Search Results (3,482)

Introduction: Two small, preliminary pilot studies report that 2 weeks of daily tart cherry juice consumption (half of the dose in the morning, half of the dose at night) may increase sleep quantity (assessed via a sleep diary or 1 night of polysomnography) in older adults with insomnia. A study of longer duration, with doses closer to bedtime, and daily objective monitoring of sleep via a wearable device may potentiate the observed impact of tart cherry juice intake on sleep. With the proposed changes to the study protocol, it is paramount to evaluate the study’s feasibility. Methods: The current study is a single-site, randomized, double-blind, cross-over pilot study in 20 older adults with self-reported insomnia. Eligible individuals will be randomly assigned to consume 16 oz. of tart cherry juice/day or placebo juice for 4 weeks each, separated by a 3-week washout period. Information on study feasibility, including recruitment rate, retention rate, safety, compliance, and study practicality, will be collected, as well as pre- and post-arm evaluations of sleep quantity/quality and biomarkers related to melatonin, cortisol, serotonin, and inflammation. Discussion: Identification of a dietary intervention that improves sleep quantity and quality may serve as a novel and feasible approach for older adults who suffer from insomnia. If successful, such a strategy would help mitigate the plethora of health consequences associated with poor sleep. Full article

The inherent bottleneck of the von Neumann architecture and the limited power budget of edge devices necessitate energy-efficient hardware solutions for artificial intelligence. Memristor-based In-Memory Computing (IMC) has emerged as a promising candidate; however, the high-power consumption of peripheral circuits, particularly Analog-to-Digital Converters (ADCs), and the reliability issues of memristive devices remain significant challenges. In this paper, we propose a hybrid Convolutional Spiking Neural Network (CSNN) architecture designed for resource-constrained edge computing. Our approach integrates digital Non-Leaky Integrate-and-Fire (NLIF) neurons with Knowm Self-Directed Channel (SDC) memristor-based synapses in a 1T1R crossbar array. To maximize power efficiency, we replace conventional high-resolution ADCs with a streamlined readout circuit utilizing a Current Sense Amplifier (CSA) and a 1-bit comparator. Furthermore, we employ an intensity-to-latency temporal coding scheme to minimize spike activity and mitigate device endurance degradation. We validated the proposed system using the MNIST dataset, achieving a classification accuracy of 97.8%, which is comparable to state-of-the-art floating-point SNNs using supervised learning methods. Power analysis confirms that our 1-bit readout method consumes only 18.4% of the energy required by an 8-bit ADC-based approach while maintaining negligible accuracy loss. Additionally, the deterministic single-spike nature of our temporal coding significantly reduces write stress on memristors compared to rate coding. These results demonstrate that the proposed hybrid CSNN offers a robust and energy-efficient solution for neuromorphic edge intelligence. Full article

In the context of the global energy transition and a strengthening climate agenda, this study provides a comparative assessment of sustainable development strategies adopted by leading national oil and gas companies in China and Russia, focusing on their contribution to decarbonisation and SDG 7 and SDG 13. The study combines content analysis of corporate ESG reporting with a quantitative assessment of key environmental indicators. The analysis covers Gazprom, Rosneft, Lukoil, Novatek and Tatneft (Russia), and CNPC (PetroChina), Sinopec and CNOOC (China) over 2020–2024. The quantitative assessment includes Scope 1–2 greenhouse gas emissions, emissions intensity per unit of energy produced, operational energy consumption, renewable energy share, water intensity and green capital expenditures. The results reveal differences in national adaptation models: Chinese companies follow a centralised, state-driven approach integrated into strategic planning and five-year programmes, while Russian companies demonstrate a more fragmented, corporate-oriented model focused on technological modernisation. The strongest divergence is observed in governance integration and low-carbon investment structures, while emissions intensity trends remain gradual in both cases. Based on these findings, recommendations are proposed to strengthen sustainability and climate governance in Russian and Chinese oil and gas companies. The findings rely on self-reported ESG data, which involve differences in reporting boundaries and calculation methodologies. Full article

Accurate forecasting of multivariate time series is essential for energy management, grid optimisation, and policy planning. This study presents a hybrid deep learning and Transformer-based forecasting framework for predicting hourly electricity consumption across Turkey using nationwide data from Energy Exchange Istanbul (EPİAŞ) between 2018 and 2025. The dataset comprises 15 variables representing diverse energy sources and market indicators, including consumption, generation, and the market-clearing price (MCP). The proposed hybrid model integrates Long Short-Term Memory (LSTM), Bidirectional LSTM (BLSTM), and Gated Recurrent Unit (GRU) layers to capture both short- and long-term temporal dependencies, while a Transformer model leveraging multi-head self-attention mechanisms is used for comparison. All models were trained using standardised preprocessing, a 24 h lookback window, and optimised hyperparameters via GridSearchCV. Experimental results reveal that the hybrid model achieved the best overall performance, with MAE = 464.01, RMSE = 663.39, and R² = 0.9902, significantly outperforming the baseline and Transformer models. The Transformer demonstrated robust long-horizon learning capability (R² = 0.9257) but at a higher computational cost. These results confirm that combining multiple recurrent architectures enhances predictive accuracy and stability for large-scale, real-time energy forecasting. The proposed framework offers a reliable foundation for smart grid operations, demand prediction, and data-driven energy policy development. Full article

Identifying real-world saturation points and grid-hosting capacity in mixed-use urban Renewable Energy Communities (RECs) requires dynamic spatial evaluation. To address this, this paper introduces a novel simulation framework that integrates GIS spatial analysis with an iterative heuristic selection algorithm. The proposed method evaluates the energetic interaction between a primary generation node and surrounding consumers, utilizing a dynamic function to calculate the collective Self-Consumption Rate (SCR). Applied to the Flaminio Stadium in Rome, the model incrementally aggregates users to determine the optimal cluster size for economic feasibility. The results demonstrate that the heuristic selection algorithm successfully refined the community from an initial pool of 854 buildings to an optimal cluster of 734. This targeted selection eliminated energy surplus and achieved a near-perfect collective SCR of 99.8%. Furthermore, by strategically reducing the required installed PV capacity by 52.6%, the initial capital investment dropped from € 89.9 million to € 42.6 million, significantly de-risking the project while maintaining a competitive payback period of approximately 13 years. Ultimately, this study presents a scalable spatial optimization tool that empowers decision makers to transform large-scale urban infrastructure into the energetic and economic engines of district wide decarbonization Full article

This study examines whether moral identity, perceived environmental responsibility, and locus of control predict green consumption behaviour among young consumers. Adopting a quantitative approach, the study follows an explanatory research design grounded in the positivist paradigm. Primary data were collected through a self-administered questionnaire delivered to respondents aged 18–35 years. Descriptive statistics were analysed using the Statistical Package for the Social Sciences (SPSS) version 30, while inferential analysis was conducted using Structural Equation Modelling (SEM) via SmartPLS 4. The findings suggest that moral identity and perceived responsibility for environmental damage are significant predictors of green consumption among youth. In contrast, locus of control shows a weak, statistically insignificant association with green consumption behaviour. After controlling for demographic variables, including gender, age, race, education, occupation, and income, the results indicate that only education level and race make significant contributions to the model. Notably, the effect of moral identity becomes insignificant once demographic factors are considered, while locus of control remains insignificant. However, perceived environmental responsibility not only retains its significance but also demonstrates a strengthened effect on green consumption behaviour. These findings highlight the persistence of the attitude–behaviour gap in sustainable consumption among young consumers, particularly in rural contexts. The study contributes to the literature by extending the Theory of Planned Behaviour through the incorporation of moral and psychological constructs within a rural African setting. Practically, the study offers insights for policymakers, educators, and marketers, emphasising the importance of environmental education, moral reinforcement, and targeted behavioural interventions to enhance youth participation in sustainable consumption practices. Full article

Background: Childhood and adolescence represent a critical period for shaping long-term dietary habits, including whole grain consumption, which remains low despite well-documented health benefits. Objective: This cross-sectional study (November–December 2024) examined Slovenian adolescents’ attitudes toward whole-grain foods in the context of organized school meals. Methods: Participants aged 10–12 years and 14–19 years (N = 501; mean age 15.6 ± 2.6) completed an online questionnaire assessing knowledge, self-reported consumption frequency, preferences, motivational factors, and food neophobia using the translated Italian Child Food Neophobia Scale (ICFNS). Based on ICFNS scores, participants were classified as low (≤17), medium (18–24), or high (≥25) in food neophobia. Results: Older adolescents demonstrated better knowledge of whole-grain health benefits; however, greater knowledge was not associated with higher self-reported consumption. Food neophobia was strongly associated with lower consumption frequency and reduced willingness to try whole-grain foods, including whole-grain bread, oatmeal, buckwheat porridge and brown rice. Across all groups, taste was the most consistent motivator for trying whole-grain foods. Older adolescents prioritized health and appearance as key reasons for eating more whole grain foods. Conclusions: Findings suggest that improving taste, increasing exposure, and leveraging institutional settings such as schools, where availability, preparation, and social cues can be managed, may be effective in promoting whole-grain food consumption. Full article

Integrating sustainability into luxury products poses fundamental challenges when brands introduce alternative materials made from recycled content that lack the intrinsic value of precious metals. This study investigates consumer perceptions and willingness to pay more for luxury jewelry made from alternative recycled materials among 357 consumers in the Bangkok Metropolitan Region. The conceptual framework examined five value dimensions (self-expression value, aesthetic value, social value, perceived natural rarity, and perceived sustainability) with pro-environmental self-identity as a moderating variable. The model explains 59.2% of the variance in willingness to pay more. Results confirm significant effects of all five dimensions, with aesthetic value as the strongest predictor. Pro-environmental self-identity significantly moderates the relationship between perceived sustainability and willingness to pay more. Despite high levels of sustainability awareness, the results reveal an attitude–behavior gap: environmental concern does not automatically translate into greater spending on sustainable luxury jewelry. This research contributes to the literature on sustainable luxury consumption by clarifying the relative importance of value dimensions and highlighting the conditional role of consumer identity in shaping the acceptance of price premiums. Full article

Soil moisture prediction is essential for precision irrigation in water-limited agricultural systems. This study presents a deep learning-driven irrigation framework for winter wheat, integrating a novel Informer–UNet model with a Comprehensive Irrigation Index for adaptive water management. The Informer–UNet combines ProbSparse self-attention mechanisms with UNet’s multi-scale feature fusion, enabling simultaneous prediction of soil moisture at 27 monitoring points across three depths, 10, 30, and 50 cm, while quantifying prediction uncertainty through Monte Carlo Dropout. A Comprehensive Irrigation Index incorporating moisture deviation, spatial variance, and confidence interval width was developed, with weights optimized via genetic algorithm. Field experiments were conducted in Chengdu, China, over two winter wheat growing seasons. The Informer–UNet achieved superior prediction accuracy, R² greater than 0.98, RMSE less than 0.65, compared to LSTM, Transformer, and standard Informer models, with the fastest convergence and lowest validation loss. The proposed DeepIndexIrr strategy maintained soil moisture within the target range, 55% to 75%, for over 81% of the irrigation period, reducing water consumption by 38.2% compared to fixed-threshold control and 19.2% compared to expert manual scheduling. These results demonstrate that integrating spatially distributed deep learning predictions with uncertainty-informed decision rules offers a promising approach for sustainable precision irrigation. Full article

In response to the growing need for accurate forecasting of electricity generation from PV installations, which is crucial both for enhancing self-consumption and for balancing the power grid, this study presents a comparative analysis of selected machine learning models. The research focuses on the XGBoost algorithm and LSTM neural networks, applied to predict PV energy production based on meteorological data and historical generation records from four medium-sized PV installations (30–50 kWp) located in Poland. Meteorological data were retrieved from open sources and combined with actual production measurements to build the training dataset. This paper discusses the challenges posed by these data at the given latitude, as well as issues related to processing data from newly launched installations. The performance of both approaches was evaluated in short- and medium-term forecasting, with particular attention to prediction accuracy, robustness to noisy data, and the ability to capture nonlinear relationships. Full article

Aiming at the problems of high energy consumption and insufficient utilization potential of clean energy in expressway service areas in severe cold and arid desert areas, this paper takes the Xinjiang Kelameili Service Area as the research object to explore the optimal configuration scheme and comprehensive benefits of a photovoltaic system in this specific scenario, providing a technical reference for the energy transformation of transportation buildings in desert areas. The field research method was used to collect measured data of energy consumption and photovoltaic operation in the service area in 2022–2024. The photovoltaic simulation model was constructed using PVsyst 7.3.1 software. The inclination and azimuth parameters were optimized by the control variable method, and the energy savings, carbon emission reductions and economic benefits of the system were calculated by the whole life cycle analysis method. The study found that the total power consumption of the service area in 2024 was 3.661 million kWh, and the actual annual power generation of the existing photovoltaic system was 438 million Wh, accounting for only 12% of the total power consumption. After optimization, the optimal inclination angle of the photovoltaic panel was determined to be 14°, and the azimuth angle was 89°/−89°. Additionally, the maximum annual power generation of the system reached 579 MWh. Throughout the whole life cycle of the photovoltaic system, it is expected to save 1692 tons of standard coal, reduce CO₂ emissions by about 10,311.98 tons, reduce carbon revenue by about 524,800 yuan, and reduce comprehensive income by about 8,097,000 yuan. The static investment recovery period is about 22 years. Reasonable optimization of photovoltaic system configuration can effectively improve the self-sufficiency rate of clean energy in desert expressway service areas. The research results have reference significance for photovoltaic applications in service areas in similar alpine arid areas. Full article

This study assesses the technical, operational, environmental, and economic feasibility of integrating alkaline water electrolysis (AEL) using on-site measured surplus electricity from two 20 MW natural-gas turbogenerators installed at a Central Processing Facility (CPF) in a Colombian oilfield. Unlike approaches based on modeled profiles, the analysis relies on more than 31,000 experimental records of gas consumption and active power, enabling an accurate characterization of the structural availability of energy surpluses under real operating conditions. A specialized industrial water treatment and purification company was consulted and provided with the physicochemical characterization results obtained from process water samples analyzed by an accredited laboratory. Based on these parameters, the technical supplier confirmed the feasibility of designing a multistage treatment train, including equalization, filtration, clarification, activated carbon, ultrafiltration, and reverse osmosis, capable of achieving final conductivities at or below 5 µS/cm. This water quality level is compatible with typical industrial alkaline electrolysis requirements and in line with technical specifications commonly aligned with ASTM and ISO standards for pressurized AEL systems. A strategic comparison between PEM and AEL technologies, supported by IFE/EFE matrices and sensitivity analyses, identified alkaline electrolysis as the optimal alternative under a stable electrical profile and capital expenditure constraints. Energy sizing for scenarios between 1.5 and 10 MW, assuming continuous 24 h operation and an average specific consumption of 50 kWh/kg H₂, yields productions between 0.5 and 3.5 t H₂/day, with electrical efficiencies above 70%. A 20-year financial analysis indicates a techno-economic threshold near 3 MW (NPV > 0; IRR > WACC), with optimal performance in the 6.5–10 MW range and payback periods between 2 and 4 years under internal valorization of the surplus electricity. From an environmental perspective, the produced hydrogen is classified as low-carbon rather than “green” due to its thermal origin; however, the integration improves the turbines’ operating regime and valorizes surplus electrical exergy that was previously unused, providing a replicable strategy for industrial assets with self-generation and treatable water availability. Full article

Curbing pornography consumption is the subject of keen debate and the object of numerous social efforts. The methods of structuring the discourse on combatting pornography use reveal a wide range of sociocultural views. This study utilizes semiotic and textual analyses of videos and advertisements (ads) dedicated to preventing pornography use in the secular and religious sectors of the Israeli Jewish public, which illuminates the differing perceptions and social norms among these groups. To this end, we conducted a comparative study of ads aimed at both audiences. By analyzing their symbolic representations and the rhetoric emerging from their content, we discovered that, while the prohibition narrative for the observant religious public centers on pornography use negatively impact the individual’s environment (their relationship family and community), the ads designed for viewing by the secular public focus on the individuals themselves. Thus, divergent socio ethical perspectives on the use of pornography emerge, illuminating how individuals relate to both their environment and their sense of self. The present study teaches us how different communities adapt words and symbols to convey social messages, particularly those associated with charged issues such as sexuality. Full article

Local governments increasingly combine power purchase agreements (PPAs) with local retail power producers and suppliers (RPPSs) to pursue decarbonization and regional revitalization. However, there is limited municipal-scale evidence on how contractual design translates into regional multiplier and employment outcomes under structural uncertainty. Using a 38-sector municipal input–output table (2015) for Fukuchiyama City, Kyoto, Japan, we conduct scenario-based simulations to quantify the output and employment multipliers of on-site and off-site solar photovoltaic PPAs. We compare Type I multipliers (household exogenous) and Type II multipliers (household endogenous) across nine scenarios that combine three PPA arrangements—off-site sales to the local RPPS [A], off-site sales to a major utility [B], and on-site self-consumption [C]—with three interregional leakage scenarios (1)–(3). A systematic sensitivity analysis (±10–20% perturbation of structural coefficients) was implemented to provide results as conditional ranges rather than point estimates. Under baseline leakage (3), off-site PPAs sold to the local RPPS [A3] yield the largest short-term total effects (1.24 million USD/year). Crucially, the error bars confirm that the policy ranking of A > B > C remains robustly invariant across all leakage conditions. Endogenizing households increases total effects by approximately 22.9% without changing this ranking, with induced effects concentrated in consumption-related services. In contrast, on-site PPAs [C] yield significantly larger long-term cumulative multipliers through stable expenditure savings from avoided electricity purchases. These results provide a transferable evaluation protocol and identify policy levers—off-taker localization, local supply chain thickening, and localized O&M—that jointly determine whether PPAs deliver broad-based regional economic benefits. Full article

Background: Housing is a social determinant of health. In 2015, a lottery assigned low-income families from a waitlist to a new subsidized building (NSB) in Chinatown, Boston, MA. In 2019–2020, we explored associations between housing status (NSB or being on waitlist) and self-rated physical and mental health; household food insecurity (FI); weekly consumption of fruits/vegetables (FV), weekly consumption of soda, and monthly consumption of fast food. Methods: Surveys were sent to NSB (n = 95) and waitlist (n = 2498) households. Logistic and linear regressions explored associations between housing status and outcomes of interest. Models were adjusted for age, sex, Asian background, household size, education, income, employment and distance to the closest food store. Results: A total of 138 respondents completed the survey (NSB = 36, waitlist = 102). Groups were demographically similar. In terms of self-reported health, most respondents reported good/better physical health (Waitlist: 62%, NSB: 60%) and good/better mental health (Waitlist: 68%, NSB: 74%). FI was prevalent among both waitlist households (63%) and NSB households (56%). FV intake was similar among NSB households (13.5 times/week) compared to waitlist households (12.8 times/week). The NSB group reported similar soda consumption (1.7 times/week) compared to the waitlist group (2.3 times/week), along with similar fast-food consumption (NSB: 2.7 times/month, Waitlist: 3.7 times/month). We found no statistically significant associations between housing status and outcomes of interest after adjusting for covariates. Conclusions: In this small sample, outcomes were not significantly different between groups. Future studies should explore mechanisms through which NSB residence affects nutrition and health, particularly in minority populations. Full article