Search Results (409)

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

Background/Objectives: Schizophrenia exhibits symptoms linked to the hippocampus and parahippocampal gyrus. This includes the entorhinal cortex (ERC) and perirhinal cortex (PRC) as anterior parts, along with the posterior segment known as the parahippocampal cortex (PHC). However, recent research has detailed atlases based on cytoarchitectural characteristics and the hippocampus divided into four subregions: cornu ammonis (CA), dentate gyrus (DG), subiculum (SUB), and hippocampal–amygdaloid transition (HATA). This study aimed to explore the functional connectivity (FC) changes between these hippocampal subregions and the parahippocampal gyrus structures (ERC, PRC, and PHC) as well as between hippocampal subregions and various functional brain networks in schizophrenia. Methods: In total, 50 individuals with schizophrenia and 50 matched healthy subjects were examined using resting state functional magnetic resonance imaging (rs-fMRI). Results: The results showed alterations characterized by increases and decreases in the strength of the positive connectivity between the parahippocampal gyrus structures and the four hippocampal subregions when comparing patients with schizophrenia with healthy subjects. Alterations were observed among the hippocampal subregions and functional brain networks, as well as the formation of new connections and absence of connections. Conclusions: There is strong evidence that the different subregions of the hippocampus have unique functions and their connectivity with the parahippocampal cortices and brain networks are affected by schizophrenia. Full article

Current rehabilitation protocols for transitioning patients to late-stage recovery, evaluating return-to-play (RTP) clearance, and assessing tendon characteristics exhibit significant heterogeneity. Clinicians frequently interpret and apply research findings based on individual philosophies, resulting in varied RTP criteria and performance expectations. Despite medical clearance, patients recovering from Achilles tendon (AT) injuries often exhibit persistent impairments in muscle volume, tendon structure, and force-generating capacity. Inconsistencies in assessment frameworks, compounded by a lack of quantitative data and the utilization of specific metrics to quantify certain strength characteristics (endurance, maximal, explosive, etc.) and standardized protocols, hinder optimal functional recovery of the plantar flexors during the final stages of rehabilitation and RTP. With this in mind, the aim of this integrative review was to provide an overview of AT rehabilitation, with particular critique around mid-stage strengthening and the use of the heel-raise assessment in milestoning rehabilitation progress. From this critique, new perspectives in mid-stage strengthening are suggested and future research directions identified. Full article

The ongoing secular changes in human movement development means that an assessment of primitive reflexes is now required not only in disabled but also in neurotypical children. This study had three aims: (1) presenting the TASHUN test battery as suitable for the assessment of primitive reflex activity in normal children and child athletes; (2) analyzing reflex characteristics of neurotypical children; (3) verifying validity and reliability of tests. Spearman’s rank correlation and ROC analysis were used for validation. Intraclass Correlation Coefficient and RM ANOVA analyzed reliability. The test on 242 schoolgirls has demonstrated that retained primitive reflexes are present in almost every individual (84.7–95.7%). Correlations showed strong positive association, with all values exceeding 0.8, and ROC analysis demonstrated excellent predictive strength (AUC values over 0.9). Interobserver reliability showed excellent agreement (ICC values above 0.9). No significant offset was present among the scoring by evaluators. Therefore, testing for primitive reflexes is necessary in neurotypical children in order to obtain a realistic image about the physiology of reflexes and their role in motor development. Our screening could be useful for practicing sport professionals, researchers and academics, to identify deficiencies, to further explore reflexes and to train future PE teachers and trainers. Full article

Aramid fibre has become a critical material for individual soft body armour due to its lightweight nature and exceptional impact resistance. To investigate its energy absorption mechanism, quasi-static and dynamic tensile experiments were conducted on Kevlar^® 29 plain-woven fabric using a universal material testing machine and a Split Hopkinson Tensile Bar (SHTB) apparatus. Tensile mechanical responses were obtained under various strain rates. Fracture morphology was characterised using scanning electron microscopy (SEM) and ultra-depth three-dimensional microscopy, followed by an analysis of microstructural damage patterns. Considering the strain rate effect, a viscoelastic constitutive model was developed. The results indicate that the tensile mechanical properties of Kevlar^® 29 plain-woven fabric are strain-rate dependent. Tensile strength, elastic modulus, and toughness increase with strain rate, whereas fracture strain decreases. Under quasi-static loading, the fracture surface exhibits plastic flow, with slight axial splitting and tapered fibre ends, indicating ductile failure. In contrast, dynamic loading leads to pronounced axial splitting with reduced split depth, simultaneous rupture of fibre skin and core layers, and fibrillation phenomena, suggesting brittle fracture characteristics. The modified three-element viscoelastic constitutive model effectively captures the strain-rate effect and accurately describes the tensile behaviour of the plain-woven fabric across different strain rates. These findings provide valuable data support for research on ballistic mechanisms and the performance optimisation of protective materials. Full article

Addressing the stability control challenges of roadways with composite roofs in the No. 34 coal seam of Donghai Mine under high-strength mining conditions, this study employed integrated methodologies including laboratory experiments, numerical modeling, and field trials. It investigated the mechanical response characteristics of the composite roof and developed a synergistic control system, validated through industrial application. Key findings indicate significant differences in mechanical behavior and failure mechanisms between individual rock specimens and composite rock masses. A theoretical “elastic-plastic-fractured” zoning model for the composite roof was established based on the theory of surrounding rock deterioration, elucidating the mechanical mechanism where the cohesive strength of hard rock governs the load-bearing capacity of the outer shell, while the cohesive strength of soft rock controls plastic flow. The influence of in situ stress and support resistance on the evolution of the surrounding rock zone radii was quantitatively determined. The FLAC^3D strain-softening model accurately simulated the post-peak behavior of the surrounding rock. Analysis demonstrated specific inherent patterns in the magnitude, ratio, and orientation of principal stresses within the composite roof under mining influence. A high differential stress zone (σ₁/σ₃ = 6–7) formed within 20 m of the working face, accompanied by a deflection of the maximum principal stress direction by 53, triggering the expansion of a butterfly-shaped plastic zone. Based on these insights, we proposed and implemented a synergistic control system integrating high-pressure grouting, pre-stressed cables, and energy-absorbing bolts. Field tests demonstrated significant improvements: roof-to-floor convergence reduced by 48.4%, rib-to-rib convergence decreased by 39.3%, microseismic events declined by 61%, and the self-stabilization period of the surrounding rock shortened by 11%. Consequently, this research establishes a holistic “theoretical modeling-evolution diagnosis-synergistic control” solution chain, providing a validated theoretical foundation and engineering paradigm for composite roof support design. Full article

Background: Heart failure (HF) is frequently associated with skeletal muscle wasting, reduced functional capacity, and malnutrition. High-protein diets offer a promising nutritional intervention to improve these outcomes in individuals with HF. Objective: This systematic review evaluated randomized controlled trials of high-protein dietary interventions in HF populations, with emphasis on intervention characteristics, quantitative benefits, and risk of bias. Methods: We conducted a comprehensive search in PubMed, MEDLINE, Embase, and Cochrane CENTRAL from inception to June 2025. Eligible studies enrolled adults (≥18 years) with HF, implemented high-protein regimens (≥1.1 g/kg/day or ~25–30% of energy), and reported on functional capacity, body composition, muscle strength, clinical outcomes, or biochemical markers. Two reviewers independently screened, extracted data, and assessed bias (Cochrane RoB 2). Heterogeneity in dosing, duration, and outcomes precluded meta-analysis; we therefore provide a narrative synthesis. Results: Ten trials (nine randomized controlled trials, one pilot) involving 1080 patients (median n = 38; range 21–652) were included. High-protein interventions yielded mean improvements in six-minute walk distance of +32 ± 14 m, lean body mass gain of +1.6 ± 0.9 kg, and 9 ± 4% enhancement in quality-of-life scores; muscle strength effects varied from −2% to +11%. Two studies reported an 18% reduction in HF readmissions (p < 0.05). The risk-of-bias assessment identified two low-risk, three moderate-risk, and one high-risk study. Key limitations include small sample sizes, varied protein dosing (1.1–1.5 g/kg/day), short follow-up (2–6 months), and outcome heterogeneity. Conclusions: High-protein dietary strategies appear to confer modest, clinically relevant gains in functional capacity, nutritional status, and HF readmission risk. Larger, well-powered trials with standardized dosing and longer follow-up are necessary to establish optimal protein targets, long-term efficacy, and safety. Full article

Objectives: Shoulder injury prevalence appears to be the highest among all injuries in CrossFit (CF) athletes. Nevertheless, there is no evidence deriving from prospective studies to explain this phenomenon. The purpose of this study was to document shoulder injury incidence in CF participants over a 12-month period and prospectively investigate the risk factors associated with their demographic, epidemiological, and functional characteristics. Methods: The sample comprised 109 CF athletes in various levels. Participants’ data were collected during the baseline assessment, using a specially designed questionnaire, as well as active range of motion, muscle strength, muscle endurance, and sport-specific tests. Non-parametric statistical tests and inferential statistics were employed, and in addition, linear and regression models were created. Logistic regression models incorporating the study’s continuous predictors to classify injury occurrence in CF athletes were developed and evaluated using the Area Under the ROC Curve (AUC) as the performance metric. Results: A shoulder injury incidence rate of 0.79 per 1000 training hours was recorded. Olympic weightlifting (45%) and gymnastics (35%) exercises were associated with shoulder injury occurrence. The most frequent injury concerned rotator cuff tendons (45%), including lesions and tendinopathies, exhibiting various severity levels. None of the examined variables individually showed a statistically significant correlation with shoulder injuries. Conclusions: This is the first study that has investigated prospectively shoulder injuries in CrossFit, creating a realistic profile of these athletes. Despite the broad spectrum of collected data, the traditional statistical approach failed to identify shoulder injury predictors. This indicates the necessity to explore this topic using more sophisticated techniques, such as advanced machine learning approaches. Full article

Driver models, which provide mathematical or computational representations of human driving behavior, are crucial for intelligent driving systems by enabling stable and repeatable operations. However, existing models typically employ fixed weighting parameters to simulate preview delay, failing to reflect individual driver differences and real-time dynamic behaviors. This paper proposes a Brain-Memory Driver Model (BMDM) that emulates human brain memory mechanisms to dynamically adjust preview weights by integrating global path curvature, real-time vehicle speed, and steering torque. This emulation involves a three-stage process: capturing data in an Instantaneous Memory (IM) region, filtering data via a forgetting mechanism in a Short-Time Memory (STM) region to reduce scale, and retaining data based on correlation strength in a Long-Time Memory (LTM) region for persistent mining. By deploying a trained behavioral memory database, the model dynamically calibrates preview weights based on the driver’s state and real-time curvature variations under different road conditions. This enables the model to more accurately simulate authentic preview characteristics and improves its adaptability. Simulation results from an automated steering case study demonstrate that the improved model exhibits control performance closer to the real driving process, reproducing authentic steering behavior within the human–vehicle–road closed-loop system from an intelligent biomimetic perspective. Full article

Background: Food addiction (FA) is an emerging construct that mirrors the behavioral and neurobiological characteristics of substance use disorders. Despite growing interest, its association with specific psychiatric disorders among bariatric patients remains understudied. Objective: Our aim was to examine the prevalence and strength of associations between FA and seven major psychiatric disorders in individuals who underwent bariatric surgery. Methods: In a sample of 100 post-bariatric patients referred for psychiatric evaluation, FA was assessed using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0), and psychiatric disorders were diagnosed using the Mini International Neuropsychiatric Interview (MINI). Logistic regression models were used to estimate adjusted odds ratios (aORs) for the association between FA and each psychiatric disorder, controlling for sex, age, body mass index (BMI), employment status, the number of children, clinical comorbidities, physical activity, family psychiatric history, and region of residence. Results: FA was present in 51% of the sample. Descriptive analyses revealed a significantly higher prevalence of major depressive disorder, panic disorder, generalized anxiety disorder, social anxiety disorder, agoraphobia, obsessive–compulsive disorder, and bulimia nervosa among individuals with FA. Multivariate models showed robust associations between FA and bulimia nervosa (aOR = 19.42, p < 0.05), generalized anxiety disorder (aOR = 2.88, p < 0.05), obsessive–compulsive disorder (aOR = 6.64, p < 0.05), agoraphobia (aOR = 3.14, p < 0.05), social anxiety disorder (aOR = 4.28, p < 0.05) and major depressive disorder (aOR = 2.79, p < 0.05). Conclusions: FA is strongly associated with a range of psychiatric comorbidities in post-bariatric patients, reinforcing the need for comprehensive mental health screening in this population. These findings underscore the potential role of FA as a clinical marker for stratified risk assessment, supporting more personalized approaches to mental health monitoring and intervention following bariatric surgery. Full article

Background: Sport plays an important role in the health promotion of people with cerebral palsy (CP). However, risk factors may impair sport performance and health in non-ambulatory athletes. Therefore, the aim of the present study was to explore body composition and bone health in a group of world-class Boccia players with CP. Methods: Five BC2-class players with CP, aged 15–42 years old, were assessed using Dual-Energy X-Ray Absorptiometry (DXA) for body composition and bone mineral density (BMD) and content (BMC). The fat mass index (kg/m²) was used to define obesity, and the BMD Z-score used to analyze bone health. A preliminary indicator of sarcopenia was considered using the appendicular lean mass index. Results: Players 1 and 3 exhibited similar body compositions (obesity class 1 and BMD Z-score are below the expected range for age). Player 5 exhibited multiple health-related risk factors. The results regarding youth players (Player 2 and Player 4) should be analyzed with caution. Conclusions: Overall, due to Boccia’s specific characteristics, players may benefit from close monitoring by multidisciplinary teams and supplementary strategies (e.g., strength training, individualized diet plans) to promote quality of life and performance. However, further research is needed to confirm the data, since these preliminary findings do not allow for broader generalizations. Full article

Background: Borderline personality disorder (BPD) is characterised by significant clinical heterogeneity. Classifying subtypes of BPD may offer deeper insights into the disorder’s complexity and inform more tailored therapeutic strategies. The exploration of data-driven subtyping using cluster-analytic approaches represents a promising avenue for capturing variability in symptomatology and comorbidity profiles. Aim: This systematic review aims to synthesise and critically evaluate the empirical studies that have applied cluster-analytic methods to identify subtypes of BPD in adult populations. It further assesses the consistency of findings and their alignment with theoretical models of the disorder. Methods: A comprehensive search of PubMed, Scopus, and PsycNet was conducted in accordance with the PRISMA guidelines. Eligible studies employed either traditional or probabilistic clustering techniques to classify adult individuals diagnosed with BPD based on the DSM criteria. A total of 29 studies, encompassing 24,345 participants, met the inclusion criteria. The study quality and risk of bias were assessed using the AXIS tool. Results: Most studies identified clinically meaningful BPD subtypes based on dimensions such as affective regulation, effortful control, interpersonal style, and impulsivity or aggression. Several findings supported the existence of internalizing and externalizing profiles, which converge with long-standing theoretical conceptualisations of BPD. However, substantial heterogeneity was observed in subtyping bases, sample characteristics, and analytic procedures. Discussion: Although this review highlights the recurring subtype patterns, the methodological inconsistencies and a lack of longitudinal and treatment-outcome data limit the strength of the conclusions. The future research should prioritise standardised subtyping frameworks and explore the prognostic and therapeutic utility of BPD subtypes in clinical settings. Full article

Background/Objectives: Emotion and cognition, two essential components of human mental processes, have traditionally been studied independently. The exploration of emotion and cognition is fundamental for gaining an understanding of human mental functioning. Despite the availability of various methods to measure and evaluate emotional states and cognitive processes, physiological measurements are considered to be one of the most reliable methods due to their objective approach. In particular, electroencephalography (EEG) provides unique insight into emotional and cognitive activity through the analysis of event-related potentials (ERPs). In this study, we discriminated pleasant/unpleasant emotional moods and low/high cognitive states using graph-theoretic features extracted from spatio-temporal components. Methods: Emotional data were collected at the Physiology Department of Istanbul Medical Faculty at Istanbul University, whereas cognitive data were obtained from the DepositOnce repository of Technische Universität Berlin. Wavelet coherence values for the N100, N200, and P300 single-trial ERP components in the delta, theta, alpha, and beta frequency bands were investigated individually. Then, graph-theoretic analyses were performed using wavelet coherence-based connectivity maps. Global and local graph metrics such as energy efficiency, strength, transitivity, characteristic path length, and clustering coefficient were used as features for classification using support vector machines (SVMs), k-nearest neighbor(K-NN), and linear discriminant analysis (LDA). Results: The results show that both pleasant/unpleasant emotional moods and low/high cognitive states can be discriminated, with average accuracies of up to 92% and 89%, respectively. Conclusions: Graph-theoretic metrics based on wavelet coherence of ERP components in the delta band with the SVM algorithm allow for the discrimination of emotional and cognitive states with high accuracy. Full article

Background/Objectives: Individuals with similar hearing sensitivity exhibit varying levels of tolerance to background noise, a trait tied to unique individual characteristics that affect their responsiveness to noise reduction (NR) processing in hearing aids. The present study aimed to capture such individual characteristics by employing electrophysiological measures and subjective noise-tolerance profiles, and both were analyzed in relation to speech-in-noise performance and NR outcomes. Methods: From a sample of 42 participants with normal hearing, the neural signal-to-noise ratio (SNR)—a cortical index comparing the amplitude ratio between auditory evoked responses to target speech onset versus noise onset—was calculated, and individual noise-tolerance profiles were also derived using k-means cluster analysis to classify participants into distinct subgroups. Results: The neural SNR showed significant correlations with speech-in-noise performance and NR outcomes with varying strength. In contrast, noise-tolerance subgroups did not show meaningful group-level differences in either speech-in-noise or NR outcomes. The neural SNR and noise-tolerance profiles were found to be statistically independent. Conclusions: While the neural SNR reliably predicted perceptual performance in background noise and NR outcomes, our noise-tolerance profiles lacked sufficient sensitivity. Still, subjective ratings of individual noise tolerance are clinically accessible, and thus, integrating both physiology and subjective measures in the same cohort is a valuable strategy. Full article

Introduction: Humanitarian Corridors are part of Protected Entry Procedures (PEPs), which allow for the safe and legal arrival in Europe of refugees in need of protection (art. 25 of Regulation (CE) n.810/2009) and were implemented for the first time in Italy in 2015. They represent an alternative to dangerous journeys, and they also bring benefits to local communities. The National Institute for Health, Migration and Poverty (NIHMP) represents the first filter at entry with regard to health needs, as it guarantees an overall individual health assessment to verify the possible presence of ongoing infectious pathologies and of issues that may require management and medical investigation. The aim of this study is to expose the organizational activity and analyze the sociodemographic and health data relating to the Humanitarian Corridors implemented in Italy and in which the NIHMP has participated from 2018 to 2024. Materials and Methods: The organizational lists and health data of each corridor were collected. The analysis was carried out in relation to trend, sociodemographic characteristics of the sample, identification, reception and health. The p-value was considered statistically significant if less than 0.01. In all cases in which the p-value was found to be statistically significant, Cramer’s V was calculated to evaluate the strength of the individual correlation. Results: The NIHMP has participated in 14 Humanitarian Corridors, allowing 1250 refugees to enter Italy; a total of 174 unaccompanied foreign minors (UFMs) arrived, and there were 451 refugees reported as vulnerable (36.1%). Contagious infectious conditions were identified in 223 refugees (17.8%), and other health findings were identified in 414 refugees (33.1%). In the analysis by sex, there are statistically significant differences in the reports of vulnerability and in nationality and education. The inferential analysis carried out by age groups presents statistically significant differences in the reports of vulnerability and in sex and nationality. The analysis relating to the differences by nationality shows statistically significant results in the individual corridors in the reports of vulnerability and in schooling, skin manifestations and infectious diseases and the presence of other health findings. Conclusions: The data reported and analyzed in this work can be considered the first attempt at a complete and detailed analysis regarding the actual implementation and effectiveness of Humanitarian Corridors as programs for legal and safe entry into Italy. The significant gap in research is the reason why there are characteristics of our sample that have no counterpart in the literature. Despite this, for other characteristics, it is possible to find statistical significance and scientific value in line with the data reported both on the websites dedicated to the topic and on the limited scientific literature on the subject. Full article