Search Results (6,965)

New energy vehicles face a critical challenge in balancing the thermal safety management of high-specific-energy battery systems with the simultaneous improvement of energy density. With the large-scale application of high-energy-density systems such as silicon-based anodes and solid-state batteries, their inherent thermal runaway risks pose severe challenges to battery thermal management systems (BTMS). Currently, the thermal insulation performance, temperature resistance, and fire protection capabilities of flame-retardant materials (e.g., foam cotton, fiber felts) used in automotive batteries are inadequate to meet the demands of intense combustion and high temperatures generated during thermal failure in high-energy-density batteries. Against this backdrop, thermal insulation and fireproof aerogel materials are emerging as a revolutionary solution for the next generation of power battery thermal protection systems. Leveraging their nanoporous structure’s exceptional thermal insulation properties (thermal conductivity of 0.013–0.018 W/(m·K) at room temperature) and extreme fire resistance (temperature resistance > 1100 °C/UL94 V-0 flame retardancy), aerogels are gaining prominence. This article provides a systematic review of thermal runaway phenomena in automotive batteries and corresponding protective measures. It highlights recent breakthroughs in the selection of material systems, optimization of preparation processes, and fiber–matrix composite technologies for automotive fireproof aerogel composites. The core engineering values of these materials, such as blocking thermal runaway propagation, reducing system weight, and improving volumetric efficiency, are quantitatively validated. Furthermore, the paper explores future research directions, including the development of low-cost aerogel composites and the design of organic–inorganic hybrid composite structures, aiming to provide a foundation and industrial pathway for the research and development of next-generation high-performance battery thermal management systems. Full article

Spiking neural networks (SNNs) have emerged as a promising model for energy-efficient, event-driven processing of asynchronous event streams from Dynamic Vision Sensors (DVSs), a class of neuromorphic image sensors with microsecond-level latency and high dynamic range. Nevertheless, challenges persist in optimising training and effectively handling spatio-temporal complexity, which limits their potential for real-time applications on embedded sensing systems such as object tracking and recognition. Targeting this neuromorphic sensing pipeline, this paper proposes the Dynamic Tracking with Event Attention Spiking Network (DTEASN), a novel framework designed to address these challenges by employing a pure SNN architecture, bypassing conventional convolutional neural network (CNN) operations, and reducing GPU resource dependency, while tailoring the processing to DVS signal characteristics (asynchrony, sparsity, and polarity). The model incorporates two innovative, self-developed components: an event-driven multi-scale attention mechanism and a spatio-temporal event convolver, both of which significantly enhance spatio-temporal feature extraction from raw DVS events. An Event-Weighted Spiking Loss (EW-SLoss) is introduced to optimise the learning process by prioritising informative events and improving robustness to sensor noise. Additionally, a lightweight event tracking mechanism and a custom synaptic connection rule are proposed to further improve model efficiency for low-power, edge deployment. The efficacy of DTEASN is demonstrated through empirical results on event-based (DVS) object recognition and tracking benchmarks, where it outperforms conventional methods in accuracy, latency, event throughput (events/s) and spike rate (spikes/s), memory footprint, spike-efficiency (energy proxy), and overall computational efficiency under typical DVS settings. By virtue of its event-aligned, sparse computation, the framework is amenable to highly parallel neuromorphic hardware, supporting on- or near-sensor inference for embedded applications. Full article

Background/Objectives: Astaxanthin, a naturally occurring carotenoid renowned for its potent antioxidant properties, has been proposed as a dietary supplement for weight management due to its potential effects on adipose tissue and skeletal muscle metabolism, as well as its anti-inflammatory properties. This meta-analysis systematically evaluated the impact of astaxanthin supplementation on body mass index (BMI) and body weight in adult populations. Methods: Comprehensive searches of reputable databases were conducted, adhering to the PRISMA guidelines, with statistical analyses performed using Jamovi. Results: The study incorporated data from nine clinical trials. Pooled results indicated no significant reduction in the context of BMI (−0.2162; 95% CI: −0.4697 to 0.0374) and a non-significant decrease in body weight (0.0230; 95% CI: −0.4534 to 0.4994) relative to control groups. The heterogeneity observed across studies was 30.1251% (p = 0.1593) for BMI and 73.3885% (p = 0.0002) for body weight management. The dose–response analysis showed no statistically significant association between astaxanthin dosage and outcomes related to BMI and body weight management. Additionally, statistical assessment of funnel plot asymmetry indicated no evidence of publication bias. Conclusions: The findings indicate that astaxanthin does not provide benefits in BMI regulation nor in weight control management, highlighting the need for additional large-scale and long-term clinical trials. This study contributes to the growing body of evidence on the role of nutraceuticals in metabolic health, providing a foundation for future clinical recommendations. Full article

In greenhouse and plant factory production, improper design of the ventilation system and increasing scales will lead to a stagnant airflow zone, which could inhibit plant growth and induce physiological disease, such as tipburn. To increase the airflow within the plant canopy, simplify the equipment complexity, and improve operation convenience, a cultivation system was designed to provide a constant airflow within the plant canopy by integrating ventilation ducts with cultivation tanks. A three-dimensional computational fluid dynamics (ANSYS Fluent 2021R2) model was developed and validated through simulating the airflow distribution within the plant canopy under different intake air velocities. According to the simulated results, an intake air velocity of 10 m s⁻¹ showed better airflow uniformity, and the proportion of the suitable zone reached the highest value of 83% at an intake air velocity of 20 m s⁻¹. To validate the practical effectiveness of cultivation, a cultivation experiment was conducted. Five different canopy air velocities were set at 0 (CK), 0.35 (T₁), 0.5 (T₂), 0.65 (T₃), and 0.8 (T₄) m s⁻¹, respectively. The results showed that the photosynthetic and transpiration rate, as well as the fresh and dry weights of lettuce plants (Lactuca sativa cv. ‘Tiberius’), increased by 17.8%, 21.7%, 29.6%, and 29.9%, respectively, under treatment T₄ compared to those under the control, while the canopy air temperature and relative humidity decreased by 1.3 °C and 3.2%, respectively. The above results indicate that the newly designed cultivation system can be considered an effective system for improving lettuce plant growth and its canopy environment. Full article

We present a (Light)weight (S)atellite–(A)erial feature (M)atching framework (LiteSAM) for robust UAV absolute visual localization (AVL) in GPS-denied environments. Existing satellite–aerial matching methods struggle with large appearance variations, texture-scarce regions, and limited efficiency for real-time UAV applications. LiteSAM integrates three key components to address these issues. First, efficient multi-scale feature extraction optimizes representation, reducing inference latency for edge devices. Second, a Token Aggregation–Interaction Transformer (TAIFormer) with a convolutional token mixer (CTM) models inter- and intra-image correlations, enabling robust global–local feature fusion. Third, a MinGRU-based dynamic subpixel refinement module adaptively learns spatial offsets, enhancing subpixel-level matching accuracy and cross-scenario generalization. The experiments show that LiteSAM achieves competitive performance across multiple datasets. On UAV-VisLoc, LiteSAM attains an RMSE@30 of 17.86 m, outperforming state-of-the-art semi-dense methods such as EfficientLoFTR. Its optimized variant, LiteSAM (opt., without dual softmax), delivers inference times of 61.98 ms on standard GPUs and 497.49 ms on NVIDIA Jetson AGX Orin, which are 22.9% and 19.8% faster than EfficientLoFTR (opt.), respectively. With 6.31M parameters, which is 2.4× fewer than EfficientLoFTR’s 15.05M, LiteSAM proves to be suitable for edge deployment. Extensive evaluations on natural image matching and downstream vision tasks confirm its superior accuracy and efficiency for general feature matching. Full article

Detecting surface cracks on sugarcane is a critical step in ensuring product quality control, with detection precision directly impacting raw material screening efficiency and economic benefits in the sugar industry. Traditional methods face three core challenges: (1) complex background interference complicates texture feature extraction; (2) variable crack scales limit models’ cross-scale feature generalization capabilities; and (3) high computational complexity hinders deployment on edge devices. To address these issues, this study proposes a lightweight sugarcane surface crack detection model, SCS-YOLO (Surface Cracks on Sugarcane-YOLO), based on the YOLOv10 architecture. This model incorporates three key technical innovations. First, the designed RFAC2f module (Receptive-Field Attentive CSP Bottleneck with Dual Convolution) significantly enhances feature representation capabilities in complex backgrounds through dynamic receptive field modeling and multi-branch feature processing/fusion mechanisms. Second, the proposed DSA module (Dynamic SimAM Attention) achieves adaptive spatial optimization of cross-layer crack features by integrating dynamic weight allocation strategies with parameter-free spatial attention mechanisms. Finally, the DyHead detection head employs a dynamic feature optimization mechanism to reduce parameter count and computational complexity. Experiments demonstrate that on the Sugarcane Crack Dataset v3.1, compared to the baseline model YOLOv10, our model achieves mAP50:95 to 71.8% (up 2.1%). Simultaneously, it achieves significant reductions in parameter count (down 19.67%) and computational load (down 11.76%), while boosting FPS to 122 to meet real-time detection requirements. Considering the multiple dimensions of precision indicators, complexity indicators, and FPS comprehensively, the SCS—YOLO detection framework proposed in this study provides a feasible technical reference for the intelligent detection of sugarcane quality in the raw materials of the sugar industry. Full article

The U.S. loneliness epidemic is associated with substantial morbidity and mortality. While higher social media use (SMU) has been associated with higher loneliness among youth, these associations have not been sufficiently examined in adult populations. Additionally, insufficient research has assessed both SMU time and frequency in the same study. Therefore, the primary aim was to evaluate associations between SMU, both by time and frequency, and loneliness in a nationally representative sample of U.S. adults. We recruited 1512 U.S. adults ages 30–70 in 2023. We assessed loneliness using the NIH PROMIS four-item scale and self-reported SMU time and SMU frequency. Survey-weighted logistic regression models determined associations between both SMU measures and loneliness, controlling for gender, age, sexual orientation, educational attainment, employment status, and marital status. Both SMU time and SMU frequency were independently and linearly associated with loneliness (p < 0.001 for both). Although odds of loneliness increased for each increase in frequency, the association between time spent on social media and loneliness demonstrated an inverted U-shape with maximal loneliness in the third quartile of SMU. Results suggest that both time and frequency of SMU may be useful targets for interventions aimed at curbing the negative impact of SMU on loneliness. Full article

The accurate assessment of Chinese bayberry (Myrica rubra) maturity is critical for intelligent harvesting. This study proposes a novel cascaded framework combining instance segmentation and multi-feature regression for accurate maturity detection. First, a lightweight SOLOv2-Light network is employed to segment each fruit individually, which significantly reduces computational costs with only a marginal drop in accuracy. Then, a multi-feature extraction network is developed to fuse deep semantic, color (LAB space), and multi-scale texture features, enhanced by a channel attention mechanism for adaptive weighting. The maturity ground truth is defined using the a*/b* ratio measured by a colorimeter, which correlates strongly with anthocyanin accumulation and visual ripeness. Experimental results demonstrated that the proposed method achieves a mask mAP of 0.788 on the instance segmentation task, outperforming Mask R-CNN and YOLACT. For maturity prediction, a mean absolute error of 3.946% is attained, which is a significant improvement over the baseline. When the data are discretized into three maturity categories, the overall accuracy reaches 95.51%, surpassing YOLOX-s and Faster R-CNN by a considerable margin while reducing processing time by approximately 46%. The modular design facilitates easy adaptation to new varieties. This research provides a robust and efficient solution for in-field bayberry maturity detection, offering substantial value for the development of automated harvesting systems. Full article

The scope of this research was to quantify the mid-term impact of different soil tillage on carbon/nitrogen agronomical key context under optimal growing conditions of the European moderate continental climate. A large-scale on-farm experiment was established in winter wheat/soybean two-crop long-term cultivation without fertilization on fertile Luvic Chernozem. Four treatments were conducted: (T1) ‘Deep Loosening’ with tillage depth of 50 cm, (T2) ‘Plowing’ to 30 cm, (T3) ‘Strip-Till’ with tillage depth of 20 cm, and (T4) ‘No-Till’; the tillage frequency at T1 and T2 was reduced and applied to soybean only, therefore, once per 2 years during the trial period 2020/21–2024/25. Unlike the crop yield, which decreased with tillage intensity decreasing (21.38 > 19.30 > 18.88 > 18.62 t/ha in dry matter cumulatively; T2 > T3 > T1 > T4), the carbon/nitrogen key agronomical parameters either increased (root nodules count/weight: thus confirmed convergent, occasionally reverse indicators; soil compaction: penetrometric resistance) or differed in varying patterns and extent (soil chemical indicators). In fertile Chernozem soils, tillage and indicators have different importance within the nexus studied; plowing still gives the most stable yields. To improve nitrogen fixing, farmers’ practices need to balance yield vs. soil health, including eliminating soil compaction. Full article

Objective: This study aimed to evaluate food insecurity and associated factors during and after remote learning among undergraduates at Brazilian public universities. Methods: This is a comparative study of two cross-sectional studies; the first had its data collection from August 2020 to February 2021, and the second from May 2023 to December 2023. The questionnaire contained socio-economic variables, the Brazilian Food Insecurity Scale, the Diet Quality Scale, and the Perceived Stress Scale. Results: In total, 4799 undergraduates of Brazilian public universities responded in the first study, and 2897 responded in the second. Food insecurity was present in 36.5% of the students in 2020/2021 and 35.9% of the students in 2023. In the correspondence analysis, low income, poor health, stress and poor diet were associated with food insecurity in 2020/2021. Low income, reduced income, poor health, stress and diet quality were associated with food insecurity in 2023. Logistic regressions demonstrated that the year of collection, whether during or after remote learning, did not significantly contribute to food insecurity. However, students from low-income families had the highest AOR for food insecurity; no change in income or weight and lower perceptions of stress were associated with a lower AOR for food insecurity. Full article

Assessing the quality of groundwater, a primary source of water in many sectors, is of paramount importance. To this end, modeling the geospatial distribution of chemical contaminants in groundwater can be of great utility. Machine learning (ML) models are being increasingly used to overcome the shortcomings of conventional predictive techniques. We report here a systematic review of the nature and utility of various supervised and unsupervised ML models during the past two decades of machine learning groundwater hazard mapping (MLGHM). We identified and reviewed 284 relevant MLGHM journal articles that met our inclusion criteria. Firstly, trend analysis showed (i) an exponential increase in the number of MLGHM studies published between 2004 and 2025, with geographical distribution outlining Iran, India, the US, and China as the countries with the most extensively studied areas; (ii) nitrate as the most studied target, and groundwater chemicals as the most frequently considered category of predictive variables; (iii) that tree-based ML was the most popular model for feature selection; (iv) that supervised ML was far more favored than unsupervised ML (94% vs. 6% of models) with tree-based category—mostly random forest (RF)—as the most popular supervised ML. Secondly, compiling accuracy-based comparisons of ML models from the explored literature revealed that RF, deep learning, and ensembles (mostly meta-model ensembles and boosting ensembles) were frequently reported as the most accurate models. Thirdly, a critical evaluation of MLGHM models in terms of predictive accuracy, along with several other factors such as models’ computational efficiency and predictive power—which have often been overlooked in earlier review studies—resulted in considering the relative merits of commonly used MLGHM models. Accordingly, a flowchart was designed by integrating several MLGHM key criteria (i.e., accuracy, transparency, training speed, number of hyperparameters, intended scale of modeling, and required user’s expertise) to assist in informed model selection, recognising that the weighting of criteria for model selection may vary from problem to problem. Lastly, potential challenges that may arise during different stages of MLGHM efforts are discussed along with ideas for optimizing MLGHM models. Full article

Background/Objectives: Overweight and obesity continue to increase globally, a trend that has been exacerbated by the pandemic. This retrospective study examines the impact of suspending sports activities during the pandemic on the physical and psychological well-being of young Italian adults engaged in sports, paying particular attention to their nutritional status. In particular, the study focused on sex-related differences in perceptions, lifestyle, and body mass index (BMI) changes. Methods: A cross-sectional online survey was conducted among 515 students enrolled in the Sport Sciences program. All the students were aged 18 years or older. Participants completed an 18-item questionnaire assessing their experiences during the pandemic on a five-point Likert scale and retrospectively reported changes in physical activity and body weight. Results: Overall, 38.3% of males and 43% of females reported that restrictions negatively affected their lives, with notable differences emerging in habits, behaviors, and perceptions between sexes. Multivariate regression analysis showed that current BMI was influenced by anthropometric characteristics and variables related to lockdown experiences, in both sexes. Conclusions: The current BMI of examined students was significantly influenced by changes in weight status during the pandemic, resulting from an increased sedentary lifestyle and changes in eating habits, especially among males. These findings highlight that the pandemic differentially affected the lifestyles and perceptions of physically active young adults, with sex-specific consequences for mental and physical health. Full article

Cross-view geo-localization is a challenging task due to the significant changes in the appearance of target scenes from variable perspectives. Most existing methods primarily adopt Transformers or ConvNeXt as backbone models but often face high computational costs and accuracy degradation in complex scenarios. Therefore, this paper proposes a visual Mamba framework based on the state-space model (SSM) for cross-view geo-localization. Compared with the existing methods, Vision Mamba is more efficient in modeling and memory usage and achieves more efficient cross-view matching by combining the twin architecture of shared weights with multiple mixed losses. Additionally, this paper introduces Dice Loss to handle scale differences and imbalance issues in cross-view images. Extensive experiments on the public cross-view dataset University-1652 demonstrate that Vision Mamba not only achieves excellent performance in UAV target localization tasks but also attains the highest efficiency with lower memory consumption. This work provides a novel solution for cross-view geo-localization tasks and shows great potential to become the backbone model for the next generation of cross-view geo-localization. Full article

In security applications, visible-light pedestrian detectors are highly sensitive to changes in illumination and fail under low-light or nighttime conditions, while infrared sensors, though resilient to lighting, often produce blurred object boundaries that hinder precise localization. To address these complementary limitations, we propose a practical multimodal pipeline—Adaptive Energy–Gradient–Contrast (EGC) Fusion with AIFI-YOLOv12—that first fuses infrared and low-light visible images using per-pixel weights derived from local energy, gradient magnitude and contrast measures, then detects pedestrians with an improved YOLOv12 backbone. The detector integrates an AIFI attention module at high semantic levels, replaces selected modules with A2C2f blocks to enhance cross-channel feature aggregation, and preserves P3–P5 outputs to improve small-object localization. We evaluate the complete pipeline on the LLVIP dataset and report Precision, Recall, mAP@50, mAP@50–95, GFLOPs, FPS and detection time, comparing against YOLOv8, YOLOv10–YOLOv12 baselines (n and s scales). Quantitative and qualitative results show that the proposed fusion restores complementary thermal and visible details and that the AIFI-enhanced detector yields more robust nighttime pedestrian detection while maintaining a competitive computational profile suitable for real-world security deployments. Full article

Polyvinyl chloride (PVC)-dibutyl adipate (DBA) gels are a fascinating dielectric elastomer actuator showing promise in soft robotics. When actuated with high voltages, the gel deforms towards the anode. A recent application of PVC gels in electrohydraulic actuators motivates elastic and hyperelastic constitutive relationships for tensile loading modes. PVC gels with plasticizer-to-polymer weight ratios of 2:1, 4:1, 6:1, and 8:1 w/w were evaluated. PVC gels exhibit a linear elastic region up to 25% strain. The elastic modulus decreased with increasing plasticizer content from 288.8 kPa, 56.1 kPa, 24.7 kPa, to 11 kPa. Poisson’s ratio also decreased with increasing plasticizer content from 0.42, 0.43, 0.39, to 0.35. We suggest that the decrease in polymer concentration facilitates a weakly interconnected polymer network susceptible to chain slippage that hinders the network response, thus lowering Poisson’s ratio. Our work suggests that PVC gels can be treated as isotropic and incompressible for large strains and hyperelastic modeling; however, highly plasticized gels tend to act less incompressible at small strains. The power scaling law between the elastic modulus and plasticizer weight ratio showed high agreement, making the elastic modulus deterministic for any plasticizer content. The Neo–Hookean, Mooney–Rivlin, Yeoh, Gent, Ogden, and extended tube hyperelastic constitutive models are investigated. The Yeoh model shows the highest feasibility when evaluated up to 3.5 stretch, showing a maximum normalized root-mean-square-error of 6.85%. Together, these findings establish a constitutive basis for PVC-DBA gels, incorporating small strain elasticity, large strain non-linear behavior, and network analysis while providing suggestive insight into the network structure required for accurately modeling the EPIC. Full article