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The free energy cost of forming a cavity in a solvent is a fundamental concept in rationalizing the solvation of molecules and ions. A detailed understanding of the factors governing cavity formation in bulk solutions has inter alia enabled the formulation of models that account for this contribution in coarse-grained implicit solvation methods. Here, we employ classical molecular dynamics simulations and multistate Bennett acceptance ratio free energy sampling to systematically study cavity formation at a wide range of metal–water interfaces. We demonstrate that the obtained size- and position-dependence of cavitation energies can be fully rationalized by a geometric Gibbs model, which considers that the creation of the metal–cavity interface necessarily involves the removal of interfacial solvent. This so-called competitive adsorption effect introduces a substrate dependence to the interfacial cavity formation energy that is missed in existing bulk cavitation models. Using expressions from scaled particle theory, this substrate dependence is quantitatively reproduced by the Gibbs model through simple linear relations with the adsorption energy of a single water molecule. Besides providing a better general understanding of interfacial solvation, this paves the way for the derivation and efficient parametrization of more accurate interface-aware implicit solvation models needed for reliable high-throughput calculations toward improved electrocatalysts. [ABSTRACT FROM AUTHOR]

Zirconium carbide (ZrC) is commonly used for energy sector research, as well as a surrogate for the proposed advanced nuclear fuel candidate uranium carbide. This study investigates structural modifications to nanocrystalline and microcrystalline ZrC resulting from dense electronic excitations induced by swift heavy ion exposure. Samples were irradiated with 946 MeV Au ions to various fluences up to 6 × 1013 ions cm−2 and characterized using synchrotron-based x-ray diffraction. The evolution of the unit-cell parameter and heterogeneous microstrain were evaluated as a function of fluence and compared with those of nanocrystalline and microcrystalline CeO2 (a surrogate for UO2 fuel) irradiated under identical conditions. Distinct differences were observed in the radiation responses of the carbide and oxide across both grain sizes. Most notably, microcrystalline ZrC exhibits swelling characterized by two distinct regimes, which does not result in saturation at the ion fluences achieved. This contrasts with CeO2, which exhibits the well-documented direct-impact defect accumulation mechanism, reaching a steady-state saturation of swelling at higher fluences. Nanocrystalline CeO2 undergoes more pronounced swelling compared with microcrystalline CeO2, in contrast to nanocrystalline ZrC, which exhibits only minimal unit-cell changes. These results demonstrate that swift heavy ion-induced structural changes can be quite different in carbides and oxides, which must be considered when extrapolating fission-fragment type damage in current fuels to advanced fuels. [ABSTRACT FROM AUTHOR]

Recent management research suggests that firms try to orchestrate conformity and distinctiveness in their behavior to reconcile opposing demands of norm compliance and competitive distinction in order to appear optimally distinct. Whether they succeed or not depends on how audiences perceive the multiple signals they elicit. Extant literature has not fully uncovered how the media combine these different signals to form a representation of firms' behavior and their subsequent reactions to these combinations. We adopt a configurational approach to address this gap in the environmental sustainability context. Drawing on unique proprietary data on 118 cases in the energy utility industry and using fsQCA, we determine that different equifinal signal configurations are associated with positive media coverage regarding environmental behavior or the lack thereof. We then generate five patterns of signals that explain media reactions and disentangle the underlying logics behind them. Our study adds to the research on the multifaceted nature of signal (in)congruence perception by discussing the role of conformity and distinctiveness and highlighting that this perception depends on the signals' credibility. Delivering a balanced orchestration of conformity and distinctiveness signals is possible, but complex, because audiences tend to penalize incongruent postures they cannot associate with a given category. [ABSTRACT FROM AUTHOR]

Free energy simulations that employ combined quantum mechanical and molecular mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly demanding. Here, we present a machine-learning-facilitated approach for obtaining AI/MM-quality free energy profiles at the cost of efficient semiempirical QM/MM (SE/MM) methods. Specifically, we use Gaussian process regression (GPR) to learn the potential energy corrections needed for an SE/MM level to match an AI/MM target along the minimum free energy path (MFEP). Force modification using gradients of the GPR potential allows us to improve configurational sampling and update the MFEP. To adaptively train our model, we further employ the sparse variational GP (SVGP) and streaming sparse GPR (SSGPR) methods, which efficiently incorporate previous sample information without significantly increasing the training data size. We applied the QM-(SS)GPR/MM method to the solution-phase SN2 Menshutkin reaction, N H 3 + C H 3 C l → C H 3 N H 3 + + C l − , using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. For 4000 configurations sampled along the MFEP, the iteratively optimized AM1-SSGPR-4/MM model reduces the energy error in AM1/MM from 18.2 to 4.4 kcal/mol. Although not explicitly fitting forces, our method also reduces the key internal force errors from 25.5 to 11.1 kcal/mol/ A ̊ and from 30.2 to 10.3 kcal/mol/ A ̊ for the N–C and C–Cl bonds, respectively. Compared to the uncorrected simulations, the AM1-SSGPR-4/MM method lowers the predicted free energy barrier from 28.7 to 11.7 kcal/mol and decreases the reaction free energy from −12.4 to −41.9 kcal/mol, bringing these results into closer agreement with their AI/MM and experimental benchmarks. [ABSTRACT FROM AUTHOR]

This article proposes a dynamic economic load dispatch (DELD) including wind power involvement process, which is capable to produce least possible generation cost of electrical systems by integrating large-scale wind power penetration. The primary component of hybridized power system comprises of conventional thermal generators and wind generators. To expedite the convergence flexibility and escape the clarifications from the confined optimal purpose, quasi-oppositional based learning (Q-OBL) is combined with HBA, which results in quasi oppositional chaotic honey badger algorithm (QOHBA). Moreover, chaotic strategy is integrated with QOHBA to boost the convergence speed as well as enhance the performance of the basic honey badger algorithm (HBA). The proposed QOCHBA is suggested to explain wind-based shared DELD problem to reduce thermal-wind generation energy cost. The superiority and expediency of the prospective technique is proved through its operation in two benchmark investigation systems. We use probability density functions (PDF) for wind energy (WE) forecasting to assessment dynamic working reserve necessities, based on the readjust of uncertainty in the prediction. To establish the efficacy of the recommended QOCHBA method in explaining non-linear, non-convex, and constrained DELD problem, the suggested structures are implemented on benchmark of 10-unit in addition to 15-unit test methods. [ABSTRACT FROM AUTHOR]

The need for clean energy to meet the world's increasing energy needs has compelled global leaders to identify other sources of energy generation. Since fossil fuels must be replaced in the energy sector to combat climate change, hydrogen may be the best possible low-emitting alternative energy source. Currently, three energy sources are used worldwide to produce hydrogen: renewable, nuclear, and fossil fuels. Using a bibliometric approach, this study examines how nuclear energy or power has been used to produce hydrogen over the past 20 years. Biblioshiny, a package in R-studio, and VOSviewer were the tools used for the analysis. With an annual growth rate of 7.81%, research on the topic of study has advanced significantly over the years. Over the years, 999 individuals have contributed to the research, using a total of 668 keywords for their studies. Countries with operational nuclear power plants are more interested in nuclear hydrogen production research, potentially due to their quest to diversify their energy use compared to those planning to add nuclear energy. Similarly, studies on how to reduce impact of accidents at such facilities have gained interest since 2010–2023, indicating a growing interest in how to reduce such incidences. The study also identified the trend and evolution of nuclear hydrogen production during the period of study. • This study presents an overview of nuclear hydrogen production. • The bibliometric approach was used to analyze data between 2003 and 2023. • A total of 398 relevant documents were analyzed in this study. • Countries with operational NPPs researched most on nuclear hydrogen production. • United States of America leads the world in terms of research on the topic. [ABSTRACT FROM AUTHOR]

This study analyzes a techno-economic optimization tool developed in-house to size on-grid and off-grid green hydrogen plants and estimate their minimum production costs. The sizing is determined through an optimization process that minimizes the levelized cost of hydrogen for a specific plant location and hydrogen demand. The analysis considers the a PV and wind power plant, electrolyzer, hydrogen conversion/processing, storage, land transportation and grid energy trade. A location in Argentina was selected as a case study, in order to describe some of the model considerations, the optimization procedure and analyze the obtained results. The results show that grid connection has a significant impact on plant sizing. Its convenience over an off-grid hydrogen hub will depend greatly on the grid energy commercialization schemes and costs, as well as the interconnection cost. The obtained results show that the minimum levelized cost of energy is around 3. 35 and 3. 20 U S D / k g H 2 for off-grid and on-grid hydrogen hubs, respectively. • An adaptable tool was developed to evaluate on-grid green hydrogen production costs. • LCOH estimation for H2 production, process, transport, storage and grid connection. • Energy prices and unsupplied H2 costs affect project sizing and demand fulfillment. • H2 storage is viable for low grid energy price scenarios, considering not supplied H2. • Grid connection is viable if its cost increases the total CAPEX by less than 5%. [ABSTRACT FROM AUTHOR]

The Equatorial Guinea Malaria Vaccine Initiative (EGMVI) highlights how long-term African government and international energy industry investment, plus novel partnerships, can enable clinical development of vaccines in Africa, for Africa. We review achievements and challenges of this pioneering, award-winning, public–private partnership which offers a model for future Africa-centric clinical research and development (R&D). [ABSTRACT FROM AUTHOR]

The Arab Gulf states and Russia have traditionally been major suppliers of hydrocarbons to the energy-poor countries of Asia. This article considers the extent to which engagement by the Arab Gulf states in the Asian energy market is likely to be significantly impacted by the Russia-Ukraine war, which began in February 2022. On the one hand, the war is likely to increase the intensity of Russia engagement with Asia and to recalibrate relations with certain Asian partners, with implications for the Gulf states. On the other hand, Asia as theatre of competition for energy suppliers – and the Gulf states as the dominant oil and gas suppliers for the region – has roots in pre-2022 trends and is unlikely to change. The qualitative analysis, supported by extensive use of diverse secondary datasets on the energy trade, concludes that for the Gulf states, the Russia-Ukraine war is less an exogenous, sudden, and "critical juncture" event than an acceleration of existing trends in the Asian energy market. [ABSTRACT FROM AUTHOR]

Due to the volatile nature of renewable energy sources (RES), energy storage systems (ESS) are the primary impetus for increased dependence on RES in current power networks. Because of their quick responsiveness and rapid transition from charge to discharge in less than 1 ms, batteries are necessary even in the presence of alternative storage devices. Lithium-ion batteries (LIBs) have demonstrated their viability as ESS for RES. Given the significance of LIBs, it was vital to correctly and effectively quantify their costs in energy systems. Most studies did not offer a true real-world assessment of these batteries' aging but instead averaged their performance indices, which vary substantially depending on the system's working circumstances such as temperature, voltage, state of charge, and current level. Failure to use precise values for each charging/discharging operation might result in disastrous judgments and future techno-economic issues. This paper provides a novel and highly accurate piecewise modeling of LIBs for each charging and discharging operation based on real-world operational conditions. This novel aging model is used for calculating hourly aging and its impact on energy costs. The musical chair algorithm (MCA) is used to determine the optimal parameters' values of the LIB's model and the appropriate size of autonomous hybrid RES. When compared to other optimization techniques, the findings of optimal parameter determination using MCA revealed an 80% reduction in convergence time and better accuracy. The novel proposed aging model of the batteries predicts a 60% cost share of the battery, meanwhile, the existing strategy predicts 51% only. The levelized cost of energy is $0.1291/kWh and $0.0731/kWh for the new proposed aging model and existing strategy, respectively. [ABSTRACT FROM AUTHOR]

The main objective of this research study is to improve the performance of a standalone hybrid power system (SHPS) that consists of photovoltaic modules (PVMs), wind turbines (WTs), battery system (BS), and diesel engine (DE). The emphasis is on optimizing the system's design by incorporating demand response strategies (DRSs). Incorporating these strategies into the system can enhance system performance, stability, and profitability while also reducing the capacity of SHPS components and, consequently, lowering consumers' bills. To achieve this objective, the sizing model incorporates a novel indicator called the load variation factor (LVF). This paper assesses and contrasts various scenarios, including SHPS without DRS, with DRS, and with DRS but no DE. In this article, interruptible/curtailable (I/C) as one of the DRSs is incorporated into the model used for sizing issues. A newly developed optimization algorithm called the mountain gazelle optimizer (MGO) is utilized for the multi-objective design of the proposed SHPS. The utilization of MGO will facilitate achieving the lowest possible values for each of the following: cost of energy (COE), loss of power supply probability (LPSP), and carbon dioxide (CO2) emissions. This work introduces a mathematical model for the entire system, which is subsequently simulated using MATLAB software. The results reveal that among all the scenarios analysed, scenario iii — which has an LVF of 30% — is the most cost-effective. It has the lowest COE, at 0.2334 $/kWh, hence the lowest net present cost (NPC), at 6,836,445.5 $. [ABSTRACT FROM AUTHOR]

Glycerol (GL) represents a widespread agro‐industrial waste. Its valorization is pivotal for a sustainable society because GL is a renewable compound deriving from biomass, but it has a high oxygen to carbon ratio, compared with feedstock used in the energy and chemistry sectors. Oxygen‐poor derivatives are easily and immediately transferable to the industry, avoiding a deep and pressing modification of the plants. From this perspective, keeping the carbon content but with an oxygen content reduction, we could effectively obtain the enhancement of the recovery and the use of GL converting it into attractive industrial building‐blocks. In this Review, we present and discuss the up‐to‐date results about the chemical reduction of GL into products with 3 carbon and 0, 1, or 2 oxygen atoms. The focus is on the transition metal (TM) catalysts that have made the hydrogenation reactions of GL possible, partitioning the metals into early and late, based on their position in the periodic table. This discussion will contribute to select and develop new catalysts aimed at the improvement of the yield and of the selectivity in the hydrogenation reactions of GL. [ABSTRACT FROM AUTHOR]

Recently, the solar energy sector has been greatly interested in lead (Pb)-free inorganic halide perovskites due to their remarkable mechanical, optical, electronic, and structural characteristics. Our study comprehensively assessed these attributes in cubic A3SbI3(A = Ba, Sr, Ca) perovskite materials via first principles density functional theory (FP-DFT) and SCAPS-1D simulation. These materials, similar to lead-free inorganic metal halide perovskites, demonstrated favorable tolerance factors, direct bandgaps, mechanical robustness, minimal losses, and high absorption coefficients. We aimed to explore how A-cation size influences their properties and solar cell performance, enabling effective comparisons. We systematically investigated novel A3SbI3-based structures with tin (IV) sulfide (SnS2) buffers, varying layer thickness, doping density, and defect density to evaluate photovoltaic (PV) capabilities. The Ba3SbI3 absorber exhibited the highest power conversion efficiency (PCE) at 30.26% with JSC of 44.24 mA/cm2, FF of 85.65%, and VOC of 0.80 V, while Sr3SbI3 and Ca3SbI3 absorbers achieved PCE of 26.93% and 20.87%, respectively, with corresponding JSC of 34.5 and 21.87 mA/cm2, FF of 86.90% and 85.85%, and VOC of 0.90 and 1.11 V. Our A3SbI3-based solar cell structures offer innovative alternatives to conventional designs. [ABSTRACT FROM AUTHOR]

With its ability to store and transport energy without releasing greenhouse gases, hydrogen is considered an important driver for the decarbonisation of energy systems. As future hydrogen import prices from global markets are subject to large uncertainties, it is unclear what impact different hydrogen and derivative import prices will have on the future German energy system. To answer that research question, this paper explores the impact of three different import price scenarios for hydrogen and its derivatives on the German energy system in a climate-neutral setting for Europe in 2045 using three different energy system models. The analysis shows that the quantities of electricity generated as well as the installed capacities for electricity generation and electrolysis increase as the hydrogen import price rises. However, the resulting differences between the import price scenarios vary across the models. The results further indicate that domestic German (and European) hydrogen production is often cost-efficient. • Comparison of the energy system models REMod, SCOPE SD and Enertile. • Detailed derivation of three import price scenarios for hydrogen and its derivatives. • Transformation pathways in Germany towards a climate-neutral energy system in 2045. • Increasing capacities for electricity generation and electrolysis in all models as the hydrogen import price rises. • Comparison of effects on storage technologies and electricity imports as well as on the industry and transport sectors. [ABSTRACT FROM AUTHOR]

This article introduces and analyzes an integrated energy system that may generate electricity and potable water at a low cost, speeding up the desired clean transition process. The proposed system uses flame-assisted fuel cells to improve upon the inefficiencies of traditional power generation setups by increasing fuel utilization and reducing waste heat. The product's energy cost and overall efficiency are improved by using fuel gas condensation combined with a multi-effect desalination process. The engineering equation solver software examines the feasibility of the proposed system using energy, exergy, exergoeconomic, environmental, and sustainability analyses. The parametric analysis also compares the effects of changing key design variables and performance metrics. Then, the genetic algorithm integrated with an artificial neural network model is applied to minimize the power cost while enhancing the exergy efficiency. According to the results, the proposed efficient hybrid system has an affordable energy cost of 0.123 $/kWh and an environmental impact of 524 kg/MWh. The parametric study demonstrates that increasing the current density improves potable water production while reducing the efficiency and increasing the unfavorable power costs. Also, it can be shown that the sustainability indices improve significantly when a lower fuel utilization factor and equivalence ratio are chosen. The results further reveal that the optimization achieves a higher exergy efficiency of 6.2% and a reduced power cost of 0.03 $/kWh than the design condition. The scatter dispersal of key variables indicates that the fuel utilization factor lacks effectiveness, and keeping the current density and fuel temperature close to their upper limits is techno-economically favorable. Due to the significant temperature increase, mixing of the fuel and air, and chemical interactions between the reactants, the irreversibility analysis shows that the mixer and the flame-assisted fuel cell have the greatest destruction rate under the optimal conditions. • A new hybrid system is introduced based on the flame-assisted solid oxide fuel cell. • The proposed system's feasibility is assessed from all aspects. • A genetic algorithm optimizer integrated with machine learning is applied. • Optimization achieves higher efficiency of 6.2% and a lower cost of 0.03 $/kWh. • Choosing a lower fuel utilization factor improves sustainability indices. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions, including CO2 emissions, are an issue in the energy sector that must be addressed urgently. The energy sector, including electricity, has been given a global aim of net zero emissions (NZE). This article examines three scenarios for reaching net-zero emissions in power supply. These scenarios are baseline, NZE1, and NZE2. The baseline scenario represents power plant capacity planning based on existing regulations in the base year. The net zero emissions consisting of the NZE1 and NZE2 scenarios aim to achieve net zero emissions by 2060. The NZE1 and NZE2 scenarios differ in the usage of nuclear power plant technology. The NZE1 scenario employs advanced costs for small modular reactors and large reactors technology, whilst the NZE2 scenario employs the low cost of small modular reactors and large reactors. The three scenarios were implemented and examined using the low emissions analysis platform software. The analytical results demonstrate that the NZE1 and NZE2 scenarios can meet the net zero emission objective by 2058. The baseline scenario results in power plant capacity planning with an average annual CO2 emission growth rate of 3.58%. On the other hand, the baseline scenario has the lowest investment expenses, at only 44 billion USD. [ABSTRACT FROM AUTHOR]

Agrivoltaics can be used to supply energy and produce agricultural products in order to meet the growing demand for energy and food. The amount of power generation is affected by the solar panel direction, spacing, tilt, and panel technology; however, there is insufficient empirical data-based research on the operation of agrivoltaics. This study estimates the levelized cost of energy (LCOE) for a fence-based agrivoltaics system using bifacial modules. This study installed and operated photovoltaic (PV) systems on a rice paddy and saltern in South Korea to estimate the input variables that could affect their economic efficiency and LCOE. For the research methods, this study used Monte Carlo simulation (a stochastic analysis method that reflects the uncertainty of the input variables), a deterministic LCOE analysis, and a sensitivity analysis of the input variables. In terms of space utilization, the LCOE of the paddy system (139.07~141.19 KRW/kWh) was found to be relatively lower than that of the saltern system (145.43~146.18 KRW/kWh), implying that the PV system on the paddy was economically favorable. In terms of installation direction, it was more economical to operate the southwest-facing panels (139.07~145.43 KRW/kWh) than the southeast-facing panels (141.19~146.18 KRW/kWh). This study provides foundational policy data for the adoption of fence-based agrivoltaics and contributes to the widespread and active use of agrivoltaics. [ABSTRACT FROM AUTHOR]

A grid blackout is an intractable problem with serious economic consequences in many developing countries. Although it has been proven that microgrids (MGs) are capable of solving this problem, the uncertainties regarding when and for how long blackouts occur lead to extreme difficulties in the design and operation of the related MGs. This paper addresses the optimal design problem of the MGs considering the uncertainties of the blackout starting time and duration utilizing the kernel density estimator method. Additionally, uncertainties in solar irradiance and ambient temperature are also considered. For that, chance-constrained optimization is employed to design residential and industrial PV-based MGs. The proposed approach aims to minimize the expected value of the levelized cost of energy ( L C O E ), where the restriction of the annual total loss of power supply ( T L P S ) is addressed as a chance constraint. The results show that blackout uncertainties have a considerable effect on calculating the size of the MG's components, especially the battery bank size. Additionally, it is proven that considering the uncertainties of the input parameters leads to an accurate estimation for the LCOE and increases the MG reliability level. [ABSTRACT FROM AUTHOR]

Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model's performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape. [ABSTRACT FROM AUTHOR]

This review underscores the pivotal role of Cognitive Systems (CS) in enhancing energy efficiency within the industrial sector, exploring the application of sophisticated algorithms, data analytics, and machine learning techniques to the real-time optimization of energy consumption. This methodology has the potential to reduce operational expenses and further diminish environmental repercussions; however, it also leverages data-driven insights and predictive maintenance to foresee equipment malfunctions and modulate energy utilization accordingly. The viability of integrating renewable energy sources is emphasized, supporting a transition towards sustainability. Furthermore, this research includes a bibliometric literature analysis from the past decade on the deployment of CS and Artificial Intelligence in enhancing industrial energy efficiency. [ABSTRACT FROM AUTHOR]

Co-firing wood pellets with coal is an attractive alternative energy generation method with economic, social, and environmental benefits for the US energy generation sector. One way to sustainably use wood pellets for co-firing is to create consumer-supported green pricing programs (GPPs). Our study surveyed residents of five states (Alabama, New Jersey, New York, Pennsylvania, and Virginia) to investigate preferences for the attributes of a hypothetical GPP. The study applied the Best Worst Choice method, which employs the Best Worst Scaling (BWS) and binary choice (BC) task. The BWS analysis showed that residents of all five states most value the flexibility of contracts, the location of energy generation, and the reduction of carbon emissions as attributes of GPPs. The BC analysis, however, showed that residents are willing to pay a premium for length of contract, followed by reduction of carbon emissions and variability of payments. This study shows that the adoption of optimal GPP attributes can create real customer value. The success of GPPs will depend on increasing enrollment and public support; hence, the next step will be to increase awareness levels and green consciousness through sensitization in the form of public education exercises and media campaigns. Such measures will serve to inform and educate residents on the benefits of GPPs and lessen the gap between intrinsic value and willingness to pay for select attributes. [ABSTRACT FROM AUTHOR]

To reach climate neutrality by 2050, a goal that the European Union set itself, it is necessary to change and modify the whole EU's energy system through deep decarbonization and reduction of greenhouse-gas emissions. The study presents a current insight into the global energy-transition pathway based on the hydrogen energy industry chain. The paper provides a critical analysis of the role of clean hydrogen based on renewable energy sources (green hydrogen) and fossil-fuels-based hydrogen (blue hydrogen) in the development of a new hydrogen-based economy and the reduction of greenhouse-gas emissions. The actual status, costs, future directions, and recommendations for low-carbon hydrogen development and commercial deployment are addressed. Additionally, the integration of hydrogen production with CCUS technologies is presented. [ABSTRACT FROM AUTHOR]

Sprinting is a decisive action in soccer that is considerably taxing from a neuromuscular and energetic perspective. This study compared different calculation methods for the metabolic power (MP) and energy cost (EC) of sprinting using global positioning system (GPS) metrics and electromyography (EMG), with the aim of identifying potential differences in performance markers. Sixteen elite U17 male soccer players (age: 16.4 ± 0.5 years; body mass: 64.6 ± 4.4 kg; and height: 177.4 ± 4.3 cm) participated in the study and completed four different submaximal constant running efforts followed by sprinting actions while using portable GPS-IMU units and surface EMG. GPS-derived MP was determined based on GPS velocity, and the EMG-MP and EC were calculated based on individual profiles plotting the MP of the GPS and all EMG signals acquired. The goodness of fit of the linear regressions was assessed by the coefficient of determination (R2), and a repeated measures ANOVA was used to detect changes. A linear trend was found in EMG activity during submaximal speed runs (R2 = 1), but when the sprint effort was considered, the trend became exponential (R2 = 0.89). The EMG/force ratio displayed two different trends: linear up to a 30 m sprint (R2 = 0.99) and polynomial up to a 50 m sprint (R2 = 0.96). Statistically significant differences between the GPS and EMG were observed for MP splits at 0–5 m, 5–10 m, 25–30 m, 30–35 m, and 35–40 m and for EC splits at 5–10 m, 25–30 m, 30–35 m, and 35–40 m (p ≤ 0.05). Therefore, the determination of the MP and EC based on GPS technology underestimated the neuromuscular and metabolic engagement during the sprinting efforts. Thus, the EMG-derived method seems to be more accurate for calculating the MP and EC in this type of action. [ABSTRACT FROM AUTHOR]

Coal, extensively used in many countries across various industries, particularly as a fuel in the energy sector, poses significant environmental challenges due to process-related implications and their complex characteristics. A notable obstacle arises from its heterogeneous and complex submicron–nano-molecular structure, often leading to operational issues that are challenging to predict. This research aims to comprehensively assess the environmental impact of bituminous coal sourced from the Barapukuria Coal Mine (NW Bangladesh). This study focuses on elucidating the intrinsic properties of coal, with specific attention to its thermal behavior, structural composition, and surface morphology, aiming to understand the implications for energy production. Advanced techniques, such as TGA/DTG, CHNS-O, XRD, FTIR, XRF, ICP-OES, XPS, SEM with EDX, and TEM, were employed. Rigorous experimentation and analysis provided valuable insights into combustion and pyrolysis processes, illuminating the release of environmentally hazardous pollutants and their negative consequences associated with the utilization of Barapukuria coal for energy generation. Thermogravimetric analysis indicates coal bond breakdown at 573–933 K, releasing gases and liquids during the breakdown stage, resulting in mass loss from moisture evaporation, protogenic gas release, and volatile matter loss. Despite being of the bituminous type, the coal exhibits a mean carbon concentration of 78.24% and a low sulfur content of 0.42%, signifying relative environmental friendliness compared to high-sulfur coals, which is crucial for preventing acid rain. The average higher heating value (31.04 ± 0.74 MJ kg−1) and calorific value (30.20 ± 0.95 MJ kg−1) indicate that the studied coals have high energy potential. This study also suggests that optimizing thermochemical conversion for power generation may enhance energy efficiency and mitigate potential environmental impacts. The research findings contribute to the scientific understanding of coal thermal properties, serving as a foundation for informed decision-making in energy production strategies that prioritize both efficiency and environmental sustainability. [ABSTRACT FROM AUTHOR]

Energy management systems are the most significant field in the potential electric vehicles (EVs). Also, EVs are widely used because of their pollution-free nature worldwide and their specific feature of flexibility. However, the EV charging system's significant challenges are power loss and high power consumption. Therefore, a robust load-balancing strategy is required to address the EV charging system's issues. Therefore, a novel intelligent hybrid Buffalo-based Recurrent Energy Management System (BREMS) has been proposed to reduce the power consumption of EVs and mitigate power losses. Here, two primary functions have been done. Initially, hydro and solar sources are modeled with the help of energy storage components and a power grid to enhance the system's efficiency. Then, the optimization fitness module is initiated to the hidden layer of the neural framework to monitor the loaded grids and distributes the loads to imbalanced grids. Implementation and designing processes have been done on the MATLAB platform. The simulation outcomes of the proposed design have been compared with conventional models in terms of power loss, cost of energy, utilization of power, and execution time. The comparative assessment shows that the presented system obtained better outcomes than other models. [ABSTRACT FROM AUTHOR]

This article concentrates on the fixed‐time synchronization (FTS) and energy cost (EC) problems of delayed coupled neural networks (NNs). A switching controller is proposed and some sufficient conditions are advanced to ensure FTS of delayed coupled NNs based on the comparison theorem. Following that, the required EC is estimated during the control process. The main results of FTS and EC for considered NNs are obtained based on the Lyapunov functions in the sense of vector 1‐norm, 2‐norm, and ∞$$ \infty $$‐norm, respectively. Under the same controller parameters, this article shows that the minimum estimation of settling time and EC are sometimes not in one norm sense. Finally, numerical examples are given to show the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China. [ABSTRACT FROM AUTHOR]

This study examines the feasibility of using hydrogen as a clean energy source for residential consumers in the UK through a low-carbon energy hub. Two cases were compared: a solid oxide fuel cell (SOFC) integrated combined heat and power (CHP) system fuelled by natural gas and hydrogen; and a SOFC-heat pump (HP) integrated CHP system fuelled by natural gas and hydrogen. The study used the actual electricity and heating demands of a UK cluster to model the CHP systems. The results indicate that the SOFC-based CHP system with hydrogen as fuel is more energy-efficient than the natural gas-fuelled system, with energetic efficiencies of 92.12% and 66.98%, respectively. The study also found that the system incorporating a heat pump is more economically viable, regardless of the fuel source, with the hydrogen-powered system equipped with a heat pump having a levelised cost of energy (LCOE) of 0.2984 £ per kW h. The study also evaluated the environmental impact of the natural gas-powered SOFC and SOFC-HP systems, with estimated levelised CO2 emissions of 0.308 kg per kW h and 0.213 kg per kW h, respectively. The study's findings provide insights into the potential of hydrogen as a cleaner energy source for residential consumers in the UK and highlight the importance of exploring low-carbon energy alternatives. [ABSTRACT FROM AUTHOR]

Modulating metal‐metal and metal‐support interactions is one of the potent tools for augmenting catalytic performance. Herein, highly active Co/VN nanoparticles are well dispersed on three‐dimensional porous carbon nanofoam (Co/VN@NC) with the assistance of dicyandiamide. Studies certify that the consequential disordered carbon substrate reinforces the confinement of electrons, while the coupling of diverse components optimizes charge redistribution among species. Besides, theoretical analyses confirm that the regulated electron configuration can significantly tune the binding strength between the active sites and intermediates, thus optimizing reaction energy barriers. Therefore, Co/VN@NC exhibits a competitive potential difference (ΔE, 0.65 V) between the half‐wave potential of ORR and OER potential at 10 mA cm−2, outperforming Pt/C+RuO2 (0.67 V). Further, catalyst‐based aqueous/flexible ZABs present superior performances with peak power densities of 156 and 85 mW cm−2, superior to Pt/C‐based counterparts (128 and 73 mW cm−2). This research provides a pivotal foundation for the evolution of bifunctional catalysts in the energy sector. [ABSTRACT FROM AUTHOR]

Over the last decades, porous organic materials (POMs) have been extensively employed in various industrial approaches including gas separation, catalysis and energy production due to possessing indisputable advantages like great surface area, high permeability, controllable pore size, appropriate functionalization and excellent processability compared to traditional substances like zeolites, Alumina and polymers. This review presents the recent breakthroughs in the multifunctional POMs for potential use in the membrane-based CO2 separation. Some examples of highly-selective membranes using multifunctional POMs are described. Moreover, various classifications of POMs following with their advantages and disadvantages in CO2 separation process es are explained. Apart from reviewing the state-of-the-art POMs in CO2 separation, the challenges/limitations of POMs with tailored structures for reasonable application are discussed. [ABSTRACT FROM AUTHOR]

Algeria, richly-endowed with renewable resources, is well-positioned to become a vital green hydrogen provider to Europe. Aiming to aid policymakers, stakeholders, and energy sector participants, this study embodies the first effort in literature to investigate the viability and cost-effectiveness of implementing green hydrogen production projects destined for exports to Europe via existing pipelines. A land suitability analysis utilizing multi-criteria decision making (MCDM) coupled with geographical information system (GIS) identified that over 43.55% of Algeria is highly-suitable for hydrogen production. Five optimal locations were investigated utilizing Hybrid Optimization of Multiple Electric Renewables (HOMER), with solar-hydrogen proving the most cost-effective option. Wind-based production, offering higher output volumes reaching 968 kg/h, requires turbine cost reductions of 17.50% (Ain Salah) to 54.50% (Djanet) to achieve a competitive levelized cost of hydrogen (LCOH) of $3.85/kg with PV systems. A techno-economic sensitivity analysis was conducted, identifying Djanet as the most promising location for a 100 MW solar-hydrogen plant, with a competitive LCOH ranging from $1.96/kg to $4.85/kg. • Assessing the cost-effectiveness of exporting Algerian green hydrogen to Europe via pipelines. • AHP-GIS method identified 43% of Algerian territory suitable for realizing green hydrogen projects. • HOMER Pro analyses identified solar-hydrogen as the most cost-effective option. • Wind turbine costs require 17.50%–54.50% reduction to compete with the $3.85/kg PV-LCOH. • Djanet suits a 100 MW solar-hydrogen plant with a LCOH range of $1.96/kg to $4.85/kg. [ABSTRACT FROM AUTHOR]

Energy cost is a long‐neglected but crucial issue for electrocatalytic carbon dioxide reduction reactions (CO2RRs). So far, achieving efficient CO2RR at a low energy cost is a major unresolved challenge. Herein, energy‐efficient CO2‐to‐CH3OH conversion by synergistically increasing the amount of favorable intermediates and depressing H2 generation is reported. The designed precursor electrocatalyst undergoes in situ reduction, forming Cu−C60 and ZnO−Cu dual interfaces. Cu−C60 induces an *H‐rich surface, decreasing the hydrogenation barrier and lowering the required voltage. *H‐modified ZnO‐Cu alters the mechanism of electron transfer and improves the conversion selectivity. As a result, at an applied potential as low as −0.63 V versus a reversible hydrogen electrode, a cathodic energy efficiency of 50.5% and a faradaic efficiency of 78.3% for CH3OH is obtained. This work unlocks an unconventional route for improving the catalytic performance and energy efficiency of electrocatalysts, addressing the concern of energy costs for electrocatalyzed CO2RR. [ABSTRACT FROM AUTHOR]

Electricity market violations affect the overall operations of the electricity market. This paper explores the evolutionary stability strategies of electricity generation enterprises and electricity consumers under two modes: traditional regulation and blockchain regulation to analyze blockchain technology's mechanism and conditions in solving electricity market violations. The experimental results indicate that the likelihood of consumers accepting electricity and the regulatory capacity of regulatory agencies play a crucial role in determining the violation approach adopted by electricity generation enterprises. Under traditional regulatory models, due to information asymmetry, regulatory agencies may not be able to detect violations promptly. Meanwhile, electricity consumers may choose to accept violations by power generation companies due to high appeal costs. Blockchain technology enables regulatory agencies to improve their regulatory capabilities by eliminating information asymmetry, reducing the cost of complaints from electricity consumers, thereby elevating the risk for enterprises engaging in market violations and optimizing the evolutionary game towards an optimum state. [ABSTRACT FROM AUTHOR]

Due to its widespread availability and inexpensive cost of energy conversion, solar power has become a popular option among renewable energy sources. Among the most complete methods of utilizing copious solar energy is the use of photovoltaic (PV) systems. However, one major obstacle to obtaining the optimal performance of PV technology is the need to maintain ideal operating temperature. Maintaining constant surface temperatures is critical to PV systems' efficacy. This review looks at the latest developments in PV cooling technologies, including passive, active, and combined cooling methods, and methods for their assessment. As advances in research and innovation progress within this domain, it will be crucial to tackle hurdles like affordability, maintenance demands, and performance in extreme conditions, to enhance the efficiency and widespread use of PV cooling methods. In essence, PV cooling stands as a vital element in the ongoing shift towards sustainable and renewable energy sources. [ABSTRACT FROM AUTHOR]

In this article, a distributed charging strategy problem for plug-in electric vehicles (PEVs) with feeder constraints based on generalized Nash equilibria (GNE) in a novel smart charging station (SCS) is investigated. The purpose is to coordinate the charging strategies of all PEVs in SCS to minimize the energy cost of SCS. Therefore, we build a non-cooperative game framework and propose a new price-driven charging control game by considering the overload constraint of the assigned feeder, where each PEV minimizes the fees it pays to satisfy its optimal charging strategy. On this basis, the existence of GNE is given. Furthermore, we employ a distributed algorithm based on forward–backward operator splitting methods to find the GNE. The effectiveness of the employed algorithm is verified by the final simulation results. [ABSTRACT FROM AUTHOR]

In modern and future communication systems, we expect peaks of traffic that largely exceed the capacity of the system, since they are originally designed to support normal traffic loads. Such peaks can be caused by emergency events and cultural or sporting gatherings, among others. Indeed, implementing more channels than the ones required in normal traffic conditions would entail higher costs and energy consumption. As such, when a traffic peak arrives, the system performance is greatly affected. To this end, we propose the use of mobile channels that assist cellular systems to increase the capacity of the network for a certain period. In this paper, we derive the blocking probability of a UAV (Unmanned Aerial Vehicle)-assisted cellular system to temporarily increase the capacity of the communication network in case of a traffic overload. The analysis presented in this work allows a careful design of future communication systems requiring fewer channels, that can serve users in normal traffic load conditions while using UAVs to maintain an adequate blocking probability when the traffic load increases. To this end, we develop the ErlangU formula, similar to the ErlangB formula for a conventional voice service cellular system. [ABSTRACT FROM AUTHOR]

This study examines the systemic risk caused by major events in the international energy market (IEM) and proposes a management strategy to mitigate it. Using the tail-event driven network (TENET) method, this study constructed a tail-risk spillover network (TRSN) of IEM and simulated the dynamic spillover tail-risk process through the cascading failure mechanism. The study found that renewable energy markets contributed more to systemic risk during the Paris Agreement and the COVID-19 pandemic, while fossil energy markets played a larger role during the Russia-Ukraine conflict. This study identifies systemically important markets (SM) and critical tail-risk spillover paths as potential sources of systemic risk. The research confirms that cutting off the IEM risk spillover path can greatly reduce systemic risk and the influence of SM. This study offers insights into the management of systemic risk in IEM and provides policy recommendations to reduce the impact of shock events. [ABSTRACT FROM AUTHOR]

Mining and processing enterprises are large consumers of electricity, with a large share of energy costs in the cost of production. Thus, the price of electricity in the cost of production takes higher values for Hydrometallurgical Plant 1. In this regard, the development of energy consumption models that can be used as a basis for managing the energy consumption process is an important component of learning. It is important to analyze the energy consumption to estimate the electricity consumption under different operating conditions of the mining industry. In order to study and plan electricity consumption and develop methods for regulating electricity consumption, it is necessary to determine the place of each technological stage in the energy consumption balance [1]. This article examines the laws of electricity consumption in mining and processing enterprises. The study of such laws helps to determine the level of energy consumption management for the mining and processing plant. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive investigation to enhance understanding of subsurface structural complexities within the Upper Cretaceous Pab Sandstone reservoir level in a gas-producing field within the Kirthar Fold Belt of the Southern Indus Basin, Pakistan. The seismic interpretation for accurate structural delineation and characterization of intricate fault systems is often challenging in Fold and Thrust Belt settings. The 3D structural maps of Upper Cretaceous Pab Sandstone at the reservoir level and the application of the ant-tracking attribute for fault extraction improve the structural understanding of the gas-producing field in the Kirthar Fold Belt in the Southern Indus Basin, Pakistan. Attributeassisted 3D seismic interpretation has revealed several subsurface structure elements, including a large thrusted anticline extended towards the north-south, the pattern of north-south oblique ramp thrusts on the southeastern flank, and a combination of easterly vergent thrusts with a counterback thrust creating a local pop-up structure in the area. A 3D structural model at the reservoir level was generated using horizon and fault framework volume-based modeling approach, serving as a structural model for Pab sandstone. The structural smoothing, variance and ant-tracking attribute were applied iteratively to get the best results and correlated with manual interpretation. 3D seismic interpretation and application of anttracking for fault extraction identifies discrete structural styles and revealed that all thrusting occurred due to compressional tectonics of the Plio- Pleistocene period. The apparent different styles result from the reactivation of earlier extensional fault systems and probably during phased periods of compression. The resulting 3D reservoir model can be used as an input to populate petrophysical properties and results can be extended for future field development plans. Integration of well and 3D seismic data interpretation and the application of automatic fault extraction techniques are highly recommended in structurally complex areas and have an equal implication for worldwide basins with similar conditions for reliable structural interpretation in Fold and Thrust Belts with structural complexity. [ABSTRACT FROM AUTHOR]