The housing sector accounts for a high percentage of total energy consumption in Iraq, with most energy usage on air-conditioning systems in summer to provide comfort to residents. This study simulates energy consumption for a typical 200 m², two-story, single-family building in Al Amarah city, Iraq, to compare heating, cooling, and total energy use across three different building configurations. Locally manufactured hollow concrete blocks made with 40 × 20 × 20 cm3 dimensions were adopted to improve their thermal performance by filling the cavities with Polystyrene insulation. The research examined three residential building configurations: (i) a base case built with traditional fired-clay brick, (ii) hollow concrete block walls free of insulation, and (iii) hollow concrete block walls incorporating thermal insulation. Energy simulations using eQUEST software were conducted, utilising the thermal response factor method as the primary tool to analyse the impact of external environmental conditions on cooling and heating loads. The results demonstrated significant annual energy savings for the building with hollow concrete blocks with and without insulation. However, insulated hollow concrete blocks showed reduced annual energy consumption compared to the common brick building system. Specifically, the insulated and uninsulated blocks attained energy savings by 29.4% and 16.08%, respectively, for north-facing orientation.

This study investigates the impact of energy consumption on industrial production in Nigeria from 1986 to 2024. Using secondary time-series data on total energy consumption, electricity consumption, petroleum product consumption, and the Industrial Production Index (IPI), the study applies the Johansen cointegration technique and Vector Error Correction Model (VECM) to examine both long-run and short-run dynamics. The results reveal a significant long-run positive relationship between total energy consumption and industrial output, highlighting the critical role of aggregate energy availability in driving industrial growth. Conversely, electricity consumption and petroleum product consumption exhibit significant negative long-run relationships with industrial production, reflecting structural inefficiencies, unreliable power supply, and high operational costs associated with self-generation and petroleum reliance. The short-run analysis identifies a significant error correction mechanism, indicating that industrial output adjusts to restore long-run equilibrium at a moderate annual speed of 15.5%. These findings underscore that while the quantity of energy is important, the efficiency, reliability, and cost of energy supply are decisive factors for industrial performance. The study concludes with recommendations for policy interventions to enhance energy infrastructure, promote alternative energy sources, and improve energy efficiency to foster sustainable industrial growth in Nigeria.

The development of intelligent transportation systems and the introduction of V2X (Vehicle-to-Everything) architecture place high demands on the characteristics of network communication, such as minimum latency, high reliability, and energy efficiency. At the same time, a decrease in one of the parameters entails an increase in another, which makes the task of their balanced tuning relevant and practically significant. It is especially important to consider not only the total delay (communication network delay and computational delay) and energy consumption, but also the expected arrival time of a mobile computing node based on a vehicle, which is an integral indicator of service quality in a dynamically changing environment. In this paper, we propose a mathematical model for multi-parameter optimization of V2X-system operation parameters that takes into account three interrelated indicators: total delay, energy consumption, and expected arrival time. The model formalizes the structure of the transport system as a directed graph with specified traffic routes, the location of stationary and mobile computing nodes of the transport infrastructure, and the parameters of video data exchange between them and terminal devices. The model is presented as an optimization problem and allows tuning the system according to external conditions and application goals. Simulation modeling methods with realistic vehicle traffic scenarios and variable network load are used as a research tool. The results of numerical experiments demonstrate that the use of the proposed model will allow achieving more balanced modes of system operation, reducing the total delays and energy consumption without deterioration of the arrival time parameters. Compared to traditional approaches based on single- or two-criteria optimization, the proposed method provides greater adaptability and stability of V2X systems to changing operating conditions. The findings can be useful for researchers in designing and implementing energy-efficient and reliable distributed architectures in modern transportation networks.

Quality inspection is a crucial step in ensuring product conformity and avoiding rework waste, while job priority constraints are prevalent in the production of complex products with assembly structures. This paper presents a modeling and solution framework for the multi-objective flexible job shop scheduling problem that incorporates both quality inspection activities and job priority constraints. An optimization model is constructed with the objectives of minimizing the makespan, minimizing the total energy consumption, and maximizing the processing quality. To solve this model, an improved multi-objective evolutionary algorithm based on decomposition is developed, which integrates several well-established mechanisms into a unified framework. The algorithm integrates multi-product assembly structures via virtual nodes, employs a two-vector encoding scheme, and incorporates a product—group repair mechanism based on binary sorting tree to handle job priority constraints. To maintain diversity among non-dominated solutions, a niching-based elite archive strategy is adopted. Furthermore, a quality enhancement strategy and a memory vector-based local search mechanism are embedded to strengthen the algorithm’s search capability. Simulation results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of both convergence and diversity.

To address the challenges of energy-matching uncertainty and precise temperature and humidity control in multi-energy drying systems, this study proposes an adaptive control strategy for a solar-assisted air-source heat pump (SAHP) drying system based on the proximal policy optimization (PPO) algorithm. Due to the intermittent and uncertain nature of solar energy, conventional drying processes struggle to ensure consistent drying quality for alfalfa, often leading to uneven moisture retention and quality degradation. This study developed a synergistic optimization mechanism, integrating the Gym-DryNet simulation environment with a deep reinforcement learning framework, to effectively balance energy input fluctuations and drying process stability. This approach achieved a steady-state control accuracy of 0.3 °C for temperature (relative error of 0.92%) and a humidity regulation accuracy of ± 0.2% RH. Experimental results demonstrate the excellent dynamic anti-interference performance of the intelligent control system. In a 5-h drying cycle, the optimized system produced 160 kg of dried alfalfa (10.3% moisture content) from an initial moisture content of 65.2%, evaporating approximately 250 kg of water at an average rate of 50 kg/h. The total electrical energy consumption was only 3.9 kWh, representing merely 2.3% of the latent heat load associated with the evaporated water. Compared to traditional solar or single heat pump drying systems, this system exhibits significantly reduced temperature fluctuations under full load conditions, demonstrating improved stability and drying efficiency. This fully validates the advantages of the multi-energy synergistic control strategy in optimizing drying efficiency and energy consumption, providing an innovative and intelligent control solution for high-precision drying processes in grass product processing.

Abstract Efficient task scheduling in the Internet of Vehicles (IoV) is crucial for optimizing communication and computational resources, especially under stringent latency and reliability constraints. Traditional task scheduling methods often struggle to handle the complex interactions between vehicle-side energy consumption, edge-side operational costs, and system stability. To address this, we propose a novel two-layer hybrid framework that integrates the Improved Whale Optimization Algorithm (IWOA) with fractional programming (FP) and Lyapunov Drift-Plus-Penalty (DPP) for IoV task scheduling. Our approach decouples the global search of discrete decisions from the optimization of continuous variables, ensuring both efficiency and stability. Experimental results show that our method outperforms benchmark algorithms in terms of energy consumption and latency, achieving up to a 27.08% reduction in total energy consumption and a 25.56% improvement in average latency compared to existing solutions.

Residential heating, ventilation, air conditioning (HVAC), and water heating systems account for approximately 51% of total household energy consumption in the United States, representing over 5.5 quadrillion BTUs annually [1]. Despite widespread adoption of connected thermostats and smart water heaters, contemporary residential energy management platforms remain fundamentally constrained by device-centric architectures that lack semantic interoperability, suffer from sparse telemetry collection, and operate without predictive optimization capabilities. These systems function as isolated control points rather than as integrated climate ecosystems capable of responding to building thermal dynamics, occupant behavior patterns, distributed energy resource availability, and grid conditions. This paper introduces a comprehensive reference architecture for Climate Intelligence Systems (CIS) that transcends current limitations through four foundational pillars: cryptographically anchored device identity frameworks, metadata-driven equipment modeling hierarchies, cloud-hosted digital twin simulation environments, and predictive machine learning optimization pipelines [2], [3]. The proposed architecture enables anticipatory comfort management that pre-conditions spaces based on forecast weather patterns and predicted occupancy, orchestrates distributed energy resources including rooftop photovoltaic arrays and battery storage systems, and provides proactive grid-responsive demand flexibility without compromising occupant comfort or safety. We present a complete four-layer architectural model encompassing device/field infrastructure, connectivity/identity frameworks, cloud intelligence platforms, and human-facing experience layers. The architecture is augmented with detailed system interaction diagrams, digital twin synchronization pipelines, and demand response control flows that demonstrate practical implementation patterns. Preliminary deployment insights indicate 18-24% reductions in compressor short-cycling events, 12-15% improvements in thermal prediction accuracy under varying weather conditions, and 35-42% increases in reliable demand response participation compared to rule-based approaches. The resulting framework provides a coherent, cryptographically secure, and operationally scalable climate management ecosystem that addresses fundamental architectural limitations in today's smart home platforms while establishing a foundation for next-generation residential cyber-physical systems capable of supporting both individual household optimization and grid-scale energy orchestration.

To address seed decay in direct-seeded rice caused by waterlogging resulting from inadequate field leveling, this study conducted split-split-plot field experiments in Chongzhou City, Sichuan Province (103°38’31’’–103°39’22’’ E, 30°33’16’’–30°33’54’’ N). Specifically, two hybrid rice varieties previously identified as flood-resistant (V1: Jinyou 1319) and flood-sensitive (V2: Jingliangyou 1377) were assigned to the main plots, wet direct seeding (P1) and water direct seeding (P2) were compared in the subplots, and the coating (C1) and no-coating (C2) treatments were applied to the sub-subplots. In the coating treatment with water direct seeding, the seedling percentage of V1 and V2 increased by 25.58% and 78.54%, respectively, the number of effective panicles increased by 4.69% and 12.95%, respectively, and the seed setting rate improved by 15.05% and 16.64%, respectively. This synergy boosted the yields of the two varieties by 23.15% and 31.77%. In particular, the yield of V2 with water direct seeding with coating matched that under wet direct seeding without coating. With little difference in total energy consumption (≤ 1.88%), the sensitive variety with water direct seeding and coating saved irrigation water and labor inputs by 13% and 17%, respectively, in the demonstration area (calculated based on the input of the demonstration area). With water direct seeding, the stable oxygen supply from the coating improved the seed germination rate and seedling growth vitality, especially for the sensitive variety. Thus, the oxygen-releasing coating achieved yield increases, resource conservation, and efficiency enhancement synergistically, providing a valuable solution for the development of direct-seeded rice in China’s hilly regions.

Purpose This study aims to develop an efficient and scalable optimization framework to address the growing need for high-performance, low-noise tiltrotor designs, particularly for electric vertical takeoff and landing (eVTOL) aircraft in urban air mobility applications. Design/methodology/approach To manage the high dimensionality and complexity of tiltrotor design, a multi-objective Bayesian optimization framework is established, targeting simultaneous improvements in aerodynamic efficiency and tonal noise reduction. A comprehensive parametric model encompassing 40 design variables is constructed, including pitch angles, blade number, rotor diameter, airfoil shapes using class shape transformation and chord and twist distributions. Aerodynamic performance is evaluated using a modified blade element vortex theory, while tonal noise is predicted through an acoustic analogy-based model. Four-objective optimization is performed under both cruise and hover conditions. Findings The proposed framework efficiently identifies Pareto-optimal designs, capturing trade-offs between total mission energy consumption and average perceived noise levels across multiple flight modes. Results demonstrate that the method achieves up to 22% reduction in total energy consumption and over 75% reduction in average noise compared with the baseline design. In the balanced optimization scenario, the framework yields a 17.5% decrease in total energy, a 16.3% reduction in average noise, demonstrating the framework’s capability to efficiently explore trade offs in high dimensional design spaces. Originality/value This study introduces a high-dimensional, multi-objective optimization methodology tailored to tiltrotor systems, integrating advanced aerodynamic and aeroacoustic modeling with Bayesian optimization. It offers a robust tool for next-generation rotor design under diverse operational scenarios, contributing to the development of quiet and efficient eVTOL propulsion systems.

In this study, a hybrid deep learning model that combines the VGG16 and ResNet101V2 architectures is proposed for image-based fire detection. In addition, a balanced drone guidance algorithm is developed to efficiently assign tasks to available UAVs. In the fire detection phase, the hybrid model created by combining the VGG16 and ResNet101V2 architectures has been optimized with Global Average Pooling and layer merging techniques to increase classification success. The DeepFire dataset was used throughout the training process, achieving an extremely high accuracy rate of 99.72% and 100% precision. After fire detection, a task assignment algorithm was developed to assign existing drones to fire points at minimum cost and with balanced load distribution. This algorithm performs task assignments using the Hungarian (Kuhn–Munkres) method and cost optimization, and is adapted to direct approximately equal numbers of drones to each fire when the number of fires is less than the number of drones. The developed system was tested in a Python-based simulation environment and evaluated using performance metrics such as total intervention time, energy consumption, and task balance. The results demonstrate that the proposed hybrid model provides highly accurate fire detection and that the task assignment system creates balanced and efficient intervention scenarios.

This study quantitatively analyzed the effects of repetitive fault currents occurring in an accelerator environment on the breaking performance of molded-case circuit breakers (MCCBs). To this purpose, four MCCB samples are subjected to one, two, and three repeated fault tests. The interrupting process is divided into the arc stretch and moving (t1–t2) section and the absorption in the splitter plate (t2–t3) section, and the energy and time are analyzed. The experimental results show that the total energy consumption increased by an average of 1.8–1.9 times in the second and third tests compared to the first test, and the interruption time is also extended by 1.6–2.0 times. In particular, the energy increase rate in the t2–t3 section is the highest, at an average of 220%, indicating that the splitter plate is thermally saturated and significantly affected by hot gas due to repeated breaking. These results imply that the thermal and electrical performances of MCCBs deteriorates in a repetitive fault environment, with the interrupting speed delayed and internal energy loss increased. This study suggests the possibility of energy-based condition diagnosis using the energy consumption ratio of each section. Furthermore, the ratios can be used as basic data for evaluating the reliability of circuit breakers under repetitive failure conditions and building predictive maintenance models.

This study presents an Adaptive PID controller designed to enhance temperature stability and energy performance in household refrigerator systems subject to non-stationary disturbances. Classical PID control is limited by fixed gains and the assumption of linear time-invariant dynamics, which is frequently violated by door opening, load variation, and compressor cycling. To address this issue, the proposed approach introduces a Laplace-distribution-based adaptive gain function L(t) that adjusts controller sensitivity according to the statistical rarity of the composite temperature error. The method preserves the conventional PID control structure while introducing a lightweight gain-scaling mechanism suitable for embedded implementation. Experimental validation using a commercial two-compartment refrigerator demonstrated substantial improvements in performance compared with a classical PID controller. The Adaptive PID achieved reduced temperature deviations in both compartments, significantly smoother compressor and fan actuation, and a 4.6% reduction in total energy consumption under an identical disturbance schedule. These results confirm that the proposed controller provides a practical, embedded-friendly solution that improves thermal regulation, actuator longevity, and energy efficiency under the tested disturbance schedule representative of typical household usage.

Abstract The digital economy's explosive growth as a new driver of global economic expansion has highlighted its effects on human health and carbon emissions. The study uses a digital economy module to construct a computable general equilibrium model of China's dynamic energy environment. Through scenario simulation, the study quantifies the impact of developing the digital economy industry on carbon emissions and human health. The study found that in terms of energy consumption, total energy consumption fell to 250 million tce in 2030 under the green digital economy transformation scenario, a reduction of 19.4% from the base scenario. In terms of environmental and health impacts, the PM2.5 concentration under the green digital economy transformation scenario decreased from 25.14 µg/m 3 in 2015 to about 22.36 µg/m 3 in 2030, which was 11.5% lower than the base scenario. In terms of economic performance, the GDP growth rate of the green digital economy transformation scenario was significantly higher, increasing by 0.0579 in 2030 compared to the base scenario. This study demonstrates that developing the digital economy and green energy together can reduce carbon intensity, improve air quality, and minimize health losses. This provides an important policy rationale for balancing economic growth and environmental sustainability. Graphical Abstract

This paper presents an energy-focused analysis of structural materials used in passenger cars, with a particular emphasis on the impact of construction materials on total energy consumption throughout the vehicle’s life cycle. Three production periods (2000, 2010, and 2020) were analysed for B- and C-segment vehicles using inventory data from Life Cycle Assessment databases, the scientific literature, and certified dismantling stations. The embodied energy of key material groups—steel, aluminium, plastics, and other materials—was calculated based on representative mass shares and material-specific energy intensity indicators. The computational model was supplemented with statistical analyses (Kolmogorov–Smirnov test, Levene’s test, ANOVA, and Tukey’s post hoc tests) to verify whether observed temporal trends were statistically significant. The results indicate that total material-related energy inputs increased from approximately 57 GJ to 64 GJ per vehicle, while the specific energy intensity per kilogram decreased from 47.6 MJ/kg to 42.6 MJ/kg. Aluminium exhibited a pronounced reduction in unit energy intensity due to the rising share of secondary materials, whereas plastics and other materials showed substantial increases. Steel remained the largest contributor in absolute terms because of its dominant mass share. This study highlights the growing importance of the production phase in the environmental balance of modern vehicles, particularly in the context of the rising share of lightweight materials and recycling-based components. The results emphasise the importance of energy-efficient material use and underscore the significance of material selection and recycling strategies in reducing energy demand within the automotive sector.

ABSTRACT This study seeks to offer fresh evidence on the relationship between FinTech development and environmental sustainability. Traditional approaches that measure welfare solely through economic indicators often overlook environmental costs, underscoring the need for a more comprehensive and inclusive evaluation framework. Accordingly, this work examines the outcome of FinTech on environmental degradation using a panel dataset covering 20 countries from 2012 to 2022. Two distinct environmental indicators—CO₂ emissions and ecological footprint—are employed to ensure a robust assessment. Additionally, income level, total energy consumption and renewable energy usage are incorporated into the model as control variables. The analysis is based on the method of moments quantile regression, while the robustness of the outcomes is verified through the Driscoll–Kraay estimator. Causal relationships are further explored using the Dumitrescu–Hurlin panel causality test. The research's original contribution lies in the construction of an innovative FinTech index focused on payment services, enabling a nuanced analysis of FinTech's multidimensional structure within an environmental context. The study shows that FinTech has the capacity to improve environmental performance, emphasizing a potential alignment between digital finance and ecological protection. Furthermore, the results indicate that income level and total energy consumption exacerbate environmental pressure, whereas the use of renewable energy contributes to its mitigation. These outcomes provide crucial insights for decision‐makers interested in advancing environmentally sustainable financial policies.

This article proposes a modified method for calculating specific energy intensity and constructs a panel cost model to analyze the impact of energy costs on the cost of milk and meat. The modification was implemented to assess the impact of direct and indirect energy costs on the cost of livestock production. The data source used was reports from 26 agricultural enterprises of various types in southern Russia for the pe-riod 2022–2024, as well as tariff and agricultural statistics data. The relevance of this issue is that farms annually consume 930–1,540 kWh per head of cattle. Pig farming requires 28.7–48.7 kWh to raise one animal from birth to fattening, depending on the housing system. In cow-calf systems, total energy consumption varies from 3,000 to 12,600 megajoules per head of cattle per year, with indirect energy costs for feed ac-counting for the largest portion. Energy costs affect production through direct operat-ing costs (heating, ventilation, lighting, equipment) and indirectly, through feed costs. Methods include elimination, numerical modeling, scenario analysis, and calculation of energy costs in physical units. A methodology for accounting for indirect energy costs is proposed. A factor model has been built that allows for a quantitative assess-ment of the impact of energy prices on production costs, highlighting differences by farm type. Practically significant results of a scenario analysis have been obtained for energy cost increases (10%, 20%, 50%), highlighting differences by enterprise type. The methodology for accounting for indirect energy costs (feed production, transport) has been refined. A model has been built that allows for studying energy price increase scenarios and assessing differences by farm type. A positive and statis-tically significant relationship has been established between the specific energy cost and production costs; With a 10% increase in energy prices, the average cost of live-stock production increases by 1.26%, while with a 20% increase in energy prices, the increase is 2.63%. We summarized various energy costs in standard physical units (J, kWh). We developed a methodology for accounting for direct and indirect energy costs at the enterprise level. The research results can serve as the basis for proposals for energy conservation and support measures for livestock production to improve its sustainability.

ABSTRACT In an era of intensifying global competition where nations aggressively pursue economic advancement, the imperative to balance progress with ecological preservation has become paramount. However, the race for advancement should not harm nature or future generations. Our study investigates the drivers that can lead to green growth, aligning with sustainable development principles that integrate economic growth, environmental stewardship, and social equity as per the UN's Sustainable Development Goals (SDGs), across nine Western European countries from 2010 to 2019. By utilizing panel data from reputable sources, this research investigates the influence of globalization, natural resource rents, renewable energy consumption, trade openness, and total energy consumption on green growth. Employing contemporary panel diagnostic tests, cointegration analyses, and fixed‐ and random‐effects models, the study also validates its findings through quantile regression, fully modified ordinary least squares, and dynamic ordinary least squares. Our study has fulfilled its destiny by finding the right drivers. According to various analyses, globalization and trade openness consistently and significantly promote green growth, confirming their potential as reliable mechanisms for achieving green growth. The complex impact of renewable energy consumption and natural resource rents opens a new door for exploration by revealing the transitional barriers, such as initial costs and policy lags, in contrast to maintaining the resource rent tendency. However, the beneficial impact of total energy consumption of carbon and fossil fuel underscores the urgency of effective resource utilization and a shift toward renewable sources to decouple growth from unsustainable consumption before running out.

ABSTRACT The separation of propane–propylene mixture is an important process in the petrochemical industry as propylene is a key component for various industrial processes. The separation process with conventional methods is energy intensive, costly, and challenging due to the similar physical properties of both components. Extractive distillation (ED) using aqueous N-methyl-2-pyrrolidone (NMP) as extracting agent can overcome these challenges. However, optimizing process conditions to achieve high purity with minimal energy consumption remains a complex challenge. The aim of the study is to make the separation process efficient and cost effective by combining the extractive distillation (ED) simulation with machine learning (ML) algorithms and optimization. In this work, comprehensive data from simulation of ED using Aspen Plus V11 was used to train ML models i.e. Random Forest, XGBoost, and KNN. The models were used to predict propylene purity, condenser and reboiler duties for extractive and recovery columns. RF, XGBoost, and KNN performed well on the data and obtained R2 scores higher than 0.99. The RF model was then integrated into Genetic Algorithm (GA) to optimize the operating conditions that maximize the propylene purity and minimize the total energy consumption. The GA successfully optimized the separation process with a 99.66% propylene purity and total energy consumption less than the initial ED simulation. The ED process with the aid of ML optimization reduced the total annual cost (TAC) by significant amounts compared to conventional high-pressure binary distillation and has very promising potential to yield significant economic benefits in industry.

The accurate and understandable carbon dioxide (CO2) emission prediction is necessary in developing effective climate policies especially in fast developing nations such as India. Although some highly developed machine learning (ML) models (e.g., XGBoost and LSTM) have a high predictive accuracy, they are black-box models and do not permit application directly in policy making. To fill this gap, this paper explores the possibility of interpretable ML models to predict CO2 emission with a small yet critical set of predictors: total energy production (TEP) and total energy consumption (TEC). Decision Trees, Explainable Boosting Machines (EBMs), and Generalized Additive Models (GAMs) were constructed to compare annual 1990–2023 data and compare them against traditional black-box solutions. These findings indicate that, in terms of accuracy and interpretability, EBMs and GAMs outperform traditional models, and their error measurements prove their high level of performance. SHAP (SHapley Additive Explanations) analysis also presented the fact that the increasing TEP and TEC have a great impact that contributes to the emissions, so it is necessary to consider renewable energy and energy-efficient solutions on a large scale. This paper, which combines strong forecasting with clear understanding, can assist in replicable analysis of applying interpretable models to climate policy, to achieve more specific interventions and effective monitoring of the reduction of emissions.

Globally, around 15%–20% of all produced electrical energy is used to power compressors due to the constant operation of compressors. Thus, even a slight enhancement in their efficiency can lead to significant reductions in total energy consumption. This present work introduced an analytical model of drive bearing considering gas force models to estimate power loss and friction moment with impact of rotation speed in an electrical scroll compressor by grease lubrication and analyzed the relative effect on the compressor performance. The frictional moment and power losses have been estimated in the case of load independence and dependence following two global models: Harris and Palmgren, in which the power loss due to driving bearing impact is 17.2 and 7.2 W according to the Palmgren and Harris models, respectively, which mean that the Palmgren model and the Harris model suggest that the drive bearing needs 1.4% and 0.6%, respectively, of total power to overcome friction caused. The analysis helps predict power losses and allows designers to achieve a more accurate estimation of the performance of electrical scroll compressors.