Search Results (2,321)

Lithium-ion batteries are the most widely used as energy storage devices in electric mobility applications. However, due to complex electrochemical processes of battery degradation, it is challenging to predict accurately the battery end-of-life (EOL) to ensure their reliability, safety, and extended usage. In this context, the introduction of machine learning techniques can provide relevant solutions based on data collection and analysis. Indeed, we compared in this study the prediction performance of numerous machine learning approaches that predict if the battery EOL bypasses a predefined threshold. Based on the variation of different indicators during the first several hundred cycles, such as charge and discharge capacity, internal resistance, and energy efficiency, extensive numerical tests have been executed and compared in terms of accuracy score, precision score, recall score, etc. All the studied machine learning approaches are trained and validated using an open-access database of 124 commercial lithium iron phosphate/graphite cells cycled under different fast-charging conditions. As a result, the classification prediction performance score reached up to 98.74% depending on the percentage of data and cycles used for training and validation as well as the predefined EOL threshold. The comparative results can be used to improve the existing health-aware energy management strategy by taking the state-of-health (SOH) of batteries into consideration. Overall, the presented research findings are relevant to battery system reliability and safety engineering. Full article

Background: In China’s expanding dairy industry, a lack of oversight regarding antimicrobial use and increasing antimicrobial resistance are evident. Selective treatments of dairy cows for clinical mastitis or dry cow therapy are proposed to promote judicious antimicrobial use without adversely impacting cattle health. These approaches have been successfully implemented on farms in other countries. Methods: On 28 October 2023, a 2-day in-person seminar was held in Beijing, China, on selective antimicrobial treatments of dairy cows for clinical mastitis or dry cow therapy on large Chinese dairy farms. Concurrently, a qualitative study involving 15 technical managers from the 13 largest Chinese dairy groups used focus group discussions and questionnaires to explore perspectives on selective treatments of dairy cows for clinical mastitis or dry cow therapy. The main outcomes assessed were opinions and concerns regarding implementing selective antimicrobial treatments. Results: Although there was diversity of cognition on AMR and selective treatments, the technical managers were generally positive regarding adoption of selective treatments. However, they expressed a need for more evidence and tools, including anticipated economic impacts, effects of delaying treatment until diagnosis, accurate interpretation of milk recording data, safe use of internal teat sealants, and spread of pathogens. Participants stressed the need for awareness, staff training, farm management, and China-specific standards, suggesting large-scale trials to assess efficacy of selective treatments. Conclusion: The findings revealed key challenges and barriers currently impeding selective AMU practices. These insights could inform efforts to promote judicious AMU on farms through targeted treatment regimens, reducing mounting selective pressure driving resistance. Full article

Background: Exercise and dietary interventions are essential for maintaining weight and reducing fat accumulation. With the growing popularity of various dietary strategies, evidence suggests that combining exercise with dietary interventions offers greater benefits than either approach alone. Consequently, this combined strategy has become a preferred method for many individuals aiming to maintain health. Calorie restriction, 5/2 intermittent fasting, time-restricted feeding, and the ketogenic diet are among the most popular dietary interventions today. Aerobic exercise, resistance training, and mixed exercise are the most widely practiced forms of physical activity. Exploring the best combinations of these approaches to determine which yields the most effective results is both meaningful and valuable. Despite this trend, a comparative analysis of the effects of different exercise and diet combinations is lacking. This study uses network meta-analysis to evaluate the impact of various combined interventions on body composition and to compare their efficacy. Methods: We systematically reviewed literature from database inception through May 2024, searching PubMed, Web of Science, Embase, and the Cochrane Library. The study was registered in PROSPERO under the title: “Effects of Exercise Combined with Different Dietary Interventions on Body Composition: A Systematic Review and Network Meta-Analysis” (identifier: CRD42024542184). Studies were meticulously selected based on specific inclusion and exclusion criteria (The included studies must be randomized controlled trials involving healthy adults aged 18 to 65 years. Articles were rigorously screened according to the specified inclusion and exclusion criteria.), and their risk of bias was assessed using the Cochrane risk of bias tool. Data were aggregated and analyzed using network meta-analysis, with intervention efficacy ranked by Surface Under the Cumulative Ranking (SUCRA) curves. Results: The network meta-analysis included 78 randomized controlled trials with 5219 participants, comparing the effects of four combined interventions: exercise with calorie restriction (CR+EX), exercise with time-restricted eating (TRF+EX), exercise with 5/2 intermittent fasting (5/2F+EX), and exercise with a ketogenic diet (KD+EX) on body composition. Intervention efficacy ranking was as follows: (1) Weight Reduction: CR+EX > KD+EX > TRF+EX > 5/2F+EX (Relative to CR+EX, the effect sizes of 5/2F+EX, TRF+EX and KD+EX are 2.94 (−3.64, 9.52); 2.37 (−0.40, 5.15); 1.80 (−1.75, 5.34)). (2) BMI: CR+EX > KD+EX > 5/2F+EX > TRF+EX (Relative to CR+EX, the effect sizes of 5/2F+EX, TRF+EX and KD+EX are 1.95 (−0.49, 4.39); 2.20 (1.08, 3.32); 1.23 (−0.26, 2.71)). (3) Body Fat Percentage: CR+EX > 5/2F+EX > TRF+EX > KD+EX (Relative to CR+EX, the effect sizes of 5/2F+EX, TRF+EX and KD+EX are 2.66 (−1.56, 6.89); 2.84 (0.56, 5.13); 3.14 (0.52, 5.75).). (4) Lean Body Mass in Male: CR+EX > TRF+EX > KD+EX (Relative to CR+EX, the effect sizes of TRF+EX and KD+EX are −1.60 (−6.98, 3.78); −2.76 (−7.93, 2.40)). (5) Lean Body Mass in Female: TRF+EX > CR+EX > 5/2F+EX > KD+EX (Relative to TRF+EX, the effect sizes of CR+EX, 5/2F+EX and KD+EX are −0.52 (−2.58, 1.55); −1.83 (−4.71, 1.04); −2.46 (−5.69,0.76).). Conclusion: Calorie restriction combined with exercise emerged as the most effective strategy for reducing weight and fat percentage while maintaining lean body mass. For women, combining exercise with time-restricted eating proved optimal for preserving muscle mass. While combining exercise with a ketogenic diet effectively reduces weight, it is comparatively less effective at decreasing fat percentage and preserving lean body mass. Hence, the ketogenic diet combined with exercise is considered suboptimal. Full article

Obesity is a global health crisis that is closely interrelated to many chronic diseases, such as cardiovascular disease and diabetes. This review provides an in-depth analysis of specific chemokines involved in the development of obesity, including C-C motif chemokine ligand 2 (CCL2), CCL3, CCL5, CCL7, C-X-C motif chemokine ligand 8 (CXCL8), CXCL9, CXCL10, CXCL14, and XCL1 (lymphotactin). These chemokines exacerbate the symptoms of obesity by either promoting the inflammatory response or by influencing metabolic pathways and recruiting immune cells. Additionally, the research highlights the positive effect of exercise on modulating chemokine expression in the obese state. Notably, it explores the potential effects of both aerobic exercises and combined aerobic and resistance training in lowering levels of inflammatory mediators, reducing insulin resistance, and improving metabolic health. These findings suggest new strategies for obesity intervention through the modulation of chemokine levels by exercise, providing fresh perspectives and directions for the treatment of obesity and future research. Full article

Background/Objectives: Traditional weight-loss methods often result in the loss of both fat and muscle mass. For individuals with sarcopenic obesity (SO), additional muscle loss can exacerbate sarcopenia, leading to further declines in muscle strength and function, ultimately worsening quality of life. To mitigate this risk, weight-loss strategies should emphasize the preservation and building of muscle mass through adequate protein intake and tailored resistance training. This study aimed to evaluate changes in SO status following a 4-week multidisciplinary weight-loss intervention program in hospitalized patients with obesity. Methods: This study included adult patients with obesity (BMI > 30 kg/m², aged 18–90 years). The SO diagnosis was performed using the handgrip strength (HGS) test and skeletal muscle mass (SMM) by bioelectrical impedance analysis (BIA) according to ESPEN/EASO-2022 guidelines. Results: A total of 2004 patients were enrolled, 64.8% female, with a mean age of 56 (±14) years and a BMI of 40.7 (±6.48) kg/m². SO was present in 9.38% (188 patients) at baseline. At discharge, 80 patients (42.55%) were no longer classified as sarcopenic and showed significant improvements in HGS. The likelihood of resolving SO was not modified in patients with only phase angle (PhA) improvement (p-value = 0.141). Patients with HGS increment had a 65% probability to be No-SO at discharge and this probability, with the concomitant PhA increment, rose to 93% (p-value < 0.0001), indicating that functional changes and good nutrition status are crucial in improvement of SO. Muscle mass (MM) and SMMI remained unchanged in the studied cohort. Conclusions: Improvements in HGS and the PhA are potential markers for the efficacy of weight-loss programs tailored to patients with SO. These findings suggest that specific interventions focusing on these markers could be beneficial in managing SO patients. Full article

Background: High-intensity anaerobic physical training frequently leads to muscle fatigue among boxers and kickboxers. Vibrational therapy (VT) and passive rest (PR) have been employed as methods to enhance muscular recovery and performance. This study evaluates the effectiveness of these two recovery methods on upper limb muscle strength and lactate levels in plasma after high-intensity exertion. Methods: Eighteen elite boxers and kickboxers, aged 19–32 years, underwent tests employing VT and PR as recovery methods in a controlled, crossover study. Muscle performance was assessed via isokinetic dynamometry, and lactate levels were measured pre-exercise, post-exercise, and post-recovery. The study adhered to the Declaration of Helsinki guidelines and was approved by the relevant bioethics committee. Results: The results showed that VT led to a faster recruitment of muscle fibers and improved muscle endurance as indicated by decreased fatigue work indices compared to PR. However, no significant differences were observed in peak torque or lactate levels between the two recovery methods. The VT group exhibited quicker recovery times in torque generation and better performance in fatigue resistance. Conclusions: VT appears to provide superior muscular recovery compared to PR following intense anaerobic effort, particularly in terms of muscle strength endurance and activation speed. These findings support the potential of VT in sports recovery protocols, although similar lactate response suggests that metabolic recovery rates are not significantly affected. Full article

Abiotic stresses, including drought, salinity, extreme temperatures and nutrient deficiencies, pose significant challenges to crop production and global food security. To combat these challenges, the integration of bioinformatics educational tools and AI applications provide a synergistic approach to identify and analyze stress-responsive genes, regulatory networks and molecular markers associated with stress tolerance. Bioinformatics educational tools offer a robust framework for data collection, storage and initial analysis, while AI applications enhance pattern recognition, predictive modeling and real-time data processing capabilities. This review uniquely integrates bioinformatics educational tools and AI applications, highlighting their combined role in managing abiotic stress in plants and crops. The novelty is demonstrated by the integration of multiomics data with AI algorithms, providing deeper insights into stress response pathways, biomarker discovery and pattern recognition. Key AI applications include predictive modeling of stress resistance genes, gene regulatory network inference, omics data integration and real-time plant monitoring through the fusion of remote sensing and AI-assisted phenomics. Challenges such as handling big omics data, model interpretability, overfitting and experimental validation remain there, but future prospects involve developing user-friendly bioinformatics educational platforms, establishing common data standards, interdisciplinary collaboration and harnessing AI for real-time stress mitigation strategies in plants and crops. Educational initiatives, interdisciplinary collaborations and trainings are essential to equip the next generation of researchers with the required skills to utilize these advanced tools effectively. The convergence of bioinformatics and AI holds vast prospects for accelerating the development of stress-resilient plants and crops, optimizing agricultural practices and ensuring global food security under increasing environmental pressures. Moreover, this integrated approach is crucial for advancing sustainable agriculture and ensuring global food security amidst growing environmental challenges. Full article

Proteins involved in antibiotic resistance (resistome) and with antimicrobial activity are present in biological specimens. This study aims to explore the presence and abundance of antimicrobial peptides (AMPs) and resistome proteins in bovine milk from diverse breeds and from intensive (Pezzata rossa, Bruna alpina, and Frisona) and non-intensive farming (Podolica breeds). Liquid atmospheric pressure matrix-assisted laser desorption/ionization (LAP-MALDI) mass spectrometry (MS) profiling, bottom-up proteomics, and metaproteomics were used to comprehensively analyze milk samples from various bovine breeds in order to identify and characterize AMPs and to investigate resistome proteins. LAP-MALDI MS coupled with linear discriminant analysis (LDA) machine learning was employed as a rapid classification method for Podolica milk recognition against the milk of other bovine species. The results of the LAP-MALDI MS analysis of milk coupled with the linear discriminant analysis (LDA) demonstrate the potential of distinguishing between Podolica and control milk samples based on MS profiles. The classification accuracy achieved in the training set is 86% while it reaches 98.4% in the test set. Bottom-up proteomics revealed approximately 220 quantified bovine proteins (identified using the Bos taurus database), with cathelicidins and annexins exhibiting higher abundance levels in control cows (intensive farming breeds). On the other hand, the metaproteomics analysis highlighted the diversity within the milk’s microbial ecosystem with interesting results that may reflect the diverse environmental variables. The bottom-up proteomics data analysis using the Comprehensive Antibiotic Resistance Database (CARD) revealed beta-lactamases and tetracycline resistance proteins in both control and Podolica milk samples, with no relevant breed-specific differences observed. Full article

Aims: To determine the intrasession and intersession reliability of the isometric force at three different starting block positions, to compare the intrasession and intersession reliability of the peak and average isometric force of three different starting block positions, and to compare the intrasession and intersession reliability of three different starting block positions. Methods: Eighteen male college students participated in this study. A repeated measures design was used to evaluate the intrasession and intersession reliability of isometric force in three different starting block positions. Results: Very high and extremely high reliability of the average and peak isometric force of the three positions of the starting blocks were obtained, with ICC ranging from 0.63 to 0.91 and a CV close to 10%. Peak force was able to determine the outcomes of the bilateral position with higher reliability than the mean force, and the dominant was the most reliable position for assessing the starting blocks. Conclusion: The functional electromechanical dynamometer can be used with a high level of reliability to assess the force exerted in the starting blocks. Full article

Background: Transcranial direct current stimulation (tDCS) is a non-invasive technique known to enhance athletic performance metrics such as vertical jump and lower limb strength. However, it remains unclear whether combining tDCS with the post-activation effects of high-load resistance training can further improve lower limb performance. Objective: This study investigated the synergistic effects of tDCS and high-load resistance training, using electroencephalography to explore changes in the motor cortex and vertical jump dynamics. Methods: Four experiments were conducted involving 29 participants. Each experiment included tDCS, high-load resistance training, tDCS combined with high-load resistance training, and a control condition. During the tDCS session, participants received 20 min of central stimulation using a Halo Sport 2 headset, while the high-load resistance training session comprised five repetitions of a 90% one-repetition maximum weighted half squat. No intervention was administered in the control group. Electroencephalography tests were conducted before and after each intervention, along with the vertical jump test. Results: The combination of tDCS and high-load resistance training significantly increased jump height (p < 0.05) compared to tDCS or high-load resistance training alone. As for electroencephalography power, tDCS combined with high-load resistance training significantly impacted the percentage of α-wave power in the frontal lobe area (F3) of the left hemisphere (F = 6.33, p < 0.05). In the temporal lobe area (T3) of the left hemisphere, tDCS combined with high-load resistance training showed a significant interaction effect (F = 6.33, p < 0.05). For β-wave power, tDCS showed a significant main effect in the frontal pole area (Fp1) of the left hemisphere (F = 17.65, p < 0.01). In the frontal lobe area (F3) of the left hemisphere, tDCS combined with high-load resistance training showed a significant interaction effect (F = 7.53, p < 0.05). The tDCS combined with high-load resistance training intervention also resulted in higher β-wave power in the parietal lobe area (P4) and the temporal lobe area (T4) (p < 0.05). Conclusions: The findings suggest that combining transcranial direct current stimulation (tDCS) and high-load resistance training significantly enhances vertical jump performance compared to either intervention alone. This improvement is associated with changes in the α-wave and β-wave power in specific brain regions, such as the frontal and temporal lobes. Further research is needed to explore the mechanisms and long-term effects of this combined intervention. Full article

The main novelty of this paper consists of presenting a statistical artificial neural network (ANN)-based model for a robust prediction of the frequency-dependent aeroacoustic liner impedance using an aeroacoustic computational model (ACM) dataset of small size. The model, focusing on percentage of open area (POA) and sound pressure level (SPL) at a zero Mach number, takes into account uncertainties using a probabilistic formulation. The main difficulty in training an ANN-based model is the small size of the ACM dataset. The probabilistic learning carried out using the probabilistic learning on manifolds (PLoM) algorithm addresses this difficulty as it allows constructing a very large training dataset from learning the probabilistic model from a small dataset. A prior conditional probability model is presented for the PCA-based statistical reduced representation of the frequency-sampled vector of the log-resistance and reactance. It induces some statistical constraints that are not straightforwardly taken into account when training such an ANN-based model by classical optimizations methods under constraints. A second novelty of this paper consists of presenting an alternate solution that involves using conditional statistics estimated with learned realizations from PLoM. A numerical example is presented. Full article

To tackle the issue of low detection accuracy in insulator images caused by intricate backgrounds and small defect sizes, as well as the requirement for real-time detection on embedded and mobile devices, this research introduces the YOLOv5s-KE model. Integrating multiple strategies, YOLOv5s-KE aims to boost detection accuracy significantly. Initially, an enhanced anchor generation method utilizing the K-means++ algorithm is proposed to generate more appropriate anchor boxes for insulator defects. Moreover, an attention mechanism is integrated into both the backbone and neck networks to enhance the model’s capacity to focus on defect features and resist interference. To improve the detection of small defects, the EIoU loss function is implemented in place of the original CIoU loss function. In order to meet the real-time detection needs on embedded and mobile devices, the model is further refined through the integration of Ghost convolution for lightweight feature extraction and a linear transformation to reduce the computational burden of standard convolution. A channel pruning strategy is deployed to optimize the sparsely trained network, diminishing redundancy, and improving model generalization. Additionally, the CARAFE operator replaces the original upsampling operator to minimize model parameters and elevate detection speed. Experimental outcomes demonstrate that YOLOv5s-KE achieves a detection accuracy of 92.3% on the Chinese transmission line insulator dataset, marking a 5.2% enhancement over the original YOLOv5s. The streamlined version of YOLOv5s-KE achieves a detection speed of 94.3 frames per second, indicating an improvement of 30.1 frames per second compared to the original model. Model parameters are condensed to 9.6 M, resulting in a detection accuracy of 91.1%. This study underscores the precision and efficiency of the proposed approach, suggesting that the advanced strategies explored introduce novel possibilities for insulator defect detection. Full article

Background: The study evaluated the effects of resistance exercise training and açaí supplementation on cardiac parameters in hypertensive animals. Methods: For this study, rats from the Wistar and SHR lines (spontaneously hypertensive rats) were used. The animals were divided into 5 groups: Wistar Control (C); Control Hypertensive (H); Trained Hypertensive (HT); Hypertensive and Supplemented with Açaí (HA); and Hypertensive Trained and Supplemented with Açaí (HAT). Resistance exercise training was carried out through climbing. The supplemented groups received 3 g of açaí/kg of body mass. The animals’ systolic blood pressure (SBP), body mass, and physical test were measured at the beginning and end of the intervention. At the end, an echocardiographic analysis was performed. Histological analysis and oxidative stress of the LV were performed. Results: It was found that hypertensive animals showed an increase in SBP, and the treatments reduced this parameter. The trained groups achieved higher values of maximum carrying load. Hypertension increased the dimension of the left ventricular free wall in diastole and reduced ejection and shortening fractions. The trained groups showed improvement in ejection and shortening fractions. The H group increased the proportion of extracellular matrix and reduced the proportion of cells, with the HAT group attenuating this change. Cell diameter was greater in group H, and all treatments reduced this parameter. Hypertension increased the concentration of malondialdehyde and decreased catalase activity in LV. The treatments managed to mitigate this damage. Conclusions: It is concluded that the treatments managed to generate positive cardiovascular adaptations, and their combination enhanced these effects. Full article

Homography estimation is a crucial task in aligning template images with target images in stamping monitoring systems. To enhance the robustness and accuracy of homography estimation against random vibrations and lighting variations in stamping environments, this paper proposes an improved unsupervised homography estimation model. The model takes as input the channel-stacked template and target images and outputs the estimated homography matrix. First, a specialized deformable convolution module and Group Normalization (GN) layer are introduced to expand the receptive field and enhance the model’s ability to learn rotational invariance when processing large, high-resolution images. Next, a multi-scale, multi-stage unsupervised homography estimation network structure is constructed to improve the accuracy of homography estimation by refining the estimation through multiple stages, thereby enhancing the model’s resistance to scale variations. Finally, stamping monitoring image data is incorporated into the training through data fusion, with data augmentation techniques applied to randomly introduce various levels of perturbation, brightness, contrast, and filtering to improve the model’s robustness to complex changes in the stamping environment, making it more suitable for monitoring applications in this specific industrial context. Compared to traditional methods, this approach provides better homography matrix estimation when handling images with low texture, significant lighting variations, or large viewpoint changes. Compared to other deep-learning-based homography estimation methods, it reduces estimation errors and performs better on stamping monitoring images, while also offering broader applicability. Full article

The fault diagnosis of rolling bearings is faced with the problem of a lack of fault data. Currently, fault diagnosis based on traditional convolutional neural networks decreases the diagnosis rate. In this paper, the developed adaptive residual shrinkage network model is combined with transfer learning to solve the above problems. The model is trained on the Case Western Reserve dataset, and then the trained model is migrated to a small-sample dataset with a scaled-down sample size and the Jiangnan University bearing dataset to conduct the experiments. The experimental results show that the proposed method can efficiently learn from small-sample datasets, improving the accuracy of the fault diagnosis of bearings under variable loads and variable speeds. The adaptive parameter-rectified linear unit is utilized to adapt the nonlinear transformation. When rolling bearings are in operation, noise production is inevitable. In this paper, soft thresholding and an attention mechanism are added to the model, which can effectively process vibration signals with strong noise. In this paper, the real noise is simulated by adding Gaussian white noise in migration task experiments on small-sample datasets. The experimental results show that the algorithm has noise resistance. Full article