Search Results (1,559)

Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle to effectively capture nonlinear degradation patterns and long-term dependencies. To tackle these challenges, we introduce an innovative framework that combines evolutionary learning with deep learning for RCP. This framework integrates Temporal Convolutional Networks (TCNs), Bidirectional Gated Recurrent Units (BiGRUs), and an attention mechanism to extract comprehensive time-series features and improve prediction accuracy. Additionally, we introduce a hybrid optimization algorithm that combines the Sparrow Search Algorithm (SSA) with Bayesian Optimization (BO) to enhance the performance of the model. The experimental results validate the superiority of our framework, demonstrating its capability to achieve significantly improved prediction accuracy compared to existing methods. This study provides researchers in battery management systems, electric vehicles, and renewable energy storage with a reliable tool for optimizing lithium-ion battery performance, enhancing system reliability, and addressing the challenges of the new energy industry. Full article

The pathophysiology of cognitive impairment (CI) in multiple sclerosis (MS) remains unclear. Meningeal B cell aggregates may contribute to cortical grey matter pathology. Cerebrospinal fluid (CSF), kappa free light chains (KFLC), and KFLCs-Index (kappa-Index) are reliable quantitative markers of intrathecal synthesis, but few data have been presented exploring the association with CI, and no data are present for lambda FLC (LFLC) in MS. We evaluated cognition using the Brief International Cognitive Assessment for MS (BICAMS) battery and collected serum and CSF at diagnosis in newly diagnosed drug-naïve MS patients. We observed that patients with impaired verbal memory and overall CI showed increased CSF KFLCs (respectively p: 0.0003 and p: 0.003) and kappa-Index (respectively p: 0.01 and p: 0.02) compared to those with normal verbal memory and no CI. Patients with CI also displayed lower CSF LFLCs (p: 0.04) and lambda-Index (p: 0.001); however, only CSF KFLC negatively correlated with normalized results of verbal memory (for age, sex, and educational levels), even after correction for EDSS (r: −0.27 p: 0.01). Finally, CSF FKLC and kappa-Index were significant predictors of verbal memory in a multivariate analysis. Our results, suggest that intrathecal B cell activity might contribute to CI development in MS patients. Full article

In this paper, the deconvolution of Electrochemical Impedance Spectroscopy (EIS) data into the Distribution of Relaxation Times (DRTs) is employed to provide a detailed examination of degradation mechanisms in lithium-ion batteries. Using an nth RC model with Gaussian functions, this study achieves enhanced separation of overlapping electrochemical processes where Gaussian functions yield smoother transitions and clearer peak identification than conventional piecewise linear functions. The advantages of employing Tikhonov Regularization (TR) with Gaussian functions over Maximum Entropy (ME) and FFT methods are highlighted as this approach provides superior noise resilience, unbiased analysis, and enhanced resolution of critical features. This approach is applied to LIB cell data to identify characteristic peaks of the DRT plot and evaluate their correlation with battery degradation. By observing how these peaks evolve through cycles of battery aging, insights into specific aging mechanisms and performance decline are obtained. This study combines experimental measurements with DRT peak analysis to characterize the impedance distribution within LIBs which enables accelerated detection of degradation pathways and enhances the predictive accuracy for battery life and reliability. This analysis contributes to a refined understanding of LIB degradation behavior, supporting the development of advanced battery management systems designed to improve safety, optimize battery performance, and extend the operational lifespan of LIBs for various applications. Full article

Background/Objectives: Sarcopenia, characterized by reduced muscle mass and strength, is associated with osteoarthritis (OA), particularly in middle-aged women, and may worsen postoperatively. Resistance exercise (RE) can resolve sarcopenia; however, recovery is often suboptimal. Nitrate (NO₃⁻) supplementation may enhance muscle recovery and complement RE. We investigated whether NO₃⁻ supplementation combined with RE improves thigh muscle mass and strength in middle-aged women during postoperative rehabilitation. Methods: We conducted a prospective randomized placebo-controlled double-blind study including 36 middle-aged women with sarcopenia and cartilage defects undergoing mesenchymal stem cell implantation. Participants were assigned to RE with NO₃⁻ supplementation (NG, n = 18) or with placebo (PG, n = 18) groups. Both groups underwent 12 weeks of supervised RE. The primary outcomes were thigh muscle cross-sectional area (CSA) and knee strength, whereas functional and clinical measures, including the Short Physical Performance Battery (SPPB), skeletal muscle index (SMI), International Knee Documentation Committee (IKDC), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, were secondary outcomes. Results: Thigh muscle CSA decreased in the PG but was maintained in the NG. Knee extension strength improved significantly in the NG compared with that in the PG at 6 and 12 weeks. Knee flexion strength also improved rapidly in the NG, with a significant increase at 6 weeks. SPPB and IKDC scores improved significantly in the NG. However, similar improvements were observed for WOMAC scores in both groups. Conclusions: NO₃⁻ supplementation combined with RE effectively prevented muscle atrophy and enhanced muscle strength in our study participants, indicating potential for improving postoperative recovery. Full article

This critical review paper delves into the complex and evolving landscape of the state of health (SOH) and state of charge (SOC) in electric vehicles (EVs), highlighting the pressing need for accurate battery management to enhance safety, efficiency, and longevity. With the global shift towards EVs, understanding and improving battery performance has become crucial. The paper systematically explores various SOC estimation techniques, emphasizing their importance akin to that of a fuel gauge in traditional vehicles, and addresses the challenges in accurately determining SOC given the intricate electrochemical nature of batteries. It also discusses the imperative of SOH estimation, a less defined but critical parameter reflecting battery health and longevity. The review presents a comprehensive taxonomy of current SOC estimation methods in EVs, detailing the operation of each type and succinctly discussing the advantages and disadvantages of these methods. Furthermore, it scrutinizes the difficulties in applying different SOC techniques to battery packs, offering insights into the challenges posed by battery aging, temperature variations, and charge–discharge cycles. By examining an array of approaches—from traditional methods such as look-up tables and direct measurements to advanced model-based and data-driven techniques—the paper provides a holistic view of the current state and potential future of battery management systems (BMS) in EVs. It concludes with recommendations and future directions, aiming to bridge the gap for researchers, scientists, and automotive manufacturers in selecting optimal battery management and energy management strategies. Full article

Background/Objectives: This study aimed to develop a questionnaire to describe and diagnose the physical activity and sport (PAS) habits of 8–12-year-old schoolchildren, assessing its content, ecological validity and reliability, from a multidimensional perspective aligned with Global Matrix 4.0 indicators. Methods: The questionnaire design phase involved seven individuals from the university sector and sport managers from the Gipuzkoa Provincial Council. Seventeen experts later evaluated the questionnaire’s content and ecological validity. For reliability testing, 276 schoolchildren aged 8 to 12 completed the questionnaire twice, with a time interval of two weeks to two months. Statistical analyses included the Wilcoxon test to compare expert ratings, effect size and percentage change calculations for magnitude assessment, and McNemar, McN-Bowker or Wilcoxon tests to compare differences between initial and repeat responses. Cohen’s Kappa was used to assess agreement. Results: The initial battery of items, submitted to the validation process, comprised 31 items across 10 dimensions, derived from validated questionnaires and published works. Following content and ecological validity evaluations, modifications were made and nine items were removed due to improved wording, clarification of concepts, redundancy or lack of relevance. Expert quantitative analyses indicated improved overall questionnaire values. Reliability analysis revealed significant differences in five of the twenty-two items, though substantial agreement (from slight to almost perfect) was observed in twenty items. Conclusions: The study confirmed the questionnaire’s validity and reliability as a suitable tool for assessing PAS practices among 8–12-year-old schoolchildren in Gipuzkoa, Spain, in both Basque and Spanish languages. Full article

Introduction: Information on the long-term maintenance of short-term exercise fitness gains measured by field-based tests is scarce in older adults. This study aimed to investigate short- and long-term changes in various physical fitness parameters after an 8-week exercise program. Methods: In this longitudinal study, a total of 265 participants (62% women; mean age 71.4 ± 4.7 years) completed a field-based test battery of 12 fitness tests (22 parameters) at 2 pre-tests and 1 post-test following an 8-week exercise program (2 sessions/week, combining aerobic and strength activities) in 2 consecutive years. The tests assessed muscle endurance, muscle strength, cardiorespiratory fitness, and motor fitness. Results: Significant short-term improvements were observed, e.g., in isometric trunk flexion and extension endurance (21–37%) for both sexes in both years. Lower-body muscular endurance improved in the first year (9–12%) for both sexes, while cardiorespiratory fitness (6-min walk test) improved only for men in both years (3%). No changes were seen in submaximal cycle test heart rates or any balance tests in any year. Most fitness parameters did not significantly decrease during the 9-month inter-intervention period, with a few exceptions in trunk strength and walking distance. Conclusions: This study demonstrates physical fitness improvements in older adults following short-term exercise interventions and that some of these improvements were maintained long term, whereas a few of these physical fitness test improvements decreased significantly over 9 months in older adults. Full article

To comprehensively evaluate the current and future aging states of lithium-ion batteries, namely their State of Health (SOH) and Remaining Useful Life (RUL), this paper proposes a joint prediction method based on Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) networks. First, health features (HFs) are extracted from partial charging data. Subsequently, these features are fed into the GPR model for SOH estimation, generating SOH predictions. Finally, the estimated SOH values from the initial cycle to the prediction start point (SP) are input into the LSTM network in order to predict the future SOH trajectory, identify the End of Life (EOL), and infer the RUL. Validation on the Oxford Battery Degradation Dataset demonstrates that this method achieves high accuracy in both SOH estimation and RUL prediction. Furthermore, the proposed approach can directly utilize one or more health features without requiring dimensionality reduction or feature fusion. It also enables RUL prediction at the early stages of a battery’s lifecycle, providing an efficient and reliable solution for battery health management. However, this study is based on data from small-capacity batteries and does not yet encompass applications in large-capacity or high-temperature scenarios. Future work will focus on expanding the data scope and validating the model’s performance in real-world systems, driving its application in practical engineering scenarios. Full article

Dual-ion batteries (DIBs) were demonstrated as a promising technology for large-scale energy storage due to their low cost, recyclability, and impressively fast charge capability. Graphite as a commonly used cathode material in DIBs, however, suffers from poor compatibility with commercial Li-ion electrolytes and graphite anodes, making it difficult to directly utilize the well-established infrastructure for Li-ion batteries. Herein, we report a small aromatic amine molecule 4,4′,4″-tris(diphenylamino)triphenylamine (N4) functioning as a compatible anion host in the EC-containing Li-ion electrolyte. With an average discharge voltage of 3.6 V (vs. Li⁺/Li), the N4 electrode delivers a reversible specific capacity of 108 mAh/g, which is much higher than 29 mAh/g for the graphite cathode at the same condition. The high capacity retention of 91.3% was achieved after 500 cycles at 1 A/g. The N4 electrode also exhibited good rate performance. Via different characterization techniques like Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy, the energy storage mechanism of N4 was revealed as a conversion between amine and quaternary amine cations, accompanied by PF₆⁻ (de-)insertion. As consequences, the assembled N4||graphite DIB w showed a high discharge capacity of 90 mAh/g within 1.5–4.1 V, and good cycling stability with a 98% capacity retention after 40 cycles. Decent rate performance was achieved in the N4||graphite DIB as well. This work provides new insights into designing a compatible anion host for affordable DIBs. Full article

This study examines the link between language abilities and Theory of Mind (ToM) development in individuals with Williams Syndrome (WS) and Down Syndrome (DS). We compared the results of 16 participants with WS, aged 6.3 to 27.2 years (Mean = 15.9 years, SD = 6.8 years), to those of 16 participants with DS, aged 10.7 to 23.9 years (Mean = 16.8 years, SD = 3.6 years). Using the French version of the ToM test-Revised (ToM test-R), we assessed three levels of ToM development: prerequisites, first-order beliefs, and second-order beliefs. Language abilities were evaluated using the Isadyle French language assessment battery, focusing on word comprehension, word production, syntax comprehension and production, and emotional lexicon. The results showed that the WS group performed significantly better in overall ToM skills in the ToM test-R compared to the DS group. Moreover, language skills were significantly associated with ToM development in the WS group, but not in the DS group. These findings underscore the importance of language development, particularly syntax and emotional understanding, in ToM acquisition. Through the application of a cross-syndrome approach, this study provides insights into how each syndrome impacts ToM development and the role of language in this process. Full article

This paper provides a comprehensive analysis of the lithium battery degradation mechanisms and failure modes. It discusses these issues in a general context and then focuses on various families or material types used in the batteries, particularly in anodes and cathodes. The paper begins with a general overview of lithium batteries and their operations. It explains the fundamental principles of the electrochemical reaction that occurs in a battery, as well as the key components such as the anode, cathode, and electrolyte. The paper explores also the degradation processes and failure modes of lithium batteries. It examines the main factors contributing to these issues, including the operating temperature and current. It highlights the specific degradation mechanisms associated with each type of material, whether it is graphite, silicon, metallic lithium, cobalt, nickel, or manganese oxides used in the electrodes. Some degradations are due to the temperature and the current waveforms. Then, the importance of thermal management and current management is emphasized throughout the paper. It highlights the negative effects of overheating, excessive current, or inappropriate voltage on the stability and lifespan of lithium batteries. It also underscores the significance of battery management systems (BMS) in monitoring and controlling these parameters to minimize the degradation and the risk of failure. This work provides a summary of valuable insight into the development of BMS. It emphasizes the importance of understanding the degradation mechanisms and failure modes specific to different families of lithium batteries, as well as the critical influence of temperature and current quality. Rational management or efficient controlling of these parameters can enhance the performance, reliability, and lifespan of lithium batteries. Full article

Investigating the temperature behavior of lithium-ion battery cells has become an important part of today’s research and development. The main reason for this is that the temperature profile of a battery cell changes during aging. By using Differential Thermal Voltammetry (DTV), new possibilities are opened up, especially since this diagnostic method is designed to work in operando by only requiring voltage and temperature readings. In this study, a batch of NMC-21700 cells were aged in calendar and cyclic manners. After a specified aging cycle was complete, a check-up measurement was performed. During this time, the cycler collected the electrical measuring values, while a negative temperature coefficient thermistor, which was located on the cell, was used to record the temperature fluctuations. The data were then evaluated by using the DTV analysis technique. By comparing the characteristic points of DTV, correlations between the changing curve characteristics and the capacity loss, and therefore the aging of the respective cell, were established. Based on these results, a simple model suitable for online State of Health (SoH) is derived and validated, showing an estimation accuracy of $1.1$%. Full article

Li-ion batteries are at risk of explosions caused by fires, primarily because of the high energy density of Li ions, which raises the temperature. Battery cases are typically made of plastic, aluminum, or SAF30400. Although plastic and aluminum aid weight reduction, their strength and melting points are low. SAF30400 offers excellent strength and corrosion resistance but suffers from work hardening and low high-temperature strength at 700 °C. Additionally, Ni used for plating has a low current density of 25% international copper alloy standard (ICAS). SAF2507 is suitable for use as a Li-ion battery case material because of its excellent strength and corrosion resistance. However, the heterogeneous microstructure of SAF2507 after casting and processing decreases the corrosion resistance, so it requires solution heat treatment. To address these issues, in this study, SAF2507 (780 MPa, 30%) is solution heat-treated at 1100 °C after casting and coated with Ag (ICAS 108.4%) using physical vapor deposition (PVD). Ag is applied at five different thicknesses: 0.5, 1.0, 1.5, 2.0, and 2.5 μm. The surface conditions and electrochemical properties are then examined for each coating thickness. The results indicate that the PVD-coated surface forms a uniform Ag layer, with electrical conductivity increasing from 1.9% ICAS to 72.3% ICAS depending on the Ag coating thickness. This enhancement in conductivity can improve Li-ion battery safety on charge and use. This result is expected to aid the development of advanced Li-ion battery systems in the future. Full article

Background/Objectives: Decompensated cirrhosis is characterized by systemic inflammation and innate and adaptive immune dysfunction. Hepatic encephalopathy (HE) is a prevalent and debilitating condition characterized by cognitive disturbances in which ammonia and inflammation play a synergistic pathogenic role. Extraskeletal functions of vitamin D include immunomodulation, and its deficiency has been implicated in immune dysfunction and different forms of cognitive impairment. The aim was to assess changes in cognitive function and inflammation in decompensated patients with cirrhosis receiving vitamin D supplementation. Methods: Patients with cirrhosis discharged from decompensation in two tertiary hospitals in Spain (from September 2017 to January 2020) were assessed before, at 6 and 12 months after vitamin D supplementation. A comprehensive neuropsychological battery and neuroinflammatory markers were examined. In a subgroup of patients, peripheral immune blood cells were analyzed. Results: Thirty-nine patients were recruited. Of those, 27 completed the 6 months evaluation and were analyzed [age 62.4 ± 11.3 years; 22 men; Model for End-Stage Liver Disease (MELD) 11.7 ± 4.0; prior overt HE 33%; median 25-hydroxyvitamin D (25OHD) plasma level 12.7 µgr/L] and 22 achieved 12 months assessment. At baseline, learning and memory (R = 0.382; p = 0.049) and working memory (R = 0.503; p = 0.047) subtests correlated with plasma 25OHD levels. In addition, processing speed (R = −0.42; p = 0.04), attention (R = −0.48; p = 0.04), Tinnetti balance (R = −0.656; p < 0.001) and Tinnetti score (R = −0.659; p < 0.001) were linked to neuroinflammation marker IL-1β. Patients with lower 25OHD had a greater proportion of TH1cells at baseline and a larger amelioration of IL-1β and IL-6 following supplementation. An improvement in working memory was found after 25OHD replacement (46.7 ± 13 to 50 ± 11; p = 0.047). Conclusions: This study supports that vitamin D supplementation modulates low-grade inflammation in decompensated cirrhosis providing cognitive benefits, particularly in working memory. Full article

To enhance the utilization efficiency of wind and solar renewable energy in industrial parks, reduce operational costs, and optimize the charging experience for electric vehicle (EV) users, this paper proposes a real-time scheduling strategy based on the “Dual Electricity Price Reservation—Surplus Refund Without Additional Charges Mechanism” (DPRSRWAC). The strategy employs a Gaussian Mixture Model (GMM) to analyze EV users’ charging and discharging behaviors within the park, constructing a behavior prediction model. It introduces reservation, penalty, and ticket-grabbing mechanisms, combined with the Interval Optimization Method (IOM) and Particle Swarm Optimization (PSO), to dynamically solve the optimal reservation electricity price at each time step, thereby guiding user behavior effectively. Furthermore, linear programming (LP) is used to optimize the real-time charging and discharging schedules of EVs, incorporating reservation data into the generation-side model. The generation-side optimal charging and discharging behavior, along with real-time electricity prices, is determined using Dynamic Programming (DP). In addition, this study explicitly considers the battery aging cost associated with V2G operations and proposes a benefit model for EV owners in V2G mode, thereby incentivizing user participation and enhancing acceptance. A simulation analysis demonstrates that the proposed strategy effectively reduces park operation costs and user charging costs by 8.0% and 33.1%, respectively, while increasing the utilization efficiency of wind and solar energy by 19.3%. Key performance indicators are significantly improved, indicating the strategy’s economic viability and feasibility. This work provides an effective solution for energy management in smart industrial parks. Full article