Search Results (12,622)

The development of the electric vehicle sector increasingly requires optimal intelligent and embedded energy management. Electric vehicles are now positioning themselves as a strategic alternative to traditional fuel vehicles. This article therefore highlights the design of an embedded system capable of evaluating and transmitting in real time the state-of-charge (SoC) of an electric vehicle battery to a cloud platform, while optimizing energy consumption and data reliability. The methodological approach proposes an experimental study involving the development of two deep learning models, LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit), in the MATLAB 2024.b environment, associated with the design of an embedded prototype for data collection and transmission via Arduino IoT Cloud. Then, a comparative analysis of the models’ performances is also carried out. The results obtained show that the GRU model offers the best performance, with an accuracy of 83.6%, an MSE of 0.0715, and an RMSE of 0.2589, thus validating the relevance of the proposed approach for the intelligent estimation of the state-of-charge in a real application context. Full article

This study presents a comprehensive analysis of the modeling and optimization of sensor installation nodes for weight measurement in the hoppers of a charging car utilized in coke production. The research highlights the critical role of precise load monitoring in preventing technological disruptions, minimizing equipment degradation, and optimizing energy consumption. Conventional sensor technologies, including capacitive, ultrasonic, and laser-based systems, are evaluated, with weight sensors mounted on hopper supports identified as the most robust solution for real-time mass determination under industrial conditions characterized by high dust levels, temperature fluctuations, and mechanical vibrations. A finite element analysis (FEA) was conducted to assess the structural behavior of sensor installation nodes under three distinct loading scenarios, corresponding to different operational conditions of the charging car. The four-point support structure of the hopper experienced the highest loads and non-uniformities. A stress–strain analysis of the sensor mounting assembly, performed using the Ansys software package, confirmed that both the sensor and its support structure maintain a sufficient safety margin (version 2024 R1, Ansys Inc., Canonsburg, PA, USA, the academic license provided to Wrocław University of Science and Technology). The findings validate the structural integrity and operational reliability of the proposed sensor configuration, contributing to the advancement of automated monitoring and control systems in coke production. Full article

Sustainable machining is gaining attention in modern manufacturing due to its cleaner operations, improved resource utilization, and reduced environmental impact. Among sustainable machining methods, minimum quantity lubrication (MQL) successfully minimizes cutting fluid consumption while maintaining adequate cooling and lubrication. This review examines recent developments and future directions in MQL-assisted machining, with particular emphasis on machine learning (ML)-based modeling and optimization techniques. A systematic review comprising literature identification, screening, scientometric analysis, and critical evaluation was employed to analyze 120 papers published mainly between 2010 and 2026. The reviewed studies employed ML models such as artificial neural networks, support vector machines, random forests, gradient boosting, and hybrid optimization approaches to predict machinability parameters, including surface roughness, tool wear, cutting force, cutting temperature, energy consumption, and chip morphology. The findings indicate that ML-assisted MQL processes improve prediction accuracy, machining efficiency, process monitoring, and sustainability performance by reducing energy consumption, minimizing cutting fluid usage, and improving machining quality. The analysis also identifies key research gaps and prospects for intelligent and sustainable machining. Full article

Model predictive control enhances building energy performance; however, its reliability is highly dependent on the robustness of internal prediction models under severe operating conditions. To address this, a risk-aware model-then-control (RAMC) training framework is proposed in this study. This approach augments conventional prediction loss with a conditional value-at-risk (CVaR) penalty on operational costs under perturbed inputs, embedding tail-risk awareness directly into the prediction model. The framework is trained via standard backpropagation, avoiding the computational burden of differentiating through the controller. The proposed methodology is evaluated on a simulated commercial building equipped with a hydronic heating system under three weather scenarios. Compared to a standard fidelity-trained baseline, the strongest risk-aware configuration reduced occupied cold degree-hours by 22–26% and peak cold violations by 14–27%, demonstrating the greatest benefit under forecast bias. These comfort improvements were achieved alongside a 17–31% increase in weekly heating energy consumption. The results indicate that embedding tail-risk awareness into model training improves closed-loop comfort robustness relative to standard accuracy-based training. An ablation study attributes this improvement directly to the CVaR tail term, while the risk weight formalizes a tunable energy–comfort trade-off dictated by operational priorities. revtwogreenIn this case study, a fixed setpoint-margin baseline reached comparable cold protection at lower energy; the distinct contribution of RAMC is that it relocates a tunable tail-risk preference into the prediction model itself, leaving the downstream controller unchanged. Full article

China’s road transport, especially private vehicles, has experienced continuous growth in energy consumption and carbon emissions in recent years. Electrification-driven net-zero pathways and their impacts on the power sector have drawn broad concern. Current research insufficiently explores vehicle-to-grid (V2G) advantages and fails to update data and assumptions aligned with the latest policies. This study establishes a provincial bottom-up model to calculate the energy demand and carbon emissions of private vehicles and evaluates decarbonization paths and their impacts on the power sector across different scenarios. Private vehicle ownership will rise first and then fall, hitting around 453 million by 2060. Near-term improvements in energy efficiency combined with the long-term diffusion of new energy vehicles can drive private transport toward net-zero emissions after 2050. Vehicle electrification raises electricity consumption remarkably, whereas V2G effectively mitigates carbon shift and offsets over half of cumulative power generation emissions. Marked regional disparities prevail in vehicle usage and emissions, with eastern China presenting higher values compared with western regions. Decarbonization of road transport is more than just addressing carbon shifting, and V2G facilitates cross-sector coordinated emission reduction. Future research is needed to explore the technical, economic and institutional potential for deepening decarbonization. Full article

Machine learning should be judged by how well it predicts, and computational resources are not accounted for in predictive accuracy. Given the growing emphasis on energy consumption and resource efficiency, decision-supporting frameworks should go beyond accuracy. This study presents an energy-based benchmarking approach for supervised learning models. Ten classical algorithms were evaluated on three textual and tabular datasets. The energy consumption of preprocessing, training, and inference was monitored with Intel RAPL via pyRAPL along with the runtime, peak memory usage, and predictive performance statistics (accuracy, precision, recall, F1-score, and AUC). Experiments were conducted in a controlled CPU-based environment to ensure comparability. The computational role of this feature is found to be appreciably diverse. Results show that Random Forest achieved the highest overall balance between predictive performance and efficiency (CI = 0.950, PPI = 0.907), while Logistic Regression provided a competitive trade-off (CI = 0.905, EI = 0.998). Gaussian Naïve Bayes was the most energy-efficient model with a mean energy consumption of 127 J, whereas Support Vector Classifier (SVC) incurred the highest computational cost, consuming 45,758 J and requiring 3925 s on average. The Pareto analysis identified Random Forest, Logistic Regression, Passive Aggressive, and Decision Tree as non-dominated solutions. These findings demonstrate that accuracy alone can be misleading for model evaluation and that integrating energy, runtime, and memory metrics enables more sustainable and resource-aware machine learning model selection. The proposed framework provides practical guidance for Green AI, Tiny Machine Learning (TinyML), edge computing, and other resource-constrained deployment environments. Full article

Sustainable agricultural development requires regional agricultural systems to balance output growth, resource efficiency, ecological protection, and long-term resilience. In mountainous and ecologically sensitive regions, identifying the development constraints and statistically derived marginal contribution thresholds of agriculture is essential for promoting green transformation and sustainable land use. Taking Yunnan Province, China, as a representative plateau mountainous agricultural region, this study uses provincial annual data from 1990 to 2023 to quantitatively identify the key drivers and threshold characteristics of agricultural development under multidimensional constraints. A multidimensional indicator system was constructed covering fiscal and investment support, agricultural production inputs, rural infrastructure, and labor and population conditions. Ridge regression was employed to address multicollinearity among explanatory variables, Bootstrap approximate inference was used to improve the robustness of coefficient estimation, and the SHAP interpretation framework was introduced to rank key driving factors and identify marginal contribution thresholds. By integrating ridge regression, Bootstrap approximate inference, SHAP-based contribution ranking, and threshold identification, the proposed framework advances prior agricultural sustainability studies by linking coefficient-based factor analysis with interpretable marginal contribution thresholds under conditions of high multicollinearity and multidimensional resource constraints. The results show that agricultural development in Yunnan is characterized by multidimensional resource and infrastructure constraints. Rural electricity consumption, total reservoir storage capacity, fixed asset investment in agriculture, forestry, animal husbandry and fisheries, local public fiscal budget expenditure, and agricultural population generally act as positive supporting factors. Rural electricity consumption is the most stable and core driver across the aggregate and three sectoral models. In contrast, pesticide and fertilizer inputs show significant negative associations in most models, suggesting that future agricultural development in Yunnan is unlikely to be sustainably supported by continued expansion of high-intensity chemical inputs. Sectoral heterogeneity is also evident: agriculture and animal husbandry are more dependent on energy, water resources, and mechanization, whereas forestry shows a more distinct operational structure. The SHAP dependence analysis identifies several statistically derived marginal contribution thresholds, including rural electricity consumption of approximately 6.055 billion kWh, total reservoir storage capacity of approximately 10.395 billion m³, total agricultural machinery power of approximately 19.8324 million kW, pesticide use of approximately 37,500 tons, and fertilizer application of approximately 1.5238 million tons. These values should be interpreted as empirical transition points in the modeled marginal contributions rather than definitive biophysical ecological limits. They indicate that the sustainability-related constraint structure of agricultural development in Yunnan is not a single output ceiling but a composite interval shaped by infrastructure support capacity, factor allocation conditions, and the declining marginal contribution of high-intensity chemical inputs. The findings provide directional quantitative evidence for sustainable agricultural governance, agricultural green transformation, and differentiated policy discussion in mountainous agricultural regions and offer reference implications for advancing SDG 2 and SDG 15 through the coordination of food-related production, resource use efficiency, and ecosystem conservation. The identified thresholds should be interpreted as model-derived marginal contribution transition points rather than operational policy cutoffs or directly enforceable ecological standards. Full article

Adding an upper reservoir to conventional cascade hydropower stations to create pumped-storage systems is an effective strategy for enhancing hydropower regulation capacity and promoting high proportion of variable renewable energy consumption. To leverage the cross-seasonal energy and intra-day power regulation capabilities of such hybrid systems, this paper proposes a multi-scale nested dispatch and benefit assessment method. The coordination principles between pumped storage and cascade hydropower under high variable renewable energy penetration are first analyzed. Subsequently, a dynamic time-of-use electricity pricing mechanism is developed by capitalizing on the temporal characteristics of net load, and a multi-scale nested scheduling model that incorporates grid regulation demands is established. A techno-economic assessment framework is further developed to assess the comprehensive benefits of the pumped-storage retrofitting. The Wujiang Basin case study demonstrates significant benefits: a 4.5% improvement in peak–valley difference reduction, a decrease of 1039 GWh in annual variable renewable energy curtailment (8.8% of the system’s total), and a 30.8% rise in generation benefits. Under wet and dry hydrological years, generation benefits increase by 787 million and 645 million CNY, respectively. These results indicate that implementing pumped-storage retrofitting in cascade hydropower basins with abundant but seasonally uneven inflow can better align grid regulation requirements with project economic viability. Full article

Urban ecological resilience (UER) has become a central concern in rapidly urbanizing regions where development pressures increasingly interact with ecological constraints. Focusing on the Guanzhong Plain Urban Agglomeration (GPUA), a semi-arid urban agglomeration in western China, this study examines the non-static and locally heterogeneous drivers of UER across 11 prefecture-level cities from 2000 to 2023. UER is measured through resistance, adaptability, and recovery. An extended STIRPAT model, Elastic Net with stability selection, two-way fixed-effects period interactions, and Geographically and Temporally Weighted Regression (GTWR) are integrated to identify robust drivers, test post-2011 shifts, and estimate city-year local associations. Residual Moran’s I diagnostics and Spatial Lag GTWR (SLM-GTWR) are used as supplementary checks. The results show that UER remains relatively stable at the aggregate regional level but becomes increasingly divergent across cities. Ten robust drivers are retained, with fiscal investment intensity, human capital, medical and health level, and total energy consumption emerging as key variables. Period heterogeneity results indicate that fiscal investment becomes more favorably associated with UER after 2011, while the marginal association of energy consumption weakens. GTWR reveals clear local heterogeneity: human capital shows the most stable positive association, medical and health level remains generally negative, fiscal investment is positive but context-dependent, and energy consumption is predominantly negative but locally differentiated. Supplementary spatial diagnostics suggest that the GTWR specification captures the main spatiotemporal structure of UER, while spatial-lag checks broadly support the robustness of the local coefficient patterns, although estimates of spatial interaction remain sensitive to how inter-city linkages are defined. These findings indicate that UER drivers are dynamic rather than fixed, with resilience formation shaped mainly by governance-regime shifts and localized heterogeneity. The study contributes a sequential screening–heterogeneity framework for identifying non-static resilience drivers and suggests that resilience governance should combine stage-sensitive policy adjustment, place-based intervention, and regional coordination where ecological functions and environmental risks cross administrative boundaries. Full article

Typhoon-eye sensing places unmanned aerial vehicle (UAV) swarms in a setting where the wind field that carries the target signal also displaces aircraft, drains energy, weakens links, and increases failure risk. A rule that improves only routing or only motion can therefore move the swarm into another failure mode. This paper proposes a risk-driven cross-layer coordination scheme for such missions. A bounded risk index, computed from isolation, connectivity loss, and wind intensity, acts as a supervisory variable for multi-hop reachability maintenance, isolated-node recovery, and layered altitude adaptation. For evaluation, graph reachability is separated from useful data return through a degraded multi-hop aggregation model that includes distance loss, wind-dependent reliability, rain-induced packet loss, relay forwarding loss, and mothership collection capacity. The simulator combines a bounded Holland-type storm field, stochastic turbulence, nonlinear propulsion energy consumption, and wind-dependent structural failure. Against three literature-inspired baselines, two AI-inspired comparators, and six ablation variants, the method keeps a balanced profile across connectivity, isolation, wind exposure, data collection, and survival. In 30-run steady-state robustness tests under heavy-rain attenuation, the full strategy showed clear gains over routing-only and multi-agent reinforcement learning (MARL)-routing comparators in connectivity and isolation, but did not uniformly dominate topology reconstruction or the multi-agent deep deterministic policy gradient–artificial potential field (MADDPG-APF) recovery comparator. The results indicate that, in storm-dominated swarm sensing, resilience comes mainly from coordinating exposure reduction with topology stabilization, rather than from optimizing a single layer. Full article

Reducing emissions is increasingly critical for mitigating the environmental impact of the iron and steel industry. Achieving this transition requires an accurate evaluation of carbon emission intensity for steel production, which relies on an in-depth analysis of carbon metabolism mechanisms across the entire steel production chain. Existing approaches predominantly focus on carbon tracing within material flows, which cannot deeply integrate carbon migration pathways with energy flows and thus fail to reveal the actual sources and transmission mechanisms of carbon emissions. To address this gap, this study develops a carbon metabolism simulation model of the steel manufacturing process that considers the coupling of material production with the energy network. The differentiated carbon metabolism patterns are characterized in terms of carbon fixation, migration, and dissipation to support more accurate carbon emission accounting and enable the formulation of targeted decarbonization strategies. The results show that the coking process fixes 72.51% of its carbon input. The sintering and pelletizing process shows typical carbon dissipation characteristics, with nearly 100% of input carbon discharged. Carbon emissions from steelmaking and the rolling process are mainly induced by indirect energy consumption. The total carbon dissipation of integrated steel production system is 440.62 kg-C/t-CS, with the ironmaking process contributing the largest share of 33.92%. Full article

Electric vehicle (EV) charging demand introduces significant variability in power systems, requiring forecasting approaches capable of representing both aggregated consumption trends and stochastic charging behaviors. While machine learning methods often provide strong predictive performance, they generally require large datasets and substantial computational resources. This paper proposes a stochastic framework based on compound Poisson and Cox processes to model EV charging demand using real charging station data collected at one-minute resolution. The proposed methodology jointly models charging-event arrivals, charging duration, and charging power through probabilistic distributions calibrated from historical observations. A compound homogeneous Poisson process (CHPP) and a double stochastic compound Poisson process (Cox process) are investigated and compared for the generation of synthetic EV charging profiles and short-term forecasting applications. The framework is validated using 1863 charging sessions recorded at a workplace charging infrastructure composed of 37 charging terminals. Monte Carlo simulations are performed to generate synthetic daily charging profiles and evaluate the capability of the models to reproduce key operational indicators, including daily energy consumption and peak grid power demand. The CHPP process achieves average forecasting errors up to 0.8% for daily energy and 6.2% for maximum grid power demand. The results show that Poisson-based stochastic models can generate diverse and realistic charging profiles while requiring only limited historical data and having low computational complexity. The proposed approach provides an interpretable and computationally efficient probabilistic framework for EV charging demand forecasting, synthetic profile generation, and power system operational studies. Stochastic compound Poisson processes may therefore constitute a valuable tool to support the ongoing electrification of mobility and the digital transformation of future smart grids and smart cities. Full article

Within the food industry, research on mobile gastronomy has increased from the consumer perspective. Food trucks play an important role as economic units worldwide, serving as a culinary alternative to traditional restaurants. They have emerged as an innovative initiative and business model that offers a disruptive alternative to home cooked meals. One of the aspects most appreciated by consumers is the charcoal-grilled food offered by food trucks. Globally, charcoal is widely used as an energy source and cooking fuel, with an annual production of approximately 53.2 million tons. Its characteristics and low cost make charcoal a dominant energy resource, and it plays a fundamental role in cooking in both low- and high-income countries due to the distinctive flavor and texture it imparts to food. Research has focused on air pollution and health risks, supplemented with information on the types of charcoal, characteristics and properties, production techniques, and added value. Charcoal ash residue production has not been fully analyzed, providing an opportunity for research to obtain data and evaluate various criteria, such as kilograms of charcoal purchased and food trucks’ residual charcoal ash. To address this gap, the authors propose a horizontal collaboration perspective through the application of the COHRV model to (1) collect data and create a database from food-truck business owners in Ciudad Juarez, Chihuahua; (2) develop a circular economy model for charcoal ash as a sustainable strategy within the food industry; and (3) estimate charcoal consumption during the grilling process and the generation of charcoal ash residue in the food truck sector. Full article

Driven by the demand for high-quality housing, fourth-generation residential buildings—known internationally as “Vertical Forest” and in China as “Urban Forest Garden”—have developed rapidly. Initially built in mild southern regions, they have recently expanded to colder northern areas, with over 50 projects underway in provinces such as Shanxi, Hebei, Shaanxi, and Gansu. Several cities have introduced design standards and incentives, and the China Association for Standardization of Engineering Construction has issued the “Design Standards for Urban Forest Garden Housing.” However, in cold regions, where winters are long and cold and summers are short and hot, there is a lack of systematic quantitative research on how balcony design affects building carbon reduction, energy efficiency, and indoor thermal comfort. To address this research gap, this paper poses the following research questions: (1) In fourth-generation residential buildings in cold regions, how do different combinations of balcony orientations affect annual energy consumption and indoor thermal comfort? (2) Which balcony configurations offer the best balance between carbon reduction, energy efficiency, and thermal comfort? Based on statistical analysis of terrace configurations from more than 40 projects, 12 typical configuration models were identified. Using Ladybug and Honeybee tools on the Grasshopper platform, building energy consumption and indoor thermal comfort were simulated. Multi-objective trade-off analysis was performed using the Pareto front method. In this study, indoor thermal comfort was evaluated using the PMV (Predicted Mean Vote) index. PMV is an index proposed by Professor Fanger that comprehensively reflects human thermal sensation, taking into account air temperature, humidity, wind speed, mean radiant temperature, human metabolic rate, and clothing thermal resistance. Its typical range is −3 (cold) to +3 (hot); in this study, the comfort zone was defined as −1 ≤ PMV ≤ 1. Key findings: (1) The southwest + south terrace configuration shows the highest annual energy consumption, exceeding the lowest (northwest + west) by 2.7%, indicating that south-facing terraces are less favorable for carbon reduction. (2) The best thermal comfort is achieved with east, west, and south orientations. Compared to the least comfortable combination (southwest + northwest), the difference in PMV comfort percentage reaches 2.4%. (3) The Pareto front reveals that beyond a certain comfort level, energy consumption increases sharply. The west + south and east + south combinations yield the highest thermal comfort (49.4%) while maintaining relatively low energy consumption (17.98 kWh/m²). Therefore, in cold regions, fourth-generation residential designs should prioritize terrace combinations integrating south-facing and side-facing orientations and avoid pure corner configurations to balance winter solar gain and summer shading. Full article

This study investigates the asymmetric and heterogeneous effects of renewable energy, non-renewable energy, capital stock, labour, and trade openness on CO₂ emissions in BRICS countries over the period 1991–2022. The study applies a panel nonlinear autoregressive distributed lag (PNARDL) model to capture short- and long-run asymmetries, complemented by a panel quantile nonlinear ARDL (QNARDL) to assess distributional heterogeneity. Robustness is ensured using Fully Modified Ordinary Least Squares (FMOLS) and Robust Least Squares (RLS) estimators. The study is grounded in the Environmental Kuznets Curve (EKC) and Just Energy Transition Theory. The results reveal a stable long-run cointegrating relationship among the variables, with a significant error correction mechanism confirming convergence toward equilibrium. Renewable energy consumption consistently reduces CO₂ emissions in both the short and long run, while non-renewable energy significantly increases emissions, exhibiting strong asymmetric effects. Capital stock shows mixed dynamics, increasing emissions in the short run but reducing them in the long run when directed toward productive and efficient investments. Labour is found to reduce emissions in the long run, highlighting the role of human capital in supporting cleaner production. Trade openness generally increases emissions, reflecting energy-intensive trade structures. Quantile results confirm heterogeneity, with stronger renewable energy effects at higher emission levels and greater environmental gains from reducing fossil fuel dependence than from increasing it. The FMOLS and RLS estimations confirm robustness, reinforcing the negative relationship between renewable energy and emissions and the positive impact of non-renewable energy. The study recommends accelerated renewable energy deployment, fossil fuel phase-down strategies, and targeted green capital investment. It further emphasizes grid modernization and energy storage systems to enhance renewable integration, alongside labour reskilling and green trade policies. These coordinated strategies are essential for achieving sustainable decarbonization in BRICS economies. Full article