Search Results (1,293)

Lithium-ion batteries (LIBs) dominate energy storage for electric vehicles (EVs) due to their high energy density, long cycle life, and low self-discharge. However, high costs, complex manufacturing, and the requirement for advanced battery management systems (BMSs) constrain their broader deployment. Therefore, extending the utility of LIBs through reuse is essential for economic and environmental sustainability. Retired EV batteries with 70–80% state-of-health (SOH) can be repurposed in battery energy storage systems (BESSs) to support power grids. Effective reuse depends on accurate and rapid assessment of SOH and state-of-safety (SOS), which relies on precise state-of-charge (SOC) detection, particularly for aged LIBs with elevated thermal and electrochemical risks. This review systematically surveys SOC, SOH, and SOS detection methods for second-life LIBs, covering model-based, data-driven, and hybrid approaches, and highlights strategies for a fast and reliable evaluation. It further examines power electronics topologies and control strategies for integrating second-life LIBs into power grids, focusing on safety, efficiency, and operational performance. Finally, it analyzes key factors within the closed-loop supply chain, particularly reverse logistics, and provides guidance on enhancing adoption and supporting the establishment of circular battery ecosystems. This review serves as a comprehensive resource for researchers, industry stakeholders, and policymakers aiming to optimize second-life utilization of traction LIBs. Full article

In today’s fast-paced world, the escalating workloads faced by individuals have rendered fatigue a pressing concern that cannot be overlooked. Fatigue not only signals the need for individuals to take a break but also has far-reaching implications for both individuals and society across various domains, including health, safety, productivity, and the economy. While numerous prior studies have explored fatigue monitoring, many of them have been conducted within controlled experimental settings. These experiments typically require subjects to engage in specific tasks over extended periods to induce profound fatigue. However, there has been a limited focus on assessing daily fatigue in natural, real-world environments. To address this gap, this study introduces a daily fatigue monitoring system. We have developed a wearable device capable of capturing subjects’ ECG signals in their everyday lives. We recruited 12 subjects to participate in a 14-day fatigue monitoring experiment. Leveraging the acquired ECG data, we propose machine learning models based on manually extracted features as well as a deep learning model called C-BL to classify subjects’ fatigue levels into three categories: normal, slight fatigue, and fatigued. Our results demonstrate that the proposed end-to-end deep learning model outperforms other approaches with an accuracy rate of 83.3%, establishing its reliability for daily fatigue monitoring. Full article

Cancer remains a leading global cause of morbidity and mortality. Modifiable lifestyle factors, including avoidance of tobacco use and excessive ultraviolet radiation, healthy dietary patterns, regular physical activity, and weight management, play key roles in prevention and care. This narrative review synthesizes evidence on lifestyle-based interventions influencing cancer risk, treatment tolerance, and survivorship. A literature search was conducted in PubMed and Scopus, supplemented by manual screening via Google Scholar. The time frame (2001–2025) was selected to reflect evidence produced within the modern era of molecular oncology and contemporary lifestyle medicine research. Eligible publications addressed carcinogen exposure (tobacco, alcohol, ultraviolet radiation), diet and nutritional strategies, physical activity, sedentary behavior, obesity, metabolic health, complementary therapies, and cancer outcomes. Evidence indicates that reducing exposure to tobacco and ultraviolet radiation remains central to cancer prevention. Adherence to predominantly plant-based diets, regular physical activity, and maintenance of healthy body weight are consistently associated with lower incidence of several cancers, including breast, colorectal, and liver cancer. Nutritional strategies such as caloric restriction, ketogenic diets, and fasting-mimicking diets show promise in improving treatment efficacy and quality of life. Complementary and mind–body therapies may alleviate treatment-related symptoms, although high-quality evidence on long-term safety and effectiveness is limited. Integrating lifestyle medicine into oncology offers a cost-effective, sustainable strategy to reduce cancer burden and enhance survivorship. Comprehensive programs combining carcinogen avoidance, dietary regulation, structured exercise, and effective radiation risk mitigation may extend healthspan, improve treatment tolerance, and help prevent recurrence. Full article

Background: Type 2 diabetes mellitus (T2DM) is a growing global health challenge requiring effective dietary management strategies. While the Mediterranean diet shows promise for cardiovascular and metabolic health, the last comprehensive meta-analysis of randomized controlled trials (RCTs) examining its effects on glycemic control and body mass index (BMI) in T2DM was published in 2015. Multiple RCTs, including culturally adapted interventions with extended follow-up, have since been completed, but remain unsynthesized. Methods: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines (PROSPERO: CRD420251147035), searching PubMed, Web of Science, and Embase from inception through 17 August 2025. Unlike previous syntheses that combined observational cohorts or mixed dietary approaches, our analysis focused strictly on RCTs in adults with established T2DM and incorporated trials published after 2015. We included RCTs comparing Mediterranean diet interventions against non-Mediterranean control diets in adults with T2DM. Primary outcomes included glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), and body mass index (BMI). Secondary outcomes comprised low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Pooled effects were estimated using random-effects models. Results: Eleven RCTs (10 publications) involving diverse populations met inclusion criteria. Compared with control diets, Mediterranean diet interventions showed reductions in HbA1c (mean difference [MD] −0.307%, 95% CI: −0.451 to −0.163), FPG (MD −0.845 mmol/L, 95% CI: −1.307 to −0.384), and BMI (MD −0.828 kg/m², 95% CI: −1.4 to −0.256). Secondary analyses revealed reductions in LDL-C (MD −8.060 mg/dL, 95% CI: −14.213 to −1.907), SBP (MD −5.130 mmHg, 95% CI: −10.877 to 0.617), and DBP (MD −2.008 mmHg, 95% CI: −3.027 to −0.989). Sensitivity analyses supported stability of findings, with no substantial publication bias detected. Subgroup analyses revealed geographic variation in blood pressure responses, with greater benefits observed in non-Mediterranean populations. Conclusions: Mediterranean dietary patterns were associated with modest improvements in glycemic control, body composition, and cardiometabolic risk factors among adults with T2DM. The cultural adaptability of this approach may support implementation in clinical practice, though larger multicenter trials with standardized protocols and extended follow-up remain necessary. Full article

Background/Objectives: Low physical activity (PA) is becoming an increasingly serious health problem among overweight school-age children. This study aimed to evaluate the influence of elevated PA during school hours on the nutritional behavior and fat mass of overweight and obese children. Methods: The study involved 11-year-old children (n = 148) who were overweight and obese. In the control group, children received physical education lessons in the standard dimension (4 h a week) while the intervention group received 10 h. Body mass index (BMI), fat mass (FM), and nutritional behavior were analyzed. Results: Compared to baseline, at the end of the intervention, the proportion of obese children increased in the control group and decreased in the intervention group. Regarding nutritional behavior, low consumption of vegetables, fruits, whole grains, poultry, and fish was observed. After a 12-month period, the intervention group showed a slight decrease in the consumption of fruits, vegetables, cold cuts, fried dishes, sweets, and fast food, and an increase in the consumption of white bread, whole-grain bread, poultry, red meat, and dairy products (milk, yogurt). In the control group, children exhibited an average increase in the consumption of fruits, vegetables, poultry, red meat, sweets, and fast food Conclusions: Although extended physical activity during school hours was correlated with a decrease in the number of obese children, the observational nature of the study precludes the drawing of definitive conclusions. The intervention may have contributed to an increase in energy expenditure, which could account for the improvements in BMI and FM. Nevertheless, the impact on nutritional behavior was limited. Full article

Time-qualified dietary interventions, including time-restricted eating (TRE), intermittent fasting (IF), and periodic fasting-mimicking diets (FMDs), have emerged as strategies to improve metabolic health. While preclinical studies consistently demonstrate robust effects on energy metabolism, cardiometabolic function, and longevity, translation to humans remains heterogeneous. In free-living settings, most metabolic improvements observed with TRE and IF appear primarily driven by spontaneous caloric restriction rather than meal timing per se, and isocaloric randomized controlled trials generally show no additional benefits compared to standard calorie restriction. Evidence supporting circadian-specific advantages, particularly for early TRE, is promising but inconsistent and often context-dependent. Important uncertainties also persist regarding long-term efficacy, lean mass preservation, safety in specific populations, and the physiological impact of extended fasting windows. Despite these controversies, time-qualified diets represent a paradigm shift in nutritional science by integrating chronobiology with dietary patterns. Future directions include tailoring eating windows to individual chronotypes, combining fasting regimens with high-quality dietary patterns and structured physical activity, and clarifying the molecular mechanisms that may mediate calorie-independent benefits. Large, long-term, mechanistically informed human trials are essential to determine whether aligning eating behaviors with circadian biology can produce durable clinical improvements. Such work will ultimately shape the role of personalized chrononutrition in preventive and therapeutic nutrition. Full article

This study proposes a multi-station high-frequency microtremor surface-wave exploration method for high-resolution characterization of shallow subsurface structures in coastal engineering environments. Three representative layered geological models were established, and Rayleigh-wave theoretical dispersion curves were calculated using a fast vector transfer algorithm to analyze dispersion characteristics associated with different stratigraphic conditions. Five array geometries were then employed to acquire high-frequency ambient-noise data, and dispersion curves were extracted using the Extended Spatial Autocorrelation (ESPAC) method. Comparative analysis revealed that the rectangular, triangular, and circular arrays provided the most stable and accurate dispersion imaging, with mismatch errors below 0.5%, and their inverted S-wave velocity structures closely matched theoretical models. Field application on the Dongzhou Peninsula in Fujian Province further demonstrated the effectiveness of the proposed method. The inverted shear-wave (S-wave) velocity profiles from three survey lines successfully delineated the original and reclaimed coastlines, showing strong agreement with known geological boundaries. These results demonstrate that the proposed approach provides a non-invasive, cost-effective, and high-resolution tool for evaluating geological conditions in coastal engineering settings. It shows substantial potential for broader application in coastal site characterization and marine engineering development. Full article

The pursuit of youth and longevity has accompanied human societies for millennia, evolving from mythological and esoteric traditions toward a scientific understanding of aging. Early concepts such as Greek ambrosia, Taoist elixirs, and medieval “aqua vitae” reflected symbolic or spiritual interpretations. A major conceptual transition occurred between the late nineteenth and early twentieth centuries, when aging began to be framed as a biological process. Pioneering ideas by Metchnikoff, together with early and sometimes controversial attempts such as Voronoff’s grafting experiments, marked the first efforts to rationalize aging scientifically. In the mid-twentieth century, discoveries including the Hayflick limit, telomere biology, oxidative stress, and mitochondrial dysfunction established gerontology as an experimental discipline. Contemporary geroscience integrates these insights into a coherent framework linking cellular pathways to chronic disease risk. Central roles are played by nutrient-sensing networks such as mTOR, AMPK, and sirtuins, together with mitochondrial regulation, proteostasis, and cellular senescence. Interventions, including caloric restriction, fasting-mimicking diets, rapalogues, sirtuin activators, metformin, NAD⁺ boosters, senolytics, and antioxidant combinations such as GlyNAC, show consistent benefits across multiple model organisms, with early human trials reporting improvements in immune function, mitochondrial activity, and biomarkers of aging. Recent advances extend to epigenetic clocks, multi-omic profiling, gender-specific responses, and emerging regenerative and gene-based approaches. Overall, the evolution from historical elixirs to molecular geroscience highlights a shift toward targeting aging itself as a modifiable biological process and outlines a growing translational landscape aimed at extending healthspan and reducing age-related morbidity. Full article

To address the limitations of existing fault diagnosis methods for excitation dry-type transformers, such as inadequate utilization of multi-axis vibration data, low recognition accuracy under complex operational conditions, and limited computational efficiency, this paper presents a lightweight fault diagnosis approach based on the fusion of multi-channel vibration signals and visual features. Initially, a multi-physics field coupling simulation model of the excitation dry-type transformer is developed. Vibration data collected from field-installed three-axis sensors are combined to generate typical fault samples, including normal operation, winding looseness, core looseness, and winding eccentricity. Due to the high dimensionality of vibration signals, the Symmetrized Dot Pattern (ISDP) method is extended to aggregate and map time- and frequency-domain information from the x-, y-, and z-axes into a two-dimensional feature map. To optimize the inter-class separability and intra-class consistency of the map, Particle Swarm Optimization (PSO) is employed to adaptively adjust the angle gain factor (η) and time delay coefficient (t). Keypoint descriptors are then extracted from the map using the Oriented FAST and Rotated BRIEF (ORB) feature extraction operator, which improves computational efficiency while maintaining sensitivity to local details. Finally, an efficient fault classification model is constructed using an Adaptive Boosting Support Vector Machine (Adaboost-SVM) to achieve robust fault mode recognition across multiple operating conditions. Experimental results demonstrate that the proposed method achieves a fault diagnosis accuracy of 94.00%, outperforming signal-to-image techniques such as Gramian Angular Field (GAF), Recurrence Plot (RP), and Markov Transition Field (MTF), as well as deep learning models based on Convolutional Neural Networks (CNN) in both training and testing time. Additionally, the method exhibits superior stability and robustness in repeated trials. This approach is well-suited for online monitoring and rapid diagnosis in resource-constrained environments, offering significant engineering value in enhancing the operational safety and reliability of excitation dry-type transformers. Full article

Active-control coils on Keda Torus eXperiment (KTX) are used to suppress error fields and mitigate MHD instabilities, thereby extending discharge duration and improving plasma confinement quality. Achieving effective active MHD control imposes stringent requirements on the coil power supplies: wide-bandwidth and high-precision current regulation, deterministic low-latency response, and tightly synchronized operation across 136 independently driven coils. Specifically, the supplies must deliver up to ±200 A with fast slew rates and bandwidths up to several kilohertz, while ensuring sub-100 μs control latency, programmable waveforms, and inter-channel synchronization for real-time feedback. These demands make the power supply architecture a key enabling technology and motivate this work. This paper presents the design and simulation of the KTX active-control coil power supply. The system adopts a modular AC–DC–AC topology with energy storage: grid-fed rectifiers charge DC-link capacitor banks, each H-bridge IGBT converter (20 kHz) independently drives one coil, and an EMC filter shapes the output current. Matlab/Simulink R2025b simulations under DC, sinusoidal, and arbitrary current references demonstrate rapid tracking up to the target bandwidth with ±0.5 A ripple at 200 A and limited DC-link voltage droop (≤10%) from an 800 V, 50 mF storage bank. The results verify the feasibility of the proposed scheme and provide a solid basis for real-time multi-coil active MHD control on KTX while reducing instantaneous grid loading through energy storage. Full article

The phenomenon of fast fashion has resulted in high yarn consumption and growing textile waste from both manufacturing and consumers. Rising environmental awareness and evolving legislation, including landfill restrictions, have prompted the search for sustainable recycling methods to manage textile end-of-life. This study investigates the mechanical recycling of polyamide 6.6 (PA66) yarn using a chain extender (Joncryl) and antioxidant (Irganox). Thermogravimetric analysis (TGA) confirmed that thermal stability in recycled PA66 was maintained compared to the original yarn, and the presence of Joncryl further enhanced this stability. Oxidative-onset temperature (OOT), measured by differential scanning calorimetry (DSC), supported these improvements. Gas chromatography–mass spectrometry (GC/MS) identified key degradation products, which were correlated with changes in the polymer matrix. Mechanical testing showed a 31% decrease in Young’s modulus after initial recycling, which was reversed with further processing. This behavior suggests the formation of shortened semi-crystalline chains and new linkages promoted by Joncryl. Viscosity and limiting viscosity number increased by up to 50%, depending on both additive concentrations. Overall, Joncryl and Irganox enhanced viscosity, mechanical strength, and notably thermal stability, confirming their suitability for recyclable textile-grade PA66 yarns. Full article

The Bloom filter remains one of the most influential constructs in probabilistic computation, a structure that achieves a mathematically elegant balance between accuracy, space efficiency, and computational speed. Since the original formulation of Dr. Burton H. Bloom in 1970, its design principles have expanded into a family of approximate membership query (AMQ) structures that now underpin a wide spectrum of modern computational systems. This review synthesizes the theoretical, algorithmic, and applied dimensions of Bloom filters, tracing their evolution from classical bit-vector models to contemporary learned and cryptographically reinforced variants. It further underscores their relevance in artificial intelligence and blockchain environments, where they act as relevance filters. Core developments, which include counting, scalable, stable, and spectral filters, are outlined alongside information-theoretic bounds that formalize their optimality. The analysis extends to adversarial environments, where cryptographic hashing and privacy-oriented adaptations enhance resilience under active attack, and to data-intensive domains such as network systems, databases, cybersecurity, and bioinformatics. Through the integration of historical insight and contemporary advances in learning, security, and system design, the Bloom filter emerges not merely as a data structure but as a unified paradigm for computation under uncertainty. The results presented in this review support practical advances in network traffic control, cybersecurity analysis, distributed storage systems, and large-scale data platforms that depend on compact and fast probabilistic structures. Full article

As one of the vital research directions in hyperspectral image (HSI) processing, anomaly detection is dedicated to identifying anomalous pixels in HSIs that have significant spectral differences from the surrounding background, and it has attracted extensive attention from numerous scholars in recent years. Anomaly detectors based on collaborative representation have achieved favorable performance in this field. Based on CRD, scholars have proposed many different variants. However, most of these methods only focus on the spectral information of HSIs, and they suffer from slow detection speed and poor robustness. In this paper, we combine the Extended Multi-Attribute Profile (EMAP) with the CRD algorithm, propose a fast collaborative representation anomaly detection algorithm based on the extended multi-attribute profile. First, we use EMAP to extract the spatial structural information of the HSI. Then, before the anomaly detection, we employ the k-means clustering algorithm to separate anomalous pixels with similar features, and obtain a reconstructed background dictionary matrix. This further separates the background from anomalies and improves the robustness of anomaly detection. Finally, we apply a collaborative representation-based anomaly detector to detect anomalies. The proposed method is compared with other algorithms through experiments on four real HSI datasets and one synthetic HSI dataset. The experimental simulation results verify the effectiveness of our proposed method. Full article

The accurate execution of aeroengine flight environment simulation tests relies on the electro-hydraulic servo valve control system in the altitude test facility. However, time delays arising from various factors, such as friction or sensor latency, impose significant constraints on system responsiveness and control precision. To address this challenge, an enhanced active disturbance rejection control has been developed. The proposed method employs an improved output prediction constructed by tracking differentiator to mitigate delay effects, introduces the Taylor compensator to more accurately capture future signal trends, and incorporates a dynamic adjustment mechanism based on error variation to optimize the parameters of the extended state observer in real time, thereby enhancing robustness under varying operating conditions. The simulation results demonstrate that under fixed-delay conditions, the proposed algorithm exhibits fast response characteristics; under varying-delay conditions, unlike model-dependent approaches, it remains less affected by delay fluctuations and maintains superior response speed and stability, thereby ensuring the accuracy of flight environment simulation tests. Full article

Flow regimes of vertical upflow for slightly cohesive Geldart A powders at high solids mass flux ($G_s$$\gtrsim$ 500 kg/m²s) are not fully resolved. In particular, Dense Suspension Upflow (DSU) as a distinct flow regime and its transition boundaries are not broadly accepted. Furthermore, the locus of the pressure gradient minimum, which is the broadly accepted dense–dilute transition at low $G_s,$ requires validation at high $G_s$. In our recent work, by adapting the phase map of Wirth and by Eulerian modeling, DSU was defined as a distinct flow regime with gross upflow of solids and with granular temperature at the wall greater than that in the bulk. This study has further validated the definition of DSU and its transition boundaries by extending the modeling to areas not fully explored in the earlier work. Furthermore, this study has identified (a) the possibility of a phase of DSU between fast fluidization and turbulent regime at all $G_s$; and (b) the need to review the suitability of the locus of the pressure gradient minimum as the dense–dilute transition at high $G_s$. Additionally, our work has demonstrated (a) a new provisional correlation that the upper transport velocity for Geldart A powders is significantly greater than hitherto predicted; and (b) the slip velocity in the transport regimes increases with $G_s$ to peak within fast fluidization and falls thereafter to attain low multiples of the terminal settling velocity within DSU. Full article