Search Results (2,604)

Routine monitoring in racehorses requires indicators that are reproducible and practical under real training conditions. This observational study evaluated the short-term reproducibility of cardiovascular and speed indicators in French Standardbred trotters, with a particular focus on cardiac cost (CC), defined as the ratio of heart rate to speed (beats·m⁻¹). The full dataset comprised 483 sessions from 60 trotters and was used to describe age-related patterns. For reproducibility analyses, consecutive monitored sessions within the same horse were grouped into follow-up blocks when the interval between two successive sessions did not exceed 7 days. Only follow-up blocks containing at least three sessions were retained, resulting in 36 blocks, 126 sessions, and 18 horses. Each session included a warm-up, two 2000 m work blocks at increasing intensity, and recovery periods, while heart rate and speed were recorded using a Polar Team Pro system. Adjusted intraclass correlation coefficients indicated moderate reproducibility for CC during the first work block (CC B1: 0.67, 95% CI 0.48–0.78), heart rate recovery (HRR) after B1 (0.60, 0.40–0.73) and B2 (0.66, 0.47–0.78), and V150 (0.59, 0.39–0.73), whereas V180, recovery speed, and CC during B2 showed poor reproducibility. Reproducibility of CC B1 and HRR was preserved after adjustment for ambient temperature. In the full dataset, V200 increased with age, consistent with previous field-test literature. The minimal detectable change was 0.04 beats·m⁻¹ for CC B1 and 26 bpm for HRR after B1. These findings suggest that CC B1, HRR, and V150 may be useful indicators for short-term monitoring, although results should be interpreted considering the single-yard design. Full article

The construction sector is undergoing rapid digital transformation, creating opportunities to enhance environmental safety in urban areas. One critical application lies in air pollution forecasting, particularly regarding fine dust (PM₁₀) emissions. While machine learning (ML) models are widely used for city-wide air quality monitoring, a significant research gap exists in the high-resolution (5 min interval) forecasting of dust at localized “point-pattern” development sites. These densely built urban zones present unique challenges due to highly volatile microclimates and intermittent emission sources that directly affect nearby residents. The purpose of this study is to perform a preliminary performance analysis of eight predictive algorithms—ARIMA, EMA, Prophet, NNAR, Random Forest, SVM, and XGBoost—to identify the most robust approach for short-term PM₁₀ forecasting under limited data (N = 1728). Special attention is paid to the non-linear relationship between meteorological conditions and dust concentrations. Unlike previous studies which focused on general urban backgrounds, this work contributes a validated methodological framework for localized monitoring. The results demonstrate that tree-based ensemble models provide the highest stability and accuracy, offering a reliable basis for future real-time environmental management and active pollution mitigation strategies on urban construction sites. Full article

This study presents a short-term, IoT-enabled sensor-based assessment of treated municipal water and decentralized groundwater in Bragança, northeastern Portugal. Two drinking-water supply contexts were compared: treated surface-water-derived municipal water from the public supply system and groundwater from a decentralized supply system serving part of a higher education campus. Five sampling points were monitored during three campaigns between January and March 2026. At each point, pH, electrical conductivity, temperature, oxidation–reduction potential, and total dissolved solids were recorded at 10 s intervals over approximately 10 min monitoring windows using a multiparameter probe integrated into an IoT-enabled data acquisition workflow. Microbiological analyses were performed on groundwater samples as complementary information. Treated municipal water showed lower mineralization, narrower parameter ranges, and higher oxidation–reduction potential, reflecting source-water characteristics, treatment, and operational control. Groundwater showed higher mineralization, lower oxidation–reduction potential, and greater variability among sampling points and campaigns, consistent with stronger local hydrogeochemical and operational influences. The repeated short-interval readings provided more detailed physicochemical profiles than isolated spot measurements, although the short monitoring windows do not represent continuous long-term high-frequency monitoring. Overall, the results support standardized IoT-enabled sensor-based monitoring as a complementary tool for short-term water-quality assessment and indicate the need for longer seasonal datasets and laboratory confirmation. Full article

Indoor temperature time series in district-heating buildings are often contaminated by anomalies embedded in nonstationary, multiscale thermal dynamics. This study proposes a hybrid Empirical Wavelet Transform and Long Short-Term Memory (EWT-LSTM) framework for adaptive anomaly detection and localized reconstruction. Evaluated on 15 min interval data from 45 residential units over a 112-day heating season, the framework operates via a highest-frequency branch for anomaly detection and a full-modal branch for signal repair. Quantitative results show that the EWT Highest-Frequency LSTM (EWT(HF)-LSTM) achieved the best anomaly discrimination among decomposition variants with an average F1-score of 0.531. For anomaly repair, the full EWT-LSTM produced the highest fidelity with a localized Root Mean Square Error ($RMS{E}_a$) of 0.818 °C. Furthermore, thermal comfort validation demonstrated that EWT-LSTM successfully prevented the severe comfort degradation of up to −82% in Exceeded Degree-Hours caused by unstable Empirical Mode Decomposition (EMD)-based reconstructions. These concrete results confirm that the proposed framework effectively provides clean, physically coherent temperature data for downstream district heating operations. Full article

With the increasing application of microseismic monitoring technology in mines, it is still difficult to automatically distinguish effective signals from noise signals, which limits its popularization and practical performance to a certain extent. This paper systematically analyzes six major identifiable signal patterns in high-noise mine environments, including rock drilling, trackless equipment operation, ore pass dumping, electromagnetic interference, blasting, and effective signals. The effective signals are further subdivided into low-energy and high-energy subcategories, and the generation mechanism of each pattern is discussed in depth. Based on a large number of collected sample data, the AIC algorithm, short-to-long window amplitude ratio, short-window amplitude average, and single-point amplitude triggering method are adopted to extract the recognition features of the above six patterns, including waveform interval time Δt, waveform duration t_c, dominant frequency f_d, number of independent events, and their combinations. Probability statistics are performed on each characteristic value, and an automatic pattern recognition algorithm for mine microseismic waveform characteristics is constructed. Meanwhile, a two-stage intelligent recognition model is established using the convolutional neural network machine learning method. A total of 1500 typical samples are selected and divided into training and test sets at a ratio of 7:3. After 5000 training iterations, the average accuracies of the three classifiers reached 87%, 84%, and 90%, respectively. The intelligent microseismic signal recognition method developed on this basis was field-tested at the Xianglushan Tungsten Mine, achieving a recognition accuracy of 94.9% for low-energy effective events. It shows favorable engineering adaptability and meets the expected research objectives. Full article

Air quality in the San Joaquin Valley (SJV) ranks among the worst in the US. Exposures to traffic-related air pollutants have been associated with pediatric health complications, and few studies have investigated respiratory complications in relation to short-term exposures to PM less than 2.5 microns in diameter (PM_2.5) in the SJV. We used Bayesian Poisson spatiotemporal conditional autoregressive models to analyze the association between PM_2.5 and pediatric respiratory emergency department (ED) visits in Fresno County, California. Additional analyses stratified respiratory outcomes by sex and age group. Weekly ambient PM_2.5 levels were estimated for each zip code using community science and regulatory air monitors. Weekly residential zip code counts of respiratory ED visits were provided by Fresno County Department of Public Health and Valley Children’s Hospital from 2 April 2022 to 31 December 2024. A ten-fold increase in PM_2.5 was associated with increased asthma ED visits among females (Relative Risk (RR):1.15; 95% Credible Interval (CrI):1.01, 1.32) and children aged 0 to 4 (RR:1.18; 95% CrI:1.03, 1.34) and other chronic respiratory conditions among males (RR:1.93; 95% CrI:1.19, 3.16) and ages 10 to 14 (RR:2.90; 95% CrI:1.32, 6.30). Findings suggest that efforts to better assess and reduce pollution exposures will improve public health in the SJV. Full article

Purpose: To evaluate the anatomical and functional efficacy of photobiomodulation (PBM) therapy in patients with dry age-related macular degeneration (AMD) and to investigate structural predictors of visual response. Methods: This retrospective study included 47 eyes of 30 patients with dry AMD treated with PBM. Best-corrected visual acuity (BCVA) was recorded as Early Treatment Diabetic Retinopathy Study (ETDRS) letter score, and visual change (ΔBCVA) was calculated. Spectral-domain optical coherence tomography parameters—central retinal thickness (CRT), central macular volume (ETDRS 9-subfield central zone), and photoreceptor layer integrity (external limiting membrane [ELM], ellipsoid zone [EZ], and interdigitation zone [IZ])—were assessed pre- and post-treatment. Age-Related Eye Disease Study (AREDS) stage was graded per eye. Because both eyes from some patients were included, generalized estimating equation (GEE) models with patient-level clustering were used to account for inter-eye correlation. Effect estimates were reported as unstandardized coefficients with 95% confidence intervals. Results: Visual acuity improved following PBM therapy, with mean ETDRS letter scores increasing from 75.0 ± 14.1 to 78.0 ± 12.1 letters. In the GEE model accounting for patient-level clustering, the estimated mean gain was 2.97 ETDRS letters (95% CI: 1.15 to 4.79; p = 0.001). Mean CRT showed no significant change following PBM therapy (210.32 ± 48.61 µm vs. 211.23 ± 50.27 µm; GEE estimate: +0.91 µm; 95% CI: −5.52 to 7.35; p = 0.780). Central macular volume likewise remained stable (0.1913 ± 0.030 vs. 0.1919 ± 0.033 mm³; GEE estimate: +0.0006 mm³; 95% CI: −0.0054 to 0.0067; p = 0.836). Photoreceptor layer integrity demonstrated limited structural change, with no significant time effect for EZ or IZ integrity in binary GEE models and no observed pre–post change in ELM integrity. In multivariable GEE analysis, baseline BCVA (p < 0.001), ELM integrity (p < 0.001) and central macular volume (p = 0.041) were associated with change in ETDRS letter score, whereas AREDS category, EZ integrity, and IZ integrity were not. Conclusions: PBM therapy demonstrated limited short-term anatomical change but variable functional outcomes in dry AMD. Baseline BCVA emerged as the primary determinant of visual response, suggesting that treatment benefit may be influenced predominantly by pre-treatment functional reserve. Full article

Background/Objectives: Regular physical exercise is strongly recommended for individuals with type 1 diabetes mellitus (T1DM) because of its beneficial effects on cardiovascular fitness, insulin sensitivity, metabolic control, and overall health. Nevertheless, participation in physical activity remains limited, largely due to the fear of exercise-induced hypoglycemia and glycemic instability. Glycemic responses to exercise in T1DM are influenced by the interaction between exercise modality, circulating insulin levels, nutritional status, and diabetes technologies. Continuous aerobic exercise, resistance training, high-intensity interval exercise, and mixed intermittent activities elicit distinct metabolic and hormonal responses, resulting in heterogeneous glycemic trajectories. This narrative review aimed to provide a clinically oriented synthesis of the physiological mechanisms underlying exercise-related glycemic fluctuations in T1DM and to discuss integrated insulin- and carbohydrate-based strategies to support safer participation in physical activity in the context of modern diabetes technologies. Methods: A structured narrative review was conducted using PubMed/MEDLINE, Scopus, and complementary searches in Google Scholar to identify experimental studies, observational studies, systematic reviews, consensus statements, and clinical guidelines focused on exercise-related glycemic responses in individuals with T1DM. Only articles published in English were considered. Evidence was selected and synthesized according to relevance to exercise modality, insulin therapy strategies, carbohydrate management, and diabetes technologies, including continuous glucose monitoring, continuous subcutaneous insulin infusion, and automated insulin delivery systems. The final narrative synthesis was based on 44 selected studies, reviews, consensus statements, and guidance documents considered most relevant to the objectives of this narrative review. Results: Available evidence indicates that continuous moderate-intensity aerobic exercise is most consistently associated with progressive glucose declines and increased risk of hypoglycemia, particularly when performed in the presence of elevated insulin on board. In contrast, resistance exercise and short-duration high-intensity or anaerobic exercise more frequently induce stable glycemia or transient hyperglycemia through adrenergic stimulation and increased hepatic glucose output. Mixed and intermittent exercise modalities often produce more variable responses depending on exercise sequencing, nutritional status, and insulin exposure. Across studies, integrated adjustment of basal and prandial insulin doses together with individualized carbohydrate supplementation emerged as the most effective strategy to reduce exercise-related glycemic instability. Continuous glucose monitoring and insulin pump technologies improved glucose trend awareness and management flexibility; however, physical exercise remains a challenging condition for current automated insulin delivery algorithms and still requires active user-driven decision-making. Conclusions: Exercise management in T1DM should be based on an individualized interpretation of exercise modality, glucose trends, insulin exposure, and nutritional context rather than on fixed glucose thresholds alone. Combining anticipatory insulin adjustments, tailored carbohydrate strategies, and appropriate use of diabetes technologies may substantially reduce glycemic variability and improve confidence toward physical activity participation. Structured education and individualized clinical guidance remain essential to translate physiological knowledge into effective real-world exercise management. Full article

Background and Objectives: Perforated peptic ulcer (PPU) is an emergent condition managed by surgical intervention. No conclusive evidence has been produced regarding the need for drain placement after primary repair. Our meta-analysis aimed to provide insight into the short-term outcomes by comparing the two strategies of drain omission or intra-operative placement of at least one drain. Materials and Methods: We performed a systematic review following the PRISMA guidelines. PubMed/MEDLINE, Web of Science, Cochrane Library, and Embase databases were utilized to identify articles of interest. Meta-analysis was performed using RevMan Version 5.4. Eligible studies were comparative studies (RCTs and observational studies) enrolling adult patients (≥18 years) undergoing emergency primary repair for PPU, with or without prophylactic intra-abdominal drain placement; case reports and series of fewer than 10 patients were excluded. The literature search covered January 2010 to 22 February 2026. Risk of bias was assessed using the Cochrane RoB 2.0 tool for RCTs, and the ROBINS-I V2 tool for observational studies; certainty of evidence was graded using the GRADE framework. Pooled effect estimates were calculated using a random-effects model and expressed as odds ratios (OR) or mean differences (MD) with 95% confidence intervals (CI); statistical heterogeneity was quantified using the I² statistic. Results: Five studies were considered for comparison, for a total of 1354 patients (744 and 610 in the drain and no-drain groups, respectively). Three were randomized controlled trials, and two were retrospective cohort studies, conducted across four countries (India, the USA, Egypt, and Japan). Meta-analysis of the pooled results showed that drain omission was associated with a shorter length of stay (LOS) (MD −2.13, 95% CI [−3.91–−0.35], p < 0.0001) and a lower rate of superficial surgical site infections (SSIs) (16.7% vs. 52.7%, OR 0.24, 95%CI [0.11–0.55], p = 0.0007). No difference was observed regarding the rate of leaks, reoperation, or deep SSIs. Low-certainty evidence suggested higher postoperative mortality in the no-drain group (OR: 1.96; 95% CI: 1.10 to 3.48; p = 0.02; I² = 0%), largely driven by retrospective studies with a high risk of bias. This mortality finding is of very low certainty and is most likely attributable to confounding in the observational studies rather than a true causal effect of drain omission. Several outcomes were based on data from only two to three studies, and the overall certainty of evidence was low to very low. Conclusions: Drain omission after primary repair for PPU may be associated with better outcomes in terms of LOS and superficial SSIs, primarily in lower-acuity patients, as reflected by the inclusion criteria of the contributing RCTs. Pooled analysis showed a higher postoperative mortality in the no-drain group; however, given the significant biases among included studies, our results should be interpreted as non-causal and thus require careful interpretation. Further research encompassing the full clinical spectrum of PPU is needed to confirm our results. Evidence certainty was low to very low across all outcomes, primarily due to a risk of bias, high heterogeneity (I² up to 95% for LOS), indirectness, and imprecision. Full article

Progesterone (P4) exerts important vascular and immunomodulatory effects that influence platelet (PLT) activation and serotonin (5-HT) handling across mammalian species; nevertheless, its role in modulating PLT physiology during diestrus in mares remains poorly defined. This study hypothesized that physiological variations in luteal activity during diestrus are associated with changes in PLT activation and 5-HT-related parameters. The first objective was to determine whether changes in circulating P4 during diestrus are associated with alterations in PLT aggregation, circulating 5-HT, and PLT morphological indices in healthy mares; the second objective was to identify a diestrus day providing consistent physiological conditions for assessing PLT-related biomarkers. Twenty clinically healthy Spanish Purebred mares aged 4–9 years old were monitored. Blood samples were collected on days 5, 14, and 16 post-ovulation, with luteal status confirmed by ultrasonography. P4 concentrations were determined using a solid-phase I-125 radioimmunoassay (RIA), 5-HT concentrations were quantified using a competitive enzyme immunoassay, and PLT indices were measured using an ADVIA 2120i hematology analyzer. Data were compared using appropriate parametric or non-parametric tests after assessing distribution, and correlations were analyzed using rank-based correlation analysis, using Pearson or Spearman coefficients according to variable distribution. P4 concentrations were higher on days 14 and 16 compared with day 5 (p < 0.05), with no significant differences between days 14 and 16. Platelet aggregates (AGREG) showed the greatest variation, with significantly higher values on day 14 compared with day 5 (p < 0.05). In contrast, circulating 5-HT and all PLT morphological indices (PLT count, PCT, MPV, PLCR, PDW, PCDW, MPM, and PMDW) remained unchanged across diestrus. PLT aggregation showed a strong positive association with circulating P4 concentrations (r = 0.88, p < 0.05), whereas no meaningful correlations were observed between 5-HT and AGREG or between 5-HT and PLT morphological parameters. Internal correlations among PLT indices followed expected biological patterns, confirming the stability of structural PLT traits over short physiological intervals. These findings demonstrate that during diestrus, PLT activation—but not PLT morphology or circulating 5-HT—varies in parallel with P4 in mares. Day 14, corresponding to mid-diestrus, characterized by high luteal activity, represents an informative time point for assessing PLT activation and related biomarkers, providing a framework for standardizing sampling protocols for PLT-derived products in equine reproductive medicine. Full article

Background: Oncology nurses are highly vulnerable to impaired mental health and reduced quality of life due to the emotionally demanding nature of their work. Although mental health is a well-established determinant of quality of life, the mechanisms underlying this relationship remain insufficiently understood. Objective: This study examined the effect of the mental health continuum on quality of life among oncology nurses and tested the serial mediating roles of exercise self-efficacy and psychological resilience. Methods: A cross-sectional design was conducted with 604 oncology nurses in Türkiye. Data were collected using the Mental Health Continuum—Short Form, the Exercise Self-Efficacy Scale, the Psychological Resilience Scale, and the WHOQOL-BREF. Serial mediation analysis was performed using PROCESS Model 6 with 5000 bootstrap resamples. Results: The mental health continuum had a significant positive effect on exercise self-efficacy (a1 = 0.08, p < 0.001) and psychological resilience (a2 = 0.05, p < 0.001). Exercise self-efficacy significantly predicted psychological resilience (d1 = 0.51, p < 0.001). Both exercise self-efficacy (b1 = 0.88, p < 0.001) and psychological resilience (b2 = 1.60, p < 0.001) were significant predictors of quality of life. The direct effect of the mental health continuum on quality of life remained significant (c′ = 0.65, p < 0.001), indicating partial mediation. Bootstrap results further confirmed that all indirect effects were statistically significant, as their 95% confidence intervals did not include zero. Conclusions: Quality of life is influenced not only by mental health but also by sequential cognitive and adaptive processes. Interventions targeting exercise self-efficacy and psychological resilience may enhance well-being among oncology nurses. Full article

Delayed decompression for metastatic epidural spinal cord compression (MESCC) is a common real-world problem, yet short-interval recovery after patients have already remained nonambulatory for at least 48 h is poorly defined. We retrospectively evaluated 41 surgically selected patients with MRI-confirmed epidural MESCC (Bilsky grade 2–3) and preoperative nonambulatory neurological deficit (Frankel grades A–C) lasting at least 48 h. The primary outcome was early neurological improvement, defined as a gain of at least one Frankel grade by postoperative days 10–14. The secondary outcome was early ambulation recovery, defined as postoperative Frankel grade D or E at the same interval. Early neurological improvement occurred in 20/41 patients (48.8%), and early ambulation recovery occurred in 15/41 (36.6%). No patient received postoperative index-level radiotherapy before the POD10–14 neurological assessment. Recovery was most common among patients with baseline Frankel grade C. In exploratory adjusted Firth-penalized models, ECOG performance status 3–4 was associated with lower odds of both outcomes. Rapid-growth tumors, classified using a pragmatic adapted growth-category framework, were associated with lower odds of early neurological improvement. Baseline Frankel grade C favored early ambulation recovery. Higher standardized HALP showed an exploratory association with early neurological improvement but did not alter the main clinical interpretation. Meaningful early recovery was observed in a subset of surgically selected MESCC patients despite delayed surgery, although these findings do not establish equivalence to earlier surgery or isolate the effect of surgery from multimodal oncologic care. Full article

Background/Objectives: The integration of Artificial Intelligence (AI) into clinical workflows raises critical questions regarding decision-making responsibility, as fully autonomous systems inevitably carry a margin of error that can be fatal in high-stakes fields like surgery. This study addresses this challenge by evaluating a “Human-in-the-Loop” (HITL) workflow, using intraoperative Optical Coherence Tomography (OCT) for glioma detection. We aimed to determine if integrating Machine Learning (ML)-generated segmentation maps with human contextual analysis resolves the tension between automation and clinical responsibility, yielding superior diagnostic reliability compared to structural or quantitative imaging alone. Methods: We retrospectively analyzed 86 intraoperative OCT scans from 27 patients. Five neurosurgeons blindly assessed the data across three progressive levels of processing: (1) structural scans, (2) physics-based parametric maps, and (3) SVM-based generated segmentation maps. Crucially, the HITL inference performance on segmentation maps was benchmarked against “models-only” inference pipeline: a SVM and a state-of-the-art multimodal reasoning model, Gemini 3.1 Pro. To evaluate interpretability and the operator’s ability to confidently exercise their authority, we measured inter-rater consistency alongside diagnostic performance. Results: The results demonstrate that, while quantitative parametric maps improved Global Accuracy (87% [95% CI: 82–92%]) compared to structural scans (80% [95% CI: 73–86%]), they suffered from an “interpretability gap,” resulting in a moderate inter-rater consistency of 0.68 [95% CI: 0.59–0.78]. In contrast, the HITL approach using segmentation maps maximized consensus to 0.98 [95% CI: 0.95–1.00] and achieved the highest performance (Accuracy 94% [95% CI: 88–98%] and Sensitivity 98% [95% CI: 92–100%]). Compared to the standalone models, the HITL approach significantly outperformed the SVM baseline (Accuracy 84% [95% CI: 81–87%]; Sensitivity 83% [95% CI: 78–88%]). Furthermore, it surpassed the SOTA Gemini 3.1 Pro model (Accuracy 90% [95% CI: 83–95%]; Sensitivity 86% [95% CI: 74–95%]). While the HITL sensitivity demonstrated a definitive and statistically significant edge over the Gemini model, the accuracy improvement fell just slightly short of undisputed statistical significance due to overlapping confidence intervals. Conclusions: By utilizing their clinical domain knowledge of tumor invasion patterns and topological priors, surgeons effectively filtered algorithmic noise—overriding ML errors in 69% (9 out of 13) false positive cases that models alone could not resolve. This demonstrates exactly how and where HITL optimally utilizes human contextual intelligence to outperform autonomous “models-only” pipelines, confirming a human-ML synergy that augments the objectivity of machine learning with human domain knowledge. This paradigm ensures that the ultimate responsibility for diagnostic inference remains safely and practically in human hands. Open Data Initiative: To ensure essential reproducibility, enable independent multi-center validation and support open science, all examples of intraoperative in vivo OCT brain scans used in this study are made publicly available. To the best of our knowledge, this represents the first open-access data of its kind globally. Full article

Background: Evidence comparing high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) for obesity remains inconsistent, particularly with respect to cycling-based protocols. Therefore, this meta-analysis aimed to evaluate randomized controlled trials comparing bicycle-ergometer HIIT with cycling-based MICT in adults with overweight or obesity. Methods: PubMed, Scopus, Cochrane Library, and Google Scholar were searched from January 2015 to September 2025; Google Scholar was used as a supplementary source. Eligible studies included adults aged 18–60 years and compared cycling-based HIIT with MICT interventions conducted over a minimum duration of 5 weeks. The primary outcomes were changes in body mass and fat mass. Between-group standardized mean differences (SMDs) were pooled using random-effects models. Results: Nine RCTs involving 394 enrolled participants were included, although analyzable samples varied by outcome. No significant between-group difference was observed for body mass (SMD: 0.04; 95% CI: −0.19 to 0.27; p = 0.710; I² = 0%; τ² = 0.00) or fat mass (SMD: 0.01; 95% CI: −0.23 to 0.26; p = 0.810; I² = 0%; τ² = 0.00). The pooled effects were close to zero and should be interpreted as short-term findings because interventions lasted 5–12 weeks. Conclusion: Current evidence from randomized controlled trials does not demonstrate the superiority of either cycling-based HIIT or MICT for reducing body mass or fat mass in adults with overweight or obesity. These findings do not establish equivalence and should therefore be interpreted with caution, given the small sample sizes, short follow-up, limited dietary control, and possible measurement error in body-composition outcomes. Full article

Lithium-ion battery energy storage systems (BESSs) face significant safety challenges arising from internal short-circuit (ISC) faults, which can ultimately trigger thermal runaway. To address this, this paper proposes an ISC fault diagnosis method based on multivariate information entropy (MIE). The proposed approach fuses voltage and temperature time series from battery cells to extract fault features via MIE. Furthermore, a hierarchical diagnosis framework incorporating statistical confidence intervals is developed to enable robust ISC fault diagnosis. Experiments were conducted on 180 Ah lithium iron phosphate batteries, utilizing external resistors to simulate ISC faults of varying severity. The method was further validated using real-world fault data from an electric vehicle accident. Results demonstrate that the proposed method effectively distinguishes between normal and faulty cells, with MIE values exhibiting a monotonic increase as fault severity intensifies. In the real-world dataset, the method identifies the faulty cell 240 s before a discernible voltage drop, demonstrating its capability for early ISC detection. Full article