Search Results (5,128)

Medical cannabis cultivation requires substantial energy for heating, lighting, and climate control. This study evaluates the economic feasibility of an innovative biomass-fired micro-CHP system in a greenhouse facility for medicinal cannabis cultivation. The system comprises an 80 kW_th boiler retrofitted for biomass and a 7 kW_el ORC engine and is assessed against a diesel-boiler Business-As-Usual (BAU) benchmark. Thermal load simulations for two growing periods (1 March–30 June and 1 September–30 December) estimate an annual heating demand of 91,065.20 kWh_th. The micro-CHP system delivers 8195.87 kWh_el per year, exceeding the greenhouse’s 7839.90 kWh_el consumption. Over a 30-year lifespan at a 7% discount rate, Life Cycle Costing yields EUR 196,421.33 for micro-CHP versus EUR 229,468.46 for BAU, a 14.4% reduction. Under all-equity financing, the project achieves an NPV of EUR 59,591.88, IRR of 27.32%, and a DPBP of 12.1 years; with 70% debt financing, NPV rises to EUR 61,211.39 and DPBP shortens to 10.5 years. Levelized Cost of Energy (LCOE) and Heat (LCOH) are EUR 0.122 per kWh_el and EUR 0.062 per kWh_th, respectively. While the LCOE is below the Greek and EU non-household averages (EUR 0.1578 and EUR 0.1515 per kWh_el), the LCOH exceeds the corresponding heat price benchmarks (EUR 0.0401 and EUR 0.0535 per kWh_th). These results indicate that, in the modeled context, biomass-ORC cogeneration can be a financially attractive and lower-carbon option for medicinal cannabis greenhouse operations. Full article

Underwater object detection is critical for marine resource utilization, ecological monitoring, and maritime security, yet it remains constrained by optical degradation, high energy consumption, and vulnerability to adversarial perturbations. To address these challenges, this study proposes an Adaptive Vision Transformer (A-ViT)-based detection framework. At the hardware level, a systematic power-modeling and endurance-estimation scheme ensures feasibility across shallow- and deep-water missions. Through the super-resolution reconstruction based on the Hybrid Attention Transformer (HAT) and the staged enhancement with the Deep Initialization and Deep Inception and Channel-wise Attention Module (DICAM), the image quality was significantly improved. Specifically, the Peak Signal-to-Noise Ratio (PSNR) increased by 74.8%, and the Structural Similarity Index (SSIM) improved by 375.8%. Furthermore, the Underwater Image Quality Measure (UIQM) rose from 3.00 to 3.85, while the Underwater Color Image Quality Evaluation (UCIQE) increased from 0.550 to 0.673, demonstrating substantial enhancement in both visual fidelity and color consistency. Detection accuracy is further enhanced by an improved YOLOv11-Coordinate Attention–High-order Spatial Feature Pyramid Network (YOLOv11-CA_HSFPN), which attains a mean Average Precision at Intersection over Union 0.5 (mAP@0.5) of 56.2%, exceeding the baseline YOLOv11 by 1.5 percentage points while maintaining 10.5 ms latency. The proposed A-ViT + ROI reduces inference latency by 27.3% and memory usage by 74.6% when integrated with YOLOv11-CA_HSFPN and achieves up to 48.9% latency reduction and 80.0% VRAM savings in other detectors. An additional Image-stage Attack QuickCheck (IAQ) defense module reduces adversarial-attack-induced latency growth by 33–40%, effectively preventing computational overload. Full article

As urban built-up areas are the main generators of carbon emissions, scientific and accurate estimation of carbon emission levels in urban built-up areas is an important method to help implement the carbon neutrality target. Nowadays, developing a spatial data–based carbon emission estimation model that reduces dependence on energy consumption data, shortens the estimation cycle, and enhances its applicability to urban spatial development remains an urgent challenge. In this study, we developed a spatial data-based carbon emission estimation model for urban built-up areas using data from five winter cities in China over a 15-year period as an example. The estimation model not only strengthens the connection between carbon emission results and urban spatial elements, but also gets rid of the over-reliance on energy data, which in turn greatly shortens the estimation cycle of urban carbon emissions. We also used the model to investigate the distribution of carbon emissions in urban built-up areas. Compared with the traditional carbon emission estimation model based on energy consumption, the correlation coefficient between the two models is greater than 0.95, and the error between the two models is extremely small, indicating that this model has important practical value. On this basis, we propose applications for this model. We apply the model to Harbin, China, to estimate built-up area carbon emissions without using energy consumption data, thereby improving estimation efficiency. We also assess how the current urban planning strategy influences low-carbon construction. Additionally, we use the SHAP method to rank each spatial element’s contribution to carbon emissions. Based on these results, we propose low-carbon optimization strategies for winter cities in China. Full article

Countries encounter significant challenges in the context of the climate crisis, prompting the implementation of environmentally sustainable measures in vulnerable sectors such as tourism. Nevertheless, inadequate levels of public awareness often delay or—in certain cases—hinder the adoption of such measures. This study focuses on the tourism sector in Greece, which is particularly resource-intensive in terms of energy and water consumption, especially in regions frequently affected by extreme weather events. The study’s objective is to evaluate the level of environmental awareness and behavioral profiles of tourists. Firstly, our study provides a literature review addressing the tourism vulnerabilities related to climate change, the nexus between tourism and environmental pressures, the role of public awareness in shaping policy obstacles, and finally issues related to environmental social and individual responsibility and attitudes. Subsequently, a relevant survey was conducted using a structured questionnaire to outline the profile and preferences of home domestic tourists in Greece. Our results indicate a generally moderate to low level of awareness: approximately 80% of respondents believe tourism has minimal or no impact on climate change, while only about 15% endorsed the need for stricter regulatory measures. Moreover, our findings underscore a significant knowledge gap regarding ongoing challenges related to water resource management. Notably, respondents with higher levels of awareness exhibited more positive attitudes towards sustainability-oriented measures. Overall, our study indicates that enhancing environmental awareness through targeted campaigns and effective communication strategies is crucial. In this respect, cultivating the notion of the “responsible tourist” emerges as a key prerequisite for ensuring the long-term sustainability of the tourism sector in Greece, as a responsible tourist contributes to the long-term sustainability and the tourist profile of a destination for both visitors and residents. Full article

The development of large-scale, flexible, and safe hydrogen storage is critical for enabling a low-carbon energy system. Deep saline aquifers (DSAs) offer substantial theoretical capacity and broad geographic distribution, making them attractive options for underground hydrogen storage. However, hydrogen storage in DSAs presents complex technical, geochemical, microbial, geomechanical, and economic challenges that must be addressed to ensure efficiency, safety, and recoverability. This study synthesizes current knowledge on hydrogen behavior in DSAs, focusing on multiphase flow dynamics, capillary trapping, fingering phenomena, geochemical reactions, microbial consumption, cushion gas requirements, and operational constraints. Advanced numerical simulations and experimental observations highlight the role of reservoir heterogeneity, relative permeability hysteresis, buoyancy-driven migration, and redox-driven hydrogen loss in shaping storage performance. Economic analysis emphasizes the significant influence of cushion gas volumes and hydrogen recovery efficiency on the levelized cost of storage, while pilot studies reveal strategies for mitigating operational and geochemical risks. The findings underscore the importance of integrated, coupled-process modeling and comprehensive site characterization to optimize hydrogen storage design and operation. This work provides a roadmap for developing scalable, safe, and economically viable hydrogen storage in DSAs, bridging the gap between laboratory research, pilot demonstration, and commercial deployment. Full article

Renewable energy sources provide environmental and economic benefits by replacing conventional energy sources. In Korea, photovoltaic (PV) systems are increasingly deployed in apartment complexes and residential buildings. In self-consumption PV systems, surplus generation exceeding local demand often leads to a reverse power flow. This phenomenon becomes more frequent in microgrid environments where multiple distributed energy resources are interconnected. Accordingly, inverter control strategies based on generation forecasting have emerged as critical challenges. In this paper, we propose an on-device artificial intelligence model for inverter control that integrates net power forecasting with time-series neural networks. Two novel forecasting methods were proposed and introduced: Prediction-to-Prediction (P–P) and Net-Power Prediction (N–P). Various neural network models were trained and evaluated using multiple performance metrics. A novel threshold adjustment mechanism based on the mean absolute error was designed for inverter control. The control scenarios were analyzed by comparing the actual power losses with the forecast-based power losses, and the energy savings were quantified by adjusting the correction factor. The proposed forecasting methods achieved a reduction of approximately 40–70% in energy losses compared with the actual loss levels. The threshold adjustment strategy enhances flexibility in balancing the number of on/off switching events and the power loss, contributing to improved energy efficiency and system stability. Full article

This paper presents an evaluation of the use of deep learning architectures for forecasting electrical energy consumption in residential environments. The main contribution of this study lies in the development and assessment of a hybrid forecasting framework that integrates multiscale temporal analysis and weather data, enabling evaluation of predictive performance across different temporal granularities, forecast horizons, and aggregation levels. Single and hybrid models were compared, trained with high-resolution data from a single residence, both considering only endogenous variables and including exogenous variables (weather data). The results showed that, among all models tested in this study, the hybrid LSTM + GRU model achieved the highest predictive performance, with R² values of 94.62% using energy data and 95.25% when weather variables were included. Intermediary granularities, particularly the 6 steps, offered the best balance between temporal detail and predictive robustness for the tests performed. Furthermore, short-time windows aggregation (1 to 5 min) showed better accuracy, while the inclusion of weather data in scenarios with larger aggregation windows and longer horizons provided additional gains. The results reinforce the potential of hybrid deep learning models as effective tools for forecasting residential electricity consumption, with possible practical applications in energy management, automation, and integration of distributed energy resources. Full article

Ultraviolet B (UVB) light exerts biological effects beyond the skin; however, its influence on systemic energy metabolism remains unclear. We investigated the effects of chronic, low-dose narrowband UVB irradiation on substrate utilization, circulating metabolites, and thermogenesis of brown adipose tissue (BAT) in mice. Male and female C57BL/6J mice were daily exposed to sub-erythemal UVB (308 nm, 50 or 100 mJ/cm², 3 h) for up to 7 weeks using a custom light-emitting diode-based device. Metabolic outcomes were assessed by indirect calorimetry, locomotor activity monitoring, and infrared thermography. Plasma metabolites were profiled by capillary electrophoresis–time-of-flight mass spectrometry. Gene expression in BAT and skin was measured by reverse transcription quantitative polymerase chain reaction. UVB exposure lowered the respiratory exchange ratio at specific time points, indicating greater lipid utilization, and transiently increased oxygen consumption. Metabolomic profiling revealed reduced succinate levels and enrichment of nicotinate/nicotinamide and propanoate metabolism pathways. Infrared thermography showed elevated surface temperature after irradiation and that prolonged UVB exposure modestly upregulated thermogenic genes in BAT, along with increased cutaneous expression of Cidea. These findings suggested that sub-erythemal UVB exposure modestly modulates systemic metabolism, circulating metabolites, and BAT activity, highlighting UVB as a potential environmental regulator of energy balance. Full article

The Energy–Economy–Environment (3E) nexus within basin economic zones has received significant scholarly attention. As a major river basin economic belt in China, the Yangtze River Economic Belt (YREB) serves as an important case for examining the status and drivers of coordinated 3E development. The findings of this study may also offer valuable insights for promoting sustainable development in river basin economies globally. Encompassing 11 provinces and municipalities, the YREB represents not only a vital socioeconomic region in China but also one of the nation’s largest energy consumers, facing considerable environmental pressures. Using panel data spanning 2009–2019, this study applies the coupling coordination degree (CCD) model, spatial Durbin model, and Moran’s I to assess the coordination level of the 3E system in the YREB. The main findings are as follows: (1) The CCD demonstrated a trend that was fluctuating but generally on the rise throughout the study period. Higher values were observed in eastern provinces and lower ones in western provinces, which reveals a distinct east–west spatial gradient. (2) A significantly positive spatial correlation was observed in provincial 3E coordination, although this correlation fluctuated and showed a slowly weakening trend over time. Local spatial clustering patterns also shifted, marked by the persistence of high-high clusters, an increase in low-low clusters, and the emergence of low-high outliers. (3) Estimates from the spatial Durbin model indicate that urbanization, automobile consumption, and foreign trade exert positive overall effects on the CCD, whereas industrial structure exerts a negative overall effect. Environmental policy is not statistically significant in the static model but shows a negative overall effect when the CCD is lagged by one period. Full article

In arid and water-stressed regions, groundwater desalination plants are critical for ensuring reliable potable water supplies, making improvements in their operational efficiency and cost effectiveness a priority for utilities. In many such facilities, lime and soda ash softening remain common pretreatment practices, which increase chemical consumption and sludge generation, prompting the need for alternative low-chemical strategies. This study evaluates the technical, operational, and economic implications of transitioning a full-scale brackish groundwater desalination plant, from lime–soda ash softening (old plan) to a low-chemical pretreatment strategy based on antiscalant dosing (new plan) upstream of reverse osmosis (RO). Key parameters, including pH, total hardness, calcium and magnesium hardness, silica, iron, alkalinity, and total dissolved solids (TDS), were measured and compared at multiple locations within the treatment plant under both the old and new plans. Removing lime and soda ash caused higher levels of hardness, alkalinity, and silica in the water before RO treatment, increasing the risk of scaling. Operationally, the feed pressure increased from 11.43 ± 0.16 bar (old plan) to a peak of 25.50 ± 0.10 bar in the new plan, accompanied by a decline in water production. Chemical cleaning effectively restored performance, reducing feed pressure to 13.13 ± 0.05 bar, confirming that fouling and scaling were the primary, reversible causes. Despite these challenges, the plant consistently produced water that complied with Saudi Standards for Unbottled Drinking Water (e.g., pH = 7.18 ± 0.09, TDS = 978.27 ± 9.26 mg/L). Economically, the new strategy reduced operating expenditure by approximately 54% (0.295 → 0.135 $/m³), largely due to substantial reductions in chemical and sludge handling costs, although these savings were partially offset by higher energy consumption and more frequent membrane maintenance. Overall, the findings emphasize the importance of systematic performance evaluation during operational transitions, providing guidance for utilities seeking to optimize pretreatment design while maintaining compliance, long-term membrane protection, and environmental sustainability. Full article

Buildings account for approximately one-third of global energy usage and associated carbon emissions, making energy benchmarking a crucial tool for advancing decarbonization. Current benchmarking studies have often been limited to mainly the annual scale, relied heavily on simulation-based approaches, or employed regression methods that fail to capture the complexity of diverse building stock. These limitations hinder the interpretability, generalizability, and actionable value of existing models. This study introduces a hybrid AI framework for building energy benchmarking across two time scales—annual and monthly. The framework integrates supervised learning models, including white- and gray-box models, to predict annual and monthly energy consumption, combined with unsupervised learning through neural network-based Self-Organizing Maps (SOM), to classify heterogeneous building stocks. The supervised models provide interpretable and accurate predictions at both aggregated annual and fine-grained monthly levels. The model is trained using a six-year dataset from Washington, D.C., incorporating multiple building attributes and high-resolution weather data. Additionally, the generalizability and robustness have been validated via the real-world dataset from a different climate zone in Pittsburgh, PA. Followed by unsupervised learning models, the SOM clustering preserves topological relationships in high-dimensional data, enabling more nuanced classification compared to centroid-based methods. Results demonstrate that the hybrid approach significantly improves predictive accuracy compared to conventional regression methods, with the proposed model achieving over 80% $R^2$ at the annual scale and robust performance across seasonal monthly predictions. White-box sensitivity highlights that building type and energy use patterns are the most influential variables, while the gray-box analysis using SHAP values further reveals that Energy Star^® rating, Natural Gas (%), and Electricity Use (%) are the three most influential predictors, contributing mean SHAP values of 8.69, 8.46, and 6.47, respectively. SOM results reveal that categorized buildings within the same cluster often share similar energy-use patterns—underscoring the value of data-driven classification. The proposed hybrid framework provides policymakers, building managers, and designers with a scalable, transparent, and transferable tool for identifying energy-saving opportunities, prioritizing retrofit strategies, and accelerating progress toward net-zero carbon buildings. Full article

Background: Degenerin proteins, such as Acid-Sensing Ion Channel 2 (ASIC2) and β Epithelial Na⁺ Channel (βENaC), have been implicated in cardiovascular function. We previously demonstrated that mice lacking normal levels of βENaC and ASIC2 are protected from diet-induced obesity, metabolic disruption, and hepatic steatosis. Methods: To investigate the specific role of βENaC proteins in the progression of metabolic disease, we examined the impact of a high-fat diet (HFD) in the βENaC hypomorph mouse model (βMUT). Body composition and metabolic and behavioral phenotypes were examined in male and female and βMUT and WT mice (n = 6–14/group) fed a normal chow diet (NFD) from weaning until 16 weeks of age, then a 60% kcal-fat diet for 5 weeks. Results: Compared to WT mice, βMUT male mice have reduced lean and total body mass. No remarkable differences in energy expenditure, motor activity, or food consumption patterns were detected. HFD-fed male βMUT mice exhibited reduced liver fat content (mass and Oil Red O staining) yet increased abdominal fat depots. HFD-fed female βMUT mice exhibited lower heart mass. Conclusions: These novel findings suggest a role for βENaC in the maintenance of metabolic homeostasis and adipose tissue distribution. Full article

Background/Objectives: Childhood obesity remains a critical public health concern in Greece, particularly among socioeconomically vulnerable groups. This study conducted a secondary analysis of five large-scale epidemiological datasets to examine the association between energy balance-related behaviours (EBRBs) and obesity in children in need. Methods: Data were compiled from five nationally or regionally representative studies (Genesis, ToyBox, Healthy Growth, ENERGY, and Feel4Diabetes) involving children aged 1–12 years. Stratified and subgroup analyses were performed to examine associations between weight status and EBRBs, including dietary habits, physical activity, and sedentary behaviour. Determinants of EBRBs were also analysed using the socio-ecological model framework. Results: Children in need demonstrated a higher prevalence of overweight and obesity compared to the general child population. Key risk factors for EBRBs included frequent consumption of sugar-sweetened beverages, sweet snacks, and high screen time. Protective behaviours associated with lower obesity risk included regular breakfast consumption, adequate sleep duration, and physical activity. Determinants of high-risk EBRBs were primarily interpersonal and, to a lesser extent, individual and community-level factors. Conclusions: These findings highlight the disproportionate burden of childhood obesity among vulnerable populations and identify modifiable behaviours and determinants that can inform targeted interventions. These results provide a robust evidence base to guide national public health policies, including the development of school- and community-based obesity prevention programmes aligned with the goals of Greece’s National Action Against Childhood Obesity. Prioritising children in need in such initiatives is essential to reduce health inequities and improve long-term health outcomes. Full article

The proliferation of artificial intelligence (AI) and online services has significantly escalated the demand for computing and storage resources, which are fundamentally enabled by cloud computing infrastructure. As the backbone of cloud computing services, data centers have undergone continuous expansion in scale, consequently leading to significant energy consumption and a significant carbon footprint. To effectively mitigate the environmental impact, the strategy should prioritize the integration of renewable energy, while simultaneously minimizing other contributing factors such as energy consumption. Achieving both objectives simultaneously requires a fine-grained, dynamic approach to workload management. To this end, this study proposes a comprehensive container placement strategy that integrates a dynamic priority-based container selection algorithm with a multi-factor single-objective container placement algorithm based on the Dream Optimization Algorithm (DOA). The placement algorithm converts multiple factors—including load balancing in multi-data center environments, energy consumption, renewable energy utilization rate, carbon emissions, Service Level Agreement Violation (SLAV), and container migration costs—into a comprehensive fitness metric. Experimental results on Google and Alibaba datasets show our method consistently achieves the highest renewable energy utilization rates (up to 92.08%) and the lowest carbon emissions. Furthermore, our integrated strategy demonstrates a superior trade-off, reducing migration counts by up to 16.3% and SLAV by 12.4% compared to baselines, while maintaining excellent green performance. This establishes our method as a practical and effective solution for sustainable cloud computing. Full article

Background: Energy drink (ED) consumption has increased significantly among adolescents worldwide, constituting a high-risk behavior with important public health implications. These beverages are associated with cardiovascular disturbances, sleep disorders, anxiety symptoms, and risky behaviors, especially when combined with alcohol. In Chile, monthly per capita ED consumption among individuals aged 14 to 30 increased ninefold between 2010 and 2020. Objectives: To examine the prevalence of ED consumption and its associated determinants among Chilean adolescents enrolled in grade eight through grade twelve. Methods: Data were drawn from the national representative survey 2023 entitled Fifteenth National Study on the School Population of Chile (ENPE). The final sample included 45,042 adolescents. Besides descriptive analyses, both bivariate and multivariate logistic regression models were used to examine associations between sociodemographic characteristics, parental presence, monitoring, and ED consumption outcomes. Results: Lifetime prevalence of ED consumption was 71.0%, with higher rates in females (72.8%) than males (69.4%). Past-month consumption was reported by 46.2%, with a higher prevalence in males (48.3%) than females (43.9%). Lifetime consumption of ED mixed with alcohol (AmED) was 23.2%, being more frequent among females (26.4%) than males (19.9%). Age, grade level, and indigenous identity were consistently associated with higher odds of consumption. Parental monitoring and involvement indicators were inversely associated with both ED and AmED consumption. Conclusions: This study reveals a high prevalence of ED consumption among Chilean adolescents, with notable gender differences. Family protective factors, particularly parental monitoring and cohesion, emerge as key determinants of this risky behavior and warrant prioritization in public health prevention efforts. Full article