Search Results (21,828)

The increasing penetration of renewable energy sources (RES) introduces significant variability and instability in modern power systems, creating a growing need for advanced and coordinated energy storage solutions. However, a key unresolved challenge remains the integrated modeling and optimal sizing of hybrid energy storage systems (ESS) that combine technologies with different temporal characteristics under high RES penetration. This study addresses this challenge by developing a unified techno-economic and physical–mathematical framework for hybrid ESS integrating lithium-ion (Li-ion), vanadium redox flow batteries (VRFB), and hydrogen (H₂) technologies. Unlike conventional approaches that treat storage technologies independently or use simplified hybrid representations, the proposed framework jointly considers dynamic energy balance, degradation-aware lifecycle behavior, and multi-criteria cost optimization. The model was implemented using Python 3.10-based simulation tools and evaluated under renewable penetration scenarios of 30%, 50%, and 70%. The results indicate that increasing RES penetration leads to higher power fluctuations, reaching ±15–20% at 50% RES and ±20–25% at 70% RES. The optimized hybrid system achieves an overall efficiency of up to 92%, reduces total system cost to approximately 450 USD/kWh, and extends operational lifetime to 25 years, demonstrating a balanced techno-economic performance compared to standalone storage technologies. The proposed framework addresses this gap by coupling dynamic energy balance analysis with degradation-aware techno-economic optimization, enabling coordinated allocation of storage functions across short-, medium-, and long-duration timescales. In this way, the study not only evaluates hybrid storage performance, but also provides a practical decision-support framework for renewable-dominated power systems, particularly in the context of Kazakhstan’s energy transition. Full article

This paper evaluates the efficiency of modernization of the regional railway line Prievidza–Jelšovce in Slovakia using cost–benefit analysis (CBA), reflecting increased investment costs and the potential electrification of the line. The assessment is based on a detailed analysis of transport demand and infrastructure conditions, where daily railway passenger volumes range between 2300 and 3700 passengers, while individual car transport exceeds 10,000 passengers per day in most sections. Two alternative modernization variants were evaluated. The results show that the project generates socio-economic benefits, particularly through travel time savings amounting to approximately €42.3 million and reductions in operating costs and externalities. Significant environmental benefits were identified, especially in the case of the more advanced variant, with reductions in air pollution reaching €56.3 million and greenhouse gas emissions reaching €42.2 million. Despite these benefits, the economic evaluation indicates negative net economic outcomes for both variants. The total economic investment costs (excluding VAT and adjusted for economic appraisal) reach €543.4 million for the EIA variant and €511.9 million for the proposed variant, resulting in net economic values of −€186.2 million and −€70.8 million, respectively. The results therefore suggest that neither variant achieves full economic efficiency under the given assumptions, although the proposed variant performs significantly better. The findings highlight the strong sensitivity of project efficiency to investment costs and the scope of modernization. The study confirms the necessity of regularly updating CBA analyses in transport projects, as changes in input parameters can substantially influence investment decision-making. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

Urban agglomerations have become the dominant spatial platform of urbanization, regional coordination, and economic transformation in China. Yet whether the expansion of agglomeration scale at the urban-agglomeration level alleviates or intensifies energy use remains insufficiently understood. Extending the scale of analysis from individual cities to integrated urban agglomerations, this study investigates 64 cities in four major Chinese urban agglomerations, including Beijing–Tianjin–Hebei, the Yangtze River Delta, the Pearl River Delta, and Chengdu–Chongqing, over the period 2006–2023. Using panel data models, this study examines the impact of the scale agglomeration within urban agglomeration on urban energy intensity. The results show that the overall agglomeration scale generated by urban agglomeration formation significantly suppresses energy intensity while indicating a robust energy-saving effect: every 10% increase in agglomeration scale is associated with a decline of approximately 0.0893 million tons of standard coal per CNY 100 million of GDP. This finding remains stable after addressing endogeneity concerns and performing a series of robustness checks. Mechanism analyses further suggest that this effect operates primarily through talent agglomeration, technological progress, and public transportation expansion. In addition, the energy-saving effect is more pronounced in smaller cities, cities with lower administrative rank, cities with weaker factor mobility, and cities characterized by poorer air quality but stronger public environmental attention. These findings contribute to the literature on urban agglomeration and green development by showing that the agglomeration scale within urban agglomerations can generate inclusive energy-efficiency gains, especially for relatively disadvantaged cities, thereby offering important implications for spatial governance and low-carbon transition in rapidly urbanizing economies. Full article

Ground source heat pump (GSHP) systems, while energy-efficient, often face persistent soil thermal imbalance in heating-dominated severe cold regions, which undermines their long-term performance and sustainability. This study proposes a TRNSYS-GenOpt framework for the life-cycle cost optimization of hybrid GSHP systems integrating electric boilers and geothermal regulation towers. A transient model for a 5650 m² fire station in Changchun was developed, employing the Hooke–Jeeves algorithm to co-optimize boiler capacity, borehole depth, and geothermal regulation tower airflow under constraints on heating supply temperature and soil thermal balance. Time-of-use electricity pricing was incorporated for realistic operational economics. The optimized configuration (148 m, 864.8 kW, 290,400 m³/h) achieved a minimum 20-year life-cycle cost of CNY 1.13 million. Sensitivity analysis revealed “rigid design, flexible cost” characteristics: optimal parameters remained invariant across discount rate variations (3.5–7.5%) and equipment costs (±20%), while life-cycle cost showed the highest sensitivity to electricity pricing and discount rates. The long-term simulation confirmed compliance with all physical constraints. This methodology demonstrates that thermodynamic constraints supersede economic trade-offs in severe cold climates, providing engineers with a reliable tool for sustainable hybrid geothermal system design. Full article

Uncertainties in generation and dynamic load behavior provide new problems for radial distribution systems (RDS) caused by the growing integration of renewable distributed generators (RDGs), including solar photovoltaic (PV) systems and wind turbines (WTs), as well as electric vehicle charging stations (EVCS). This article offers a thorough techno-economic evaluation of how to best distribute RDG resources (solar PV, wind, and EVCS) inside a 28-bus distribution test system in India, taking into account generation volatility due to the seasons. Optimization of installation and operating costs, enhancing voltage stability, and decreasing active power loss are done all at once using a new Catch Fish Optimization Algorithm (CFOA). Integrating beta and Weibull distributions, respectively, into the probabilistic modeling of solar irradiance and wind speed allows for economic analysis to adhere to recognized approaches from contemporary multi-objective optimization frameworks. The simulation findings confirm that the proposed CFOA-based placement method improves economic efficiency, decreases energy loss, and increases system performance. Full article

Corn processing waste represents an abundant, renewable, and low-cost lignocellulosic resource with considerable potential for environmental remediation applications. Large quantities of residues generated during corn processing, including cobs, husks, bran, and other by-products, are produced annually and can be utilized directly as native biomass or converted through thermochemical processes into hydrochars and biochars. This systematic review provides a comparative analysis of native corn processing biomass, hydrochars produced via hydrothermal carbonization, and biochars obtained through pyrolysis, with a focus on their potential as adsorbents for the removal of organic and inorganic pollutants from soil and water systems. Particular attention is given to the influence of thermochemical conversion processes on the physicochemical properties of the materials, including surface chemistry, porosity, functional groups, and structural characteristics, which govern adsorption mechanisms such as ion exchange, electrostatic interactions, surface complexation, hydrogen bonding, and π–π interactions. Furthermore, the advantages and limitations of each material type are discussed, together with key environmental and techno-economic considerations related to their production and practical application, including indicative production costs (USD per kg of adsorbent) and cost–performance relationships in terms of adsorption capacity. By linking biomass conversion processes, material properties, and adsorption performance, this review aims to provide a comprehensive overview of corn processing waste valorization and to support the development of sustainable adsorbent materials for soil and water remediation. A total of 36 studies were included in the qualitative synthesis following PRISMA guidelines. Full article

Reservoir characterization of the Abu Roash “G” (AR/G) Member in the Karama Field, Abu Gharadig Basin, Western Desert of Egypt, is complicated by structural deformation, facies variability, and lithologic heterogeneity, which introduce uncertainties in reservoir evaluation and hydrocarbon estimation. This study aims to provide a comprehensive reservoir assessment through an integrated three-dimensional (3D) static modeling workflow. Well-log data from four wells were combined with the interpretation of seventeen seismic lines to construct structural, stratigraphic, and petrophysical models of the AR/G reservoir. The results indicate that reservoir thickness ranges from 9 to 14 ft and is structurally controlled by nine normal faults forming a horst–graben configuration that significantly influences compartmentalization and hydrocarbon distribution. Petrophysical modeling reveals favorable reservoir quality, with effective porosity ranging from 14% to 20%, an average shale volume of approximately 19%, and hydrocarbon saturation averaging 56%. Two prospective zones were identified, with estimated original oil in place (OOIP) of 10.76 MMSTB and 3.23 MMSTB, respectively, representing recoverable volumes within structurally defined closures rather than the entire field volume. The model also explains the relatively poor performance of Karama-5 and Karama-11 wells due to their peripheral structural positions outside the main closures and their higher water saturation (44–53%). These findings demonstrate that integrated structural and petrophysical modeling improves reservoir understanding and helps identify optimal drilling targets in structurally complex reservoirs of the Abu Gharadig Basin and comparable North African settings. Although the estimated volumes correspond to relatively small accumulations, they are considered economically viable within mature basins such as the Abu Gharadig Basin, where existing infrastructure and optimized development strategies enable efficient exploitation of marginal reserves. Full article

Despite their excellent torsional and bending strength, the economical production of hypotrochoidal profiles (H-profiles) remains an obstacle to their use. Due to the tool clearance angle, the commercially available twin-spindle turning process has limited ability to manufacture many of the profiles standardized according to DIN 3689 (Deutsches Institut für Normung). On the other hand, the manufacturing of cycloidal as a non-involute special geometry using generating processes (hobbing or continuous generating grinding) depends critically on the accuracy of the tool geometry—whether a hobbing cutter or a grinding worm. Conventional tool design methods—based on approximations, involute-derived profiles, or iterative trial-and-error corrections—face fundamental limitations: unpredictable cutting force variations, elevated surface roughness, and limited process capability. However, if the exact tool geometry has been determined analytically, the same machine achieves significantly better performance. In this work, the exact tool geometry conjugated to the H-profile for profile manufacturing is determined based on the gearing law. This provides modular H-profile manufacturing without deviations. Consequently, a design concept that enables the implementation of all existing rolling processes—including gear hobbing, gear shaping, gear planning, and other variants such as gear grinding—is presented. For profile shaping of hollow contours, the transfer ratio is considered and a curve conjugated to the profile contour is determined for the tool. A CAD-based simulation shows very good consistency with the analytically determined tool geometry. Full article

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a $1.9$% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises. Full article

Thamnaconus septentrionalis is an economically important marine aquaculture species in China. However, the acceptance rate of formulated feeds in commercial farming is only 30–40%, substantially lower than the 80–90% achieved with fresh feeds, which severely constrains the intensive development of this industry. The gut microbiota-mediated regulatory mechanisms underlying the effects of different feed types on growth performance remain unclear, limiting the precise development of efficient formulated feeds. This study established four feed types (commercial pellet feed K, custom-formulated feed P, frozen shrimp X, and fresh fish meat Y) through a 60-day feeding trial. Growth performance data, 16S rRNA sequencing, and untargeted metabolomics were analyzed. Random Forest-Partial Least Squares Regression models were employed to identify key microbial-metabolite features. Results indicated that the Y group exhibited the optimal feed conversion ratio (1.14), with intestinal Firmicutes abundance (45.3%) significantly higher than the K group (28.5%). Short-chain fatty acid levels increased by more than 350-fold, enriching short-chain fatty acid-producing bacteria such as Lactobacillus and Faecalibacterium. The P group, formulated with high fishmeal content (40%), achieved performance levels comparable to the Y group across most indicators. Machine learning models identified key microbial-metabolite features predicting growth performance, providing a multi-omics framework for developing efficient formulated feeds for marine carnivorous fish. Full article

Indoor Environmental Quality (IEQ) strongly influences health, comfort, and learning performance in academic buildings, yet assessment practices remain fragmented and rarely aligned with sustainability goals. This study conducted a PRISMA 2020-guided systematic literature review to identify, screen, and map IEQ indicators for educational facilities and to develop a sustainability-aligned framework for classroom evaluation. Searches of Google Scholar, Scopus, and Web of Science (2010–2025) yielded 365 records; after de-duplication and eligibility screening, 142 peer-reviewed studies were included. From these, 118 unique IEQ indicators were extracted and classified into six domains: thermal comfort, indoor air quality, acoustic quality, visual comfort, environmental quality, and spatial quality. Using sustainability-oriented screening criteria (measurability, relevance, reliability, data accessibility, understandability, and long-term applicability), 50 indicators (42%) were retained as methodologically robust, while 68 (58%) were excluded due to weak standardization or limited practical applicability. The retained indicators were systematically mapped to the environmental, social, and economic pillars and aligned with key SDGs (3, 4, 7, 11, and 13). The resulting Sustainability-Aligned IEQ Indicator Framework integrates quality-screened indicators with pillar/SDG alignment and a mixed-method pathway that combines objective monitoring and occupant perception, supporting context-sensitive evaluation, particularly for naturally ventilated and tropical learning environments. Full article

Hydraulic flushing is an effective permeability enhancement technology for coal seams in underground coal mines and has been widely applied in several mining areas in China. However, in low-permeability coal seams, gas drainage from hydraulic flushing boreholes often enters a rapid depletion phase, and achieving secondary enhanced drainage remains a critical challenge. To address this issue, this study investigates a synergistic gas drainage technology that combines gas injection displacement with hydraulic flushing. Taking the No. 3 coal seam in the Lu’an mining area of China as the research object, the optimal process parameters of this synergistic technology are systematically determined through numerical simulation and validated by underground field tests. A fully coupled numerical model incorporating the adsorption–desorption–seepage processes of the CH₄/N₂/O₂ ternary gas system is established. The influences of injection spacing and injection pressure on drainage performance are systematically analyzed. Simulation results identify the optimal process parameters as an injection spacing of 3.5 m and an injection pressure of 1.4 MPa. Under these conditions, the relative coal permeability reaches a maximum of 1.06, the permeability enhancement zone fully covers the region between the injection and drainage boreholes, and the coal seam gas content decreases to the critical threshold of 8 m³/t in approximately 235 days. The model is quantitatively validated using 82-day field monitoring data from the synergistic module, with a relative error of approximately 1.1% between the simulated and field-derived recovery ratios. Subsequently, four sets of underground engineering trials—conventional drainage, gas injection displacement alone, hydraulic flushing alone, and the synergistic technology—are conducted in the target coal seam based on the optimized parameters. Statistical analysis of the 82-day field data shows that the synergistic technology achieves a cumulative pure methane volume of 4.83 m³, outperforming conventional drainage by 85.8% (4.83 m³ compared with 2.60 m³), gas injection alone by 23.5% (4.83 m³ compared with 3.91 m³), and hydraulic flushing alone by 52.4% (4.83 m³ compared with 3.17 m³). The mean flow rate of the synergistic module during the injection phase reaches 0.070 ± 0.012 L/min, significantly higher than that of gas injection alone (0.044 ± 0.011 L/min). This study provides economically feasible theoretical and technical support for efficient gas drainage in low-permeability coal seams in underground mines. Full article

Croatia hosts one of the most intensive recreational boating activities in the Mediterranean, with over 134,600 registered vessels along 5835 km of Adriatic coastline. This paper presents an AI-driven simulation framework for evaluating electrification pathways for the Croatian recreational vessel fleet. A key contribution is the explicit treatment of the AIS data gap: recreational vessels in Croatia are not required to carry AIS transponders, so synthetic operational profiles calibrated from manufacturer specifications and verified economic data are used instead. Six machine learning architectures are compared for vessel energy demand forecasting, with a proposed Transformer-based model achieving the best simulated performance. Fleet-weighted Monte Carlo simulation across three electrification scenarios suggests that an AI-optimised hybrid configuration can, subject to use intensity, reduce per-vessel CO₂ emissions by up to 56.8% relative to conventional engines. Techno-economic analysis shows payback periods ranging from over 15 years for low-use private owners to 7–9 years for charter operators, supporting targeted incentive design. The framework is intended to be transferable to other Mediterranean coastal regions facing comparable data and operational constraints. Full article

The classic design of the Practical Byzantine Fault Tolerance (PBFT) protocol relies on a centralized primary node, which not only creates a performance bottleneck but also introduces severe data censorship risks, threatening the data integrity and security of Edge Computing networks. To address this challenge, this paper proposes DC-PBFT (Decoupled PBFT), a censorship-resistant consensus protocol for Edge-Internet of Things (Edge-IoT) environments. The core innovation of DC-PBFT lies in the decoupling of the Proposer and Primary roles, supplemented by Verifiable Random Function (VRF)-based dynamic role rotation, which fundamentally eliminates the arbitrary power of a single node. Building on this, the protocol introduces a parallel group consensus mechanism: an elected Consensus Committee (CC) composed of Active Edge Nodes leads the consensus, while an independent Replica Network (RN) performs parallel validation. When a disagreement arises, the protocol triggers a global disagreement arbitration process involving all nodes to guarantee final consistency and attribute fault. To ensure long-term incentive compatibility, we also designed a hybrid election mechanism combining Proof-of-Stake and dynamic reputation, along with corresponding economic incentives and a tiered penalty system. Theoretical analysis proves that DC-PBFT satisfies Consistency and Liveness, and achieves strong censorship resistance guarantees. Simulation results demonstrate that DC-PBFT’s scalability significantly outperforms PBFT and RepChain; its reputation mechanism effectively improves long-term performance under sustained Byzantine attacks; and, compared to asynchronous censorship-resistant protocols like HoneyBadgerBFT, DC-PBFT achieves censorship resistance with over 45% lower transaction confirmation latency. Full article