Search Results (2,604)

Background/Objectives: Healthy longevity depends on not only lifespan but also the maintenance of physiological, metabolic, physical, and cognitive functions throughout aging. Identifying early determinants of health is crucial for preventing age-related decline. This narrative review aims to synthesize current evidence on how diet and specific nutrients relate to these early risk factors and indicators of healthy longevity. Methods: A review was performed to identify the links between dietary factors, energy balance, and gut microbiota composition and normal body weight; blood cholesterol, pressure, and glucose; healthy sleep; an active lifestyle; and normal physical function and cognitive performance. Particular attention was given to Mediterranean and other plant-based dietary models as sources of key nutrients. Evidence from observational studies, randomized controlled trials, and meta-analyses was considered. Results: Across all markers, dietary quality and nutrient adequacy emerged as consistent determinants of health outcomes. Key nutrients were associated with favorable cardiometabolic, cognitive, and musculoskeletal functions, such as omega-3 fatty acids, fiber, vitamins D and B, minerals like magnesium and potassium, and polyphenols. Common nutrition gaps included insufficient intake of fiber, unsaturated fats, and micronutrients, which was often linked to a shift toward less plant-based diets. Gut microbiota diversity may mediate several of these associations, influencing metabolism, inflammation, sleep quality, and cognitive performance, although inter-individual variability and causal pathways remain incompletely understood. Conclusions: An integrated dietary approach emphasizing the consumption of whole and plant-rich foods, with moderate amounts of animal foods, supports multiple early markers, risk factors, and indicators of healthy longevity. The modulation of the gut microbiota through plant-based diets and fermented foods represents a promising strategy for maintaining health across aging trajectories. Full article

The rapid expansion of solar photovoltaics is intensifying competition for land and highlighting the need for scalable energy solutions that can be integrated into existing power systems without displacing agricultural activity. Once the technical and agronomic viability of agrivoltaic configurations has been demonstrated at field scale, a critical next step toward their market consolidation is the assessment of their deployment potential at regional scales from an energy systems and grid integration perspective. This study presents a GIS-based framework to evaluate the large-scale implementation of vertically integrated agrivoltaic systems, using vineyard landscapes in the Region of Murcia (southeastern Spain) as a representative case study. The analysis combines high-resolution land-use data, crop distribution, regulatory constraints on grid connection distances, and existing electrical infrastructure to quantify installable capacity, energy production, self-consumption potential, and grid accessibility. Results indicate that vertically mounted bifacial PV systems could reach up to 7.06 GWp, generating approximately 11.84 TWh/year, while revealing a pronounced spatial mismatch between optimal agrivoltaic production sites and current grid connection points. This distance-dependent distribution highlights the need for differentiated deployment strategies, balancing local self-consumption, grid reinforcement, and centralized injection. Beyond the specific case examined, the proposed approach provides a transferable framework for energy system planning, supporting grid-aware agrivoltaic deployment in diverse regions and regulatory contexts. Full article

Background/Objectives: Milk and milk-based beverages have shown potential benefits for maintaining exercise-induced negative energy balance. However, this has not been systematically investigated. Therefore, this review aimed to evaluate the effects of post-exercise milk or milk-based beverages consumption on appetite regulation and energy intake. Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, the Cochrane Library, Ovid MEDLINE ALL, Open Access Theses and Dissertations, and EBSCO Open Dissertations up to 6 April 2025. Eligible studies were randomized controlled trials assessing the effects of milk or milk-based beverages on post-exercise appetite regulation in healthy adults. Study selection, data extraction, and risk of bias assessment (RoB-2) were performed independently by two reviewers. Meta-analysis was conducted where appropriate using mean differences with 95% confidence intervals (CI). Subgroup analyses were conducted by sex and intervention. Results: Twelve studies (n = 140) were included, of which 10 (n = 118) contributed to the meta-analysis of energy intake. Milk and milk-based beverages were associated with lower energy intake than carbohydrate (CHO) beverages (−72.73 kcal, 95% CI [−141.69; −3.77]; I² = 0%, p = 0.039). Subgroup analyses indicated no effect modification by sex or intervention type. For subjective appetite ratings (11 studies, n = 125), meta-analysis was not performed due to measurement and reporting heterogeneity, and no clear differences or only mild appetite-suppressive effects were observed. Appetite-related hormones were assessed in two studies (n = 23), with no overlapping outcomes. Conclusions: Post-exercise consumption of milk and milk-based beverages may reduce energy intake compared with CHO beverages, although effects on subjective appetite are inconsistent and evidence for hormonal responses remains limited. Full article

Urban separate sewer systems face significant challenges from rainfall-derived infiltration and inflow (RDII) during the wet season. To achieve the integrated optimization of operational safety, energy consumption, and carbon emissions, this study proposes a dynamic optimal control method. A real-time regulation framework was developed by coupling a Storm Water Management Model (SWMM) hydraulic model with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm within a Model Predictive Control (MPC) structure. Based on real-time water level risks, the framework adaptively adjusts the priority among three objectives: overflow reduction, pumping station energy consumption, and methane emission potential. Using a real separate sewer network in CZ city as a case study, the method was evaluated under light, moderate, and heavy rainfall scenarios. Results show that, compared with traditional rule-based control (RBC) and fixed-weight static model predictive control (SMPC), the proposed dynamic model predictive control (DMPC) strategy reduces overflow by 37.2% during heavy rain, and achieves 16.5% energy savings and a 15.8% reduction in methane emission potential during light rain. The strategy also balances network storage utilization, mitigates local overload, and demonstrates enhanced robustness to rainfall forecast errors, providing an effective technical solution for safe, energy-efficient, and low-carbon urban drainage operation. Full article

This research presents an integrated framework for operational planning of low-power robotic agricultural systems, which combines digital twins, uncertainty modeling with triangular fuzzy numbers, and multi-objective optimization in a coherent structure. The goal is to balance energy consumption, carbon emissions, operational delay, and crop yield under variable and uncertain field conditions. The proposed framework was evaluated using real and simulated data, various operational scenarios, and comparative analyses. The results showed that this approach reduced energy consumption from 248.6 to 191.5 kWh and carbon emissions from 132.4 kg CO₂ to 96.8 kg CO₂, while increasing crop yield from 148.7 to 178.4 kg/day, compared to the deterministic baseline model. Also, the use of digital twins improved the quality of decision-making in different scenarios by about 6 to 7 percent, and fuzzy modeling significantly increased the stability of results at higher levels of uncertainty. The findings show that the proposed framework can be an effective tool for sustainable, smart, and energy-efficient agriculture. Full article

Post-disaster reconstruction programmes create an irreversible window for embedding or foreclosing residential energy efficiency at scale. This study examines the structural determinants of per capita residential electricity consumption (K_MES) across all 81 provinces of Türkiye over 2013–2022 using a balanced province-year panel. We develop two complementary panel models, both estimated by two-way fixed effects (province + year) with cluster-robust standard errors, and supported by GLS-AR(1) and random-effects GLS robustness checks. Note that K_MES measures the electricity component of residential energy use only; we, therefore, also estimate the building-stock model with a constructed total-energy dependent variable that combines residential electricity (H_MES) and natural-gas consumption (X_DG) in kWh-equivalent units. Model 1 isolates the macroeconomic transmission channel through which exchange-rate volatility shapes residential electricity demand. Because the USD/TRY rate has no cross-sectional variation, its identifying power in two-way fixed effects comes from its interaction with province-level natural-gas-heating exposure (sh_gas × EV_DA). The interaction is robustly negative across all full-sample specifications (β ≈ −0.022, p < 0.01), indicating that provinces with greater gas-heating penetration are buffered against currency-depreciation pass-through into electricity demand. Provincial GDP carries the dominant direct macro coefficient (β ≈ 0.27–0.29, p < 0.01), establishing income elasticity rather than the exchange rate as the headline aggregate driver. Model 2 decomposes the building stock by structural system, filler material, heating system, and heating fuel. The dominant predictors are the share of electric heating (β ≈ 1.16–1.27, p < 0.01) and the share of AC-only heating (β ≈ −1.0 to −1.13, p < 0.05), with a total-energy specification reaching R² = 0.92. In the comparative subsample of the eleven Kahramanmaraş-affected provinces, masonry construction emerges as the dominant pre-disaster predictor of per capita electricity consumption (β = 14.04, p < 0.05), revealing structurally distinct stock characteristics that pre-date the February 2023 earthquake. Two re-framings are required. First, since the panel covers 2013–2022, the disaster-province estimates capture pre-disaster structural heterogeneity rather than post-disaster market rupture. Second, the macroeconomic mechanism that prior work attributed to the exchange-rate level is more accurately understood as a fuel-mix-mediated exposure channel. The combined evidence implies that mandatory building-code enforcement and natural-gas grid extension are complementary policy levers in the 488,000-unit Turkish Housing Development Administration reconstruction programme: gas grid expansion reduces the macroeconomic vulnerability of residential energy demand, while masonry-replacement construction standards address the largest pre-disaster structural determinant of energy intensity in the affected region. Full article

Energy homeostasis requires precise coordination between brain-derived appetitive signals and peripheral nutrient-handling mechanisms. Although Neuropeptide F (NPF) and its mammalian homolog NPY are well-established central stimulators of feeding, whether and how they regulate nutrient assimilation in the gut remains unknown. Here, using Drosophila, we identify a previously unrecognized transcriptional circuit between NPF and the purine synthesis enzyme GART trifunctional enzyme (Gart) that governs feeding by controlling gut absorptive efficiency. We show that NPF signaling acts via its receptor NPFR to positively regulate Gart expression specifically within the intestine. Conversely, Gart activity exerts negative feedback on NPF expression, forming a reciprocal regulatory loop. Functionally, gut-specific, but not glial or fat body-specific, Gart is necessary and sufficient for promoting food absorption and consumption. Genetic epistasis experiments demonstrate that Gart acts downstream of NPF to execute its function. Strikingly, peripheral NPF from the fat body and gut, rather than brain-derived NPF, serves as the primary systemic signal driving this loop. Our findings reveal a gut-centered homeostatic module where NPF activates Gart to boost nutrient absorption, while the resultant feeding activity in turn curbs the signal, ensuring calibrated energy intake. This work redefines a canonical neuropeptide’s role from a pure behavioral driver to a key regulator of peripheral metabolic efficiency, and establishes a novel framework for understanding gut–brain communication in energy balance. Full article

Wireless sensor networks (WSNs) are fundamental to the Internet of Things (IoT) and are widely used in environmental, industrial, and healthcare applications. However, their operational lifetime is constrained by the limited energy resources of sensor nodes. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol reduces energy consumption through clustering but suffers from random cluster head (CH) selection, leading to uneven energy usage and reduced stability. This study introduces a hybrid optimization approach, LEACH-CSA, which integrates the Crow Search Algorithm (CSA) with LEACH to enhance CH selection and positioning. The proposed method employs CSA’s intelligent search behavior to minimize intra-cluster distances and balance energy consumption across nodes. MATLAB simulations with 100 sensor nodes in a 100 × 100 m² area demonstrate that LEACH-CSA significantly reduces energy consumption and extends network lifetime compared with LEACH and its variants. Furthermore, CSA parameters were optimized using a progressive randomized tuning strategy with 1000, 2000, and 4000 candidate configurations. A comparative evaluation against LEACH-based GA, PSO, GWO, and WOA demonstrated that LEACH-CSA consistently improved the FND metric under different node density and area-scaling scenarios. Full article

The indoor environmental quality of primary and secondary school classrooms is crucial for students’ health and learning efficiency, yet enhancing comfort often leads to high energy consumption. Efficiently balancing the complex relationship between daylighting, visual comfort, and energy consumption during the early design stage presents a significant challenge for architects. To address the design optimization of standard classrooms in primary and secondary schools in the cold region of Zhengzhou, this paper proposes an efficient multi-objective optimization method based on metamodels. This method integrates physical performance simulation (EnergyPlus and Radiance), Latin Hypercube Sampling (LHS), an artificial neural network (ANN) metamodel, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Using Useful Daylight Illuminance (UDI), Discomfort Glare Index (DGI), and Cooling Energy Use Intensity (cEUI) as optimization objectives, ten design parameters, including classroom spatial form and envelope structure, were optimized. The aim is to replace time-consuming traditional simulation calculations and rapidly generate a Pareto optimal solution set. A case study of a typical south-facing classroom in Zhengzhou demonstrates that this method can substantially improve daylighting performance while moderately reducing cooling energy. Compared to the baseline model, the optimized schemes show an average increase in UDI of 42.9% (maximum 50.5%), an average reduction in DGI of 8.4% (maximum 9.6%), and an average reduction in cEUI of 4.7% (maximum 7.7%). Because the study focuses on summer cooling energy only, the reported cEUI improvement should not be interpreted as an annual energy reduction. Through K-means clustering and sensitivity analysis, the study further identifies different design strategies from the Pareto solution set and clarifies the key design variables affecting each performance indicator. This provides an evidence-based reference and preliminary design guidelines for the early-stage design of primary and secondary school classrooms in the region. Full article

With the rapid expansion of the smart clothing market, designers face increasing pressure to balance functional performance, material suitability, environmental impact, and development efficiency. Conventional design workflows and rule-based assistance methods often struggle to provide adaptive and data-driven support for multi-constraint decision-making. To address this issue, this study proposes an AI-driven decision support system for sustainable smart clothing design based on a multi-scale dynamic graph convolutional network (MDGCN). The proposed system integrates material properties, environmental indicators, and user-oriented design requirements into a unified decision-support framework and further enhances feature extraction through an attention mechanism. Two datasets, the Wearable Technology Material Properties Dataset (WTMPD) and the Environmental Impact Assessment Dataset (EIAD), were used to validate the model and system effectiveness. Experimental results showed that the MDGCN-based model achieved accuracies of 0.964 and 0.943, with recalls of 0.923 and 0.920 on the WTMPD and EIAD datasets, respectively. In system-level evaluation, the proposed decision support system reduced design time from 120 h to 60 h, improved material selection accuracy to 90.2%, and achieved superior operational performance in terms of resource utilization (77.45%), energy consumption (115.25 kWh), and response time (1.56 s). These results demonstrate that the proposed framework can effectively support complex design decision-making while improving efficiency, sustainability, and adaptability in smart clothing development. The study provides a practical AI-enabled system innovation approach for sustainable smart clothing design by linking flexible material selection, environmental impact prediction, and designer-oriented decision support. In addition, the prototype deployment demonstrates the feasibility of applying the proposed system as a design-stage wearable AI tool for mediating human, technological, and environmental considerations in smart clothing development. Full article

As the energy and power sector transitions toward clean and low-carbon development, the installed capacity of renewable energy sources such as wind and photovoltaic power has been rapidly increasing. Wind–solar hydrogen production via water electrolysis can enhance renewable energy utilization and enable the supply of green hydrogen. Meanwhile, the $H_2/C{O}_2$ molar ratio in the syngas produced by conventional biomass gasification generally cannot directly meet the 2:1 stoichiometric requirement for methanol synthesis. To address this issue, this paper proposes an off-grid coordinated system integrating wind–solar hydrogen production and biomass gasification for methanol synthesis. The system incorporates multi-operating-condition constraints of electrolyzers, coordinated regulation between electrochemical energy storage and hydrogen storage, and coordinated matching between biomass gasification and the water–gas shift reaction. Based on the system energy and material balance, a mixed-integer linear programming (MILP) model is formulated with the objective of minimizing the annualized total cost and is solved using the Gurobi solver in the MATLAB environment. To highlight the roles of HES and the WGS reaction, four comparative scenarios are designed for validation. The results show that the system with an annual methanol production capacity of 100,000 tons achieves an annualized total cost of 318 million CNY, with a wind–solar utilization rate of 98.86%. The system is configured with 12 electrolyzers of 5 MW each. The biomass consumption per ton of methanol is 3.06, and the $C{O}_2$ emissions per ton of methanol are 2.37. Finally, a sensitivity analysis of the levelized methanol cost (LCOM) was conducted, providing guidance for cost reduction in green methanol production. Full article

Rapid digital transformation is reshaping global trade and raising important questions about its environmental impact, particularly in energy-intensive GCC economies. Despite growing interest, existing evidence remains inconclusive and often overlooks potential nonlinear effects. This study explores how digital trade influences environmental sustainability in Gulf Cooperation Council (GCC) countries over the period 2010–2024. Using a balanced panel dataset for the six economies, the analysis applies a fixed-effects approach with Driscoll–Kraay standard errors to account for cross-sectional dependence and other econometric concerns. To better capture the complexity of the relationship, the study also adopts an asymmetric framework that distinguishes between positive and negative changes in digital trade. The findings show that digital trade does not have a significant effect in the linear model. However, once asymmetry is considered, a clearer pattern emerges. Increases in digital trade are associated with lower CO₂ emissions, while decreases tend to raise emissions. Energy consumption remains the primary driver of emissions, while technological readiness helps reduce environmental pressure. Urbanization and political stability, on the other hand, are linked to higher emissions, reflecting ongoing structural challenges in the region. Overall, the results highlight the importance of sustaining digital trade growth and strengthening technological capabilities to support environmental sustainability in GCC economies. Full article

The growth of Internet of Things (IoT) environments has expanded the attack surface of modern systems. Trojan attacks are a major challenge as they evade conventional detection mechanisms and operate silently within legitimate processes. This paper presents an automated Trojan detection framework for Windows-based IoT gateways. The framework combines custom dataset generation informative feature engineering and deep learning-driven analysis. A dataset of 3000 real world executable samples was created through controlled sandbox execution and forensic monitoring. The process captured behavioral static and network-level characteristics. An initial set contained 146 extracted features. A multi-stage feature selection process identified 33 informative attributes. This step allowed efficient learning and preserved discriminative power. A custom deep neural network model named TrDNN was developed using these features. The model captures complex nonlinear patterns linked to Trojan activity. The framework was evaluated against five classical machine learning models. It was also compared with five deep learning baselines. Results show that TrDNN achieves strong detection performance. The accuracy is 0.975. The precision is 0.972. The recall is 0.969. The F1 score is 0.970. The study also examines inference time and energy consumption. The model shows a balance between detection effectiveness, computational cost and energy efficiency. This makes it suitable for resource-constrained IoT gateway deployment. The detection model was integrated into an automated real-time monitoring pipeline. The system enables continuous process surveillance through Windows command line automation with minimal operational overhead. Statistical validation used paired t tests, Wilcoxon signed rank tests and McNemar chi-square test. The performance gains are statistically significant and do not indicate overfitting. The framework provides a reliable, efficient and deployable solution for Trojan detection in modern IoT systems. Full article

Underground Compressed Air Energy Storage (CAES) is a promising large-scale energy storage technology, yet its long-term operational safety is constrained by progressive tensile damage accumulation in lining structures under cyclic thermo-mechanical loading. Conventional steel-lined caverns are costly, while ordinary reinforced concrete linings require excessive reinforcement due to their limited tensile capacity, compromising the economic viability of CAES. This study proposes a Reinforced-Steel Fibre Concrete (R-SFC) lining as the structural load-bearing layer of CAES caverns, in which the steel fibres provide tensile and crack-propagation resistance and the rebars contribute supplementary tensile capacity. A 2D coupled thermo-mechanical damage-plasticity finite element model was developed in COMSOL Multiphysics and verified using published in situ monitoring data from operating CAES caverns. Parametric analyses of the steel fibre volume fraction, lining thickness, rebar diameter, and cavern diameter were then performed. The results show that the R-SFC lining significantly improves crack propagation resistance, reducing the maximum tensile damage by 41.3% relative to conventional reinforced concrete while lowering steel consumption. Within the lining–rock system, the concrete lining and the surrounding rock jointly resist the radial compressive load, while the steel fibres and rebars bear the hoop tensile stress. A thickness-to-diameter ratio of 1/8 to 1/5 is identified as the recommended geometric design range to balance lining damage against surrounding rock loading. Finally, an MOPSO algorithm coupled with a PSO-BP surrogate model is employed to balance lining tensile damage against cavern dimensions, yielding optimised parameter combinations particularly suitable for cavern diameters around 4 m. The study findings may provide a new lining solution and design reference for cost-effective and high-reliability underground gas storage. Full article

To address the issues with the Crocodile Ambush Optimization Algorithm (CAOA) in UAV trajectory planning—such as its tendency to get stuck in local optima, the difficulty in balancing global search and local exploration, and low convergence accuracy—this study proposes a three-dimensional trajectory planning method based on the Hybrid Particle Swarm and Crocodile Ambush Optimization Algorithm (HPSOCAOA). First, a collaborative search structure combining the Particle Swarm Optimization (PSO) algorithm and the Crocodile Ambush Optimization Algorithm (CAOA) is established; second, an adaptive energy consumption coefficient is designed to address the issues of premature individual elimination in the early stages and insufficient convergence momentum in the later stages, thereby further balancing global exploration and local exploitation; finally, crossover learning is introduced. Using a cross-group replacement mechanism for superior individuals, PSO’s fine-tuning identifies high-quality individuals, which are then substituted for lower-quality individuals in CAOA. This resolves the problems of redundant low-quality individuals within the population and low search efficiency, and enhances overall optimization performance. Standard test functions demonstrate that HPSOCAOA outperforms the comparison algorithms in terms of optimization accuracy and stability. In simulation experiments for path planning in complex 3D mountainous environments, HPSOCAOA was compared with classical intelligent algorithms, verifying its superiority and practicality in complex 3D scenarios. Full article