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.

Artificial intelligence (AI) powers breakthroughs in language processing, computer vision, and scientific discovery; yet, the increasing complexity of frontier models makes their reasoning opaque. This opacity undermines public trust, complicates deployment in safety-critical settings, and frustrates compliance with emerging regulations. In response to initiatives such as the White House AI Action Plan, we synthesize the scientific foundations and policy landscape for interpretability, control, and robustness. We clarify key concepts and survey intrinsically interpretable and post-hoc explanation techniques, discuss human-centered evaluation and governance, and analyze how adversarial threats and distributional shifts motivate robustness research. An empirical case study compares logistic regression, random forests, and gradient boosting on a synthetic dataset with a binary-sensitive attribute using accuracy, F1 score, and group-fairness metrics, and illustrates trade-offs between performance and fairness. We integrate ethical and policy perspectives, including recommendations from America’s AI Action Plan and recent civil rights frameworks, and conclude with guidance for researchers, practitioners, and policymakers on advancing trustworthy AI.

The rising incidence of myocardial infarction (MI) in individuals under 50 years of age underscores an urgent need for innovative rehabilitation strategies that extend beyond hospital care, empowering young patients to reclaim active lives through sustained physical activity and remote monitoring. Wearable health technologies hold transformative potential here, as studies demonstrate their ability to boost exercise capacity daily steps while reducing rehospitalizations in post-MI recovery. This study thus assesses the clinical value of wearable devices in remotely tracking motor activity among young adults during early MI rehabilitation. Using the SiDLY Care Pro wristband, continuous non-invasive measurements of heart rate, oxygen saturation, and physical activity were collected from 62 of 80 post-MI patients (<50 years) over seven days, alongside validated questionnaires (IPAQ, SF-36, DASS-21). Time-series clustering and principal component analysis characterized heart rate dynamics and activity patterns. Most participants showed sedentary behavior (2000–4000 steps/day), though self-reported health and psychological well-being were satisfactory. The device provided reliable, clinically meaningful data, particularly when linked to clinician feedback. Participants expressed an interest in using such technologies, especially if supported through reimbursement and professional guidance. Despite limitations—short monitoring timelines, small heterogeneous samples, and accuracy constraints—the findings suggest that wearable systems can enhance remote monitoring, patient engagement, and early intervention in post-MI care. Broader studies and supportive policies are recommended. Overall, integrating wearable technologies with professional oversight and patient participation may substantially improve recovery and outcomes for young MI survivors.

This paper examines how hour-ahead forecasting uncertainty propagates to microgrid operation under intermittent renewable generation. Using hourly public data for Ontario and focusing on the FSA K0K in 2018, we evaluate four representative months (January, April, July, and December) to capture seasonal dynamics. We benchmark three univariate forecasting approaches for load demand, photovoltaic (PV) generation, and wind generation under a consistent 24-to-1 input setup, including GRU, LSTM, and a persistence baseline. We report point-forecast metrics (RMSE, MAE, and R2) and also provide 90% prediction intervals (PI90) using conformal calibration to quantify uncertainty. To assess downstream impact, forecasts are coupled with a dual-branch MATLAB/Simulink microgrid model. One branch uses True profiles and the other uses forecast-driven Pred inputs, while both branches share the same rule-based EMS and BESS constraints. System performance is evaluated using time-series comparisons and monthly key performance indicators (KPIs) covering grid import and export, grid peak power, battery throughput, and state-of-charge (SoC) statistics. We further report an illustrative cost sensitivity under a flat tariff and a throughput-based degradation proxy. Results show that forecasting performance is target dependent. GRU achieves the best overall point accuracy for load and PV, whereas wind is strongly driven by short term persistence at the one hour horizon, and in this measurement only setup without meteorological covariates the persistence baseline can match or outperform the deep learning models. In the microgrid simulations, Pred and True trajectories remain qualitatively consistent, and SoC-related indicators and peak power remain comparatively consistent across months. In contrast, energy-flow indicators, especially grid export and battery throughput, show larger deviations and dominate the observed cost sensitivity. Overall, the findings suggest that compact hour-ahead forecasts can be adequate to preserve operational reliability under a constraint-driven EMS, while forecast improvements mainly translate into economic efficiency gains rather than reliability-critical benefits.

Unplanned downtime in industrial machinery remains a major challenge, causing substantial economic losses and safety risks across sectors such as manufacturing, food processing, oil and gas, and transportation. This systematic review investigates the application of machine learning (ML) techniques for anomaly and fault detection within the broader context of predictive maintenance. Following a hybrid review methodology, relevant studies published between 2010 and 2025 were collected from major databases including IEEE Xplore, ScienceDirect, SpringerLink, Scopus, Web of Science, and arXiv. The review categorizes approaches into supervised, unsupervised, and hybrid paradigms, analyzing their pipelines from data collection and preprocessing to model deployment. Findings highlight the effectiveness of deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and hybrid frameworks in detecting faults from time series and multimodal sensor data. At the same time, key limitations persist, including data scarcity, class imbalance, limited generalizability across equipment types, and a lack of interpretability in deep models. This review concludes that while ML-based predictive maintenance systems are enabling a transition from reactive to proactive strategies, future progress requires improved hybrid architectures, Explainable AI, and scalable real-time deployment.

Efficient object detection is vital for Remotely Operated Vehicles (ROVs) performing marine debris cleanup, yet existing lightweight designs frequently encounter efficiency bottlenecks when adapted to deeper neural networks. This research identifies a critical “Inverted Bottleneck” anomaly in the Slim-Neck architecture on the YOLO11 backbone, where deep-layer Memory Access Cost (MAC) abnormally spikes. To address this, we propose SvelteNeck-YOLO. By incorporating the proposed EHSCSP module and EHConv operator, the model systematically eliminates computational redundancies. Empirical validation on the TrashCan and URPC2019 datasets demonstrates that the model resolves the memory wall issue, achieving a state-of-the-art trade-off with only 5.8 GFLOPs. Specifically, it delivers a 34% relative reduction in computational load compared to specialized underwater models while maintaining a superior Recall of 0.859. Consequently, SvelteNeck-YOLO establishes a robust, cross-generational solution, optimizing the Pareto frontier between inference speed and detection sensitivity for resource-constrained underwater edge computing.

Background: Osteoarthritis (OA) is a prevalent chronic degenerative disorder, with coxarthrosis (hip OA) and gonarthrosis (knee OA) representing its most significant clinical manifestations. While diagnosis typically relies on imaging, such methods can be resource-intensive and insensitive to early disease trajectories. Objective: This study aims to achieve the differential diagnosis of coxarthrosis and gonarthrosis using solely routine preoperative clinical and laboratory data, benchmarking state-of-the-art machine learning algorithms. Methods: A retrospective analysis was conducted on 893 patients (617 with knee OA, 276 with hip OA) from a clinical hospital in Almaty, Kazakhstan. The study evaluated a diverse portfolio of models, including gradient boosting decision trees (LightGBM, XGBoost, CatBoost), deep learning architectures (RealMLP, TabDPT, TabM), and the pretrained tabular foundation model RealTabPFN v2.5. Results: The RealTabPFN v2.5 (Tuned) model achieved superior performance, recording a mean ROC–AUC of 0.9831, accuracy of 0.9485, and an F1-score of 0.9474. SHAP interpretability analysis identified heart rate (66.2%) and age (18.1%) as the dominant predictors driving the model’s decision-making process. Conclusion: Pretrained tabular foundation models demonstrate exceptional capability in distinguishing OA subtypes using limited clinical datasets, outperforming traditional ensemble methods. This approach offers a practical, high-performance triage tool for primary clinical assessment in resource-constrained settings.

A class of one-dimensional dynamic impact models is investigated with respect to non-smooth velocities using variational inequalities and space-time finite element approximation. For the problem of collision of a rigid obstacle by an elastic bar in the gravitational field, a benchmark based on particular solutions to the wave equation is constructed on a partition of rectangle domains. The full discretization of the collision problem is carried out over a uniform space-time triangulation and extended to distorted meshes. For the solution of the corresponding variational inequality, a semi-smooth Newton-based primal-dual active set algorithm is applied. Numerical experiments demonstrate advantages over time-step approximation: a high-precision numerical solution is computed in a few iterations without any spurious oscillations.

To achieve the automation and intelligence of mining equipment, it is essential to address the challenge of autonomous driving, with the core task being how to navigate safely from the starting point to the mining area endpoint. This paper proposes a boundary-aware multi-point preview control algorithm to tackle the strong dependency on predefined paths and the lack of foresight in the autonomous driving of underground articulated mining vehicles in highly confined underground spaces. The algorithm determines the driving direction by calculating the vehicle’s real-time state and LiDAR data, previewing road conditions without relying on preset path planning. Experiments conducted in a ROS Noetic/GAZEBO 11 simulation environment compared the proposed method with single-point and two-point preview algorithms, validating the effectiveness of the boundary-aware multi-point preview control. The results show that the proposed control strategy yields the lowest lateral deviation and the highest steering smoothness compared to single-point and two-point preview algorithms; it also outperforms the standard multi-point preview algorithm. This demonstrates its superior performance. Specifically, the proposed boundary-aware multi-point preview algorithm outperformed other methods in terms of steering smoothness and stability, significantly enhancing the vehicle system’s adaptability, robustness, and safety.

In extreme scenarios, to prevent the leakage of jamming coordination information, the jammers must proactively terminate their communication functions and implement jamming resource scheduling via Non-Networked Cooperation. However, current research on this non-networked jamming approach is relatively limited. Furthermore, existing algorithms either rely on networked interactions or lack cognitive strategies for the surrounding communication countermeasure situation. For example, they fail to adapt to dynamic changes in electromagnetic noise and struggle to determine jamming effectiveness, leading to low jamming efficiency and severe energy waste in non-networked scenarios. To address this issue, this paper establishes a game process and corresponding algorithm for non-networked communication countermeasures and designs cognitive, cooperative, and scheduling strategies for individual jammers. Meanwhile, a novel performance metric called the “Overall Communication Suppression Ratio (OCSR)” is proposed. This metric quantifies the relationship between “sustained full-suppression duration” and “ operating duration of the jamming system,” overcoming the defect that traditional metrics cannot evaluate the dynamic jamming effectiveness in non-networked scenarios. Experimental results indicate that although the OCSR of the proposed Intelligent Concentric Circle Algorithm (ICCA) is significantly lower than that of the Full-Power Jamming Algorithm (FPJA), ICCA extends the operating duration of the jamming system by 4.8%. This achieves non-uniform power setting of jammers, enabling flexible and dynamic jamming in non-networked scenarios and retaining more battery capacity for jammers after overall jamming failure.