Search Results (1,457)

Background: Female collegiate runners may be at high risk for disordered eating and poor bone health, which are characteristics of the female athlete triad. Intuitive eating can promote healthy eating behavior and adequate calorie intake, central variables in calculating energy availability, an underlying cause of low bone mass in athletes. Poor bone health can contribute to injury, preventing optimal performance for athletes. The purpose of this study was to assess intuitive eating, energy availability, and bone mineral density in female college runners with comparison to non-athletes. Methods: Female college athletes (n = 13, 19.5 ± 1.4 yrs) and non-athletes (n = 12, 19.9 ± 1.3 yrs) completed the Intuitive Eating Scale, Eating Disorder Examination Questionnaire, and menstrual history survey. Bone mineral density and body composition were measured using a dual-energy X-ray absorptiometer (DEXA). A 3-day diet record and exercise log were used to assess dietary intake, estimate energy expenditure, and calculate energy availability. Results: Intuitive eating was inversely correlated with disordered eating (r = −0.596, p = 0.002). Intuitive eating scores were not correlated to calorie intake, energy availability, bone mass, or percent body fat. Runners consumed significantly more calories, calcium, magnesium, phosphorus, and protein (g/kg) than non-athletes. Energy availability and bone mineral density were not significantly different between runners and non-athletes. Conclusions: Intuitive eating is associated with healthy eating behaviors in college-age females and was not related to energy availability, bone density, or body composition in this population. Future research could explore the use of intuitive eating principles in reducing disordered eating and addressing low energy availability in female runners and non-athletes. Full article

In the context of China’s “dual carbon” goals and rural revitalization strategy, scientifically assessing the carbon footprint of rural tourism is essential for promoting the sustainable development of the tourism sector. This study presents the first case analysis of the rural tourism carbon footprint in Guangdong Province, using Village B as a representative example. A tourism carbon footprint model for village B was developed using the life cycle assessment (LCA) method. Based on empirical survey data, the tourism carbon footprint of Village B in 2024 was estimated at 7731.23 t, with a per capita carbon footprint of 38.656 kg/p/a. Among the contributing sectors, transportation accounted for the largest share (85.18%), followed by catering (6.93%) and accommodation (5.10%). As an ecotourism-oriented rural destination, Village B exhibited a relatively low carbon footprint from recreational activities. To facilitate the low-carbon transition of rural tourism in the study area and accelerate progress toward the “dual carbon” targets, it is recommended to optimize public transport infrastructure, promote green mobility, enhance the energy efficiency of rural dining and accommodation, and raise awareness of low-carbon tourism. Full article

Amorphous boron powder, as a high-energy fuel, is widely used in the energy sector. However, its ignition and combustion difficulties have long limited its performance in propellants, explosives, and pyrotechnics. In this study, Mg/Al-coated boron powder with enhanced combustion properties was synthesized using the electrical explosion method. To investigate the effect of Mg/Al coating on the ignition and combustion behavior of boron powder, four samples with different Mg/Al coating contents (4 wt.%, 6 wt.%, 8 wt.%, and 10 wt.%) were prepared. Compared with raw B95 boron powder, the coated powders showed a significant reduction in particle size (from 2.9 μm to 0.2–0.3 μm) and a marked increase in specific surface area (from 10.37 m²/g to over 20 m²/g). The Mg/Al coating formed a uniform layer on the boron surface, which reduced the ignition delay time from 143 ms to 40–50 ms and significantly improved the combustion rate, combustion pressure, and combustion calorific value. These results demonstrate that Mg/Al coating effectively promotes rapid ignition and sustained combustion of boron particles. Furthermore, with the increasing Mg/Al content, the ignition delay time decreased progressively, while the combustion rate, combustion pressure, and heat release increased accordingly, reaching optimal values at 8 wt.% Mg/Al. An analysis of the combustion residues revealed that both Mg and Al reacted with boron oxide to form new multicomponent compounds, which reduced the barrier effect of the oxide layer on oxygen diffusion into the boron core, thereby facilitating continuous combustion and high heat release. This work innovatively employs the electrical explosion method to prepare dual-metal-coated boron powders and, for the first time, reveals the synergistic promotion effect of Mg and Al coatings on the ignition and combustion performance of boron. The results provide both experimental data and theoretical support for the high-energy release and practical application of boron-based fuels. Full article

Against the backdrop of increasing extreme weather events associated with global climate change, regulating carbon dioxide emissions, a primary contributor to atmospheric warming, has emerged as a pressing global challenge. Focusing on China as a representative case study of major developing economies, this research examines industrial carbon emission patterns during 2001–2022. Methodologically, it introduces an innovative analytical framework that integrates the Generalized Divisia Index Method (GDIM) with the Low Emissions Analysis Platform (LEAP) to both decompose industrial emission drivers and project future trajectories through 2040. Key findings reveal that:the following: (1) Carbon intensity in China’s industrial sector has been substantially decreasing under green technological advancements and policy interventions. (2) Industrial restructuring demonstrates constraining effects on carbon output, while productivity gains show untapped potential for emission abatement. Notably, the dual mechanisms of enhanced energy efficiency and cleaner energy transitions emerge as pivotal mitigation levers. (3) Scenario analyses indicate that coordinated policies addressing energy mix optimization, efficiency gains, and economic restructuring could facilitate achieving industrial carbon peaking before 2030. These results offer substantive insights for designing phased decarbonization roadmaps, while contributing empirical evidence to international climate policy discourse. The integrated methodology also presents a transferable analytical paradigm for emission studies in other industrializing economies. Full article

Driven by dual carbon targets, the scale of wind power integration has surged dramatically. However, its strong volatility causes insufficient short-term prediction accuracy, severely constraining grid security and economic dispatch. To address three key challenges in extracting temporal characteristics of strong volatility, adaptive fusion of multi-source features, and enhancing model interpretability, this paper proposes a Time-Domain Dual-Channel Adaptive Learning Model (TDDCALM). The model employs dual-channel feature decoupling: one Transformer encoder layer captures global dependencies while the raw state layer preserves local temporal features. After TCN-based feature compression, an adaptive weighted early fusion mechanism dynamically optimizes channel weights. The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. Validation on two wind farm datasets (A/B) demonstrates that the proposed method reduces RMSE by at least 8.89% compared to the best deep learning baseline, exhibits low sensitivity to time window sizes, and establishes a novel paradigm for forecasting highly volatile renewable energy power. Full article

This study focuses on electrical stimulation for composting. Using the PSALSAR method, a comprehensive systematic review analysis identified 22 relevant articles. The examined studies fall into four main systems: electric field-assisted aerobic composting (EAAC), electrolytic oxygen aerobic composting (EOAC), microbial fuel cells (MFCs), and thermoelectric generators (TEGs). Apart from the main systems highlighted above, bioelectrochemically assisted anaerobic composting (AnC_{BE, III}) is discussed as an underexplored system with the potential to improve the efficiency of anaerobic degradation. Each system is described in terms of key materials, composter design, operating conditions, temperature evolution, compost maturity, microbial community, and environmental outcomes. EAAC and EOAC systems accelerate organic matter decomposition by improving oxygen distribution and microbial activity, whereas MFC and TEG systems have dual functioning due to the energy generated alongside waste degradation. These innovative systems not only significantly improve composting efficiency by speeding up organic matter breakdown and increasing oxygen supply but also support sustainable waste management by reducing greenhouse gas emissions and generating bioelectricity or heat. Together, these systems overcome the drawbacks of conventional composting systems and promote future environmental sustainability solutions. Full article

The assessment of bone mineral density (BMD) is essential for osteoporosis diagnosis. Dual-energy X-ray Absorptiometry (DXA) is the current gold standard, but it has limitations in evaluating trabecular bone and is susceptible to different artifacts. In this study we evaluate whether Dual-Energy Computed Tomography (DECT) can be defined as an alternative method for the assessment of BMD in a sample of postmenopausal patients undergoing oncological follow-up. In this study a retrospective analysis was conducted on 41 patients who had both DECT and DXA within six months. BMD values were extracted from DECT using five different base material pairs (BMPs) and compared with DXA measurements at the femoral neck. The calcium–fat pairing showed the strongest correlation with DXA-derived BMD (Spearman’s ρ = 0.797) and excellent reproducibility (ICC = 0.983). There was a strong and significant association between the DXA results and the various BPM measurements. These findings support the possibility of DECT in the precise and opportunistic evaluation of BMD changes when employing particular BMPs. This study showed how this technique can be a useful and effective substitute for conventional DXA, particularly when patients are in oncological follow-up using DECT, minimizing additional radiation exposure. Full article

Employees’ green voice behavior (GVB), as a specific category of extra-role green behavior, plays a vital role in promoting a firm’s sustainable development. However, its underlying mechanism has not been sufficiently explored. Drawing on social learning theory (SLT), this study proposes a research model that examines the indirect influence of environmentally-specific transformational leadership (ESTFL) on GVB via psychological empowerment (PE) and ecological reflexivity (ER) as well as the moderating role of person-supervisor value congruence (PSVC). To achieve the research goals, we conducted a two-wave online survey via the convenience sampling method to collect data from 530 employees and 106 direct supervisors working in the manufacturing, hospitality and service, energy production, construction, transportation, information and communication, and finance industries in China. Regression analyses and CFA based on SPSS and Mplus were employed to test and validate the research model. Our findings show that PE and ER both partially mediated the positive association between ESTFL and GVB. Moreover, PSVC moderated the mediating effects of ESTFL on GVB via PE and ER. This study advances empirical research regarding how leadership impacts GVB by revealing dual cognitive mechanisms and identifying its boundary condition. It also offers managerial implications for leaders and enterprises in China to promote employees’ GVB and improve sustainable management. Full article

Objectives: The early detection of low bone mineral density (BMD) is essential for preventing osteoporosis and related complications. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its cost and limited availability restrict its use in large-scale screening. This study investigated whether raw bioelectrical impedance analysis (BIA) data combined with explainable machine learning (ML) models could accurately classify osteopenia in women aged 40 to 55. Methods: In a cross-sectional design, 138 women underwent same-day BIA and DXA assessments. Participants were categorized as osteopenic (T-score between −1.0 and −2.5; n = 33) or normal (T-score ≥ −1.0) based on DXA results. Overall, 24.1% of the sample were classified as osteopenic, and 32.85% were postmenopausal. Raw BIA outputs were used as input features, including impedance values, phase angles, and segmental tissue parameters. A sequential forward feature selection (SFFS) algorithm was employed to optimize input dimensionality. Four ML classifiers were trained using stratified five-fold cross-validation, and SHapley Additive exPlanations (SHAP) were applied to interpret feature contributions. Results: The neural network (NN) model achieved the highest classification accuracy (92.12%) using 34 selected features, including raw impedance measurements, derived body composition indices such as regional lean mass estimates and the edema index, as well as a limited number of categorical variables, including self-reported physical activity status. SHAP analysis identified muscle mass indices and fluid distribution metrics, features previously associated with bone health, as the most influential predictors in the current model. Other classifiers performed comparably but with lower precision or interpretability. Conclusions: ML models based on raw BIA data can classify osteopenia with high accuracy and clinical transparency. This approach provides a cost-effective and interpretable alternative for the early identification of individuals at risk for low BMD in resource-limited or primary care settings. Full article

Accurate forecasting of carbon emissions at the county level is critical to support China’s dual-carbon goals. However, most current studies are limited to national or provincial scales, employing traditional statistical methods inadequate for capturing complex nonlinear interactions and spatiotemporal dynamics at finer resolutions. To overcome these limitations, this study develops and validates a high-resolution predictive model using advanced gradient boosting algorithms—Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—based on socioeconomic, industrial, and environmental data from 2732 Chinese counties during 2008–2017. Key variables were selected through correlation analysis, missing values were interpolated using K-means clustering, and model parameters were systematically optimized via grid search and cross-validation. Among the algorithms tested, LightGBM achieved the best performance (R² = 0.992, RMSE = 0.297), demonstrating both robustness and efficiency. Spatial–temporal analyses revealed that while national emissions are slowing, the eastern region is approaching stabilization, whereas emissions in central and western regions are projected to continue rising through 2027. Furthermore, SHapley Additive exPlanations (SHAP) were applied to interpret the marginal and interaction effects of key variables. The results indicate that GDP, energy intensity, and nighttime lights exert the greatest influence on model predictions, while ecological indicators such as NDVI exhibit negative associations. SHAP dependence plots further reveal nonlinear relationships and regional heterogeneity among factors. The key innovation of this study lies in constructing a scalable and interpretable county-level carbon emissions model that integrates gradient boosting with SHAP-based variable attribution, overcoming limitations in spatial resolution and model transparency. Full article

Background/Objectives: Childhood obesity represents a growing public health concern. It is closely associated with obstructive sleep apnoea (OSA), which impairs nocturnal breathing and significantly affects neurocognitive and cardiovascular health. This review aims to analyse differences in fat distribution, anthropometric parameters, and instrumental assessments of paediatric OSA compared to adult OSA to improve the diagnostic characterisation of obese children. Methods: narrative review. Results: While adenotonsillar hypertrophy (ATH) remains a primary cause of paediatric OSA, the increasing prevalence of obesity has introduced distinct pathophysiological mechanisms, including fat accumulation around the pharynx, reduced respiratory muscle tone, and systemic inflammation. Children exhibit different fat distribution patterns compared to adults, with a greater proportion of subcutaneous fat relative to visceral fat. Nevertheless, cervical and abdominal adiposity are crucial in increasing upper airway collapsibility. Recent evidence highlights the predictive value of anthropometric and body composition indicators such as neck circumference (NC), neck-to-height ratio (NHR), neck-to-waist ratio (NWR), fat-to-muscle ratio (FMR), and the neck-to-abdominal-fat percentage ratio (NAF%). In addition, ultrasound assessment of lateral pharyngeal wall (LPW) thickness and abdominal fat distribution provides clinically relevant information regarding anatomical contributions to OSA severity. Among imaging modalities, dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and air displacement plethysmography (ADP) have proven valuable tools for evaluating body fat distribution. Conclusions: Despite advances in the topic, a validated predictive model that integrates these parameters is still lacking in clinical practice. Polysomnography (PSG) remains the gold standard for diagnosis; however, its limited accessibility underscores the need for complementary tools to prioritise the identification of children at high risk. A multimodal approach integrating clinical, anthropometric, and imaging data could support the early identification and personalised management of paediatric OSA in obesity. Full article

Background/Objectives: To investigate associations between Follistatin (FST) gene polymorphisms (SNPs) and baseline musculoskeletal traits, and their interactions with 16-week exercise interventions. Methods: A cohort of 470 untrained Northern Han Chinese adults (208 males, 262 females), sourced from the “Research on Key Technologies for an Exercise and Fitness Expert Guidance System” project, was analyzed. These participants were previously randomly assigned to one of four exercise groups (Hill, Running, Cycling, Combined) or a non-exercising Control group, and completed their respective 16-week protocols. Body composition, bone mineral content (BMC), bone mineral density (BMD), and serum follistatin levels were all assessed pre- and post-intervention. Dual-energy X-ray absorptiometry (DXA) was utilized for the body composition, BMC, and BMD measurements. FST SNPs (rs3797296, rs3797297) were genotyped using matrix assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF MS) or microarrays. To elucidate the biological mechanisms, we performed in silico functional analyses for rs3797296 and rs3797297. Results: Baseline: In females only, the rs3797297 T allele was associated with higher muscle mass (β = 1.159, 95% confidence interval (CI): 0.202–2.116, P_{_adj} = 0.034) and BMC (β = 0.127, 95% CI: 0.039–0.215, P_{_adj} = 0.009), with the BMC effect significantly mediated by muscle mass. Exercise Response: Interventions improved body composition, particularly in females. Gene-Exercise Interaction: A significant interaction occurred exclusively in women undertaking hill climbing: the rs3797296 G allele was associated with attenuated muscle mass gains (β = −1.126 kg, 95% CI: −1.767 to −0.485, P_{_adj} = 0.034). Baseline follistatin correlated with body composition (stronger in males) and increased post-exercise (primarily in males, Hill/Running groups) but did not mediate SNP effects on exercise adaptation. Functional annotation revealed that rs3797297 is a likely causal variant, acting as a skeletal muscle eQTL for the mitochondrial gene NDUFS4, suggesting a mechanism involving muscle bioenergetics. Conclusions: Findings indicate that FST polymorphisms associate with musculoskeletal traits in Northern Han Chinese. Mechanistic insights from functional annotation reveal potential pathways for these associations, highlighting the potential utility of these genetic markers for optimizing training program design. 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

This study investigates the integration of advanced optimization algorithms within energy-intensive infrastructures and industrial plants. In fact, the authors focus on the dynamic interplay between computational intelligence and operational efficiency in wastewater treatment plants (WWTPs). In this context, energy optimization is thought of as a hybrid process that emerges at the intersection of engineered systems, environmental dynamics, and operational constraints. Despite the known energy-intensive nature of WWTPs, where pumps and blowers consume over 60% of total power, current methods lack systematic, real-time adaptability under variable conditions. To address this gap, the study proposes a computational framework that combines hydraulic simulation, manufacturer-based performance mapping, and a Memetic Algorithm (MA) capable of real-time optimization. The methodology synthesizes dynamic flow allocation, auto-tuning mutation, and step-by-step improvement search into a cohesive simulation environment, applied to a representative parallel-pump system. The MA’s dual capacity to explore global configurations and refine local adjustments reflects both static and kinetic aspects of optimization: the former grounded in physical system constraints, the latter shaped by fluctuating operational demands. Experimental results across several stochastic scenarios demonstrate consistent power savings (12.13%) over conventional control strategies. By bridging simulation modeling with optimization under uncertainty, this study contributes to sustainable operations management, offering a replicable, data-driven tool for advancing energy efficiency in infrastructure systems. Full article

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article