Search Results (521)

Modern energy storage systems for both power and transportation are highly related to lithium-ion batteries (LIBs). However, their safety depends on a potentially hazardous failure mode known as thermal runaway (TR). Predicting and classifying TR causes can widely enhance the safety of power and transportation systems. This paper presents an advanced machine learning method for forecasting and classifying the causes of TR. A generative model for synthetic data generation was used to handle class imbalance in the dataset. Hyperparameter optimization was conducted using Optuna for four classifiers: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), tabular network (TabNet), and Extreme Gradient Boosting (XGBoost). A three-fold cross-validation approach was used to guarantee a robust evaluation. An open-source database of LIB failure events is used for model training and testing. The XGBoost model outperforms the other models across all TR categories by achieving 100% accuracy and a high recall (1.00). Model results were interpreted using SHapley Additive exPlanations analysis to investigate the most significant factors in TR predictors. The findings show that important TR indicators include energy adjusted for heat and weight loss, heater power, average cell temperature upon activation, and heater duration. These findings guide the design of safer battery systems and preventive monitoring systems for real applications. They can help experts develop more efficient battery management systems, thereby improving the performance and longevity of battery-operated devices. By enhancing the predictive knowledge of temperature-driven failure mechanisms in LIBs, the study directly advances thermal analysis and energy storage safety domains. Full article

A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the system. This work addresses and solves the problem of selecting and obtaining reliable measurement data by exploiting the redundant measurements of process streams together with the balance equations linking those streams. This study formulates an approach for integrating cloud technologies, machine learning methods, and forecasting into information control systems (ICSs) via predictive analytics to optimize CCP production processes. A method for combining the hybrid cloud infrastructure with an LSTM-DNN neural network model has been developed, yielding a marked improvement in TEP for copper concentration operations. The forecasting accuracy for the key process parameters rose from 75% to 95%. Predictive control reduced energy consumption by 10% through more efficient resource use, while the copper losses to tailings fell by 15–20% thanks to optimized reagent dosing and the stabilization of the flotation process. Equipment failure prediction cut the amount of unplanned downtime by 30%. As a result, the control system became adaptive, automatically correcting the parameters in real time and lessening the reliance on operator decisions. The architectural model of an ICS for metallurgical production based on the hybrid cloud and the LSTM-DNN model was devised to enhance forecasting accuracy and optimize the EPIs of the CCP. The proposed model was experimentally evaluated against alternative neural network architectures (DNN, GRU, Transformer, and Hybrid_NN_TD_AIST). The results demonstrated the superiority of the LSTM-DNN in forecasting accuracy (92.4%), noise robustness (0.89), and a minimal root-mean-square error (RMSE = 0.079). The model shows a strong capability to handle multidimensional, non-stationary time series and to perform adaptive measurement correction in real time. Full article

Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article

The timely detection of delamination is essential for preventing catastrophic failures and extending the service life of carbon fiber-reinforced polymers (CFRP). Full wavefields in CFRP encapsulate extensive information on the interaction between guided waves and structural damage, making them a widely utilized tool for damage mapping. However, due to the multimodal and dispersive nature of guided waves, interpreting full wavefields remains a significant challenge. This study proposes an end-to-end delamination imaging approach based on UNet++ using 2D frequency domain spectra (FDS) derived from full wavefield data. The proposed method is validated through a self-constructed simulation dataset, experimental data collected using Scanning Laser Doppler Vibrometry, and a publicly available dataset created by Kudela and Ijjeh. The results on the simulated data show that UNet++, trained with multi-frequency FDS, can accurately predict the location, shape, and size of delamination while effectively handling frequency offsets and noise interference in the input FDS. Experimental results further indicate that the model, trained exclusively on simulated data, can be directly applied to real-world scenarios, delivering artifact-free delamination imaging. Full article

The reliability and safety of five-axis CNC abrasive water jet machining are critical for many industries. This study employs Failure Mode and Effects Analysis (FMEA) to identify and mitigate potential failures in this machining system. Traditional FMEA, which relies on crisp numerical values, often struggles with handling uncertainty in risk assessment. To address this limitation, this paper introduces an Interval-Valued Spherical Fuzzy FMEA (IVSF-FMEA) approach, which enhances risk evaluation by incorporating membership, non-membership, and hesitancy degrees. The IVSF-FMEA method leverages the inherent rotational symmetry of interval-valued spherical fuzzy sets and the permutation symmetry among severity, occurrence, and detectability criteria, resulting in a transformation-invariant and unbiased risk assessment framework. Applying IVSF-FMEA to seven periodic failure (PF) modes in five-axis CNC water jet machining achieves a more precise prioritization of risks, leading to improved decision-making and resource allocation. The findings highlight improper fixturing of the workpiece (PF6) as the most critical failure mode, with the highest RPN value of −0.54, followed by mechanical vibrations (PF2) and tool wear and breakage (PF1). This indicates that ensuring proper fixturing stability is essential for maintaining machining accuracy and preventing defects. Comparative analysis with traditional FMEA demonstrates the superiority of the proposed fuzzy-based approach in handling subjective assessments and reducing ambiguity. The findings highlight improper fixturing, mechanical vibrations, and tool wear as the most critical failure modes, necessitating targeted risk mitigation strategies. This research contributes to advancing risk assessment methodologies in complex manufacturing environments. Full article

Right heart failure (RHF) is a major cause of morbidity and mortality, often resulting from pulmonary arterial hypertension and characterized by impaired calcium (Ca²⁺) handling and maladaptive remodeling. Phosphodiesterase 9A (PDE9A), a cGMP-specific phosphodiesterase, has been proposed as a potential contributor to RHF pathogenesis by suppressing the cardioprotective cGMP–PKG signaling pathway—a conclusion largely extrapolated from left-sided heart failure models. This review examines existing evidence regarding PDE9A’s role in RHF, focusing on its effects on intracellular calcium cycling, fibrosis, hypertrophy, and contractile dysfunction. Data from preclinical models demonstrate that pathological stress upregulates PDE9A expression in cardiomyocytes, leading to diminished PKG activation, impaired SERCA2a function, RyR2 instability, and increased arrhythmogenic Ca²⁺ leak. Pharmacological or genetic inhibition of PDE9A restores cGMP signaling, improves calcium handling, attenuates hypertrophic and fibrotic remodeling, and enhances ventricular compliance. Early-phase clinical studies in heart failure populations suggest that PDE9A inhibitors are well tolerated and effectively augment cGMP levels, although dedicated trials in RHF are still needed. Overall, these findings indicate that targeting PDE9A may represent a promising therapeutic strategy to improve outcomes in RHF by directly addressing the molecular mechanisms underlying calcium mishandling and myocardial remodeling. Full article

Aerial tracking is an important service for many Unmanned Aerial Vehicle (UAV) applications. Existing work has failed to provide robust solutions when handling target disappearance, viewpoint changes, and tracking drifts in practical scenarios with limited UAV resources. In this paper, we propose a closed-loop framework integrating three key components: (1) a lightweight adaptive detection with multi-scale feature extraction, (2) spatiotemporal motion modeling through Kalman-filter-based trajectory prediction, and (3) autonomous decision-making through composite scoring of detection confidence, appearance similarity, and motion consistency. By implementing dynamic detection-tracking coordination with quality-aware feature preservation, our system enables real-time operation through performance-adaptive frequency modulation. Evaluated on VOT-ST2019 and OTB100 benchmarks, the proposed method yields marked improvements over baseline trackers, achieving a 27.94% increase in Expected Average Overlap (EAO) and a 10.39% reduction in failure rates, while sustaining a frame rate of 23–95 FPS on edge hardware. The framework achieves rapid target reacquisition during prolonged occlusion scenarios through optimized protocols, outperforming conventional methods in sustained aerial surveillance tasks. Full article

Bilateral teleoperation systems have been widely used in many fields of robotics, such as industrial manipulation, medical treatment, space exploration, and deep-sea operation. Delays in communication, known as an inevitable issues in practical implementation, especially for long-distance operations and challenging communication situations, can destroy system passivity and potentially lead to system failure. In this work, we address the time-delayed three-channel teleoperation design problem to guarantee system passivity and achieve high transparency simultaneously. To realize this, the three-channel teleoperation structure is first reformulated to form a two-channel-like architecture. Then, the wave variable technique is used to handle the communication delay and guarantee system passivity. Two novel wave variable compensators are proposed to achieve delay-minimized system transparency, and energy reservoirs are employed to monitor and regulate the energy introduced via these compensators to preserve overall system passivity. Numerical studies confirm that the proposed method significantly improves both kinematic and force tracking performance, achieving near-perfect correspondence with only a single-trip delay. Quantitative analyses using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Dynamic Time Warping (DTW) metrics show substantial error reductions compared to conventional wave variable and direct transmission-based three-channel teleoperation approaches. Moreover, statistical validation via the Mann–Whitney U test further confirms the significance of these improvements in system performance. The proposed design guarantees passivity with any passive human operator and environment without requiring restrictive assumptions, offering a robust and generalizable solution for teleoperation tasks with communication time delay. Full article

Chatbots, an automatic dialogue system empowered by deep learning-oriented AI technology, have gained increasing attention in healthcare e-services for their ability to provide medical information around the clock. A formidable challenge is that chatbot dialogue systems have difficulty handling queries with unknown intents due to the technical bottleneck and restricted user-intent answering scope. Furthermore, the wide variation in a user’s consultation needs and levels of medical knowledge further complicates the chatbot’s ability to understand natural human language. Failure to deal with unknown intents may lead to a significant risk of incorrect information acquisition. In this study, we develop an unknown intent detection model to facilitate chatbots’ decisions in responding to uncertain queries. Our work focuses on algorithmic innovation for high-risk healthcare scenarios, where asymmetric knowledge between patients and experts exacerbates intent recognition challenges. Given the multi-role context, we propose a novel query representation learning approach involving multiple views from chatbot users, medical experts, and system developers. Unknown intent detection is then accomplished through the transformed representation of each query, leveraging adaptive determination of intent decision boundaries. We conducted laboratory-level experiments and empirically validated the proposed method based on the real-world user query data from the Tianchi lab and medical information from the Xunyiwenyao website. Across all tested unknown intent ratios (25%, 50%, and 75%), our multi-view boundary learning method was proven to outperform all benchmark models on the metrics of accuracy score, macro F1-score, and macro F1-scores over known intent classes and over the unknown intent class. Full article

Roll-to-roll (R2R) manufacturing processes demand precise control of web or yarn velocity and tension, alongside robust mechanisms for handling system failures. This paper presents an integrated approach combining high-performance control with reliable fault detection for an experimental R2R system. A model-based cascade control strategy is designed, incorporating system identification, radius compensation for varying roll diameters, and a Kalman filter to mitigate load sensor noise, ensuring accurate regulation of yarn velocity and tension under normal operating conditions. In parallel, a data-driven fault detection layer uses Gaussian Process Regression (GPR) models, trained offline on healthy operating data, to predict yarn tension and motor speeds. During operation, discrepancies between measured and GPR-predicted values that exceed predefined thresholds trigger an immediate shutdown of the system, preventing material loss and equipment damage. Experimental trials demonstrate tension regulation within ±0.02 N and velocity errors below ±5 rad/s across varying roll diameters, while yarn-break and motor-fault scenarios are detected within a single sampling interval (<100 milliseconds) with zero false alarms. This study validates the integrated system’s capability to enhance both the operational precision and resilience of R2R processes against critical failures. Full article

Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score. Full article

The increasing demand for distributed machine learning like Federated Learning (FL) in dynamic, resource-constrained edge environments, 5G/6G networks, and the proliferation of mobile and edge devices, presents significant challenges related to fault tolerance, elasticity, and communication efficiency. This paper addresses these issues by proposing a novel modular and resilient FL framework. In this context, resilience refers to the system’s ability to maintain operation and performance despite disruptions. The framework is built on decoupled modules handling core FL functionalities, allowing flexibility in integrating various algorithms, communication protocols, and resilience strategies. Results demonstrate the framework’s ability to integrate different communication protocols and FL paradigms, showing that protocol choice significantly impacts performance, particularly in high-volume communication scenarios, with Zenoh and MQTT exhibiting lower overhead than Kafka in tested configurations, and Zenoh emerging as the most efficient communication option. Additionally, the framework successfully maintained model training and achieved convergence even when simulating probabilistic worker failures, achieving a MCC of 0.9453. Full article

Radar echo extrapolation based on real-world data is a fundamental problem in meteorological forecasting. Existing extrapolation models typically assume complete radar echo sequences, but in practice, data loss frequently occurs due to equipment failures and communication disruptions. Although traditional solutions can handle missing values through a interpolation-then-prediction pipeline, they suffer from a major limitation: interpolating the missing data and then extrapolating will introduce a cumulative error. To address these issues, we propose MissPred, a radar echo extrapolation model specifically designed for missing data patterns. MissPred employs a dual encoder–decoder architecture. Specifically, the training process involves the sequential execution of interpolation and extrapolation as two distinct serial tasks. In order to circumvent the occurrence of cumulative errors, interpolation and extrapolation are required to share encoder parameters. Furthermore, a missing spatiotemporal feature fusion module (MSTF) that is absent has been designed for the purpose of extracting fine-grained complete spatiotemporal features. Finally, the incorporation of adversarial training is introduced to enhance the authenticity of the prediction results. In order to evaluate the proposed model, case studies are conducted on real radar datasets. Our dataset covers missing rates ranging from 10% to 50%. The experimental results show that the model outperforms the baseline model with the prior interpolation of missing data in the missing mode with stable robustness. Full article

The L-type calcium channels (LTCCs) function as the main entry points that convert myocyte membrane depolarization into calcium transients, which drive every heartbeat. There is increasing evidence to show that maladaptive remodeling of these channels is the cause of heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF). Recent experimental, translational, and clinical studies have improved our understanding of the roles LTCC expression, micro-domain trafficking, and post-translational control have in disrupting excitation–contraction coupling, provoking arrhythmias, and shaping phenotype specific hemodynamic compromise. We performed a systematic search of the PubMed and Google Scholar databases (2015–2025, English) and critically evaluated 17 eligible publications in an effort to organize the expanding body of work. This review combines existing data about LTCC density and T-tubule architecture with β-adrenergic and Ca²⁺/calmodulin-dependent protein kinase II (CaMKII) signaling and downstream sarcoplasmic reticulum crosstalk to explain how HFrEF presents with contractile insufficiency and how HFpEF shows diastolic calcium overload and stiffening. Additionally, we highlight the emerging therapeutic strategies aimed at restoring calcium homeostasis such as CaMKII inhibitors, ryanodine receptor type 2 (RyR2) stabilizers, and selective LTCC modulators without compromising systolic reserve. The review establishes LTCC dysregulation as a single mechanism that causes myocardial dysfunction while remaining specific to each phenotype, thus offering clinicians and researchers a complete reference for current concepts and future precision therapy approaches in heart failure. Full article

Large deformation analysis is a crucial foundation for studying the nonlinear behavior and progressive damage of materials and structures. Traditional mesh methods often struggle with large-scale mesh distortion when dealing with such issues, which can compromise solution efficiency and accuracy, and in severe cases, even cause computational interruptions. In contrast, the material point method (MPM) employs a dual framework of Lagrangian particles and Eulerian background grids, effectively integrating the advantages of both Lagrangian and Eulerian approaches, thus avoiding mesh distortion and challenges in handling convective terms. Consequently, many researchers are dedicated to developing an MPM for addressing high-speed impact and fluid–structure interaction problems that involve material failure and large deformations. This paper begins by introducing the fundamental theory and contact algorithms of the MPM. It then systematically summarizes the latest advancements and applications of the MPM, including its hybridization and coupling with other algorithms, in simulating various large deformation scenarios such as high-speed impacts, explosions, dynamic cracking, penetration, and fluid–structure interactions. This paper concludes with a summary and a prospective view on future trends. This review highlights the robustness and accuracy of the MPM in tackling large deformation problems, offering valuable insights for the analysis of large deformations and damage evolution in various materials. Full article