Search Results (958)

Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, localized, and predicted. This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications. It covers supervised, unsupervised, deep, and hybrid learning techniques, highlighting their capabilities in processing high-dimensional sensor data, managing uncertainty, and enabling real-time diagnostics. Particular focus is given to the challenges of data scarcity, operational variability, and interpretability in safety-critical environments. The review also explores emerging directions such as digital twins, transfer learning, and federated learning. By mapping current strengths and limitations, this paper provides a roadmap for future research and outlines the key enablers needed to bring ML-based SHM from laboratory development to widespread aerospace deployment. Full article

Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R² > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty. Full article

This paper proposes an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, based on the fusion of heterogeneous sensor data. To address the increasing complexity and ambiguity in modern UAV threats, this study introduces a novel three-stage fuzzy inference architecture that supports adaptive sensor evaluation and optimal pairing. The proposed methodology consists of three-layered Fuzzy Inference Systems (FIS): FIS-A quantifies sensor effectiveness based on UAV flight altitude and detection probability; FIS-B assesses operational suitability using sensor range and cost; and FIS-C synthesizes both outputs, along with sensor capability overlap, to determine the composite suitability of sensor pairs. This hierarchical structure enables detailed analysis and system-level optimization, reflecting real-world constraints and performance trade-offs. Simulation-based evaluation using diverse sensor modalities (EO/IR, Radar, Acoustic, RF), supported by empirical data and literature, demonstrates the framework’s ability to handle uncertainty, enhance detection reliability, and support cost-effective sensor deployment in Counter-UAV operations. The framework’s modularity, scalability, and interpretability represent significant advancements in intelligent Counter-UAV system design, offering a transferable methodology for dynamic threat environments. Full article

Multi-source information fusion is a key challenge in uncertainty reasoning. Dempster–Shafer evidence theory (D-S evidence theory) offers a flexible framework for representing and fusing uncertain information. However, the classical Dempster’s combination rules may yield counter-intuitive results when faced with highly conflicting evidence. To overcome this limitation, we introduce a cluster-level information fusion framework, which shifts the focus from pairwise evidence comparisons to a more holistic cluster-based perspective. A key contribution is a novel cluster–cluster divergence measure that jointly captures the strength of belief assignments and structural differences between clusters. Guided by this measure, a reward-driven evidence assignment rule dynamically allocates new evidence to enhance inter-cluster separability while preserving intra-cluster coherence. Building upon the resulting structure, we propose a two-stage information fusion algorithm that assigns credibility weights at the cluster level. The effectiveness of the framework is validated through a range of benchmark pattern classification tasks, in which the proposed method not only improves classification accuracy compared with D-S evidence theory methods but also provides a more interpretable, cluster-oriented perspective for handling evidential conflict. Full article

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across various charging contexts and control scenarios. This paper categorizes RL methodologies from value-based to actor–critic and hybrid frameworks, and explores their integration with optimization techniques, forecasting models, and multi-agent coordination strategies. By examining key design aspects—including agent structures, training schemes, coordination mechanisms, reward formulation, data usage, and evaluation protocols—this review identifies broader trends across central control dimensions such as scalability, uncertainty management, interpretability, and adaptability. In addition, the review assesses common baselines, performance metrics, and validation settings used in the literature, linking algorithmic developments with real-world deployment needs. By bridging theoretical principles with practical insights, this work provides comprehensive directions for future RL applications in EVCS control, while identifying methodological gaps and opportunities for safer, more efficient, and sustainable operation. Full article

Cardiorenal syndrome (CRS) reflects the intricate and bidirectional interplay between cardiac and renal dysfunction, commonly resulting in diagnostic uncertainty, therapeutic dilemmas and poor outcomes. While traditional biomarkers like serum creatinine (Cr) and natriuretic peptides remain widely used, their limitations in specificity, timing and contextual interpretation often hinder optimal management. This narrative review synthesizes the current evidence on established and emerging biomarkers in CRS, with emphasis on their clinical relevance, integration into real-world practice, and potential to inform precision therapy. Markers of glomerular filtration rate beyond creatinine—such as cystatin C—offer more accurate assessment in frail or sarcopenic patients, while tubular injury markers such as NGAL, KIM-1, and urinary L-FABP (uL-FABP) provide early signals of structural renal damage. The FDA-approved NephroCheck^® test—based on TIMP-2 and IGFBP7— enables risk stratification for imminent AKI up to 24 h before functional decline. Congestion-related markers such as CA125 and bio-adrenomedullin outperform natriuretic peptides in certain CRS phenotypes, particularly in right-sided heart failure or renally impaired patients. Fibrosis and inflammation markers (galectin-3, sST2, GDF-15) add prognostic insights, especially when combined with NT-proBNP or troponin. Rather than presenting biomarkers in isolation, this review proposes a framework that links them to specific clinical contexts—such as suspected decongestion-related renal worsening or persistent congestion despite therapy—to support actionable interpretation. A tailored, scenario-based, multi-marker strategy may enhance diagnostic precision and treatment safety in CRS. Future research should prioritize prospective biomarker-guided trials and standardized pathways for clinical integration. Full article

Woven composite structures are inherently influenced by uncertainties across multiple scales, ranging from constituent material properties to mesoscale geometric variations. These uncertainties give rise to both spatial autocorrelation and cross-correlation among material parameters, resulting in stochastic strength performance and damage morphology at the macroscopic structural level. This study established a comprehensive multiscale uncertainty quantification framework to systematically propagate uncertainties from the microscale to the macroscale. A novel dual-correlation sampling approach, based on multivariate random field (MRF) theory, was proposed to simultaneously capture spatial autocorrelation and cross-correlation with clear physical interpretability. This method enabled a realistic representation of both inter-specimen variability and intra-specimen heterogeneity of material properties. Experimental validation via in-plane tensile tests demonstrated that the proposed approach accurately predicts not only probabilistic mechanical responses but also discrete damage morphology in woven composite structures. In contrast, traditional independent sampling methods exhibited inherent limitations in representing spatially distributed correlations of material properties, leading to inaccurate predictions of stochastic structural behavior. The findings offered valuable insights into structural reliability assessment and risk management in engineering applications. Full article

Posture recognition is critical in modern educational and office environments for preventing musculoskeletal disorders and maintaining cognitive performance. Existing methods based on human keypoint detection typically rely on convolutional neural networks (CNNs) and single-scale features, which limit representation capacity and suffer from overfitting under small-sample conditions. To address these issues, we propose MSBN-SPose, a Multi-Scale Bayesian Neuro-Symbolic Posture Recognition framework that integrates geometric features at multiple levels—including global body structure, local regions, facial landmarks, distances, and angles—extracted from OpenPose keypoints. These features are processed by a multi-branch Bayesian neural architecture that models epistemic uncertainty, enabling improved generalization and robustness. Furthermore, a lightweight neuro-symbolic reasoning module incorporates human-understandable rules into the inference process, enhancing transparency and interpretability. To support real-world evaluation, we construct the USSP dataset, a diverse, classroom-representative collection of student postures under varying conditions. Experimental results show that MSBN-SPose achieves 96.01% accuracy on USSP, outperforming baseline and traditional methods under data-limited scenarios. Full article

Lutetium–Yttrium Oxyorthosilicate (LYSO) crystals and EJ-200 plastic scintillators are widely recognized fast scintillating materials, valued for their high light yield and mechanical robustness, which make them well suited for demanding applications in high-energy physics and space research. Their non-proportional light response, along with their non-linear behavior at low-energy X-rays, has been extensively investigated in previous studies, revealing potential systematic effects in existing measurements. In this work, light quenching in both scintillators is measured under charged-particle excitation. The results are interpreted using the modified Birks–Onsager model, which provides a theoretical framework for understanding the underlying quenching mechanisms, as well as a generalized logistic parametrization, offering experimentalists a useful tool to characterize the detector’s light yield and associated uncertainties. Full article

In the context of rising uncertainty and financial crises, the roles of financial advisors are evolving beyond technical compliance, particularly in household contexts. This article introduces a novel perspective by highlighting how these professionals contribute to resilience and stability at all levels of society by building financial literacy and acting as human barriers against systemic risk. From the datasets retrieved from Web of Science and Scopus, a final curated sample of 102 peer-reviewed articles was retained following thematic refinement and in-depth human filtering. After data harmonisation, a bibliometric analysis was conducted through VOSviewer, identifying five key thematic clusters. Beyond cartographic description, a rigorous thematic exploration was conducted. We advance an interpretive architecture consisting of mechanisms (M1–M4), advice-to-outcome pathways (P1–P3), and a conditional context (Conditions of Success (CS), Failure points (F) and Moderating Factors (MF)), enabling integrative inference and cumulative explanation across an otherwise heterogeneous corpus. Results show that financial advisors mitigate risk by educating clients, guiding decisions, and turning complexity into usable judgment. They also bear risk; as human barriers, they channel and transform these pressures through their professional practice, returning stabilizing effects to households and, by extension, to the wider financial system. Full article

This study investigates the interpretation of hand gestures in nonverbal communication, with particular attention paid to cases where gesture form does not reliably convey the intended meaning. Hand gestures are a key medium for expressing impressions, complementing or substituting verbal communication. For example, the “Thumbs Up” gesture is generally associated with approval, yet its interpretation can vary across contexts and individuals. Using participant-generated descriptive words, sentiment analysis with the VADER method, and fuzzy membership modeling, this research examines the variability and ambiguity in gesture–intention mappings. Our results show that Negative gestures, such as “Thumbs Down,” consistently align with Negative sentiment, while Positive and Neutral gestures, including “Thumbs Sideways” and “So-so,” exhibit greater interpretive flexibility, often spanning adjacent sentiment categories. These findings demonstrate that rigid, category-based classification systems risk oversimplifying nonverbal communication, particularly for gestures with higher interpretive uncertainty. The proposed fuzzy logic-based framework offers a more context-sensitive and human-aligned approach to modeling gesture intention, with implications for affective computing, behavioral analysis, and human–computer interaction. Full article

Quantifying uncertainty in complex systems is a central problem in reliability analysis and engineering applications. In this work, we develop an information-theoretic framework for analyzing linear consecutive k-out-of-n:G systems using the cumulative residual Tsallis entropy (CRTE). A general analytical expression for CRTE is derived, and its behavior is investigated under various stochastic ordering relations, providing insight into the reliability of systems governed by continuous lifetime distributions. To address challenges in large-scale settings or with nonstandard lifetimes, we establish analytical bounds that serve as practical tools for uncertainty quantification and reliability assessment. Beyond theoretical contributions, we propose a nonparametric CRTE-based test for dispersive ordering, establish its asymptotic distribution, and confirm its statistical properties through extensive Monte Carlo simulations. The methodology is further illustrated with real lifetime data, highlighting the interpretability and effectiveness of CRTE as a probabilistic entropy measure for reliability modeling. The results demonstrate that CRTE provides a versatile and computationally feasible approach for bounding analysis, characterization, and inference in systems where uncertainty plays a critical role, aligning with current advances in entropy-based uncertainty quantification. Full article

This study aimed to evaluate the scientific reliability of 3D-printed silicone boluses fabricated with patient-specific molds, focusing on fabrication-related uncertainties such as internal air bubbles, thickness variations, and density differences, thereby providing evidence for clinical quality assurance. Custom silicone boluses were fabricated using 3D-printed molds with varying vacuum degassing times (1, 5, and 10 min). Air bubble size and depth were quantified using scanner image analysis, while density and Hounsfield unit (HU) values were compared with a commercial bolus. Dosimetric evaluation was performed using a VitalBeam linear accelerator (6 MV photons, Varian Medical Systems, Palo Alto, CA, USA) and a MatriXX 2D detector (IBA Dosimetry, Schwarzenbruck, Germany), comparing treatment planning system (TPS) calculated doses with measured doses across a 3 × 3 grid. Surface dose distributions were further analyzed using EBT3 film. Results showed that bubble size increased with longer vacuum times, interpreted as coalescence due to limited degassing and silicone viscosity. The density of 3D boluses ranged from 0.980 to 1.104 g/cm³ (commercial: 0.988 g/cm³), with HU values of +240 to +250 (commercial: −110). In point-wise comparisons, mean dose differences were less than 1% for 1- and 5 min samples and approximately 1% for 10 min, with all conditions within |Δ| ≤ 3%. Film analysis confirmed equivalent surface dose distributions. These findings demonstrate, for the first time, that microscopic bubbles in 3D-printed silicone boluses have negligible clinical impact, supporting their safe adoption without requiring complex degassing procedures. Full article

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article

Electrochemical impedance spectroscopy (EIS) is widely used at the laboratory level for monitoring/diagnostics of battery cells, but the design and validation of in situ, online measurement systems based on EIS face challenges due to complex hardware–software interactions and non-idealities. This study aims to develop an integrated co-simulation framework to support the design, debugging, and validation of EIS measurement systems devoted to the online monitoring of battery cells, helping to predict experimental results and identify/correct the non-ideality effects and sources of uncertainty. The proposed framework models both the hardware and software components of an EIS-based system to simulate and analyze the impedance measurement process as a whole. It takes into consideration the effects of physical non-idealities on the hardware–software interactions and how those affect the final impedance estimate, offering a tool to refine designs and interpret test results. For validation purposes, the proposed general framework is applied to a specific EIS-based laboratory prototype, previously designed by the research group. The framework is first used to debug the prototype by uncovering hidden non-idealities, thus refining the measurement system, and then employed as a digital model of the latter for fast development of software algorithms. Finally, the results of the co-simulation framework are compared against a theoretical model, the real prototype, and a benchtop instrument to assess the global accuracy of the framework. Full article