Search Results (110)

Freshwater resources are on the verge of depletion due to the rapid increase in population, lifestyle changes, and especially during climate change in Iraq. Therefore, treating domestic wastewater correctly will significantly contribute to keeping the balance of water purity and its usage. To fulfil this, the Sustainable Project Appraisal Routine (SPeAR^TM) program, which leverages Multi-Criteria Decision Analysis with operational sustainability indicators, is used to compare the relative sustainability performance of the novel Modified Sequencing Batch Reactor by visualising the results of the degree of its sustainability compared to the Moving Bed Biofilm Reactor and the conventional Sequencing Batch Reactor system. Although selecting the most sustainable treatment depends on specific treatment goals, available resources, site conditions, and stakeholder preferences, this study considers the equal weighting of sustainability assessment across environmental, social, and economic indices to inform sustainable decision making. The results show that integrating both conventional treatment plants into the novel modified treatment plant demonstrates a comparatively more balanced and stable sustainability performance under the assessed operational conditions. As at a design capacity of 100 m³·day⁻¹, the MSBR achieved a higher organic and nutrient removal efficiencies relative to the conventional SBR and MBBR systems while operating at an intermediate energy demand (187.7 kWh·day⁻¹) compared with the SBR (121.7 kWh·day⁻¹) and the MBBR (211.8 kWh·day⁻¹). Thus, it can compensate for the weaknesses and combines the strengths of the sustainability indices of the two systems, which supports the Modified Sequencing Batch Reactor as a comparatively favourable option for wastewater treatment within the assessed sustainability framework. Full article

Frequent extreme-weather events pose severe challenges to the secure and economical operation of power systems with high renewable energy penetration. To strengthen grid resilience against such low-probability, high-impact events while maintaining good performance under normal conditions, this paper proposes an optimal energy storage allocation method for power systems with high-wind-power penetration. We first identify two representative extreme wind power events and develop a risk assessment model that jointly quantifies load-shedding volume and transmission-line security margins. On this basis, a multi-scenario joint siting-and-sizing optimization model is formulated over typical-day and extreme-day scenarios to minimize total system cost, including annualized investment cost, operating cost, and risk cost. To solve the model efficiently, a two-stage hierarchical solution strategy is designed: the first stage determines an investment upper bound from typical-day scenarios, and the second stage optimizes storage allocation under superimposed extreme-day scenarios within this bound, thereby balancing operating economy and extreme-weather resilience. Simulation results show that the proposed method reduces loss-of-load under extreme-weather scenarios by 32.46% while increasing storage investment cost by only 0.18%, significantly enhancing system resilience and transmission-line security margins at a moderate additional cost. Full article

Glacier surface albedo controls solar energy absorption and Arctic mass balance, yet comprehensive modelling approaches remain limited. This study develops and validates multiple modelling frameworks for glacier albedo prediction using automatic weather station (AWS) data from Hansbreen and Werenskioldbreen in southern Svalbard during the 2011 ablation season. We compared three point-based approaches across elevation zones. At lower elevations (190 m), linear regression models emphasising snowfall probability or temperature controls achieved excellent performance (R² = 0.84–0.86), with snowfall probability contributing 65% and daily positive temperature contributing 86.3% feature importance. At higher elevations (420 m) where snow persists, neural networks proved superior (R² = 0.65), with positive degree days (72.5% importance) driving albedo evolution in snow-dominated environments. Spatial modelling extended point predictions across glacier surfaces using elevation-dependent probability calculations. Validation with Landsat 7 imagery and multi-algorithm comparison (n = 5) revealed that while absolute albedo values varied by 12% (0.54–0.60), temporal dynamics showed remarkable consistency (27.8–35.2% seasonal decline). Point-to-pixel validation achieved excellent agreement (mean absolute difference = 0.03 ± 0.02, 5.3% relative error). Spatial validation across 173,133 pixel comparisons demonstrated good agreement (r = 0.62, R² = 0.40, RMSE = 0.15), with an accuracy of reproducing temporal evolution within 0.001–0.021 error. These findings demonstrate that optimal glacier albedo modelling requires elevation-dependent approaches combining physically based interpolation with machine learning, and that temporal pattern reproduction is more reliably validated than absolute values. The frameworks provide tools for understanding albedo-climate feedback and improving mass balance projections in response to Arctic warming. Full article

High organic loading is known to destabilize anaerobic digestion (AD). This study compared bioaugmentation and pH adjustment under increasing organic loading rate (OLR: 2.0, 4.0 and 6.0 gVS L⁻¹ d⁻¹), focusing on the responses of microbial structure, metabolic pathways, and energy metabolism. Results demonstrated that bioaugmentation maintained stable methane production of 400.54 ± 10.08 and 374.15 ± 24.32 mL·g-VS⁻¹ at 4.0 and 6.0 gVS L⁻¹ d⁻¹, respectively, whereas control and pH-adjusted reactors failed at 4.0 gVS L⁻¹ d⁻¹. The acidified system restored methane yield from 86.30 to 382.13 mL·g-VS⁻¹ after bioaugmentation, whereas pH adjustment and feeding cessation were ineffective, failing to produce methane within 25 days. Microbial analysis showed bioaugmentation enriched Methanosarcina, enhanced hydrogenotrophic/methylotrophic methanogenesis, and strengthened syntrophy with syntrophic propionate-oxidizing bacteria (SPOB), reducing volatile fatty acid accumulation via reinforced syntrophic propionate/butyrate oxidation. Upregulation of osmoregulatory (nha, kdp, proP) and energy metabolism genes (eha, mvh, hdr) maintained osmotic balance and energy supply under high load. In contrast, pH adjustment downregulated SPOB and propionate oxidation genes, causing persistent acid inhibition. This study elucidated the distinct regulatory effects of bioaugmentation and pH adjustment on high-load AD systems, providing actionable strategies for both maintaining operational stability in high-load reactors and recovering methanogenesis in acid-inhibited systems. Full article

Negative energy balance (NEB) during early lactation links spontaneous lipolysis (SL) with inflammatory signaling, yet the molecular response in dual-purpose breeds remains insufficiently characterized. This study investigated how NEB regulates circulating concentrations of interleukin-6 (IL-6) and plasminogen (PL) in Simmental cows, contextualizing these changes within concurrent metabolic adaptation. Forty-two cows were monitored from approximately two weeks prepartum to 150 days in milk across six defined stages. Energy balance (EB) was calculated from feed intake and energy-corrected milk yield, while daily milk production (DMP), milk composition, body condition score (BCS), β-hydroxybutyrate (BHBA), glucose (GLU), leptin (LEP), selected fatty acids (FAs: C16:0, C18:0, C18:1-t9, C18:2, IL-6), and PL were determined. EB declined progressively as DMP increased (r = −0.689, p ≤ 0.05). During peak NEB (SLII–SLIII), IL-6 increased from 92.16 to 109.59 ng·L⁻¹ and PL from 1.65 to 2.05 ng·L⁻¹, both inversely correlated with EB (r = −0.741 and −0.586, respectively) and positively associated with each other (r = 0.728), indicating coordinated activation of cytokine and fibrinolytic pathways. NEB severity was accompanied by elevated BHBA and LEP, decreased GLU, reduced BCS, and increased circulating FAs; nevertheless, ketosis remained moderate (peak BHBA 1.04 mmol·L⁻¹). These findings demonstrate that Simmental cows display a breed-specific molecular response in which NEB modulates IL-6 and PL in parallel with controlled lipid mobilization and efficient hepatic metabolism, supporting enhanced metabolic resilience during early lactation. Full article

Driven by the global energy transition and carbon-neutrality goals, virtual power plants (VPPs) are expected to aggregate distributed energy resources and participate in multiple electricity markets while achieving economic efficiency and low carbon emissions. However, the strong volatility of wind and photovoltaic generation, together with the coupling between electric and thermal loads, makes real-time VPP scheduling challenging. Existing deep reinforcement learning (DRL)-based methods still suffer from limited predictive awareness and insufficient handling of physical and carbon-related constraints. To address these issues, this paper proposes an improved model, termed SAC-LAx, based on the Soft Actor–Critic (SAC) deep reinforcement learning algorithm for intelligent VPP scheduling. The model integrates an Attention–xLSTM prediction module and a Linear Programming (LP) constraint module: the former performs multi-step forecasting of loads and renewable generation to construct an extended state representation, while the latter projects raw DRL actions onto a feasible set that satisfies device operating limits, energy balance, and carbon trading constraints. These two modules work together with the SAC algorithm to form a closed perception–prediction–decision–control loop. A campus integrated-energy virtual power plant is adopted as the case study. The system consists of a gas–steam combined-cycle power plant (CCPP), battery storage, a heat pump, a thermal storage unit, wind turbines, photovoltaic arrays, and a carbon trading mechanism. Comparative simulation results show that, at the forecasting level, the Attention–xLSTM (Ax) module reduces the day-ahead electric load Mean Absolute Percentage Error (${\mathrm{M}}_{\mathrm{A}\mathrm{P}\mathrm{E}}$) from 4.51% and 5.77% obtained by classical Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to 2.88%, significantly improving prediction accuracy. At the scheduling level, the SAC-LAx model achieves an average reward of approximately 1440 and converges within around 2500 training episodes, outperforming other DRL algorithms such as Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Under the SAC-LAx framework, the daily net operating cost of the VPP is markedly reduced. With the carbon trading mechanism, the total carbon emission cost decreases by about 49% compared with the no-trading scenario, while electric–thermal power balance is maintained. These results indicate that integrating prediction enhancement and LP-based safety constraints with deep reinforcement learning provides a feasible pathway for low-carbon intelligent scheduling of VPPs. Full article

Kisspeptin is a neuropeptide recognised for a pivotal role within the reproductive system, but potentially important endocrine metabolic effects are less well understood. We examined effects of twice-daily intraperitoneal administration of saline vehicle or kisspeptin-10 (25 nmol/kg), for 21 days, on glucose homeostasis, energy balance, circulating hormones as well as the morphology-function of enteroendocrine and islet cells in high-fat diet (HFD) fed female mice, with normal diet (ND) mice as an additional control group. Kisspeptin-10 decreased body weight, blood glucose and energy intake to ND levels. HFD increased circulating follicle-stimulating hormone (FSH) levels, which were further enhanced by kisspeptin-10 along with luteinising hormone (LH) concentrations. Neither HFD nor kisspeptin-10 affected progesterone or corticosterone. In the ileum, kisspeptin-10 decreased crypt depth and restored villi length to ND control levels, as well as increasing the proportion of glucose-dependent insulinotropic polypeptide (GIP) positive cells when compared to HFD mice and glucagon-like peptide-1 (GLP-1) positive cells compared to ND mice. Peptide YY (PYY) immunoreactivity was unaltered by HFD or kisspeptin-10. Plasma GIP was unchanged but circulating GLP-1 and PYY were reduced to ND levels. Within the pancreas, total islet, beta- and alpha-cell areas were similar in all mice, but kisspeptin-10 intervention restored relative insulin area to ND levels. Glucagon radius, an indicator of peripherally located alpha-cells, was reduced in HFD mice but normalised by kisspeptin-10 alongside elevated glucagon-islet area. Notably, beta-cell proliferation was increased by kisspeptin-10 with no alteration in beta-cell apoptosis. Overall, we reveal a previously uncharacterised diverse metabolic role for kisspeptin in directly modulating the gut–pancreatic axis. Full article

Street vitality has become a crucial metric for smart city management. Classical theories qualitatively explain that street vitality originates from the dynamic interaction between people and spatial carriers, yet the threshold effect within this process has not been addressed, leaving a gap in urban research. This study selects South China, one of China’s most vibrant and globally influential regions, introduces dissipative structure theory based on classical theories, and constructs a threshold effect hypothesis model for the vitality formation mechanism. Through energy efficiency conversion of data and a slope-based method for identifying balanced time periods, the periods of supply–demand balance in energy efficiency were identified, the threshold effect in vitality formation was captured, and critical thresholds were measured. The results indicate the following: (1) the hypothesis model is valid; (2) the threshold effect is inevitable and periodic, primarily occurring on workdays from 12:00 to 13:00 and 18:00 to 19:00, and on rest days from 08:00 to 09:00 and 18:00 to 19:00; and (3) the activation threshold is quantifiable and exhibits volatility, ranging from 0.40 to 1.56, varying specifically by city, season, day type, and street type. This study advances the translation of street vitality research from theory into practice and provides theoretical support and strategic guidance for smart city management globally, particularly in developing countries. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article

As the energy transition advances toward a low-carbon economy, small- and medium-sized consumers are increasingly becoming active prosumers, capable of generating, storing, and managing their own electricity. However, the intermittent nature of renewable sources poses significant challenges in matching generation with consumption, making energy storage a key element for prosumer participation in smart grids. This work assesses the performance of various energy storage technologies suitable for prosumer applications, focusing on parameters such as efficiency, lifecycle behavior, and system integration. Lithium-ion batteries, supercapacitors, and hydrogen-based technologies were tested under real-world operating conditions within residential, commercial, and industrial scenarios. The results confirm that hybrid configurations deliver the most balanced performance, with supercapacitors improving short-term stability in commercial contexts and hydrogen storage enabling long-duration autonomy in industrial settings. In terms of battery state of charge, the experimental tests showed clear differences across prosumer types: in the residential case, it dropped to about 20–25% in the morning, but recovered to nearly full capacity by midday and stabilized at around 70–75% by the end of the day; in the commercial case, it fluctuated more widely, between roughly 18% and 100%, evidencing the highest stress on batteries; while in the industrial case, it reached 25–30% at peak demand, with hydrogen sustaining autonomy under extended load and ensuring greater long-term reliability. Overall, the findings reinforce the importance of tailored storage strategies to unlock the full potential of prosumers in smart grids. Full article

To promote a more decentralized energy system, the European Commission introduced the concept of Renewable Energy Communities (RECs). Meanwhile, the increasing penetration of Electric Vehicles (EVs) may significantly increase peak power demand and consumption ramps when charging sessions are left uncontrolled. However, by integrating smart charging strategies, such as Vehicle-to-Grid (V2G), EV storage can actively support the energy balance within RECs. In this context, this work proposes a comprehensive and scalable model for leveraging smart charging capabilities in RECs. This approach focuses on an external cooperative framework to optimize incentive acquisition and reduce dependence on Medium Voltage (MV) grid substations. It adopts a hybrid strategy, combining Mixed-Integer Linear Programming (MILP) to solve the day-ahead global optimization problem with local rule-based controllers to manage power deviations. Simulation results for a six-month case study, using historical demand data and synthetic charging sessions generated from real-world events, demonstrate that V2G integration leads to a better alignment of overall power consumption with zonal pricing, smoother load curves with a 15.5% reduction in consumption ramps, and enhanced cooperation with a 90% increase in shared power redistributed inside the REC. Full article

This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy storage systems, SMRs provide a reliable and flexible baseload power source. Sector coupling systems—such as hydrogen production and heat generation—enhance grid stability by absorbing surplus energy and supporting the decarbonization of non-electric sectors. The core contribution of this study lies in its real-time data emulation framework, which overcomes a critical limitation in the current energy landscape: the absence of operational data for future technologies such as SMRs and their coupled hydrogen production systems. As these technologies are still in the pre-commercial stage, direct physical integration and validation are not yet feasible. To address this, the researchers leveraged real-time data from an existing commercial microgrid, specifically focusing on the import of grid electricity during energy shortfalls and export during solar surpluses. These patterns were repurposed to simulate the real-time operational behavior of future SMRs (ProxySMR) and sector coupling loads. This physically grounded simulation approach enables high-fidelity approximation of unavailable technologies and introduces a novel methodology to characterize their dynamic response within operational contexts. A key element of the SSNC control logic is a day–night strategy: maximum SMR output and minimal hydrogen production at night, and minimal SMR output with maximum hydrogen production during the day—balancing supply and demand while maintaining high SMR utilization for economic efficiency. The SSNC testbed was validated through a seven-day continuous operation in Busan, demonstrating stable performance and approximately 75% SMR utilization, thereby supporting the feasibility of this proxy-based method. Importantly, to the best of our knowledge, this study represents the first publicly reported attempt to emulate the real-time dynamics of a net-zero city concept based on not-yet-commercial SMRs and sector coupling systems using live operational data. This simulation-based framework offers a forward-looking, data-driven pathway to inform the development and control of next-generation carbon-neutral energy systems. Full article

Highly sensitive real-time detection of hydrogen sulfide (H₂S) is important for human health and environmental protection due to its highly toxic properties. The development of high-performance H₂S sensors remains challenging for poor selectivity, high limit detection and slow recovery from irreversible sulfidation. To solve these problems, we strategically prepared a layered structure of CuO-sensitized WO₃ flower-like hollow spheres with CuO as the sensitizing component. A 15 wt% CuO/WO₃ exhibits an ultra-high response (R_a/R_g = 571) to 10 ppm H₂S (131-times of pure WO₃), excellent selectivity (97-times higher than 100 ppm interference gas), and a low detection limit (100 ppb). Notably, its fast response (4 s) is accompanied by full recovery within 236 s. After 30 days of continuous testing, the response of 15 wt% CuO/WO₃ decreased slightly but maintained the initial response of 90.5%. The improved performance is attributed to (1) the p-n heterojunction formed between CuO and WO₃ optimizes the energy band structure and enriches the chemisorption sites for H₂S; (2) the reaction of H₂S with CuO generates highly conductive CuS, which significantly reduces the interfacial resistance; and (3) the hierarchical flowery hollow microsphere structure, heterojunction, and oxygen vacancy synergistically promote the desorption. This work provides a high-performance H₂S gas sensor that balances response, selectivity, and response/recovery kinetics. Full article

To further quantify the role of coal-fired power units in a wind–solar–thermal storage system and improve the construction of clean energy bases, this study examined the temporal production characteristics of wind and solar power and established an operational model for coal-fired power units within a wind–solar–thermal storage system. This approach ensured a stable electricity supply on the basis of power balance. The findings indicate that the correlation between the installed capacity of coal-fired power and the daily power supply capability of energy storage that meets various scheduled power demands can be obtained via the model. As the proportion of wind and solar power in the output power decreases, the influence of the minimum operational load of the coal-fired power units on the curtailment rate intensifies. Notably, the operational cost savings from reducing this minimum operational load surpass those obtained by either downsizing the installed capacity of coal-fired power units or energy storage devices. Among the parameters of this study, the lowest operational cost for the system was observed when wind and solar power generation constituted 76% of the total. This scenario, which ensured stable power output for 95% of the days in a year, had a wind and solar power curtailment rate of 11.3%. Additionally, the energy supplied by storage devices amounted to 1000 MWh, with the ratio of the installed capacity of coal-fired power to the total installed capacities of wind and solar power remaining at 25%. When the ratio of wind and solar power generation to output power was 91%, 76%, and 58%, a 1% reduction in coal consumption by coal-fired units during low-load operation resulted in a decrease in total system operating costs of 0.012%, 0.093%, and 0.089%, respectively. These findings provide valuable data support for the development of clean energy infrastructures. Full article

Quaternary NiCoMoB electrocatalysts exhibited significantly enhanced OER performance compared to their ternary NiCoMo and NiCoB counterparts. An optimal Mo/B ratio of 1 (NiCoMo_yB_y) demonstrated a superior OER activity, attributed to a balance between the electronic and structural contributions from Mo and B, maximizing the electrocatalytic site density and activity. NiCoMo_yB_y-SA, a nanoparticle version synthesized via a surfactant-assisted method, showed further improved performance. The OER activity was evaluated by comparing overpotentials at 10 mA/cm², with NiCoMo_xB_1−x, NiCoMo_yB_y, and NiCoMo_yB_y-SA exhibiting 293, 284, and 270 mV, respectively. NiCoMo_yB_y-SA also demonstrated the lowest onset potential (1.45 V), reflecting a superior efficiency. Chronoamperometry in 1 M pre-electrolyzed KOH at 30 °C highlighted NiCoMo_yB_y-SA’s stability, activating within hours at 10 mA/cm² and stabilizing over 7 days. At 50 mA/cm², the overpotential increased minimally (0.02 mV/h over 2 days), and even at 100 mA/cm² for 10 days, the activity declined only slightly, affirming a high stability. These findings demonstrate NiCoMoB electrocatalysts as cost-effective, efficient OER electrocatalysts, advancing sustainable energy technologies. Full article