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Environmentally friendly coated urea prepared using a waterborne polymer coating material is essential for promoting green and sustainable practices in modern agriculture. However, significant efforts are still urgently needed to address the undesirable properties of waterborne polymer coatings, i.e., poor hydrophobic properties and numerous micropores. Herein, dual nano-SiO₂ and siloxane-modified waterborne-polymer-coated urea was successfully developed. The characteristics of waterborne-polymer-coated urea before and after modification were compared. The results demonstrate that nano-SiO₂ and siloxane modification improved the hydrophobicity (water absorption decreased from 119.86% to 46.35%) and mechanical strength (tensile strength increased from 21.09 to 31.29 MPa, and the elongation at break exhibited an increase of 22.42%) of the waterborne polymer coatings. Furthermore, the –OH number of the modified coatings was decreased, while the coating surface formed a nano-scale rough structure, prolonging the nitrogen (N)-controlled release period from 7 to 28 days. Overall, the proposed novel dual-modification technique utilizing waterborne polymer coatings highlights the significant potential of eco-friendly coated urea with renewable coatings in modern agriculture. Full article

The increasing demand for electricity in multi-tenanted residential areas has placed unforeseen strain on sub-transformers, particularly in dense urban environments. This strain compromises overall grid performance and challenges utilities with shifting and rising peak demand periods. This study presents a novel approach to enhance the operation of a virtual power plant (VPP) comprising a microgrid (MG) integrated with renewable energy sources (RESs) and energy storage systems (ESSs). By employing an advanced monitoring and control system, the proposed topology enables efficient energy management and demand-side control within apartment complexes. The system supports controlled electricity distribution, reducing the likelihood of unpredictable demand spikes and alleviating stress on local infrastructure during peak periods. Additionally, the model capitalizes on the large number of tenancies to distribute electricity effectively, leveraging locally available RESs and ESSs behind the sub-transformer. The proposed research provides a systematic framework for managing electricity demand and optimizing resource utilization, contributing to grid reliability and a transition toward a more sustainable, decentralized energy system. Full article

Power distribution systems are witnessing a growing deployment of distributed, inverter-based renewable resources such as solar generation. This poses certain challenges such as rapid voltage fluctuations due to the intermittent nature of renewables. Volt-Var control (VVC) methods have been proposed to utilize the ability of inverters to supply or consume reactive power to mitigate fast voltage fluctuations. These methods usually require a detailed power network model including topology and impedance data. However, network models may be difficult to obtain. Thus, it is desirable to develop a model-free method that obviates the need for the network model. This paper proposes a novel model-free cooperative control method to perform voltage regulation and reduce inverter aging in power distribution systems. This method assumes the existence of time-series voltage and load data, from which the relationship between voltage and nodal power injection is derived using a feedforward artificial neural network (ANN). The node voltage sensitivity versus reactive power injection can then be calculated, based on which a cooperative control approach is proposed for mitigating voltage fluctuation. The results obtained for a modified IEEE 13-bus system using the proposed method have shown its effectiveness in mitigating fast voltage variation due to PV intermittency. Moreover, a comparative analysis between model-free and model-based methods is provided to demonstrate the feasibility of the proposed method. Full article

The large-scale integration of renewable energy into power systems poses significant challenges to reactive power and voltage stability. To enhance system stability, this work proposes a cluster partitioning and distributed control strategy for distribution networks with high-penetration distributed PV integration. Firstly, a comprehensive clustering index system, including electrical distance, voltage sensitivity, and regulation ability, is established. Considering the voltage and reactive power support capability of regional clusters, the distribution network is divided into clusters. Subsequently, based on the results of cluster division, a hierarchical partition optimization model is constructed with voltage and reactive power as the optimization objectives. Finally, a distributed optimization algorithm based on ADMM is proposed to solve the optimization model and maximize the utilization of distribution network control resources. The simulation results based on the IEEE 33-node distribution system verify the effectiveness of the proposed distributed optimization strategy. Full article

In sustainable development studies, a key question is how the abundance of natural resources influences long-run economic growth. However, there is no consensus on this issue. Some literature suggests a negative impact, while other studies find no effect at all, and other research indicates a positive impact. This study aims to examine the relationship between natural resource rents, renewable energy, and economic growth in the Gulf Cooperation Council (GCC) countries over the period from 1990 to 2023. The study utilizes the Method of Moments Quantile Regression (MMQR) to provide reliable findings across different quantiles. We also incorporate a series of control variables, including capital, labor force participation, non-renewable energy, and trade openness. The findings indicate that natural resources rent enhances economic growth in GCC countries, supporting the Rostow hypothesis. Although renewable energy has a positive impact on economic growth, it does not have an effect on natural resource rents. Additionally, capital, labor force participation, non-renewable energy, and trade openness play a critical role in raising economic growth in these countries. Based on the empirical results, this study provides several valuable recommendations for policymakers to enhance the management of natural resources in GCC countries. Full article

Pyridoxine-dependent epilepsy (PDE) represents a group of rare developmental and epileptic encephalopathies. The most common PDE is caused by biallelic pathogenic variants in ALDH7A1 (PDE-ALDH7A1; OMIM #266100), which encodes α-aminoadipate semialdehyde (α-AASA) dehydrogenase, a key enzyme in lysine catabolism. Affected individuals present with seizures unresponsive to conventional anticonvulsant medications but responsive to high-dose of pyridoxine (vitamin B6). Adjunctive lysine restriction and arginine supplementation have also shown potential in improving neurodevelopmental outcomes. Given the significant benefit of early intervention, PDE-ALDH7A1 is a strong candidate for newborn screening (NBS). However, traditional biomarkers are biochemically unstable at room temperature (α-AASA and piperideine-6-carboxylate) or lack sufficient specificity (pipecolate), limiting their utility for biomarker-based NBS. The recent identification of two novel and stable biomarkers, 2S,6S-/2S,6R-oxopropylpiperidine-2-carboxylate (2-OPP) and 6-oxo-pipecolate (oxo-PIP), offers renewed potential for biochemical NBS. We evaluated the feasibility of incorporating 2-OPP, oxo-PIP, and pipecolate into routine butylated FIA-MS/MS workflows used for biochemical NBS. A total of 9402 dried blood spots (DBS), including nine confirmed PDE-ALDH7A1 patients and 9393 anonymized controls were analyzed using a single multiplex assay. 2-OPP emerged as the most sensitive biomarker, identifying all PDE-ALDH7A1 patients with 100% sensitivity and a positive predictive value (PPV) of 18.4% using a threshold above the 99.5th percentile. Combining elevated 2-OPP (above the 99.5th percentile) with either pipecolate or oxo-PIP (above the 85.0th percentile) as secondary marker detected within the same multiplex FIA-MS/MS assay further improved the PPVs to 60% and 45%, respectively, while maintaining compatibility with butanol-derivatized method. Notably, increasing the 2-OPP threshold above the 99.89th percentile, in combination with either pipecolate or oxo-PIP above the 85.0th percentile resulted in both 100% sensitivity and 100% PPV. This study supports the strong potential of 2-OPP-based neonatal screening for PDE-ALDH7A1 within existing NBS infrastructures. The ability to multiplex 2-OPP, pipecolate and oxo-PIP within a single assay offers a robust, practical, high-throughput and cost-effective approach. These results support the inclusion of PDE-ALDH7A1 in existing biochemical NBS panels. Further prospective studies in larger cohorts are needed to refine cutoffs and confirm clinical performance. Full article

Modern microgrids face critical challenges in maintaining stability and efficiency due to renewable energy intermittency and dynamic load demands. This paper proposes a novel real-time energy management framework that synergizes a bio-inspired T-Cell optimization algorithm with decentralized voltage-based droop control to address these challenges. A JADE-based multi-agent system (MAS) orchestrates coordination between the T-Cell optimizer and edge-level controllers, enabling scalable and fault-tolerant decision-making. The T-Cell algorithm, inspired by adaptive immune system dynamics, optimizes global power distribution through the MAS platform, while droop control ensures local voltage stability via autonomous adjustments by distributed energy resources (DERs). The framework is rigorously validated through Hardware-in-the-Loop (HIL) testing using OPAL-RT, which interfaces MATLAB/Simulink models with Raspberry Pi for real-time communication (MQTT/Modbus protocols). Experimental results demonstrate a 91% reduction in grid dependency, 70% mitigation of voltage fluctuations, and a 93% self-consumption rate, significantly enhancing power quality and resilience. By integrating centralized optimization with decentralized control through MAS coordination, the hybrid approach achieves scalable, self-organizing microgrid operation under variable generation and load conditions. This work advances the practical deployment of adaptive energy management systems, offering a robust solution for sustainable and resilient microgrids. Full article

Sustainable building design is significantly impacted by the local climate response knowledge ingrained in traditional architecture. However, its integration and dissemination with contemporary green technologies are limited by the absence of a comprehensive quantitative analysis of the regulation of its humid and temperature environment. The Ganzhou Wei family compound from China’s wind–heat environmental regulation systems are examined in this study. We statistically evaluate the synergy between spatial morphology, material qualities, and microclimate using field data with Thsware and Ecotect software in a multiscale simulation framework. The findings indicate that the compound’s special design greatly controls the thermal and wind conditions. Cold alleyways and courtyards work together to maximize thermal environment regulation and encourage natural ventilation. According to quantitative studies, courtyards with particular depths (1–4 m) and height-to-width ratios (e.g., 1:1) reduce wind speed loss. A cool alley (5:1 height–width ratio) creates a dynamic wind–speed–temperature–humidity balance by lowering summer daytime temperatures by 2.5 °C. It also serves as a “cold source area” that moderates temperatures in the surrounding area by up to 2.1 °C. This study suggests a quantitative correlation model based on “spatial morphology–material performance–microclimate response,” which offers a technical route for historic building conservation renovation and green renewal, as well as a scientific foundation for traditional buildings to maintain thermal comfort under low energy consumption. Although based on a specific geographical case, the innovative analytical methods and strategies of this study are of great theoretical and practical significance for promoting the modernization and transformation of traditional architecture, low-carbon city construction, and sustainable building design. Full article

The large-scale integration of stochastic renewable energy sources necessitates enhanced dynamic balancing capabilities in power systems, positioning hydropower as a critical balancing asset. Conventional excitation systems utilizing hot-standby dual-redundancy configurations remain susceptible to unit shutdown events caused by regulator failures. To mitigate this vulnerability, this study proposes a peer-to-peer distributed excitation architecture integrating asynchronous traffic shaping (ATS) and Data Distribution Service (DDS) technologies. This architecture utilizes control channels of equal priority and achieves high redundancy through cross-communication between discrete acquisition and computation modules. This research advances three key contributions: (1) design of a peer-to-peer distributed architectural framework; (2) development of a real-time data interaction methodology combining ATS and DDS, incorporating cross-layer parameter mapping, multi-priority queue scheduling, and congestion control mechanisms; (3) experimental validation of system reliability and redundancy through dynamic simulation. The results confirm the architecture’s operational efficacy, delivering both theoretical foundations and practical frameworks for highly reliable excitation systems. Full article

The varying characteristics of grid-connected energy resources necessitate a clear and effective approach for managing and scheduling generation units. Without proper control, high levels of renewable integration can pose challenges to optimal dispatch, especially as more generation sources, like wind and solar PV, are introduced. As a result, conventional power sources require an advanced management system, for instance, a virtual power plant (VPP), capable of accurately monitoring power supply and demand. This study thoroughly explores the dispatch of battery energy storage systems (BESSs) and diesel generators (DGs) through a distributionally robust joint chance-constrained optimization (DR-JCCO) framework utilizing the conditional value at risk (CVaR) and heuristic-X (H-X) algorithm, structured as a bilevel optimization problem. Furthermore, Binomial expansion (BE) is employed to linearize the model, enabling the assessment of BESS dispatch through a mathematical program with equilibrium constraints (MPECs). The findings confirm the effectiveness of the DRO-CVaR and H-X methods in dispatching grid network resources and BE under the MPEC framework. Full article

Hydrogen, as a renewable and clean energy with a high energy density, is of great significance to the realization of carbon neutrality. In recent years, extensive research has been conducted on the electrocatalytic hydrogen evolution reaction (HER) by splitting water, with a focus on developing efficient electrocatalysts that can perform the HER at an overpotential with minimal power consumption. Tungsten oxide (WO₃), a non-noble-metal-based material, has great potential in hydrogen evolution due to its excellent redox capability, low cost, and high stability. However, it cannot meet practical needs because of its poor electrical conductivity and the limited number of active sites; thus, it is necessary to further improve HER performance. In this review, recent advances related to WO3-based electrocatalysts for the HER are introduced. Most importantly, several tactics for optimizing the electrocatalytic HER activity of WO₃ are summarized, such as controlling its morphology, phase transition, defect engineering (anion vacancies, cation doping, and interstitial atoms), constructing a heterostructure, and the microenvironment effect. This review can provide insight into the development of novel catalysts with high activity for the HER and other renewable energy applications. Full article

Improving the energy efficiency of residential buildings is essential for achieving global climate goals and reducing environmental impact. This study analyzes the Total Performance approach using the example of a modern semi-detached house built by a Polish developer, as an example. The building is designed with integrated systems that minimize energy consumption while maintaining resident comfort. The building is equipped with an air-to-water heat pump, underfloor heating, mechanical ventilation with heat recovery, and automatic temperature control systems. Energy efficiency was assessed using ArCADia–TERMOCAD 8.0 software in accordance with Polish Technical Specifications (TS) and verified by monitoring real-time electricity consumption during the heating season. The results show a PED from non-renewable sources of 54.05 kWh/(m²·year), representing a 23% reduction compared to the Polish regulatory limit of 70 kWh/(m²·year). Real-time monitoring conducted from December 2024 to April 2025 confirmed these results, indicating an actual energy demand of approximately 1771 kWh/year. Domestic hot water (DHW) preparation accounted for the largest share of energy consumption. Despite its dependence on grid electricity, the building has the infrastructure to enable future photovoltaic (PV) installation, offering further potential for emissions reduction. The results confirm that Total Performance strategies are not only compliant with applicable standards, but also economically and environmentally viable. They represent a scalable model for sustainable residential construction, in line with the European Union’s (EU’s) decarbonization policy and the goals of the European Green Deal. Full article

Island microgrids are essential for the exploitation and utilization of offshore renewable energy resources. However, voltage regulation and accurate reactive power sharing remain significant technical challenges that need to be addressed. To tackle these issues, this paper proposes an algorithm that integrates a dynamic leader election (DLE) mechanism and model-free reinforcement learning (RL). The algorithm aims to address the issue of fixed leaders restricting reactive power flow between buses during heavy load variations in island microgrids, while also overcoming the challenge of obtaining model parameters such as resistance and inductance in practical microgrids. First, we establish a voltage containment control and reactive power error model for island alternating current (AC) microgrids and construct a corresponding value function based on this error model. Second, a dynamic leader election algorithm is designed to address the issue of fixed leaders restricting reactive power flow between buses due to preset voltage limits under unknown or heavy load conditions. The algorithm adaptively selects leaders based on bus load, allowing the voltage limits to adjust accordingly and regulating reactive power flow. Then, to address the difficulty of accurately acquiring parameters such as resistance and inductance in microgrid lines, a model-free reinforcement learning method is introduced. This method relies on real-time measurements of voltage and reactive power data, without requiring specific model parameters. Ultimately, simulation experiments on offshore island microgrids are conducted to validate the effectiveness of the proposed algorithm. Full article

With the increasing integration of renewable energy sources (RESs) into power systems, batteries are playing a critical role in ensuring grid reliability and flexibility. Among them, vanadium redox flow batteries (VRFBs) have emerged as a promising solution for large-scale storage due to their long cycle life, scalability, and deep discharge capability. However, achieving optimal control and system-level integration of VRFBs requires accurate, real-time modeling and parameter estimation, challenging tasks given the multi-physics nature and time-varying dynamics of such systems. This paper presents a lightweight physics-informed neural network (PINN) framework tailored for VRFBs, which directly embeds the discrete-time state-space dynamics into the network architecture. The model simultaneously predicts terminal voltage and estimates five discrete-time physical parameters associated with RC dynamics and internal resistance, while avoiding hidden layers to enhance interpretability and computational efficiency. The resulting PINN model is integrated into a modulated model predictive control (MMPC) scheme for a dual-stage DC-AC converter interfacing the VRFB with low-voltage AC grids. Simulation and hardware-in-the-loop results demonstrate that adaptive tuning of the PINN-estimated parameters enables precise tracking of battery parameter variations, thereby improving the robustness and performance of the MMPC controller under varying operating conditions. Full article

Wax deposition, driven by the crystallization of long-chain n-alkanes, poses severe challenges across industries such as petroleum, oil and natural gas, food processing, and chemical manufacturing. This phenomenon compromises flow efficiency, increases energy demands, and necessitates costly maintenance interventions. Wax inhibitors, designed to mitigate these issues, operate by altering wax crystallization, aggregation, and adhesion over the pipelines. Classic wax inhibitors, comprising synthetic polymers and natural compounds, have been widely utilized due to their established efficiency and scalability. However, synthetic inhibitors face environmental concerns, while natural inhibitors exhibit reduced performance under extreme conditions. The advent of nano-based wax inhibitors has revolutionized wax management strategies. These advanced materials, including nanoparticles, nanoemulsions, and nanocomposites, leverage their high surface area and tunable interfacial properties to enhance efficiency, particularly in harsh environments. While offering superior performance, nano-based inhibitors are constrained by high production costs, scalability challenges, and potential environmental risks. In parallel, the development of “green” wax inhibitors derived from renewable resources such as vegetable oils addresses sustainability demands. These eco-friendly formulations introduce functionalities that reinforce inhibitory interactions with wax crystals, enabling effective deposition control while reducing reliance on synthetic components. This review provides a comprehensive analysis of the mechanisms, applications, and comparative performance of classic and nano-based wax inhibitors. It highlights the growing integration of sustainable and hybrid approaches that combine the reliability of classic inhibitors with the advanced capabilities of nano-based systems. Future directions emphasize the need for cost-effective, eco-friendly solutions through innovations in material science, computational modeling, and biotechnology. Full article