimensional imaging capabilities. This study aims to assess the differences in apical and root canal preparation across various instrumentation techniques using CBCT. A systematic search of the Medline database (via PubMed) and Web of Science was performed up to 12 April 2025, yielding a total of 70 studies, with 45 full-text articles assessed for eligibility; 28 were included in the review. Studies showed great heterogeneity in experimental design, anatomical variables, and outcome measurements. The results indicate that rotary instruments, such as ProTaper Next^® and XP-Endo Shaper^®, were reported more frequently or showed favorable shaping trends in individual studies. Although rotary systems often appeared advantageous, conclusions were limited by study design variability and a lack of correlation with clinical outcomes. The evidence highlights the need for standardized methodologies and further research, especially on manual techniques. CBCT remains a valuable research tool despite inherent spatial resolution limitations. Full article

This study investigates the impact of various data-augmentation techniques on the performance of neural networks in EEG-based motor imagery three-class event classification. EEG data were obtained from a publicly available open-source database, and a subset of 25 patients was selected for analysis. The classification task focused on detecting two types of motor events: imagined movements of the left hand and imagined movements of the right hand. EEGNet, a convolutional neural network architecture optimized for EEG signal processing, was employed for classification. A comprehensive set of augmentation techniques was evaluated, including five time-domain transformations, three frequency-domain transformations, two spatial-domain transformations and two generative approaches. Each method was tested individually, as well as in selected two- and three-method cascade combinations. The augmentation strategies were tested using three data-splitting methodologies and applying four ratios of original-to-generated data: 1:0.25, 1:0.5, 1:0.75 and 1:1. Our results demonstrate that the augmentation strategies we used significantly influence classification accuracy, particularly when used in combination. These findings underscore the importance of selecting appropriate augmentation techniques to enhance generalization in EEG-based brain–computer interface applications. Full article

The feather coverage of a laying hen is an important indicator of both its productivity and welfare. Conventional manual feather scoring procedures are laborious, subjective, and stressful for the hens. Thermography offers a modern alternative to addressing these problems. Thermal cameras capture radiative heat loss, which is comparatively greater Classification from featherless areas. Studies have been conducted to establish a standard temperature range that correlates to specific featherless areas. However, such temperature-based approaches have been inconsistent with each other. In contrast, this study used deep learning techniques to automatically assess dorsal feather scores using thermal images. Thermal images (n = 1575) of the dorsal body of cage-free laying hens with varying degrees of feather damage were captured. Manual feather scoring was performed, classifying the image into a feather score (0–2) according to the increasing severity of feather loss. A total of 1222 images were selected, filtering out images of lower quality. Two types of computer vision models, a classification model and an object detection model, were trained and evaluated. A custom convolutional neural network (CNN) was trained to classify thermal images into feather score categories. Additionally, we trained and optimized You Only Look Once (YOLO) object detection models to detect areas of feather damage and predict the feather score. The CNN model achieved an overall accuracy of 0.81, with high precision for severe feather loss. The YOLO-based object detection model was optimum using YOLO11n, which achieved a precision of 0.81, a recall of 0.73 and a mean average precision (mAP) at 0.5 intersection over union (IoU) of 0.84. Results show the potential of combining thermal imaging with deep learning techniques to perform objective, automatic, and scalable feather scoring procedures. Future studies should focus on data diversity, multiple part scoring, and semantic segmentation for robust performance. Full article

Pneumonia is one of the main reasons for child mortality, with chest radiography (CXR) being essential for its diagnosis. However, the low radiation exposure in pediatric analysis complicates the accurate detection of pneumonia, making traditional examination ineffective. Progress in medical imaging with convolutional neural networks (CNN) has considerably improved performance, gaining widespread recognition for its effectiveness. This paper proposes an accurate pneumonia detection method based on different deep CNN architectures that combine optimal feature fusion. Enhanced VGG-19, ResNet-50, and MobileNet-V2 are trained on the most widely used pneumonia dataset, applying appropriate transfer learning and fine-tuning strategies. To create an effective feature input, the Chi-Square technique removes inappropriate features from every enhanced CNN. The resulting subsets are subsequently fused horizontally, to generate more diverse and robust feature representation for binary classification. By combining 1000 best features from VGG-19 and MobileNet-V2 models, the suggested approach records the best accuracy (97.59%), Recall (98.33%), and F1-score (98.19%) on the test set based on the supervised support vector machines (SVM) classifier. The achieved results demonstrated that our approach provides a significant enhancement in performance compared to previous studies using various ensemble fusion techniques while ensuring computational efficiency. We project this fused-feature system to significantly aid timely detection of childhood pneumonia, especially within constrained healthcare systems. Full article

Research has demonstrated that axonal signaling processes are influenced by both static structural factors and dynamic metabolic and electro-dynamic processes. Imaging, computational modeling and research in molecular neuroscience have demonstrated that multiple processes contribute to axonal signal processing, including periodic rearrangement of cytoskeletal structures and membrane structures, and redistribution of ion channel clusters and organelles (such as mitochondria), which occur rapidly and transiently to modify excitability. The dynamics of energy production and distribution also vary between regions of the axon and at different time points during signal generation and transmission. Additionally, myelin-associated glia may temporarily modulate their metabolic and structural contributions to axonal conduction. Advanced AI-based techniques for mapping and simulating ultrastructure and the use of closed-loop perturbation experiments demonstrate that axons can generate multiple distinct electromechanical states, and therefore potentially influence both the timing of signals generated by the axon, the routing of signals to branches of the axon, and the robustness of signal propagation. While the existence of these adaptive microstates appears well established, there are many aspects of their influence on circuit level function that are poorly understood. In summary, these data support the concept that axonal conduction represents a continuum of reversible and state-dependent configurations generated by integrated interactions among molecular, structural and energetic processes. Therefore, this review will attempt to synthesize the available literature into a unified conceptual framework and identify areas of uncertainty that may direct future research into the adaptive processes underlying axonal computation. Full article

The assessment of structural degradation in reinforced concrete bridges is a crucial task for infrastructure maintenance and safety. Traditional inspection methods are often time-consuming, dependent on expert interpretation and weather conditions. This study explores the potential of artificial intelligence to support inspectors in the detection of typical deterioration patterns in reinforced (RC) and prestressed concrete (PRC) bridges, developing the VIADUCT (Visual Inspection and Automated Damage Understanding by Computer vision Techniques) software tool. Unlike previous studies, focusing only on a limited variety of possible defects (e.g., cracks, water stains), this study aims to train a deep learning model to be able to recognise a larger range of defects, such as those foreseen by the current Italian code for the assessment of existing bridges. The methodology relies on the YOLOv8n object detection model, which was trained, validated, and tested using a dataset including 1045 either wide-angle or detailed photographs taken during routine inspections. With these kinds of images being challenging for object detection algorithms (they include large parts of the background), multimodal attention mechanisms were implemented in the Graphical User Interface (GUI) through the semantic segmentation of the bridge surface using both the SAM and the U-Net model, as well as a tile reduction approach. These attention mechanisms allow the object detection model to focus on the relevant portions of the image (i.e., the bridge), while suppressing background information. Despite the limitation of the small size dataset used for training, results showed promising detection capabilities and precision. Furthermore, VIADUCT is ready to accept and use newer and more efficient versions of the object detection model, as soon as they become available. Full article

Decoding reaching movements from non-invasive brain signals is a key challenge for the development of naturalistic brain–computer interfaces (BCIs). While this decoding problem has been addressed via traditional machine learning, the exploitation of deep learning is still limited. Here, we evaluate a convolutional neural network (CNN) for decoding movement direction during a delayed center-out reaching task from the EEG. Signals were collected from twenty healthy participants and analyzed using EEGNet to discriminate reaching endpoints in three scenarios: fine-direction (five endpoints), coarse-direction (three endpoints), and proximity (two endpoints) classifications. To interpret the decoding process, the CNN was coupled with explanation techniques, including DeepLIFT and occlusion tests, enabling a data-driven analysis of spatio-temporal EEG features. The proposed approach achieved accuracies well above chance, with accuracies of 0.45 (five endpoints), 0.64 (three endpoints) and 0.70 (two endpoints) on average across subjects. Explainability analyses revealed that directional information is predominantly encoded during movement preparation, particularly in parietal and parietal–occipital regions, consistent with known visuomotor planning mechanisms and with EEG analysis based on event-related spectral perturbations. These results demonstrate the feasibility and interpretability of CNN-based EEG decoding for reaching movements, providing insights relevant for both neuroscience and the prospective development of non-invasive BCIs. Full article

The rapid advancement of Generative Pre-trained Transformer (GPT) models has led to their widespread adoption across applied domains such as healthcare, finance, education, and enterprise software engineering. However, the large-scale data requirements and generative capabilities of these models introduce significant challenges related to data security, privacy preservation, and regulatory compliance. This paper presents a systematic literature review conducted in accordance with the PRISMA 2020 guidelines, analyzing 60 peer-reviewed empirical studies published between 2020 and 2025 in Q1 and Q2 journals indexed in the Web of Science Core Collection. The review examines the evolution of GPT architectures and evaluates state-of-the-art security and privacy techniques, including encryption, differential privacy, federated learning, data anonymization, model distillation, and secure deployment mechanisms. Key challenges identified include unintended memorization of sensitive data, adversarial prompt-based attacks, and performance degradation resulting from privacy-preserving constraints, with reported accuracy reductions ranging from 5% to 20% depending on the applied technique. Additionally, the analysis highlights increased computational overhead, in some cases exceeding 30–40% training or inference cost when advanced cryptographic methods are employed. Regulatory and ethical implications are assessed in relation to frameworks such as GDPR, CCPA, HIPAA, and the proposed EU Artificial Intelligence Act. The findings emphasize the need for privacy-by-design approaches and scalable governance strategies to support secure and trustworthy deployment of GPT models in applied real-world environments. Full article

Many fields, including psychology, neuroscience, linguistics, computational modeling, and even philosophy, have been investigating the neuroscience of language for many years. Even so, a lack of comprehensive, interdisciplinary guidelines remains for research projects that aim to decode or model language from brain activity. Electroencephalography (EEG) is unique among neuroimaging methods in that it is a non-invasive technique. This review provides a comprehensive examination of the fundamental elements of imagined speech decoding using EEG, offering a tour of the most recent developments and perspectives in linguistic, neurological, and computational approaches over the past decade. It highlights essential findings such as the consistent involvement of sensory–motor brain regions, the strong influence of language abstraction and selection, and the superior classification performance attained with spectral and temporal features. This study was conducted and reported in accordance with the PRISMA 2020 guidelines for systematic reviews. Full article

The safety of vehicle operation is largely influenced by the performance of the brakes. The quality of automotive brake performance directly affects the lives of drivers and passengers. This paper conducts an in-depth study based on the structural characteristics of disk brakes for a specific model of sedan, analyzing the roles of key components in the brake system. Then, using simulation techniques such as finite element analysis and topology optimization, it provides strong support for optimizing the design process. First, the symmetrical structure of the disk brake is analyzed, and 3D modeling is performed in SolidWorks 2025. Next, static simulation analysis is conducted using ANSYS R1, with results showing that the maximum total deformation of the brake is 0.038 mm (not strain), and the maximum stress is 155.78 MPa, which meets the requirements for emergency braking. On this basis, modal analysis is further conducted to clarify the natural frequencies and vibration patterns of each mode, comparing the differences in vibration modes across different orders. Through computational verification, the brake does not experience resonance, effectively improving the stability of each mode and the comfort of driving and riding. Finally, the variable-density method enabled 10.49% weight reduction while maintaining resonance safety, validating the proposed ‘static–modal–topology’ workflow for brake lightweighting. Unlike previous FEA studies that merely verified static strength or performed isolated modal checks, this work establishes an integrated “static–modal–topology” sequential optimization workflow which explicitly couples the prestress-induced frequency shift with lightweighting constraints, thereby filling the gap in simultaneous resonance-risk-aware and mass-target-driven brake design. The proposed ‘static-modal-topological’ sequential framework achieves a 10.49% weight reduction rate, representing a 26.4% improvement over the 8.3% reduction rate of single-topological optimization methods in the literature. Notably, it controls the first-order frequency of prestressed coupling at 1885.7 Hz (exceeding the engine’s 200 Hz upper limit) for the first time, resolving the core contradiction of ’difficulty in balancing lightweighting and resonance risk’. Full article

The widespread connectivity of the Industrial Internet of Things (IIoT) improves the efficiency and functionality of connected devices. However, it also raises serious concerns about cybersecurity threats. Implementing an effective intrusion detection system (IDS) for IIoT is challenging due to heterogeneous data, high feature dimensionality, class imbalance, and the risk of data leakage during evaluation. This paper presents a leakage-safe hybrid intrusion detection framework that combines text-based and numerical network flow features in an IIoT environment. Each network flow is converted into a short text description and encoded using a frozen Large Language Model (LLM) called the Bidirectional Encoder Representations from Transformers (BERT) model to obtain fixed semantic embeddings, while numerical traffic features are standardized in parallel. To improve class separation, class prototypes are computed in Principal Component Analysis (PCA) space, and cosine similarity scores for these prototypes are added to the feature set. Class imbalance is handled only in the training data using the Synthetic Minority Over-sampling Technique (SMOTE). A Random Forest (RF) is used to select the top features, followed by a Histogram-based Gradient Boosting (HGB) classifier for final prediction. The proposed framework is evaluated on the Edge-IIoTset and ToN_IoT datasets and achieves promising results. Empirically, the framework attains 98.19% accuracy on Edge-IIoTset and 99.15% accuracy on ToN_IoT, indicating robust, leakage-safe performance. Full article