Search Results (3,135)

Deep learning models for power quality (PQ) disturbance classification remain critically vulnerable to adversarial perturbations, with classification performance degrading severely under white-box attacks. Existing defenses address individual models in isolation and provide no mechanism for operators to assess whether the system is under attack or which classifier remains trustworthy. This paper proposes a two-stage framework that combines adversarial training with large language model (LLM) reasoning to improve both robustness and interpretability. In the first stage, four architecturally diverse classifiers, including a proposed Multi-Scale Temporal Attention Network (MSTAN), are evaluated under four adversarial attacks (FGSM, PGD, C&W, and UAP), and their failure patterns are recorded as structured vulnerability fingerprints. The ensemble is then retrained via adversarial training on mixed clean and perturbed signals. In the second stage, an LLM analyzes the ensemble predictions alongside the fingerprint knowledge base to perform attack detection, fingerprint-guided meta-classification, and operator-facing threat report generation. On a 17-class, 255,000-signal synthetic benchmark, adversarial training recovers FGSM and PGD accuracy from below 25% to the 53–78% range, with MSTAN achieving the highest post-training robustness (78.26% under FGSM, 65.41% under PGD). The LLM reasoning layer provides an additional 3.5–6.2 percentage point improvement over majority voting by selecting the most reliable ensemble member based on the inferred attack condition, and detects adversarial attacks with 87.6% overall accuracy. To our knowledge, this is the first integration of LLM-based ensemble reasoning into the PQ adversarial robustness pipeline and the first application of the C&W optimization attack to power quality signals. Full article

End-to-end motion control of 7-degree-of-freedom (DOF) redundant manipulators under sparse reward signals presents a fundamental challenge in deep reinforcement learning (DRL) for robotics: the vast configuration space and absence of dense gradient information combine to produce severe cold-start failures and high cross-seed training variance. This paper proposes Curriculum-SAC-HER, a novel fusion framework integrating Soft Actor–Critic (SAC), Hindsight Experience Replay (HER), and a performance-driven three-stage Automatic Curriculum Learning (ACL) scheduler, designed to resolve the cold-start exploration bottleneck within a training budget of 300,000 environment interaction steps. The core methodology progressively expands the spatial target distribution across three stages of increasing difficulty, conditioning each stage transition on an 80% rolling success threshold to guarantee kinematic prior consolidation before advancing. A rigorous evaluation across 15 independent training runs (five seeds per group, all retained without filtering) demonstrates that the proposed framework achieves a final mean success rate of 84.8% (std: 11.0%), substantially surpassing the SAC + HER ablation (70.3%, Mann–Whitney U test, p = 0.028) and the DDPG baseline (22.3%, p = 0.008), while compressing cross-seed variance by 67% relative to the ablation. Zero-shot robustness evaluations under simulated domain perturbations further reveal that the learned policy maintains above 92% success across extreme friction variations and sustains 71.8% success under a 1.5× payload increase, demonstrating that the ACL module fosters generalized kinematic representations rather than over-fitting to specific contact mechanics. Full article

Medical image analysis is a cornerstone of modern healthcare, yet conventional single-modal deep learning often struggles with the unique physical constraints and structural variability inherent in data acquired from diverse medical sensors. Recently, Vision-Language Models (VLMs) have sparked a paradigm shift by bridging the semantic gap between visual sensor signals and clinical narratives. Following the PRISMA guidelines, 167 representative studies are systematically synthesized in this review to provide a comprehensive roadmap of VLM technological evolution and clinical utility. First, rather than treating VLMs as generic feature extractors, their underlying mechanisms are uniquely distilled into seven core operational principles, which are then explicitly mapped to downstream applications such as few-shot diagnosis, prompt-driven segmentation, and multi-task foundation models. To facilitate intuitive evaluation, a rigorous quantitative cross-comparison of current benchmark architectures is presented. Crucially, this review goes beyond highlighting successes by critically assessing prevalent clinical bottlenecks, including zero-shot segmentation failures, multi-modal hallucinations in diagnosing rare diseases, and the prohibitive computational complexity associated with 3D volumes and gigapixel whole slide images. Finally, a novel, forward-looking framework is proposed: the transition from static “image-text alignment” to dynamic “multi-source sensor-driven intelligence”. By addressing both physical sensor constraints and algorithmic limitations, this survey offers actionable insights for developing trustworthy, sensor-aware clinical diagnostic agents. Full article

Water loss and significant problems in the operation of water distribution networks caused by pipe failures are a global problem that needs immediate attention. This study is based on the experience-based assumption that the probability of water main breaks occurring is highest within a short time and a short distance from a previous (considered initial or base) break. The dataset used includes the historical pipe breaks recorded from 2007 to 2020 in the city of Larisa, Greece. A Geographic Information System (GIS) application is used for better data visualization, but also for effective operation and management of the developed water network database. Cluster analysis and Near and Spatial Join geoprocessing tools are the main tools used to detect and analyze trends in data related to space and time. In addition, the study attempts to identify relations between pipe attributes (material, age), environmental stressors (traffic load, soil type), and spatiotemporal clustering patterns. Finally, a machine learning-based water pipe failure Prediction Model is developed to serve as the computational engine of a Decision Support System (DSS) designed to optimize pipe replacement prioritization. Full article

Three-dimensional (3D) spatial interaction data are fundamental to understanding genome architecture. Multi-modal deep learning models that jointly learn from 3D spatial data and orthogonal modalities, such as gene expression, face a critical computational challenge: the 3D spatial modality dominates computation by over one order of magnitude, creating a structural memory bottleneck that renders heavyweight model instances untrainable on single GPU. Existing distributed training methods rely on cost-model searching and treat model components uniformly, overlooking modality-specific memory asymmetries. We propose Automatic Modality-aware Parallelization (AMP), a framework that diagnoses memory bottlenecks from data configuration signals and prescribes a set of five strategies. At the core of this framework is a hidden-dimension tensor parallelism strategy ($S_5$) that partitions the 3D decoder’s hidden dimension across GPUs, transforming five non-standard operators into sharded forms with formal equivalence proofs. Evaluated on Hi-C data and RNA-seq from the HiRES single-cell mouse brain dataset across lightweight and heavyweight configurations, AMP converts out-of-memory (OOM) failures into successful training runs. Scaling from four to eight GPUs under heavyweight configurations, the 500 kb and 100 kb variants achieve 2.0× and 3.8× training speedups respectively, with mathematical equivalence to single GPU computation guaranteed by formal proofs. Full article

Indoor air quality has a significant impact on occupant health, comfort, and productivity in residential and commercial indoor environments. This paper proposes an IoT-edge enabled deep–fuzzy hybrid framework for real-time IAQ prediction and adaptive control. The proposed system integrates IoT-based environmental sensing, Temporal Fusion Transformer-based multivariate forecasting, knowledge distillation, edge-deployed Bi-LSTM inference, and Mamdani fuzzy logic control within a unified IAQ management architecture. A composite Comfort Risk Index is introduced to combine environmental parameters and occupant discomfort feedback into a single adaptive control indicator. Experimental evaluation under varying indoor conditions demonstrated strong forecasting performance, with prediction accuracies reaching 96.3% for CO₂ and 95.7% for PM_2.5 prediction, while reducing inference latency from 575 ms to 295 ms. Comparative analysis against baseline threshold-based control strategies further indicated improved comfort stability, smoother actuator behavior, and reduced estimated actuator operating intensity during deployment. The proposed framework also demonstrated resilient operation under simulated sensor-failure conditions while maintaining low computational overhead suitable for resource-constrained IoT-edge environments. Overall, the results indicate that combining lightweight deep learning models with interpretable fuzzy control can provide an effective, scalable, and energy-aware solution for intelligent real-time IAQ optimization in smart indoor environments. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

Artificial intelligence (AI) is increasingly applied to healthcare decision-making; however, many persistent patient safety risks arise from sociotechnical conditions such as communication breakdowns, coordination failures, and organisational culture rather than diagnostic or decision error alone. While simulation can engage these dimensions of care, AI-supported simulation remains limited by heterogeneity and a lack of explicit conceptual structure. This study presents a narrative and conceptual review of the healthcare simulation and AI literature to identify structural barriers to coherent AI reasoning about simulation. Drawing on this synthesis, we introduce Transformative Simulation (TfS) as an intentional framework that can be formalised as an ontology for AI-supported simulation focused on cultural and systems-level change. TfS structures simulation through explicit Simulation-Based Intentions, an aligned design–delivery–data–debrief process, and foundational considerations of purpose, perspective, power, preparation, and possibility. Framed in this way, TfS enables AI systems to interpret simulation artefacts in relation to declared intent, sociotechnical context, and ethical boundaries. We further describe an Intentionality–Simulation–Intelligence triad and a continuous learning loop that align human values, simulation structure, and AI reasoning. The findings of this review suggest that an important challenge in applying AI to healthcare simulation may be ontological as well as technical, and that explicit representation of intention and context is necessary to support coherent, context-sensitive, and system-aligned AI reasoning in healthcare. Full article

Fatigue health monitoring of engineering structures requires continuous degradation assessment, yet ground-truth health labels are unavailable during run-to-failure tests. Existing self-supervised approaches rely on monotonic degradation assumptions that are violated by the structured non-monotonic behaviour of acoustic emission signals during fatigue. A self-supervised framework called Cross-Modal Degradation Rivalry (CMDR) is proposed, which introduces the Modal Rivalry Index (MRI) as a directional measure of cross-modal predictability between heterogeneous sensor modalities. CMDR comprises a label-free representation-learning stage trained via the Cross-Modal Prediction Asymmetry (CMPA) pretext task, followed by a lightweight supervised stage that maps MRI features to scalar health indicators (HIs) using normalised lifecycle labels. The MRI is conceptually related, under the stated assumptions only loosely met in practice, to the Transfer Entropy difference between sensor latent channels. Experiments on a structural fatigue dataset with seven specimens under two loading conditions demonstrate that CMDR achieves competitive trendability and prognosability, as well as the lowest remaining useful life (RUL) error in three of four scenarios. RUL evaluations are additionally repeated under a fully online estimator that uses only training specimens. A strictly inductive ablation that re-pre-trains the self-supervised stage within each leave-one-specimen-out fold confirms a bounded transductive-vs-inductive gap, and CMDR remains the best against three further self-supervised baselines on the within-condition and mixed-condition scenarios. Ablation studies confirm the necessity of directional asymmetry, bottleneck architecture, and momentum-updated target encoders. Full article

This study presents a comprehensive symmetry-breaking analysis framework for a twelve-legged Jansen walking robot, integrating finite element analysis (FEA) with adaptive neuro-fuzzy inference system (ANFIS) surrogate modeling. A systematic dataset of 210 cases was generated by combining 21 single- and multi-leg failure scenarios across 10 load levels (20–200 N) on the PLA-based 3D-printed prototype. Two novel dimensionless metrics are introduced: the Resilience Index (RI), quantifying the proportional stress increase relative to the baseline, and the Asymmetry Index (AI), measuring leg-reaction force distribution imbalance. Results identify a clear fault-tolerance threshold between two- and four-leg failures: single-leg failures remain at LOW risk (RI < 0.20), while three-leg asymmetric failures (S18) reach CRITICAL level (RI = 1.13, ~97% of PLA yield strength). A hybrid machine learning framework is proposed, applying ANFIS to maximum stress (R² = 0.817) and safety factor (R² = 0.936) predictions, while reserving FEA tables for bimodal outputs. The ANFIS surrogate achieves approximately 10⁶× speedup over FEA (262.6 μs vs. 5–8 min), enabling real-time fault diagnosis and digital twin applications. The framework is generalizable to other multi-legged robotic systems requiring fault-tolerance evaluation. Full article

Predictive quality systems are central to Industry 4.0 manufacturing, yet detecting rare defects that pass established quality gates remains an open problem. This study addresses post-quality-gate failure detection in automotive brake manufacturing, where 310 faulty units (1.20%) among 25,756 production records create a naturally occurring extreme class imbalance of 1:82. Fault labels are derived from warranty reports and linked to multi-station production line measurements, while negative samples may include latent failures, motivating a recall-focused evaluation. We propose a Quality 4.0 machine learning framework that compares five resampling methods (ADASYN, SMOTE-Tomek, KMeans-SMOTE, CTGAN, and TVAE) plus a no-resampling baseline across 24 classifiers and stacking ensembles. In total, 504 configurations are tested on a held-out test set. The proposed SVM-RBF model trained on ADASYN-augmented data achieves recall of 0.871, specificity of 0.982, balanced accuracy of 0.926, and ROC-AUC of 0.952, producing only 93 false positives (FPR = 1.8%). Stacking ensembles provide alternative operating points maximizing the detection rate (93.5%) and a separate operating point with the highest discrimination capacity (ROC-AUC = 0.975). Feature importance analysis through Permutation Importance and SHAP identifies Force Increment as the leading feature under both attribution methods. Friedman and Wilcoxon tests confirm statistically significant differences among strategies. The framework offers a practical way to add predictive capability to existing quality control systems. Full article

Reliable detection of small agricultural targets is fundamental to precision crop protection, phenotyping, yield estimation, and robotic intervention. Typical examples include detecting aphids such as Aphis gossypii, whiteflies such as Bemisia tabaci, planthoppers such as Nilaparvata lugens, and other tiny pests on sticky traps or crop canopies for early warning, identifying crop-like weed seedlings for site-specific herbicide spraying, locating early disease lesions for targeted treatment, and detecting young fruits, flowers, or wheat heads for yield estimation and robotic manipulation. Agricultural small-object detection differs from generic small-object detection because target visibility is jointly determined by pixel area, physical size, imaging distance, ground sampling distance, canopy structure, biological similarity, and task-specific intervention requirements. Existing reviews have summarized agricultural object detection or general small-object detection, but they rarely connect agricultural failure modes with detector-level mechanisms and reproducible evaluation practices. This review addresses this gap through a mechanism-oriented synthesis of agricultural small-object detection. First, we revisit the limitations of the COCO-style 32²-pixel threshold and propose an agricultural scale-reporting framework that combines pixel area, physical scale, relative image occupancy, and acquisition geometry. Second, we organize recent methods according to the mechanisms by which they address detail loss, scale shift, occlusion, dense distributions, foreground–background confusion, localization uncertainty, and edge-deployment constraints. Third, we summarize public datasets, quantitative evaluation metrics, reporting checklists, and real-device deployment evidence to support fair and field-oriented comparison. Finally, we identify future directions in multimodal sensing, foundation-model adaptation, label-efficient learning, and hardware-aware optimization. By linking agricultural scene characteristics, detector mechanisms, and evaluation requirements, this review aims to provide a more actionable framework for developing robust small-object detection systems in precision agriculture. Full article

High-temperature structural components are susceptible to creep deformation, which can ultimately lead to failure. In this work, an AI-based framework was developed capable of predicting the creep time of 316 austenitic stainless steel. Here, creep time refers to both the time to reach specific strain levels and the time to rupture. However, the scope of the present work is limited to rupture-time prediction, while the application of the framework to strain-level prediction will be reported in future work. The dataset consisted of creep strain curves from four heats, including both rupture and non-rupture curves. Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Support Vector Regressor (SVR), Gaussian Process Regressor (GPR), and Neural Network (NN) were employed. The effects of square-root and cube-root transformations on data distribution and model learning behaviour were analysed using model learning curves. An Optuna (version 4.3.0)-based hyperparameter tuning strategy was employed. The cube-root transformation improved the learning performance of SVR, GPR, and NN, whereas RF, GB, and XGB remained unaffected. Learning curves revealed mild overfitting for RF, GB, and XGB, and very minimal overfitting for SVR, GPR, and NN. NN achieved the best predictive performance (${\mathrm{R}}^2=0.92,\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=0.195$, deviation factor of 1.57). The findings demonstrated that the combined useof creep strain curves, data transformation, learning curve guided model selection, and rigorous hyperparameter tuning can improve the prediction accuracy under a limited dataset. Full article

Water Distribution System (WDS) pipe failures are one of the most critical issues in WDS management. In order to identify them, a machine learning approach was applied to eight years of geolocated data on pipe failures to establish priorities for WDS rehabilitation. District-level characteristics, such as network length, pressures, materials, population density, and temperature, were combined with a specific failure rate to account for differences in network size. A cost-sensitive classification approach minimized false negatives, ensuring that high-risk areas were correctly flagged. Among all models analyzed the best performance was achieved by Naive Bayes, which reliably predicted priority districts for proactive maintenance, supporting pipeline renewal strategies. Full article

Existing AISI S100 provisions for cold-formed steel (CFS) screw connections lack codified strength equations for screw shear and net section fracture, and traditional machine learning (ML) models struggle to predict these minority failure modes due to imbalanced experimental datasets. This study proposes a cascaded ML framework that first classifies the failure mode and then predicts strength using mode-specific regressors. Two cascade strategies are evaluated: a Hard Classification Cascade (HC-C) and a novel Probability-Weighted Cascade (PW-C) that weights predictions by class probabilities to mitigate error propagation from misclassification. The predictive performance of the two cascaded models is benchmarked against a single regressor without classification. The superior PW-C model is then compared with AISI S100, and its resistance factor ϕ is subsequently calibrated in accordance with LRFD. Results show that the proposed cascaded models outperform the direct regression model, with PW-C improving the R² for minority-class screw shear from 0.765 to 0.933 and for net section fracture from 0.784 to 0.912. Compared with AISI S100 provisions, PW-C extends coverage to the currently unaddressed failure modes and effectively captures screw group effects on shear strength based on a database of 564 tests. Reliability analysis yields an overall ϕ_c of 0.64 for the PW-C model, with a recommended divisor of 1.15 for direct application within the AISI design framework. This work provides a practical, data-driven pathway for updating design codes to cover failure modes beyond current specification limits. Full article