Search Results (579)

Background/Objectives: This decentralized, real-world observational cohort study assessed whether Cheers Restore (Houston, TX, USA), a commercially available dietary supplement, is associated with improved next-day subjective well-being (energy, mental clarity, physical well-being, and sleep quality) following alcohol consumption in healthy adults. A secondary aim evaluated the feasibility of integrating consumer wearables into future controlled studies. Methods: Ninety adults (56% female; mean age 44.4 years) were enrolled via the Alethios digital research platform and contributed 2958 morning surveys across three self-selected behavioral conditions: Alcohol-Only (reference), Cheers Restore, and Non-Drinking Baseline. Four subjective outcomes (energy, mental clarity, physical well-being, sleep quality rating; 1–10 scale) were modeled using linear mixed-effects models with random intercepts for participant and centered drink count as a covariate. Results: Cheers Restore use was associated with statistically significant improvements across all four subjective outcomes relative to alcohol-only nights: mental clarity (β = 0.356, 95% CI 0.214–0.499, p < 0.001), physical well-being (β = 0.335, 95% CI 0.189–0.482, p < 0.001), energy (β = 0.270, 95% CI 0.133–0.407, p < 0.001), and sleep quality rating (β = 0.263, 95% CI 0.096–0.430, p = 0.002). Standardized effect sizes were small (Cohen’s d = 0.20–0.32). Paired Alcohol Use Disorders Identification Test–Consumption (AUDIT-C) analysis confirmed stable consumption with no evidence of increased alcohol consumption over the short, self-reported follow-up. Conclusions: In this observational cohort, Cheers Restore use was associated with consistent improvements in next-day subjective well-being. These findings warrant a randomized, placebo-controlled trial to establish causality. ClinicalTrials.gov: NCT07069608, registered 16 July 2025. Full article

The sizing of rooftop solar energy systems is commonly based on the most visible load or on generic end-use allocations, leading to an inadequate distribution of the limited rooftop area between heat and electricity. This study formalizes the energy audit within a three-level deterministic framework that selects and sizes solar technologies by energy vector: demand is first decomposed by vector; the technology for the thermal vector is then selected through a levelized cost of heat selection ratio $\psi$, while the photovoltaic system of the electrical vector is sized for self-consumption; and the rooftop area is finally allocated among vectors according to marginal value per unit area. In a 75-bed university residence in Cienfuegos, Cuba, air conditioning is the dominant energy end-use in terms of installed power (accounting for $77\%$ of the connected load), whereas the thermal vector dominates annual energy consumption (domestic hot water: 127,440 versus 76,818 kWh/year for electricity; thermal-to-electric ratio $1.66$). Solar thermal technology has been selected for the thermal vector ($0.018$ versus $0.088$ USD/kWh_th; $\psi =0.21$, a robust value according to the sensitivity analysis), and the marginal value (≈111 versus ≈32 USD/(m²·year)) allocates 104 m² to solar thermal collectors and 134 m² to photovoltaic energy, thereby reversing the original design that prioritized photovoltaic energy. The resulting portfolio achieves an annual solar fraction close to $100\%$ in both vectors on an energy balance basis, avoids $86.5$ t of operational CO₂ emissions per year, and combines a simple payback of $1.1$ years (solar thermal) with a net present value of 55,327 USD and an internal rate of return of $28\%$ (photovoltaics). The sizing decision is shown to be robust to the choice of statistical design criterion (median, mean, P90, maximum), and none of the three framework decisions is reversed under $\pm 30\%$ parameter variations. By replacing the subjective weightings of multi-criteria methods with observable economic criteria, the framework provides a replicable and auditable design protocol. Full article

Trajectory prediction is a critical component of autonomous driving systems, enabling vehicles to anticipate future motion behaviors and perform safe decision-making in dynamic traffic environments. While recent trajectory forecasting methods achieve state-of-the-art prediction accuracy, many operate as black-box systems and are evaluated primarily on high-end computing platforms, limiting their interpretability and practical deployment feasibility in resource-constrained autonomous driving systems. To address these limitations, this work proposes an interpretable and edge-deployable spatio-temporal trajectory prediction framework for autonomous driving. The proposed architecture integrates a Temporal Convolutional Network with Multi-Head Self-Attention (TCN–MHSA) in ActorNet for selective temporal modeling, a Lane Graph Attention Network (LaneGAT) for structured spatial reasoning, and a multi-stage FusionNet for actor–lane interaction. To improve model interpretability, a comprehensive Explainable AI (XAI) evaluation framework is introduced, including temporal sensitivity analysis, interaction-aware perturbation studies, spatial influence analysis, and gradient-based feature attribution methods. These analyses provide insights into how the model captures temporal motion dependencies, neighboring vehicle interactions, and environmental context during trajectory prediction. To improve the robustness of the interpretability analysis, temporal sensitivity was additionally evaluated over 100 validation scenes, demonstrating that recent observations consistently exert the greatest influence on trajectory prediction, while neighboring interaction effects gradually diminish with increasing spatial separation. Furthermore, practical real-world deployment feasibility is investigated on the NVIDIA Jetson Xavier NX platform using edge-aware optimization strategies, including mixed-precision inference and graph-complexity reduction techniques for efficient resource-constrained inference, achieving 125.74 ms latency at 12.86 W. Additional edge deployment comparisons with HiVT and SIMPL approaches under identical hardware conditions demonstrate that the proposed framework provides a more favorable balance between computational efficiency and embedded deployment performance. Experimental evaluation on the Argoverse 1 dataset demonstrates a minimum Average Displacement Error (minADE) of 0.90 m, a minimum Final Displacement Error (minFDE) of 1.50 m, a Miss Rate (MR) of 0.19, and DAC = 0.95, while establishing an accuracy–deployability operating point under embedded hardware constraints with low power consumption and practical inference throughput. Full article

Industrial Internet of Things (IIoT) networks use on-demand sensing and wireless power transfer (WPT) for self-sustainable operation. Existing scheduling frameworks are fundamentally limited because they react only after energy levels decline. Consequently, IoT nodes enter charging mode only when their residual energy falls below a threshold, causing energy outages, increased latency, and missed sensing tasks while preventing proactive WPT resource allocation. This paper proposes a Digital Twin (DT)-enabled proactive scheduling framework that transforms IIoT scheduling from reactive to proactive. The key innovation is a closed-loop virtual–real integration in which a DT layer, co-located with the control centre, maintains a Kalman filter predictor to forecast node energy over an H-slot horizon, enabling scheduling decisions before energy shortages occur. Physical layer security (PLS) constraints and DT-based anomaly detection protect against eavesdropping, energy depletion, and false data injection attacks. A multi-objective formulation jointly optimises sensing utility and WPT efficiency while accounting for DT synchronisation overhead and uplink bandwidth consumption. The resulting multi-slot Binary Integer Linear Programmes (BILP) are solved using branch-and-bound with a reliability branching rule, and a fast greedy heuristic is also developed. Simulation results over 50 Monte Carlo iterations show that the proposed framework reduces energy outage events by approximately 70% compared with the reactive baseline, activates less than 50% of available sensing nodes, and schedules less than 60% of energy transmitters for WPT. Ablation studies confirm that DT prediction is the primary contributor to the outage reduction. DT-based anomaly detection achieves a false alarm rate below 3% while maintaining a detection rate above 95%. The proposed framework improves the sustainability, efficiency, and security of IIoT networks with practical computational overhead, making it well suited for Industry 5.0 deployments. Full article

Background and Objectives: Ultrasound-guided sciatic and saphenous nerve blocks are widely used as analgesic alternatives for multimodal analgesia. However, how to objectively quantify the opioid- and anaesthetic-sparing effects of an additional peripheral nerve block in patients undergoing ankle fracture surgery under general anaesthesia remains unclear. This study aimed to evaluate their efficacy in managing postoperative pain and opioid-sparing effects following ankle fracture surgery. Materials and Methods: Patients scheduled for ankle fracture surgery were randomly assigned to the nerve block (n = 30) or control group (n = 30). The primary outcome was the postoperative numeric rating scale (NRS) score. The secondary outcomes included the total doses of remifentanil and propofol, Quality of Recovery-40 Korean (QoR-40K) scores, and self-reported patient satisfaction. Results: The NRS score immediately after surgery was significantly lower in the nerve block group compared with that in the control group (2.6 ± 2.5 vs. 6.2 ± 2.6; p < 0.001). The total remifentanil dose required during surgery was also significantly lower in the nerve block group than in the control group (1148.1 ± 391.8 vs. 1487.4 ± 798.8; p = 0.041). However, the patient satisfaction scale (3 [IQR, 2–4] vs. 3 [IQR, 2–4]; p = 0.206) and QoR-40K score 24 h after surgery (169 [IQR, 155–184] vs. 175 [IQR, 154–165]; p = 0.324) did not differ significantly between the two groups. Conclusions: Ultrasound-guided sciatic and saphenous nerve blocks significantly reduced immediate postoperative pain and intraoperative remifentanil consumption under qNOX (quantitative nociception index)-guided general anaesthesia during ankle fracture surgery. Full article

The deterministic evaluation framework for traditional virtual power plants (VPP) overlooks the capacity squeeze caused by day-ahead forecasting errors in real-time settlement, resulting in a significant overestimation of the adjustable capacity committed to external parties; simultaneously, conventional distributed PV forecasting methods struggle to capture the dynamic spatiotemporal coupling characteristics arising from meteorological time-varying factors. To address these issues, this paper proposes a comprehensive evaluation method for VPP adjustable capacity that integrates front-end dynamic spatiotemporal forecasting with back-end risk-averse scheduling. Firstly, a two-step dynamic sub-region partitioning strategy based on a sliding time window is proposed. By combining the Spacetimeformer self-attention model with K-means error mining, high-precision scenario-based available power boundaries are generated. Secondly, conditional value at risk (CVaR) is introduced to construct a two-stage risk-averse stochastic programming (RA-SP) model that accounts for expected physical default penalties. Finally, a parallel control evaluation system is established based on the physical baseline anchoring without response of internal flexible resources and real-time post-event verification. Simulations using real operational data demonstrate that the proposed dynamic spatiotemporal forecasting architecture significantly improves regional power forecasting accuracy; compared with traditional deterministic models, the RA-SP model effectively eliminates artificially inflated committed capacity exceeding physical limits (reducing the assessed tracking root-mean-square error by 32.42%), and by proactively setting aside a physical risk-resistance margin, the cumulative physical default electricity consumption throughout the day was reduced by 99.99%. This not only significantly enhanced the reliability of physical delivery but also achieved a flexible and reliable settlement rate with high confidence. Full article

Renewable energy communities (RECs) are increasingly recognized as a strategic instrument for enhancing the sustainability and resilience of energy systems, promoting local renewable integration, and reducing consumer exposure to electricity market volatility. This study analyzes the Italian electricity market and assesses the economic performance of RECs relative to individual consumers using high-frequency hourly data from 2021 to 2023, covering both the 2022 European energy crisis and the subsequent Italian regulatory reform of incentive mechanisms. The optimization problem is formulated in physical terms, aiming to maximize locally utilized energy, defined as the sum of self-consumed and shared photovoltaic generation. This choice reflects the structure of the Italian regulatory framework, where incentives are directly linked to the amount of energy shared within the community. In this context, energy-based optimization is preferred to avoid embedding assumptions on discount rates, investment horizons, and financing conditions, which may vary significantly across users and introduce additional uncertainty. From a sustainability perspective, maximizing local energy utilization contributes to improving energy efficiency, reducing reliance on external energy sources, and enhancing the capacity of decentralized systems to absorb market shocks. For this reason, economic indicators such as Net Present Value (NPV) or payback period are not explicitly included in the optimization objective. This is justified by the focus of the analysis on short-term operational performance and exposure to electricity price volatility, rather than long-term investment evaluation. Moreover, given that the economic value of the REC is largely determined by shared energy volumes under the current Italian incentive scheme, maximizing local energy utilization provides a consistent proxy for economic performance. Nevertheless, the integration of financial metrics such as NPV or payback period represents a relevant extension for future research, particularly in the context of investment decision-making. Through panel econometric analysis, we estimate the sensitivity of economic value to electricity price fluctuations. Results show that RECs reduce price sensitivity by approximately 8–15% compared to individual users, as estimated by panel regression coefficients. Furthermore, the volatility of economic value decreases by around 1.95% under the community configuration, particularly during the 2022 price shock demonstrating that RECs exhibit significantly lower price dependence than standalone consumers. To assess the robustness of these findings, a machine learning framework is employed to relax linearity assumptions and capture potential non-linear effects. Results consistently show that while market prices remain an important determinant, RECs substantially attenuate their impact, particularly during periods of extreme price stress. A policy counterfactual comparison between pre- and post-reform incentive structures further indicates that the coefficient of variation decreases by approximately 4.4% under the post-reform incentive scheme, highlighting the role of policy design in supporting economically and operationally sustainable energy communities. Overall, this study develops a data-driven analysis based on a high-frequency synthetic dataset designed to reproduce realistic consumption and generation dynamics, providing robust evidence that RECs contribute not only to renewable energy deployment but also to the economic and systemic sustainability of electricity markets under conditions of high volatility. Full article

The traction performance analysis of hydro-mechanical transmission (HMT) tractors is employed to evaluate and optimize the transmission system during the design phase, thereby reducing research and development costs associated with continuously variable tractor transmission systems. However, there is currently no established methodological framework for calculating the traction performance of HMT tractors. To address this gap, this study integrated traditional tractor traction performance calculation equations with a self-developed HMT energy consumption calculation method, thereby developing a traction performance calculation model for HMT tractors. Initially, the principle of the tractor’s hydrostatic power-split transmission system was introduced. Subsequently, mathematical models for calculating transmission system energy consumption and tractor traction performance were established, with key sub-models experimentally validated to ensure the reliability of subsequent results. On this foundation, a calculation method for the traction performance of HMT tractors was proposed. Finally, models before and after energy consumption optimization, as well as models under different road conditions and transmission system configurations, were utilized as comparative models to calculate the traction performance of HMT tractors under various settings. The results indicate that energy optimization of HMT not only improves the transmission performance and reduces fuel consumption of HMT tractors, but also enhances the matching capability of HMT tractors with farm tools under high traction efficiency. Additionally, road conditions significantly impact the traction performance of HMT tractors. In wheat stubble fields, the tractor’s traction performance is substantially lower than on standard roads, with a maximum traction force decrease of 40.8% at a slip rate of 30%. Full article

Objectives: This study aimed to investigate the longitudinal associations between breakfast consumption, sleep duration and depressive symptoms among adolescents. Methods: The baseline survey (T1) was conducted from November to December 2019 using a multi-stage stratified cluster sampling method among secondary school students in Shanghai, China. The follow-up survey was conducted about one and a half years later (T2). A total of 2502 adolescents were included in the final analysis. Depressive symptoms were evaluated using the Chinese version of the Patient Health Questionnaire-2 (PHQ-2-C). Breakfast consumption frequency and sleep duration in the past week were self-reported. A cross-lagged model was constructed to examine the longitudinal associations between breakfast consumption, sleep duration and depressive symptoms. Results: The results showed that the prevalence of depressive symptoms and insufficient sleep increased from 15.31% and 89.53% at T1 to 18.47% and 92.89% at T2, respectively. The rate of daily breakfast consumption decreased from 81.10% to 76.06%. The cross-lagged model showed that daily breakfast consumption could significantly predict depressive symptoms (β = −0.109, SE = 0.030, p < 0.001) and sleep duration (β = 0.070, SE = 0.028, p = 0.013). Sleep duration could predict depressive symptoms (β = −0.076, SE = 0.022, p < 0.001) and vice versa (β = −0.039, SE = 0.018, p = 0.028). Conclusions: The rate of daily breakfast consumption among adolescents decreased, alongside the prevalence of depressive symptoms and insufficient sleep increased. Daily breakfast consumption predicted depressive symptoms and sleep duration, whereas depressive symptoms and sleep duration may have a bidirectional association. Full article

Spirulina, a cyanobacterium recognized for its nutritional and environmental advantages, has emerged as a potential ingredient for sustainable diets. Consumer acceptance remains limited despite its benefits, particularly due to sensory challenges and limited prior awareness. This study aimed to investigate knowledge, consumption patterns, and attitudes toward Spirulina among a Brazilian sample. A cross-sectional online questionnaire distributed via social media and public spaces yielded 933 valid responses, categorized into three groups based on prior awareness and consumption history. Results indicated limited prior awareness and low consumption, with more than half of consumers having tried Spirulina only once. Education, income, generation, and health-related behaviors were associated with knowledge and consumption, although most effect sizes were small. Knowledge of Spirulina was concentrated on nutritional attributes, whereas environmental and technological attributes were less widely recognized. Health and environmental benefits were most often rated as increasing willingness to consume Spirulina, while self-reported barriers included taste, smell, and issues related to powdered and capsule forms. Preferred applications were in familiar food categories such as baked goods and powdered mixes. These findings indicate that Spirulina occupies a niche position among respondents and suggest the relevance of sensorially acceptable formulations, tailored communication strategies, and inclusive educational efforts. Full article

Background: Fibromyalgia is a chronic pain syndrome associated with impaired psychosocial functioning, reduced quality of life, and substantial socioeconomic burden. Although nutritional approaches to fibromyalgia management are increasingly investigated, the relationships between dietary patterns, socioeconomic determinants, and quality of life remain unclear. This study aimed to assess associations between fibromyalgia severity, psychosocial functioning, dietary patterns (DPs), socioeconomic factors, and self-rated quality of life among Polish women with fibromyalgia. Methods: A cross-sectional online survey was conducted between March and April 2026 among members of nationwide Polish Facebook support groups for individuals with fibromyalgia. The final analysis included 201 women who self-reported a physician diagnosis of fibromyalgia. Fibromyalgia severity was assessed using the Modified Fibromyalgia Assessment Status (FAS), disease impact using the Revised Fibromyalgia Impact Questionnaire (FIQR), and quality of life using selected WHOQOL-BREF domains. Dietary patterns were identified using principal component analysis (PCA) based on the frequency of consumption of 33 food groups from the KomPAN questionnaire. Hierarchical linear regression models were applied to identify predictors of self-rated quality of life. Results: Four dietary patterns were identified: traditional, meat-and-fat, pro-healthy, and dairy-based. Participants reported low overall quality of life and moderate fibromyalgia severity. A better self-rated financial situation was associated with a higher quality of life only in the initial regression model (β = 0.325; p < 0.001). After adjustment for clinical and psychosocial variables, socioeconomic factors and dietary patterns were no longer significant predictors. Functional limitations (FIQR Functioning: β = −0.202; p = 0.022) and overall disease impact (FIQR Overall Impact: β = −0.189; p = 0.026) were negatively associated with quality of life, whereas psychological (β = 0.234; p < 0.001) and social functioning (β = 0.162; p = 0.023) were positive predictors. The final model explained 40.3% of the variance in quality of life. Conclusions: These findings support a comprehensive biopsychosocial model of fibromyalgia care. Full article

(1) Background: Obesity and dental caries disproportionately affect low-income South Asian (SA) immigrant children in the US. This CHALO! study aimed to reduce the risk of obesity and oral health risk in young SA children in the US. (2) Methods: CHALO! is a randomized controlled trial. A total of 350 low-income Bangladeshi mothers of 6-month-old children were recruited and randomized to intervention or control. Intervention participants received six home visits and six phone calls from trained community health workers who delivered health education and support. The primary outcome was frequency of combined bottle/sippy cup use over 18 months measured via self-report. Secondary outcomes included sugar consumption, maternal feeding practices, oral hygiene practices, and dental utilization measured via self-report. Secondary clinical outcomes included the presence of dental caries at follow-up (12 months post baseline) assessed through intra-oral camera, and obesity risk, measured as weight gain velocity, at each 6-month period. (3) Results: Bottle/sippy-cup use increased less in the intervention group (Poisson rate ratio = 0.36, 95% CI: 0.34–0.39, p < 0.0001) vs. controls (Poisson rate ratio = 0.58, 95% CI: 0.56–0.61), and while consistent results were noted in sugar consumption, oral hygiene practices, dental visits, and other secondary outcomes, no difference was found in caries prevalence or weight gain velocity. (4) Conclusions: The intervention improved self-reported bottle use and child diet in the intervention group. There were no significant changes in caries prevalence or weight gain velocity. Social context, particularly social networks, may act as a barrier to adopting new healthy behaviors, impacting changes in caries and obesity outcomes. Full article

The rapid proliferation of cloud data centers has intensified concerns over carbon emissions, energy efficiency, and sustainability. Virtual machine (VM) placement is a pivotal control lever, yet existing methods rarely couple carbon intensity signals with computationally tractable multi-objective optimization. In this paper, we propose CASO (Carbon-Aware Surrogate-Guided Optimization), a novel framework that integrates an online adaptive Radial Basis Function (RBF) surrogate model with a self-adaptive hybrid PSO-DE swarm optimizer for real-time VM placement in geo-distributed edge cloud environments. CASO simultaneously minimizes carbon emissions, energy consumption, SLA violation rate, and network latency under strict host capacity and Quality-of-Service (QoS) constraints. Three key innovations differentiate CASO: (i) an online surrogate update mechanism that refines fitness approximations incrementally as workload patterns evolve; (ii) a carbon intensity weighting scheme anchored to real-time Grid Emission Factor (GEF) signals; and (iii) an adaptive parameter controller that autonomously tunes swarm exploration–exploitation trade-offs without hand-crafting. Experiments on the publicly available Alibaba Cluster Trace (cluster-trace-v2026-GenAI) dataset within a CloudSim-Plus environment show that CASO reduces carbon emissions by up to $31.4\%$, energy consumption by $27.9\%$, and SLA violations by $18.8\%$ compared to the strongest baseline while converging $3.8\times$ faster than the strongest baseline (ADEDL). Full article

The escalating energy consumption in China’s rural residences necessitates the adoption of targeted energy-efficient design strategies. However, existing studies have mainly focused on urban buildings or cold-climate rural residences, and insufficient attention has been given to form-based energy optimization for rural housing in hot summer and cold winter regions. Hangzhou was selected because it is a representative city in this climate zone, where rural residences face both summer cooling and winter heating demands. This study systematically investigates passive design pathways for rural residential buildings by optimizing architectural forms. We conducted in-depth field surveys and data analysis on 76 diverse samples, including both self-built and unified construction types, to establish three representative typical residential models (rectangular, L-shaped, U-shaped) for the Hangzhou region. DesignBuilder was employed to simulate the impacts of eight morphological elements—Shape Coefficient, building area, aspect ratio, orientation, number of floors, floor height, floor height ratio, and roof slope—on building energy consumption. The Gradient Boosting Decision Tree (GBDT) method was then used to quantify the nonlinear effects and relative importance of these elements. The results indicate clear nonlinear relationships between elements and the energy-saving rate. Floor height is identified as the most critical factor affecting energy consumption, followed by roof slope, with building area and other elements also showing significant influence. Based on the quantitative analysis, this study proposes energy-efficient design optimization strategies for rural housing in Hangzhou, offering a validated methodological framework and practical design references for the sustainable development of rural residences in hot summer and cold winter regions. Full article

This study proposes a comprehensive techno-economic methodology to assess the economic viability and optimal sizing of grid-connected residential photovoltaic (PV) self-consumption systems without storage in emerging economies. The model uses net present value (NPV) as the optimization criterion and estimates internal rate of return (IRR) and discounted payback time (DPBT) as complementary profitability indicators. It integrates hourly PV generation, synthesized hourly demand profiles, local tariff structures, surplus-energy remuneration, investment and operating costs, inflation, performance losses, and discount-rate assumptions, while explicitly accounting for context-specific limitations related to data availability, storage-free operation, and financing assumptions. The methodology is applied to 30 Colombian residential scenarios, covering five cities and six socioeconomic strata, and is complemented with a replicability case in Jaén, Spain. In Colombia, PV self-consumption is economically viable in all cases, but profitability is highly uneven: maximized NPV ranges from 2.8 € in the least favorable low-income case to 2816 € in the best high-income case, IRR ranges from 5.0% to 14.7%, and DPBT ranges from 8 to 24 years. From an energy-justice perspective, tariff subsidies improve affordability but may reduce PV attractiveness for low-income users, highlighting the need for capital grants, low-interest loans, or community solar schemes. Full article