This study examines the challenge of measuring environmental sustainability in India using a multidimensional indicator framework that highlights the roles of nuclear energy and human capital in shaping long-term ecological outcomes. The analysis integrates three complementary indicators, carbon dioxide emissions, ecological footprint, and load capacity factor, which reflect emissions intensity, ecological demand, and regenerative capacity. Grounded in the Environmental Kuznets Curve and Load Capacity Curve hypotheses, the study uses annual data from 1969 to 2023 and applies the autoregressive distributed lag bounds testing approach together with robustness estimations using fully modified ordinary least squares, dynamic ordinary least squares, and canonical cointegration regression to assess both short run and long-run dynamics. The findings support the Environmental Kuznets Curve and Load Capacity Curve in India. Nuclear energy and human capital improve environmental performance, while economic growth and natural gas consumption increase ecological pressure. The study contributes by integrating carbon emissions, ecological footprint, and load capacity factor within a unified empirical framework for India, providing evidence to support energy reform, human capital investment, and sustainability oriented policy design. • Integrates ecological footprint, load capacity factor, and carbon emissions to provide a comprehensive indicator-based assessment of environmental sustainability in India. • Confirms both Environmental Kuznets Curve and Load Capacity Curve hypotheses using long-run time-series evidence from 1969–2023. • Shows that nuclear energy reduces ecological pressure and enhances ecological capacity over the long term. • Demonstrates that human capital development plays a critical enabling role in improving sustainability indicators. • Provides indicator-relevant evidence to support environmental management and policy formulation aligned with the SDGs.

The energy landscape is at a critical inflection point as geopolitical tensions disrupt conventional systems, particularly in Europe, heightening energy security concerns and testing climate goals. While domestic renewable capacity expansion and green hydrogen production are crucial, their effectiveness is constrained by geographical disparities and infrastructural bottlenecks. These limitations underscore the need for transcontinental energy corridors to diversify import portfolios and enable renewable deployment in supplying regions, advancing shared decarbonization goals. To address these challenges, this work presents a novel scenario-based framework to assess electrification and hydrogen corridors, capturing the temporal progression of domestic renewable infrastructure, alignment with climate mitigation pathways, spatial-sectoral implications, and advancements in clean energy cost-effectiveness. Through strategic scenario analyses, the framework evaluates the transformative potential of these corridors to enhance sector-specific resilience and sustainability outcomes in European recipients and unlock decarbonization potential in partnering regions. The findings suggest that electrification corridors from the Middle East and North Africa, alongside hydrogen corridors from allies such as the US, Australia, Brazil, South Africa, etc. can reduce Europe's gross natural gas consumption by up to 58.7%, while delivering climate benefits of 10.6 GtCO2 through 2050 with a land-use factor of 0.26% and creating 3,340.2 million person-days of workforce engagement.

ABSTRACT The quick pace of cyber threat has revealed important vulnerabilities in conventional antivirus tools, especially because of their centralized designs, poor visibility of threats, and slow reaction to new and polymorphic malware strains. To overcome such difficulties, this paper will propose a hybrid Blockchain-Based SDN-Cloud-IoT Collaborative Antivirus Network (BCAN), which uses the decentralized and transparent nature of blockchain technology to improve security and scalability in contemporary data management network structures. Contrary to other current blockchain-based systems of cyber threat intelligence (CTI), the proposed system suggests a single cross-layer approach to deploy Software Defined Networking (SDN), IoT-edge aggregation, cloud-based analytics, and smart-contract-based implementation of trust enforcement. In the proposed model, malware signatures, threat knowledge, and response plans are distributed among the antivirus engines, security scientists, IoT apparatus, and distributed nodes in close real time. Smart contracts can be used to authenticate devices, manage trust, and access (and) blockchain can be used to guarantee integrity, immutability, and auditability of shared intelligence. To minimize the blockchain overhead and enhance the scalability, an edge level aggregation mechanism is presented allowing to optimally record the transactions without the loss of security guarantees. Extensive experimental benchmarking, detection accuracy, false-positive rate, transactions analysis and gas consumption profiling show that detection performance, response time, and blockchain overhead are better regarding centralized antivirus solutions. The findings identify the feasibility of blockchain-based collaborating security designs of the next generation of decentralized cybersecurity infrastructures.

ABSTRACT Accurate short‐term forecasting of residential natural gas consumption (NGC) is essential for operational planning and supply reliability. Most forecasting studies rely primarily on meteorological variables, often neglecting infrastructure expansion effects. This study introduces the subscription growth ratio (SGR) as a socioeconomic indicator to enhance daily residential NGC forecasting in Qazvin Province, Iran. Two datasets were developed: Dataset A containing meteorological variables and Dataset A + SGR incorporating subscription growth information. Forecasting performance was evaluated using expanding window cross‐validation across 52 sequential folds. Multiple regression models were implemented, including multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and XGBoost (XGB). Feature importance analysis confirmed that temperature variables dominate NGC variation; however, SGR ranked as the third most influential predictor, exceeding maximum temperature. Results show that incorporating SGR significantly improves nonlinear model performance, reducing MAE by approximately 20% in RFR and XGB and increasing adjusted R ² by about 38%. Paired hypothesis testing confirmed statistically significant improvements for SVR ( p < 0.001), RFR ( p = 0.000347), and XGB ( p = 0.012654), while MLR showed no significant improvement ( p = 0.767816). The findings demonstrate that infrastructure‐driven demand growth has a nonlinear influence on residential NGC and should be integrated into operational forecasting frameworks.

Strategies for the integration of a cogeneration system in a hot-dip galvanising steel wire process

The article develops and substantiates mathematical models of a cogeneration plant based on a gas piston engine-generator GDG-90 with a capacity of 500 kW. In conditions of energy shortage in Ukraine and the need to decentralize energy supply, the authors propose a method for assessing the efficiency of power plants in variable load modes. Based on experimental data obtained using a specialized measuring complex, regression dependences of fuel gas consumption and components of heat utilization on electric power were constructed. The use of the least squares method allowed us to obtain adequate mathematical models that were verified by the Fisher criterion, multiple correlation coefficient and Student's t-statistics. The results of the study allow us to perform a highly accurate calculation of the technical and economic indicators of mini-CHPs, taking into account real energy consumption schedules, which ensures the rational use of hydrocarbon fuel and increased energy autonomy of facilities.

HighlightsElucidated the effects of P&T-induced gas state on hydrate nucleation and growth.Revealed the mechanisms of CO2 on kinetics and morphological evolution.Clarified the hydrate inhibition mechanisms of methanol and MEG in O-W systems.Analyzed the hydrate dissociation and evolution in methanol-inhibited systems.During deepwater oil and gas production and shut-in operations, the high-pressure and low-temperature environment readily induces hydrate formation of CO2-rich associated gas in oil–water systems, thereby posing serious flow assurance risks. This study systematically investigated the nucleation, growth, and morphological evolution of hydrates in oil–water systems under different gas-phase states using fully visualized high-pressure apparatus, along with the effects of temperature, pressure, CO2 concentration, and inhibitors on hydrate formation behavior. The results showed that gas phase transition significantly altered the hydrate induction time, gas consumption, and growth time. However, once the gas was liquefied, mass transfer became hindered, and the growth process exhibited pronounced dynamic fluctuations. Phase transitions caused by variations in CO2 concentration also exerted a significant influence on hydrate growth, among which the terminal subcooling had the most pronounced effect on the integrated growth index. Compared with monoethylene glycol (MEG), methanol lowered the peak value during the rapid hydrate formation stage, markedly reduced the hydrate growth rate, and led to a prolonged period during which the pressure remained above its initial value. These findings revealed the hydrate formation characteristics in oil–water systems and mechanism of thermodynamic inhibitors, providing a theoretical basis for ensuring flow safety in CO2-rich oil and gas wellbores and pipelines.

This study quantitatively evaluates the impact of Wire Arc Additive Manufacturing (WAAM) process parameters on the environmental performance of components produced in Invar 36 alloy. An experimental campaign involving 49 parameter sets was carried out by varying wire feed speed, welding voltage, and welding speed. For each condition, electrical signals, shielding gas consumption, and wire usage were measured and converted into parameter-resolved Life Cycle Inventory (LCI) data. A cradle-to-gate Life Cycle Assessment (LCA) was implemented in SimaPro 9.6 using the European CML-IA baseline v3.10 midpoint method, adopting 1 kg of as-built deposited Invar 36 as the functional unit. Results show that feedstock production represents the dominant hotspot (8.68 kg CO2-eq/kg), while the WAAM stage contributes between 1.13 and 4.12 kg CO2-eq/kg, leading to a total impact ranging from 9.81 to 12.80 kg CO2-eq/kg. As a result, this study demonstrates that process parameter selection strongly influences environmental performance. Indeed, Specific Energy Consumption (SEC) ranges from 0.44 to 1.95 kWh/kg, while argon consumption varies between 0.26 and 1.51 kg/kg of deposited material. By analysing the results and excluding unstable or manufacturing-infeasible deposition regimes, the optimal trade-off between process stability and environmental impact is achieved at approximately WFS = 7 m/min, V = 20 V, and WS = 6.5 mm/s. Beyond quantifying the environmental hotspots of Invar 36 WAAM, this study provides a dedicated, parameter-resolved cradle-to-gate LCA based on experimentally measured foreground data collected across 49 process parameter combinations. By combining environmental assessment with feasibility screening of the investigated deposition regimes, the work identifies not only environmentally favourable conditions, but also parameter regions that are technologically viable for WAAM processing of Invar 36. The resulting dataset provides a benchmark foundation for future sustainability-oriented process optimisation and decision support in WAAM.

This study develops econometric models to examine greenhouse gas emissions associated with coal and natural gas consumption in Poland between 2015 and 2023. Poland has one of the most carbon-intensive energy systems in Europe. Three complementary log–log econometric models were estimated: a model explaining total CO2 emissions, a model assessing emission intensity (CO2 per unit of GDP), and a model capturing short-term variations in emission intensity. The results demonstrate that coal consumption remains the dominant determinant of absolute emissions, whereas the expansion of renewable energy significantly contributes to lowering the carbon intensity of economic growth. However, short-term fluctuations in emission intensity are still largely influenced by changes in fossil fuel consumption patterns. The findings highlight the gradual and sequential character of Poland’s energy transition, where gains in environmental efficiency precede a consistent reduction in total emissions. The proposed modeling framework offers an empirical basis for evaluating the effectiveness of climate and energy policies and can support the formulation of decarbonization strategies in economies heavily reliant on fossil fuels.

Timely classification of cardiovascular diseases is crucial to improve medical outcomes. Emerging remote patient monitoring systems help achieve this by enabling continuous monitoring of electrocardiogram signals in home environments. However, these systems struggle with unique challenges like missing genuine medical emergencies, rising energy demands, scalability challenges, handling vast medical databases, data processing delays, and safeguarding patient records. To overcome these challenges, we propose a single framework with three main phases: (a) an embedded hardware-driven K-Nearest Neighbor (KNN)-assisted real-time ECG monitoring and classification method; (b) a differentiated communication strategy (DCS) formed with a priority-based ECG data packaging framework and multi-layered security protocols; and (c) a multi-level blockchain network (MLBN) architecture armed with adaptive security mechanisms and real-time cross-chain medical data communication bridges. Simulations are conducted using the ECG signals (1000 fragments) dataset and the Ganache Ethereum development framework. The classification accuracies obtained for patient urgent categories U1 to U5 are 91.43%, 95.71%, 94.23%, 90.00%, and 91.43%, respectively. The performance evaluation results of the KNN-guided classification method, along with DCS and MLBN simulation results obtained from average gas consumption analysis, confirms reliability and viability of our framework, while also revolutionizing remote patient monitoring technology and addressing critical challenges in existing systems.

ABSTRACT This study aims to evaluate the feasibility of using biogas as an alternative fuel for residential space heaters in Algeria, with the objective of reducing natural gas consumption and mitigating pollutant emissions. The use of the biogas produced at El Karma STEP in Oran, Algeria, as a fuel in a space heater is investigated and compared to the Algerian natural gas. A combined numerical – experimental methodology is employed. 3D CFD simulations are carried out to analyze flame structure, temperature distribution, and exhaust gas composition, and the obtained results are validated using experimental measurements on a domestic space heater operating under identical conditions for both fuels. The results show that biogas combustion reaches a maximum flame temperature of 1574 K, which is 7% lower than the 1683 K achieved by natural gas, while remaining sufficient to satisfy residential heating demands. Experimental data indicate that the natural gas space heater stabilizes after approximately 20 min, whereas the biogas-fueled space heater would require about 50 to 60 min to reach the recommended heating load. In terms of emissions, the use of biogas reduces NOx by 67% but increases CO by 12% and CO2 by 76% in comparison to natural gas under the same operating conditions. The study concluded that biogas can serve as a technically viable alternative to natural gas for residential space heating, provided that burner design and operating strategies are optimized to accommodate its lower calorific value. The results demonstrated the potential of biogas to contribute to energy diversification and reduced reliance on fossil fuels in Algeria.

With the rapid growth of natural gas consumption in my country, flow metering accuracy is crucial for energy trade settlement and transmission and distribution loss control. However, metering errors caused by multiple factors, such as liquefied natural gas vaporization, metering equipment failures, and improper parameter settings, pose challenges to traditional compensation methods that rely on simple mathematical models. This study aims to construct a high-precision natural gas flow metering error compensation model to intelligently improve metering accuracy and reduce economic losses caused by errors. Based on multi-source data from the natural gas pipeline network, after data cleaning and standardization preprocessing, this study combines the advantages of XGBoost in handling nonlinear relationships with the Stacking ensemble strategy, supplemented by Bayesian optimization and random search for parameter tuning. Ultimately, an XGBoost-Stacking ensemble learning error compensation model is constructed. Experimental results show that the proposed model achieves a mean absolute error of 0.0298, a root mean square error of 0.0451, and a coefficient of determination of 0.9432, significantly outperforming traditional regression models and single XGBoost models. The compensation accuracy meets the industry requirement of ≤0.5%, and generalization capability is stable. This model provides an effective technical path for resolving natural gas metering errors, reducing transmission losses, ensuring trade fairness, and promoting the intelligent development of the natural gas industry.

It is shown that in the technological complex of secondary condensation in ammonia production, seasonal and daily fluctuations of ambient air temperature change the heat removal conditions in air-cooled condensers, which leads to deviations of ammonia vapor condensation pressure and disturbs the stability of generation, rectification, and condensation modes. The consequences of such disturbances for the operation of jet compressors are analyzed, in particular the variation of the injection coefficient and the need for prompt coordination of the motive steam flow rate with the current condensation parameters. An approach to supervisory control is considered, within which the decision-making subsystem generates recommendations for the operator on switching individual jet compressors on or off and setting the motive steam flow rate based on the calculated assessment of the injection coefficient. Emphasis is placed on the algorithmic support based on a mathematical description of a single-phase jet compressor with a cylindrical mixing chamber and the use of gas-dynamic functions to determine limiting regimes and convergent computational procedures. It is confirmed that the proposed sequence of calculations makes it possible to coordinate the motive steam flow rate with changes in temperature and condensation pressure in air-cooled units and provides a formalized selection of the number of operating jet compressors under off-design conditions. The algorithm is implemented in the MATLAB software environment, and its validation is carried out using experimental data from industrial operation. The obtained results demonstrate the possibility of rapid switching of jet compressor configurations depending on condensation conditions and the calculated injection coefficient, which contributes to a reduction in electricity and natural gas consumption and improves the reproducibility of control decisions in the supervisory loop.

Psychological and physiological factors influencing household energy consumption among apartment occupants in major cities of Indonesia

Electrochemical bioassays based on oxidase reactions are widely used in biological sciences and medical industries. However, the enzymatic reaction kinetics are significantly restricted by the poor solubility and slow diffusion rate of oxygen in conventional solid-liquid diphase reaction system. This limitation compromises the detection accuracy, linearity and reliability of oxidase-based bioassays. In this study, an effective solid‒liquid‒air triphase bioassay system is provided that uses ZIF-7 nanoparticles (ZIF-7 NPs) as oxygen nanocarriers. We constructed a solid-liquid-air triphase enzyme electrode by encapsulating ZIF-7 NPs within an oxidase network. The hydrophobic nature of ZIF-7 NPs provides localized oxygen supply by releasing pre-stored oxygen from its hydrophobic pores, thereby enhancing the kinetics of oxidase-catalyzed reactions. Consequently, compared to the conventional diphase system, the triphase system significantly improves the enzymatic reaction kinetics with a 21-fold higher maximum reaction rate (Vmax) and expands the linear detection range for glucose from 2mM to 20mM, a 10-fold improvement. Furthermore, this triphase technique can be applied to the detection of other biomolecules, and the design strategy offers a new route to addressing the gas deficiency problem in catalytic reactions that involve gas consumption.

Blockchain users pay transaction fees to miners or block proposers who validate and add transactions to the distributed ledger. Ethereum introduces the concept of gas to decouple transaction costs from Ether's price volatility, calculating fees based on gas units. The current mechanism defined by Ethereum Improvement Proposal (EIP) 1559 dynamically adjusts the base fee according to block gas usage. However, its rule-based adjustment can lead to unstable gas consumption when demand fluctuates within a narrow range and struggles to respond efficiently to sudden demand spikes, such as during non-fungible token (NFT) drops. To address these limitations, we propose a deep reinforcement learning-based transaction fee mechanism that learns an adaptive base-fee update policy. Our approach maintains gas consumption close to the target level across various demand scenarios and stabilizes transaction fees and gas usage per block even under abrupt demand shifts. These results demonstrate that the proposed method provides a more adaptive and resilient fee adjustment mechanism compared to the current EIP-1559 model.

Blockchain-based systems increasingly require authentication mechanisms that simultaneously preserve user privacy, support accountability, and enable efficient credential revocation. However, most existing anonymous authentication schemes rely on pairing-based cryptography which introduce high computational overhead and limit deploy ability on widely adopted blockchain platforms such as Ethereum. This paper presents BAAR, a Blockchain-based Anonymous and Revocable authentication framework designed entirely within the discrete logarithm setting over the secp256k1 elliptic curve. BAAR integrates Pedersen vector commitments, Schnorr-based zero-knowledge proofs, and a Merkle-tree-based dynamic accumulator to support anonymous and unlinkable authentication with selective attribute disclosure and public, auditable revocation. Authentication and proof verification are performed off-chain, while the blockchain maintains only a compact revocation state, significantly reducing on-chain computation and gas costs. A formal security analysis demonstrates unforgeability, unlinkability, attribute privacy, and revocation soundness under standard cryptographic assumptions in the random oracle model. A prototype implementation on Ethereum confirms that BAAR achieves low gas consumption, logarithmic-time revocation, and scalable performance with respect to both the number of users and attributes. These results indicate that BAAR provides a practical balance between strong privacy guarantees and deploy ability, making it suitable for real-world blockchain-based identity and access-control systems.

This study presents the results of a comprehensive energy audit conducted at an industrial textile dyeing facility in Türkiye with the aim of evaluating its energy performance and identifying energy efficiency improvement potentials. Electricity and natural gas consumption patterns were analyzed using historical production and energy data, field measurements, and on-site observations. The audit focused particularly on thermal energy systems, including steam boiler operation, heat distribution lines, and auxiliary equipment, as well as electrical systems such as lighting. Flue gas analysis, thermal imaging, and energy monitoring were employed to determine system efficiencies and quantify energy losses. Based on the audit findings, three main energy efficiency measures were proposed: recovery of waste heat from boiler flue gases, insulation of uninsulated piping and installation elements, and conversion of conventional lighting systems to high-efficiency LED luminaires. The results indicate that natural gas accounts for more than 90% of total energy consumption, highlighting the critical importance of improving thermal systems. The proposed measures demonstrate significant energy saving and economic benefits, with simple payback periods ranging between 0.6 and 3.9 years. The findings confirm that systematic energy audits constitute an effective and economically feasible approach for reducing energy consumption, operating costs, and greenhouse gas emissions in energy-intensive textile dyeing facilities.

This paper reports a UK single centre carbon footprint data falling under GHGP scope 2 (purchased energy including electricity and gas) plus water consumption on a per patient encounter basis and further reporting the impact of our educational package on utility consumption.

Theory and Practice of Gas Consumption Forecasting in Planning the Development of Regional Gas Supply Systems