Search Results (212)

Research in on-orbit processing enables edge AI deployment over satellite optical networks. However, these operations induce frequent battery discharge cycles, particularly depth-of-discharge (DoD) events, which accelerate degradation and curtail satellite longevity. To address this, we propose green task-aware routing planning (GreenRP), a task-aware routing framework that achieves sustainable edge AI through dynamic task offloading and discharge-resilient path orchestration. GreenRP employs a novel battery aging model explicitly coupling DoD effects with laser inter-satellite link dynamics under AI workloads, enhancing system sustainability. Comprehensive evaluation on a 1152-satellite constellation demonstrates that GreenRP extends network lifetime by 176% over shortest-path routing while meeting latency and completion rate targets. This work enables reliable edge AI via sustainable satellite resource utilization. Full article

Structural cracks significantly threaten the safety and longevity of historical buildings, which are essential parts of cultural heritage. Conventional inspection techniques, which depend heavily on manual visual evaluations, tend to be inefficient and subjective. This research introduces an automated framework for crack and damage detection using advanced YOLO (You Only Look Once) models, aiming to improve both the accuracy and efficiency of monitoring heritage structures. A dataset comprising 2500 high-resolution images was gathered from historical buildings and categorized into four levels of damage: no damage, minor, moderate, and severe. Following preprocessing and data augmentation, a total of 5000 labeled images were utilized to train and evaluate four YOLO variants: YOLOv5, YOLOv8, YOLOv10, and YOLOv11. The models’ performances were measured using metrics such as precision, recall, mAP@50, mAP@50–95, as well as losses related to bounding box regression, classification, and distribution. Experimental findings reveal that YOLOv10 surpasses other models in multi-target detection and identifying minor damage, achieving higher localization accuracy and faster inference speeds. YOLOv8 and YOLOv11 demonstrate consistent performance and strong adaptability, whereas YOLOv5 converges rapidly but shows weaker validation results. Further testing confirms YOLOv10’s effectiveness across different structural components, including walls, beams, and ceilings. This study highlights the practicality of deep learning-based crack detection methods for preserving building heritage. Future advancements could include combining semantic segmentation networks (e.g., U-Net) with attention mechanisms to further refine detection accuracy in complex scenarios. Full article

The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful life (RUL) using machine and deep learning, most existing models fail to capture both short-term degradation trends and long-range contextual dependencies jointly. In this study, we introduce V2G-HealthNet, a novel hybrid deep learning framework that uniquely combines Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to model battery degradation under dynamic vehicle-to-grid (V2G) scenarios. Unlike prior approaches that treat SOH estimation in isolation, our method directly links health prediction to operational decisions by enabling SOH-informed adaptive load scheduling and predictive maintenance across EV fleets. Trained on over 3400 proxy charge-discharge cycles derived from 1 million telemetry samples, V2G-HealthNet achieved state-of-the-art performance (SOH RMSE: 0.015, MAE: 0.012, $R^2$: 0.97), outperforming leading baselines including XGBoost and Random Forest. For RUL prediction, the model maintained an MAE of 0.42 cycles over a five-cycle horizon. Importantly, deployment simulations revealed that V2G-HealthNet triggered maintenance alerts at least three cycles ahead of critical degradation thresholds and redistributed high-load tasks away from ageing batteries—capabilities not demonstrated in previous works. These findings establish V2G-HealthNet as a deployable, health-aware control layer for smart city electrification strategies. Full article

The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework’s applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework’s adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Full article

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

As electric vehicles (EVs) continue to advance toward widespread adoption, innovations in power electronics are playing a pivotal role in improving efficiency, performance, and sustainability. This review presents recent progress in bidirectional converters and regenerative braking systems (RBSs), highlighting their contributions to energy recovery, battery longevity, and vehicle-to-grid integration. Bidirectional converters support two-way energy flow, enabling efficient regenerative braking and advanced charging capabilities. The integration of wide-bandgap semiconductors, such as silicon carbide and gallium nitride, further enhances power density and thermal performance. The paper evaluates various converter topologies, including single-stage and multi-stage architectures, and assesses their suitability for high-voltage EV platforms. Intelligent control strategies, including fuzzy logic, neural networks, and sliding mode control, are discussed for optimizing braking force and maximizing energy recuperation. In addition, the paper explores the influence of regenerative braking on battery degradation and presents hybrid energy storage systems and AI-based methods as mitigation strategies. Special emphasis is placed on the integration of RBSs in advanced electric vehicle platforms, including autonomous systems. The review concludes by identifying current challenges, emerging trends, and key design considerations to inform future research and practical implementation in electric vehicle energy systems. Full article

This paper presents a hybrid fast charging strategy for static wireless power transfer (WPT) systems that synergistically combines pulsed current and multi-stage current (MCM) modes to enable rapid yet battery-health-conscious electric vehicle (EV) charging, thereby promoting sustainable transportation. The proposed approach employs a memetic algorithm (MA) to dynamically optimize the charging parameters, achieving an optimal balance between speed and battery longevity while maintaining 90.78% system efficiency at the SAE J2954-standard 85 kHz operating frequency. A neural-network-based state of charge (SOC) estimator provides accurate real-time monitoring, complemented by MA-tuned PI control for enhanced resonance stability and adaptive pulsed current–MCM profiles for the optimal energy transfer. Simulations and experimental validation demonstrate faster charging compared to that using the conventional constant current–constant voltage (CC-CV) methods while effectively preserving the battery’s state of health (SOH)—a critical advantage that reduces the environmental impact of frequent battery replacements and minimizes the carbon footprint associated with raw material extraction and battery manufacturing. By addressing both the technical challenges of high-power WPT systems and the ecological imperative of battery preservation, this research bridges the gap between fast charging requirements and sustainable EV adoption, offering a practical solution that aligns with global decarbonization goals through optimized resource utilization and an extended battery service life. Full article

Background: Understanding the multifactorial determinants of human longevity remains a major scientific challenge. Certain regions of the world—so-called “longevity hotspots”—exhibit a notably high prevalence of centenarians; one such region is the province of Ourense, in north-western Spain. Objectives: This study aimed to analyse, for the first time, the nutritional factors associated with healthy longevity among centenarians, as well as those linked to longevity irrespective of health status, in the province of Ourense. Methods: A cross-sectional, retrospective, observational, mixed-methods study was conducted. A population of 261 individuals aged 100 or over residing in Ourense was identified. A sample of 156 participants was included in the quantitative analysis; from this sample, 25 centenarians were selected for in-depth qualitative analysis through personal interviews. Results: Dietary patterns aligned with the Southern European Atlantic Diet (SEAD), combined with strong social bonds and a culture of self-sufficiency, appear to be key contributors to exceptional longevity in this population. Conclusions: Remarkable longevity in Ourense is associated with a combination of factors: adherence to an SEAD-style dietary pattern, an active and uncomplicated lifestyle, and strong social support networks. Full article

Background/Objectives: The global population is rapidly aging, raising interest in dietary practices for promoting the healthspan. This study aimed to comprehensively investigate the state of diet and longevity research over the past decade, addressing the lack of bibliometric synthesis within the field. Methods: A bibliometric analysis was performed using the keywords “diet” and “longevity” on English-language articles from the Web of Science database that were published from 2015 to 2024. Data were analyzed using Web of Science tools, InCites, and VOSviewer to identify trends in publication output, citation metrics, coauthorship networks, institutional contributions, and keyword co-occurrence patterns. Results: Overall, 2203 articles meeting the inclusion criteria were included in the analysis. Publication volume and citation counts gradually increased, peaking in 2021. Countries, including the United Kingdom, and organizations, such as the National Institutes of Health and Harvard University, had significant citation impact, and the United States and China led productivity. Molecular processes (oxidative stress and autophagy), dietary models (Mediterranean diet and calorie restriction), and public health issues (obesity and mortality) were the main thematic clusters. Model species, including Caenorhabditis elegans and Drosophila melanogaster, were frequently used. Regional disparities in research production and notable terminology variability were noted. Conclusions: This study emphasizes the development and diversity of nutrition and longevity research while highlighting novel molecular and translational topics. More international cooperation, uniform language, and multidisciplinary frameworks are warranted to promote equal scientific advancement worldwide and connect mechanistic discoveries with therapeutic outcomes. Full article

Wireless Power Transfer (WPT) enables efficient, contactless charging for mobile devices by eliminating mechanical connectors and wiring, thereby enhancing user experience and device longevity. However, conventional WPT systems remain prone to performance issues such as coil misalignment, resonance instability, and thermal losses. Addressing these challenges involves designing coil geometries that operate at lower resonant frequencies to strengthen magnetic coupling and decrease resistance. This work introduces a WPT system with a performance-driven coil design aimed at maximizing magnetic coupling and mutual inductance between the transmitting (Tx) and receiving (Rx) coils in mobile devices. Due to the nonlinear behavior of magnetic flux and the high computational cost of simulations, exploring the full design space for coils using ANSYS Maxwell becomes impractical. To address this complexity, a machine learning (ML)-based optimization framework is developed to efficiently navigate the design space. The framework integrates a hybrid sequential neural network and multivariate regression model to optimize coil winding and ferrite core geometry. The optimized structure achieves a mutual inductance of 12.52 $\mu$H with a conventional core, outperforming many existing ML models. Finite element simulations and experimental results validate the robustness of the method, which offers a scalable solution for efficient wireless charging in compact, misalignment-prone environments. Full article

Proton exchange membrane fuel cells (PEMFCs) are ideal for fuel cell vehicles due to their high specific power, rapid start-up, and low operating temperatures. However, their limited lifespan presents a challenge for large-scale deployment. Accurate assessment of remaining useful life (RUL) is essential for enhancing longevity. Automotive PEMFC systems are complex and nonlinear, making lifespan prediction difficult. Recent studies suggest deep learning approaches hold promise for this task. This study proposes a novel EMD-TCN-GN algorithm, which, for the first time, integrates empirical mode decomposition (EMD), temporal convolutional network (TCN), and group normalization (GN) by using EMD to adaptively decompose non-stationary signals (such as voltage fluctuations), the dilated convolution of TCN to capture long-term dependencies, and combining GN to group-calibrate intrinsic mode function (IMF) features to solve the problems of modal aliasing and training instability. Parametric analysis shows optimal accuracy with the grouping parameter set to 4. Experimental validation, with a voltage lifetime threshold at 96% (3.228 V), shows the predicted degradation closely aligns with actual results. The model predicts voltage threshold times at 809 h and 876 h, compared to actual values of 807 h and 872 h, with a temporal prediction error margin of 0.250–0.460%. These results demonstrate the model’s high prediction fidelity and support proactive health management of PEMFC systems. Full article

The longevity and safety of asphalt pavements, which form the foundation of our transportation infrastructure, are directly impacted by their performance. Pavement performance has traditionally been measured using the Marshall Mix Design method, which is a time- and resource-intensive laboratory procedure. Machine learning algorithms (MLAs) are increasingly popular today and are being utilized in various fields. Their performances vary; therefore, evaluating different MLAs and comparing them is important. The potential of various machine learning (ML) algorithms to predict Marshall Stability (MS) and Marshall Flow (MF) was investigated in this work. We collected data from published studies in the literature encompassing 732 data points to train and evaluate ML models. Eight key input parameters were considered for modeling. We used three feature importance analysis techniques (Random Forest, Permutation Importance, and Lasso Regression) to determine which parameters were the most significant. Linear regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), and Artificial Neural Networks (ANNs) were the six MLAs that were assessed. Robust statistical measures such as MSE, MAE, R², and RMSE were employed to evaluate each model’s performance. Our results indicate that the RF algorithm had the best performance for both MS and MF parameter prediction, followed by ANN and DT. The predicted and actual values showed a strong correlation, which was evidenced by the high R² and the lowest values in other error metrics, indicating good performance. This highlights the significance of selecting an optimal machine learning algorithm for a particular predictive task. Full article

Seed deterioration is an inevitable process during storage, characterized by a gradual loss of germination capacity and eventual seed death, which poses challenges to seed longevity and the preservation of genetic resources. Understanding the molecular mechanisms driving seed aging and inherent resistance pathways, alongside developing innovative rejuvenation strategies for deteriorated seeds, is crucial for agricultural sustainability and germplasm banking. This review systematically examines (1) redox-regulated deterioration pathways involving reactive oxygen species (ROS) and macromolecular damage cascades, (2) anti-deterioration mechanisms mediated by the antioxidant system and macromolecular repair mechanisms, (3) genetic–epigenetic networks governing seed aging resistance, particularly ABA- and IAA-mediated signaling through ABI3/ABI5/LEC1 regulons, and (4) technological advances in seed priming that restore aged seeds via metabolic resetting and repair potentiation. By integrating multi-omics insights with physiological evidence, we propose a hierarchical model of seed deterioration and establish mechanistic links between priming interventions and longevity enhancement. These insights offer a theoretical framework for cultivating anti-deterioration crop varieties and developing seed longevity-enhancement technologies. Full article

This study investigates the degradation mechanisms of fiber-reinforced polymer (FRP) matrices in composite insulators under partial discharge (PD) conditions. The degradation products may further cause deterioration of the electrical and chemical resistance (ECR) glass fibers. Using pyrolysis–gas chromatography-mass spectrometry (PY-GC-MS) and high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS-MS), the thermal degradation gas and liquid products of the degraded FRP matrix were analyzed, revealing the presence of organic acids. These acids form when the epoxy resin’s cross-linked bonds break at high temperatures, generating anhydrides that hydrolyze into carboxylic acids in the presence of moisture. The hydrolyzation process is accelerated by hydroxyl radicals produced during PD. The resulting carboxylic acids deteriorate the glass fibers within the FRP matrix by degrading surface coupling agents and reacting with the alkali metal–silica network, leading to the substitution and precipitation of metal ions. Organic acids, particularly carboxylic acids, were found to have a more severe deteriorating effect on glass fibers compared to inorganic acids, with high temperatures exacerbating this process. These findings provide critical insights into the deterioration mechanisms of FRP under operational conditions, offering valuable guidance for optimizing manufacturing processes and enhancing the longevity of composite insulators. Full article

Proton exchange membrane fuel cells (PEMFC), distinguished by rapid refueling capability and zero tailpipe emissions, have emerged as a transformative energy conversion technology for automotive applications. Nevertheless, their widespread commercialization remains constrained by technical limitations mainly in operational longevity. Precise prognostics of performance degradation could enable real-time optimization of operation, thereby extending service life. This investigation proposes a hybrid prognostic framework integrating steady-state modeling with dynamic condition. First, a refined semi-empirical steady-state model was developed. Model parameters’ identification was achieved using grey wolf optimizer. Subsequently, dynamic durability testing data underwent systematic preprocessing through a correlation-based screening protocol. The processed dataset, comprising model-calculated reference outputs under dynamic conditions synchronized with filtered operational parameters, served as inputs for a recurrent neural network (RNN). Comparative analysis of multiple RNN variants revealed that the hybrid methodology achieved superior prediction fidelity, demonstrating a root mean square error of 0.6228%. Notably, the integration of steady-state physics could reduce the RNN structural complexity while maintaining equivalent prediction accuracy. This model-informed data fusion approach establishes a novel paradigm for PEMFC lifetime assessment. The proposed methodology provides automakers with a computationally efficient framework for durability prediction and control optimization in vehicular fuel cell systems. Full article