A novel data-driven algorithm, MA-DOPS (Model Assembly with Data-driven Optimized Pathway Sampling), is introduced for the automated generation of combustion kinetic models. The method systematically assembles models by sampling and evolving reaction pathways from published models to minimize simulation error against experimental data. MA-DOPS builds models in a bottom-up manner, incorporating reactions and parameterizations from validated sources, ensuring physically meaningful model generation. The performance of the method was evaluated in three test cases with increasing complexity of the test conditions. First, algorithm control parameters were systematically analysed, identifying configurations that balance computational cost and model accuracy. Second, the constraining potential of different experimental data types was assessed, including micro flow, jet-stirred and tubular flow reactor speciation data, shock tube ignition delay times, and laminar burning velocities. This analysis showed that micro flow reactor measurements provide valuable complementary information for experimental validation conditions, demonstrating their efficiency as target for model development. Finally, a full-scale model generation task for ammonia combustion was carried out. The resulting model was compiled based on rate parameters from 72 source models and it outperformed all individual sources, showing good agreement with a comprehensive reference dataset, particularly excelling in micro flow reactor simulations. These results highlight the utility of the MA-DOPS approach in generating accurate combustion models by combining literature data with systematic algorithmic refinement.
Fuel, Energy industries. Energy policy. Fuel trade
Nano-sized boron (nB)-based composite energetic materials (CEMs) are an emerging class of high-energy-density fuels with excellent combustion performance, offering broad potential applications in space propulsion and explosives. In this paper, we review the various preparation techniques for nB-based CEMs (comparing their respective advantages and limitations) and discuss the combustion characteristics and reaction mechanisms of these materials, while also surveying current development trends and future challenges. Recent findings show that incorporating nB significantly improves the ignition characteristics, burning rates, and overall energy release efficiency of B-based energetic formulations. In particular, nB-based composites exhibit faster reaction kinetics, higher energy release rates, and greater gas generation than their micro-sized boron (μB) counterparts. These enhancements underscore the promise of nB-based CEMs for next-generation propellants, explosives, and pyrotechnics, and existing research has already laid a solid foundation for further progress in designing such advanced energetic systems.
Fuel, Energy industries. Energy policy. Fuel trade
Energy burden, the ratio of energy expenditure to household income, is a critical yet often overlooked measure of economic and environmental inequality in the United States. A high energy burden, 6% or greater, is not just a financial issue; it is a public health and environmental justice concern, as frontline communities often experience greater exposure to pollution, poorer housing efficiency, and heightened vulnerability to extreme weather events. This study uses self-organizing maps (SOMs), an unsupervised neural network, to identify contributing factors and inform policy interventions for energy-burdened communities in the North, South, Midwest, and West census regions, a novel use of this method. It is also among the first to integrate environmental justice indicators, including outdoor air quality metrics and health disparities, as determinants of energy burden. In addition to environmental justice indicators, socioeconomic status, building characteristics, and power outages are explored to assist policymakers, engineers, and advocates working within the energy transition. Results revealed statistically significant ( p < 0.05) differences in these indicators across SOM-defined energy-burden regimes. For the Midwest and South regions, all 45 indicators showed statistical significance, while 44 were significant in the Northeast, and 41 were significant for the West. These findings suggest that high energy-burden regimes tend to coincide with elevated environmental and health risk indicators, which may intensify under climate change.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Abstract With the global energy structure shifting towards clean and efficient hydrogen energy, the safety management issues of hydrogen refueling stations are becoming increasingly prominent. To address these issues, a hydrogen leak localization algorithm for hydrogen refueling stations based on a combination of reinforcement learning and hidden Markov models is proposed. This method combines hidden Markov model to construct a probability distribution model for hydrogen leakage and diffusion, simulates the propagation probability of hydrogen in different grid cells, and uses reinforcement learning to achieve fast and accurate localization of hydrogen leakage events. The outcomes denoted that the training accuracy reached 95.2%, with an F1 value of 0.961, indicating its high accuracy in hydrogen leak localization. When the wind speed was 0.8 m/s, the mean square error of the raised method was 0.03, and when the wind speed was 1.0 m/s, the mean square error of the raised method was 0.04, proving its good robustness. After 50 localization experiments, the proposed algorithm achieves a localization success rate of 93.7% and an average computation time of 42.8 s, further demonstrating its high accuracy and computational efficiency. The proposed hydrogen leakage location algorithm has improved the accuracy and efficiency of hydrogen leakage location, providing scientific basis and technical guarantee for the safe operation of future hydrogen refueling stations.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Ramin Mehdipour, Behnam Feizollah Beigi, Romina Fathiraboki
et al.
In hot seasons, residential areas consume significant amounts of electricity for refrigeration and air conditioning, leading to peak power consumption. This simultaneous increase in cooling load, combined with reduced performance of gas turbines, places considerable stress on the power grid, particularly during specific periods each year. Cold storage systems offer an effective solution by shifting electricity consumption from peak daytime hours to off-peak nighttime periods. This study evaluates and compares the economic and thermal performance of cold storage systems implemented in both power plants and office buildings for peak demand management. Tailored cold storage systems were designed for each application, with a focus on ensuring reliable performance during peak cooling demand based on load analysis. The study utilized real-world case studies, including modeling for an office building in Arak, Iran, and a nearby power plant, to understand the impact of different climatic conditions on system performance. The results indicate that, during peak hours, the turbine’s net power output improved by 15.98% and 17.97% with partial and full storage methods, respectively, compared to scenarios without cooling. Additionally, the economic analysis revealed substantial cost savings, with partial and full storage systems resulting in reductions of 97.36% and 95.54%, respectively, in power plant units compared to similar office buildings with equivalent power consumption. The analysis also highlights that full storage systems in both office and power plant contexts deliver better peak shaving performance but at a higher cost due to the larger size of tanks and equipment required for operation. These findings underscore the potential of cold storage systems as an effective strategy for enhancing electricity management and reducing operational costs.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
As the adoption of Generative AI in real-world services grow explosively, energy has emerged as a critical bottleneck resource. However, energy remains a metric that is often overlooked, under-explored, or poorly understood in the context of building ML systems. We present the ML$.$ENERGY Benchmark, a benchmark suite and tool for measuring inference energy consumption under realistic service environments, and the corresponding ML$.$ENERGY Leaderboard, which have served as a valuable resource for those hoping to understand and optimize the energy consumption of their generative AI services. In this paper, we explain four key design principles for benchmarking ML energy we have acquired over time, and then describe how they are implemented in the ML$.$ENERGY Benchmark. We then highlight results from the early 2025 iteration of the benchmark, including energy measurements of 40 widely used model architectures across 6 different tasks, case studies of how ML design choices impact energy consumption, and how automated optimization recommendations can lead to significant (sometimes more than 40%) energy savings without changing what is being computed by the model. The ML$.$ENERGY Benchmark is open-source and can be easily extended to various customized models and application scenarios.
We examine the role of export intensity and firm governance in promoting the renewable energy transition of Indian manufacturing firms. Firm-level analysis of renewable energy transition is carried out in reference to six manufacturing industries during 2010–2021. The panel Tobit model is used to estimate the impact of identified variables and other firm-specific controls on renewable energy use. Positive impact of export intensity on renewable energy use is found, with the exception of the hard-to-abate sectors. Alternate measures of firm governance and institutional and foreign investor participation yield mixed results across industries. The presence of cross-sectional dependence was also tested. Robustness checks using fixed effects with Driscoll-Kraay standard errors show a consistent sign and significance of export intensity in impacting renewable energy use in the selected industries. JEL Codes: Q42, F14, Q48
Dongsheng Li, Muhammad Yousaf Raza, Guangwei Zhang
The study detects and analyzes the driving factors underlying the CO2 emission variations in the transport sector of Bangladesh, including the carbon coefficient, fossil fuel ratio, energy use per unit turnover, turnover per unit of transport value-added, and value-added of transport from 2003 to 2021. The objective is to analyze the transportation (i.e., land, water and air) factor's effects under the logarithmic mean Divisia index, Tapio index, CO2 mitigation potential methods, and their decouplings. The results show that: (i) value-added was the main CO2 driving factor, while CO2 coefficient, fuel substitution, energy use, and turnover value mitigated CO2 emissions. (ii) Land and water transport seemed to be the main CO2 producers and meaningfully contributed to the economy. (iii) Only two decoupling states─weak decoupling and strong decoupling appeared, in which economic growth was the significant turn towards the best state. (iv) Sub-transportation presented significant and strong decouplings in the maximum intervals. (iv) The carbon mitigation rate was observed at 0.26 % during the period in which the economic structural factor was the main factor contributing to declining CO2 emissions. Finally, the study proposes frameworks that will support policymakers in estimating energy and technological policies for climate and economic sustainability.
Abstract Green finance plays a pivotal role in advancing sustainable urban development by enhancing energy efficiency and supporting low-carbon transitions. This study empirically demonstrates that green finance maturity (GFM), which reflects the development and effectiveness of green financial systems, has a significant positive impact on urban energy efficiency (UEE). Using panel data from Chinese prefecture-level cities spanning 2006 to 2021, the analysis shows that a one-unit increase in GFM improves UEE by 0.221 standard deviations. Mechanism analysis reveals that this effect is primarily mediated through technological advancements and improvements in innovation capacity. Further heterogeneity analysis highlights that GFM’s impact is more pronounced in non-resource-based cities and in regions characterized by advanced financial systems, greater global market integration, and higher levels of urbanization. These findings offer valuable, context-specific insights for policymakers seeking to leverage green finance maturity as a tool to promote sustainable urban development across diverse socio-economic and institutional settings.
Célestin Coquidé, José Lages, Dima L. Shepelyansky
We extend the opinion formation approach to probe the world influence of economical organizations. Our opinion formation model mimics a battle between currencies within the international trade network. Based on the United Nations Comtrade database, we construct the world trade network for the years of the last decade from 2010 to 2020. We consider different core groups constituted by countries preferring to trade in a specific currency. We will consider principally two core groups, namely, 5 Anglo-Saxon countries which prefer to trade in US dollar and the 11 BRICS+ which prefer to trade in a hypothetical currency, hereafter called BRI, pegged to their economies. We determine the trade currency preference of the other countries via a Monte Carlo process depending on the direct transactions between the countries. The results obtained in the frame of this mathematical model show that starting from year 2014 the majority of the world countries would have preferred to trade in BRI than USD. The Monte Carlo process reaches a steady state with 3 distinct groups: two groups of countries preferring, whatever is the initial distribution of the trade currency preferences, to trade, one in BRI and the other in USD, and a third group of countries swinging as a whole between USD and BRI depending on the initial distribution of the trade currency preferences. We also analyze the battle between USD, EUR and BRI, and present the reduced Google matrix description of the trade relations between the Anglo-Saxon countries and the BRICS+.
We discuss the theoretical framework behind reconstruction of a generic class of inflationary potentials for canonical single-field slow-roll inflation in a model-independent fashion. The Non-Bunch Davies (NBD) initial condition is an essential choice to determine the structure of potential and to accommodate the blue-tilted tensor power spectrum feature recently observed in NANOGrav. Using the reconstruction technique we found the favoured parameter space which supports blue tilted tensor power spectrum. The validity of the EFT prescription in inflation is also maintained through the use of a new field excursion formula while keeping the necessary and sufficient conditions on the sub-Planckian field values in check. We find that the reconstructed potential display inflection point behaviour, which has deeper connection with high energy physics.
The recently developed nuclear effective interaction based on the so-called N3LO Skyrme pseudopotential is extended to include the hyperon-nucleon and hyperon-hyperon interactions by assuming the similar density, momentum, and isospin dependence as for the nucleon-nucleon interaction. The parameters in these interactions are determined from either experimental information if any or chiral effective field theory or lattice QCD calculations of the hyperon potentials in nuclear matter around nuclear saturation density $ρ_0$. We find that varying the high density behavior of the symmetry energy $E_{\rm sym}(ρ)$ can significantly change the critical density for hyperon appearance in the neutron stars and thus the maximum mass $M_{\rm TOV}$ of static hyperon stars. In particular, a symmetry energy which is soft around $2-3ρ_0$ but stiff above about $4ρ_0$, can lead to $M_{\rm TOV} \gtrsim 2M_\odot$ for hyperon stars and simultaneously be compatible with (1) the constraints on the equation of state of symmetric nuclear matter at suprasaturation densities obtained from flow data in heavy-ion collisions; (2) the microscopic calculations of the equation of state for pure neutron matter; (3) the star tidal deformability extracted from gravitational wave signal GW170817; (4) the mass-radius relations of PSR J0030+0451, PSR J0740+6620 and PSR J0437-4715 measured from NICER; (5) the observation of the unusually low mass and small radius in the central compact object of HESS J1731-347. Furthermore, the sound speed squared of the hyperon star matter naturally displays a strong peak structure around baryon density of $3-4ρ_0$, consistent with the model-independent analysis on the multimessenger data. Our results suggest that the high density symmetry energy could be a key to the solution of the hyperon puzzle in neutron star physics.
[Introduction] To reduce fossil energy consumption and mitigate environmental pollution, offshore wind power is one of the effective ways to solve the problem. However, there are some problems in offshore wind power, such as strong intermittency, large volatility and bidirectional peak shaving. Therefore, it is of great significance to study the output characteristic curve of offshore wind power. [Method] In this paper, a Gaussian Mixture Model (GMM) based on Bayesian Information Criterion was proposed. The original output curve of offshore wind power was classified and the characteristic curve was extracted. [Result] The characteristic curve of offshore wind power which can reflect the characteristics of different wind areas is obtained, and it is applied to the calculation of electric quantity balance of offshore wind power output. [Conclusion] At last, the effectiveness of the proposed method is verified by taking the original offshore wind power output curves of different sea wind areas under the installed capacity of 15 GW of offshore wind power in a coastal province as the research object.
Abstract Straw returning has been demonstrated as a beneficial approach for the utilization of renewable biomass source, which contributes to reducing environmental pollution and strengthening the sustainability of agriculture. However, information on how microorganisms respond to different straw return modes (SRMs) at varying nitrogen fertilizer levels (NFLs) in the black soil is still limited. The community composition, network pattern, and modular function of bacteria and fungi are investigated under three SRMs, including straw removal (CK), crushed straw incorporation (SD), and biochar incorporation (BC) at three NFLs (0, 144, and 240 kg N ha−1, respectively) mainly using Illumina MiSeq technique based on a long‐term maize field experiment. Results showed that bacterial richness, diversity, and fungal richness decreased with NFL reduction. However, these decreases can be compensated by SD and BC, demonstrating superiority for BC at reduced NFLs. SD and BC differed in their effects on the bacterial and fungal abundances (showing increments only in SD) and fungal Shannon diversity (remaining stable only in BC irrespective of NFLs). Microbial communities were substantially affected by SRMs and interacted with NFLs, which were driven by soil NH4+‐N, available potassium, total nitrogen, and pH. In addition, SD induced a network characterized by its highly complex (average degree 10.259 vs. 3.364) and stable structure (average clustering coefficient 0.503 vs. 0.239), Ascomycota as predominating keystone taxa, and abundant N‐cycling related bacteria, while BC formed a network comprising a superior modular structure (modularity 2.599 vs. 0.912), dominant symbiotic fungi, and soil bulk density as specific shaping factor, indicating that network pattern, keystone taxa, modular function, and determining factors shifted between SD and BC co‐occurrence networks. These results deepen insights into the response divergence of bacteria and fungi to SRMs and NFLs, providing a scientific basis for selecting the suitable strategy for sustainable straw utilization in the black soil area.
Renewable energy sources, Energy industries. Energy policy. Fuel trade