Search Results (4,216)

The rapid growth of renewable energy integration in modern power systems brings new challenges in terms of stability and quality of electricity supply. Hybrid AC/DC microgrids represent a promising solution to integrate photovoltaic panels (PV), wind turbines, fuel cells, and storage units with flexibility and efficiency. However, maintaining adequate power quality (PQ) under variable conditions of generation, load, and grid connection remains a critical issue. This paper presents the modelling, implementation, and validation of a hybrid AC/DC microgrid equipped with a fuzzy-logic-based energy management system (EMS). The study combines PQ assessment, measurement architecture, and supervisory control for technical compliance and economic efficiency. The microgrid integrates a combination of PV array, wind turbine, proton exchange membrane fuel cell (PEMFC), battery storage system, and heterogeneous AC/DC loads, all modelled in MATLAB/Simulink using a physical-network approach. The fuzzy EMS coordinates distributed energy resources by considering power imbalance, battery state of charge (SOC), and dynamic tariffs. Results demonstrate that the proposed controller maintains PQ indices within IEC/IEEE standards while eliminating short-term continuity events. The proposed EMS prevents harmful deep battery cycles, maintaining SOC within 30–90%, and optimises fuel cell activation, reducing hydrogen consumption by 14%. Economically, daily operating costs decrease by 10–15%, grid imports are reduced by 18%, and renewable self-consumption increases by approximately 16%. These findings confirm that fuzzy logic provides an effective, computationally light, and uncertainty-resilient solution for hybrid AC/DC microgrid EMS, balancing technical reliability with economic optimisation. Future work will extend the framework toward predictive algorithms, reactive power management, and hardware-in-the-loop validation for real-world deployment. Full article

The waveform stacking location method achieves microseismic source localization by computing characteristic functions (CFs) and stacking multi-channel data, without phase picking. It has been widely applied in geotechnical engineering. However, the low signal-to-noise ratio (SNR) caused by weak event energy and ambient noise often degrades localization accuracy. To enhance the localization precision and stability under low SNR conditions, this study employs the Stockwell transform (S-transform) to convert noisy time-domain data into the time–frequency domain. By analyzing the energy distribution of microseismic signal and noise in the time–frequency domain, frequency and time coefficients are introduced to enhance the energy of microseismic signal. Event location is achieved through the computation of CFs and multiple-cross-correlation stacking. Comparison of the location results when computing the CFs by the new method, the short-term average to long-term average ratio (STA/LTA) method, and the envelope (Env) method under varying noise levels demonstrates the superior noise resistance and improved localization accuracy of the new method. Finally, the effectiveness of the new method is validated using real seismic data collected from a coal mine. Full article

In this paper, the real-time decentralized integrated sensing, navigation, and communication co-optimization problem is investigated for large-scale mobile wireless sensor networks (MWSN) under limited energy. Compared with traditional sensor network optimization and control problems, large-scale resource-constrained MWSNs are associated with two new challenges, i.e., (1) increased computational and communication complexity due to a large number of mobile wireless sensors and (2) an uncertain environment with limited system resources, e.g., unknown wireless channels, limited transmission power, etc. To overcome these challenges, the Mean Field Game theory is adopted and integrated along with the emerging decentralized multi-agent reinforcement learning algorithm. Specifically, the problem is decomposed into two scenarios, i.e., cost-effective navigation and transmission power allocation optimization. Then, the Actor–Critic–Mass reinforcement learning algorithm is applied to learn the decentralized co-optimal design for both scenarios. To tune the reinforcement-learning-based neural networks, the coupled Hamiltonian–Jacobi–Bellman (HJB) and Fokker–Planck–Kolmogorov (FPK) equations derived from the Mean Field Game formulation are utilized. Finally, numerical simulations are conducted to demonstrate the effectiveness of the developed co-optimal design. Specifically, the optimal navigation algorithm achieved an average accuracy of $2.32\%$ when tracking the given routes. Full article

Examining the changing patterns and underlying mechanisms of soil biomass carbon stocks constitutes a fundamental aspect of soil biology. Despite the potential influence of the sulfur cycle and the life strategies of organisms on community biomass, these factors have rarely been studied in tandem. Biocrusts are model systems for studying soil ecosystems. In this study, metagenomic analysis of biocrusts related to different life strategies from five batches over four consecutive years demonstrated that, in free-living communities, microbial biomass carbon (MBC) synthesis, via assimilatory sulfate reduction (ASR), is primarily coupled with the 3-hydroxypropionate/4-hydroxybutyrate and Calvin–Benson–Bassham cycles. These pathways are affected by the oxidation-reduction potential (Eh), pH, electrical conductivity, and nutrient levels. The decomposition of organic carbon (OC) via dissimilatory sulfate reduction (DSR) was accompanied by the production of dimethyl sulfide (DMS), which was influenced by the C/S ratio and moisture, whereas the synthesis of MBC by symbiotic communities was found to be affected by Eh and pH, and decomposition was affected by the C/S ratio. The MBC stock was influenced by all strategies, with resource strategies having the greatest impacts during the growing season, and the contribution of chemotrophic energy was most significant in free-living communities. In conclusion, the MBC in biocrusts is associated with both ASR and DSR and is facilitated by the A-, S-, and P-strategies under the regulation of the stoichiometric C/S ratio. The exploration of microbial life strategies and sulfur cycling in biocrusts within arid ecosystems in this study offers a new perspective on the patterns of change in soil biomass carbon stocks. Full article

Rising global temperatures are driving an urgent need for buildings that consume less energy while maintaining comfort. Cooling demand is surging worldwide, yet conventional air-conditioning remains energy-intensive and carbon-heavy. Against this backdrop, radiative cooling materials have gained attention as a passive solution capable of reflecting incoming solar radiation while emitting thermal energy to the sky. This study aims to establish a climate-informed framework that quantitatively predicts the energy-saving potential of façade-integrated radiative-cooling materials across diverse East Asian climates. By synergizing hour-by-hour building-energy simulation with three novel atmospheric suitability indices, we provide a transferable methodology for selecting and optimizing passive cooling strategies at urban and regional scales. Three façade configurations were tested, i.e., a conventional absorptive surface, a common radiative cooling surface, and an idealized high-reflectance and high-emissivity surface. The results show that the ideal case can reduce wall surface temperatures by up to 20 °C, suppress diurnal heat flux swings by 60–80%, and cut annual cooling demand by 5–80 kWh per square meter, depending on climate conditions. To generalize these findings, three new indices—the Weather Structure Index, Diurnal Temperature Index, and Composite Climate Applicability—were proposed. Regression models with R² values above 0.9 confirm the Composite Climate Applicability index as a robust predictor of energy-saving potential. The outcomes demonstrate that radiative cooling is not only highly effective in hot, humid regions but also unexpectedly beneficial in clear, cold climates, offering a practical, climate-informed framework for advancing low-carbon building design. Full article

This study establishes a structured framework to identify and evaluate risk factors that may hinder the achievement of sustainable development goals in green buildings and sustainable infrastructure projects. Fifty-six risk factors are identified and categorized into four risk groups, including stakeholder and management, financial and economic, technological and resource, and process and regulatory risks. The risk factors are evaluated across four risk indices related to probability of occurrence, manageability, impact on building performance, and project cost. Further, the severity of risks based on combining the four indices’ effects is quantified using a new Green Risk Index (GRI), while the relationships among all risk indices are determined. The strongest positive correlation is observed between the probability and the impact on cost, whereas a negative relationship is found between the probability and manageability. The analysis demonstrates that a risk factor related to the lack of knowledge about energy-saving procedures and environmental concerns during the design phase is the most critical, as it has the highest severity based on the GRI. “Non-compliance with environmental standards in project design” is also identified as a critical risk factor due to its high effect on building performance. Additionally, the risk factor associated with unstable funds from investors shows the highest effect on manageability. Process and regulatory is identified as the most critical risk group, encompassing the maximum number of key risk factors, and has the highest average weight related to the GRI. These findings reveal crucial vulnerabilities and underline the importance of targeted strategies to strengthen the use of nature-based solution frameworks for mitigating the risk effects in green buildings and sustainable infrastructures. Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

The memory demand of modern applications has been rapidly increasing with the continuous growth of data volume across industrial and academic domains. As a result, computing devices (i.e., IoT devices, smartphones, and tablets) often experience memory shortages that degrade system performance and quality of service by wasting CPU cycles and energy. Thus, most operating systems rely on the swap mechanism to mitigate the memory shortage situation in advance, even if the swap memory fragmentation problem occurs over time. In this paper, we analyze the fragmentation behavior of the swap memory space within storage devices over time and demonstrate that the latency of swap operations increases significantly under aged conditions. We also propose a new extension of the traditional swap mechanism, called VSwap, that mitigates the swap memory fragmentation problem in advance by introducing two core techniques, virtual migration and address remapping. In VSwap, virtual migration gathers valid swap pages scattered across multiple clusters into contiguous regions within the swap memory space, while address remapping updates the corresponding page table entries to preserve consistency after migration. For experiments, we enable VSwap on the traditional swap mechanism (i.e., kswapd) by implementing it with simple code modifications. To confirm the effectiveness of VSwap, we performed a comprehensive evaluation based on various workloads. Our evaluation results confirm that VSwap is more useful and highly valuable than the original swap mechanism. In particular, VSwap improves the overall performance up to 48.18% by harvesting available swap memory space in advance with negligible overhead; it performs close to the ideal performance. Full article

We present a time-domain direct current (DC) approach to differentiate positive- (PE) and negative-electrode (NE) contributions from two-electrode full-cell signals in lithium-ion batteries, enabling electrode-resolved diagnostics without specialized instrumentation. The responses of a LiNi_0.8Co_0.1Mn_0.1O₂ (PE)/graphite (NE) cell were recorded across −20 to 20 °C during galvanostatic pulses and subsequent open-circuit relaxations, alongside electrochemical impedance spectroscopy (EIS) measurements. These responses were analyzed using an equivalent-circuit-based model to decompose them into terms with characteristic times. Their distinct temperature dependences enabled attribution of the dominant terms to PE or NE, especially at low temperatures where temporal separation is substantial. The electrode attribution and activation energies were cross-validated against three-electrode measurements and were consistent with EIS-derived time constants. Reconstructing full-cell voltage transients from the identified terms reproduced the measured electrode-specific behavior, and quantitative comparisons showed that the DC time-domain separation aligned closely with directly measured PE/NE overpotentials during the current pulse. These results demonstrate a practical pathway to extract electrode-resolved information from cell voltage alone, offering new methodological possibilities for battery diagnostics and management while complementing three-electrode and alternating current (AC) techniques that are often constrained in field applications. Full article

Against the backdrop of intelligent manufacturing, the ball mill, as a key energy-consuming piece of equipment, requires an accurate perception of its load state, which is crucial for optimizing production efficiency and ensuring operational safety. However, its vibration signals exhibit typical nonlinear and non-stationary characteristics, intertwined with complex noise, posing significant challenges to high-precision identification. A core contradiction exists in existing diagnostic methods: convolution network-based methods excel at capturing local features but overlook global trends, while Transformer-type models, although capable of capturing long-range dependencies, tend to “average out” critical local transient information during modeling. To address this dilemma, this paper proposes a new paradigm for multi-scale feature collaborative perception. This paradigm is implemented through an innovative deep learning architecture—the Residual Block-Swin Transformer Network (RB-SwinT). This architecture subtly achieves hierarchical and in-depth integration of the powerful global context modeling capability of Swin Transformer and the excellent local detail refinement capability of the residual module (ResBlock), enabling synchronous and efficient representation of both the macro trends and micro mutations of signals. On the experimental dataset covering nine types of fine operating conditions, the overall recognition accuracy of the proposed method reaches as high as 96.20%, which is significantly superior to a variety of mainstream models. To further verify the model’s generalization ability, this study was tested on the CWRU public bearing fault dataset, achieving a recognition accuracy of 99.36%, which outperforms various comparative methods such as SAVMD-CNN. This study not only provides a reliable new technical approach for ball mill load identification but also demonstrates its practical application value in indicating critical operating conditions and optimizing production operations through an in-depth analysis of the physical connotations of each load level. More importantly, its “global-local” collaborative modeling concept opens up a promising technical path for processing a broader range of complex industrial time-series data. Full article

Quantum energy teleportation (QET) has been proposed to overcome the restrictions of strong local passivity (SLP) and to facilitate energy transfer in quantum systems. Traditionally, QET has only been considered under strict constraints, including the requirements that the initial state be the ground state of an interacting Hamiltonian, that Alice’s measurement commute with the interaction terms, and that entanglement be present. These constraints have significantly limited the broader applicability of QET protocols. In this work, we demonstrate that SLP can arise beyond these conventional constraints, establishing the necessity of QET in a wider range of scenarios for local energy extraction. This leads to a more flexible and generalized framework for QET. Furthermore, we introduce the concept of a “local effective Hamiltonian,” which eliminates the need for optimization techniques in determining Bob’s optimal energy extraction in QET protocols. As an additional advantage, the amount of energy that can be extracted using our new protocol is amplified to be 7.2 times higher than that of the original protocol. These advancements enhance our understanding of QET and extend its broader applications to quantum technologies. To support our findings, we implement the protocol on quantum hardware, confirming its theoretical validity and experimental feasibility. Full article

Due to the large emissions of greenhouse gases from the burning of fossil fuels and people’s demand for green materials and energy, the development of environmentally friendly triboelectric nanogenerators (TENGs) is becoming increasingly significant. Silk fibroin (SF) is considered an ideal biopolymer candidate for fabricating green TENGs due to its biodegradability and renewability. However, its intrinsic brittleness and relatively weak triboelectric performance severely limit its practical applications. In this study, SF was physically blended with poly(ethylenimine) (PEI), a polymer rich in amino groups, to fabricate SF/PEI composite films. The resulting films were employed as tribopositive layers and paired with a poly(tetrafluoroethylene) (PTFE) tribonegative layer to assemble high-performance TENGs. Experimental results revealed that the incorporation of PEI markedly enhanced the flexibility and electron-donating capability of composite films. By optimizing the material composition, the SF/PEI-based TENG achieved an open-circuit voltage as high as 275 V and a short-circuit current of 850 nA, with a maximum output power density of 13.68 μW/cm². Application tests demonstrated that the device could serve as an efficient self-powered energy source, capable of lighting up 66 LEDs effortlessly through simple hand tapping and driving small electronic components such as timers. In addition, the device can function as a highly sensitive self-powered sensor, capable of generating rapid and distinguishable electrical responses to various human motions. This work not only provides an effective strategy to overcome the intrinsic limitations of SF-based materials but also opens up new avenues for the development of high-performance and environmentally friendly technologies for energy harvesting and sensing. Full article

Climate-change-induced thermal stress poses a significant threat to cold-adapted aquatic species, particularly fish endemic to high-altitude ecosystems such as Gymnocypris eckloni, which is native to the Qinghai-Tibetan Plateau. To elucidate the molecular and metabolic mechanisms underlying their response to elevated temperatures, we integrated RNA-seq, miRNA-seq, and LC-MS-based metabolomic analyses of liver tissue from fish exposed to chronic thermal stress (HT) versus control (CT) conditions. Although no significant differences were observed in growth parameters, histopathological examination revealed structural damage under heat stress. Transcriptomic analysis identified widespread dysregulation of genes involved in energy metabolism, with significant downregulation of pathways related to amino acid, fatty acid, glucose, and oxidative phosphorylation. In contrast, upregulated DEGs were enriched in N-glycan biosynthesis, protein processing in the endoplasmic reticulum, and phagosome. Concomitant miRNA profiling revealed differentially expressed miRNAs, including miR-196a-5p, miR-132-3p, and miR-181b-5p, which were predicted to regulate key metabolic genes such as ugt1a1, pepck, and calr. Metabolomic analysis further demonstrated significant alterations in metabolic profiles, with glutathione metabolism, tryptophan metabolism, steroid hormone biosynthesis, and pyruvate metabolism emerging as central pathways in the heat stress response. Integrated multi-omics analysis confirmed coordinated regulation of these pathways, highlighting the critical role of glutathione and tryptophan, as well as disruptions in purine and energy metabolism. The DEMiR-DEG-DEM networks involving miR-196a-5p-pepck-PEP, miR-133a-3p-gne-UDP-GlcNAc, and miR-132-3p-ugt1a1-Bilirubin may play an important role in thermal stress. This study provided a new perspective on the molecular, regulatory, and metabolic adaptations of Gymnocypris eckloni to thermal stress, identifying potential biomarkers and regulatory networks that may inform conservation strategies for cold-water fish under global warming. Full article

To address the challenges of low solar energy utilization efficiency and rapid recombination of photogenerated charge carriers in photocatalytic hydrogen evolution, this study successfully constructed a composite photocatalyst of ZnIn₂S₄ (ZIS) supported on N-doped hollow carbon spheres (N-HCS), denoted as ZIS/N-HCS, via a combination of template etching and in situ growth strategies. Characterization results demonstrate that this hollow structure possesses a high specific surface area (48.41 m²/g) and a narrowed bandgap (2.41 eV), achieve broad-spectrum light absorption, thereby enabling the catalyst to generate a local hot spot temperature of 136 °C under AM1.5G conditions. The optimized ZIS/N-HCS-0.30 sample exhibited a significantly enhanced photocurrent response (8.26 μA cm⁻²) and improved charge separation efficiency. When evaluated at a set solution temperature of 20 °C, the material exhibited a photocatalytic hydrogen evolution rate of 17.03 mmol g⁻¹·h⁻¹, which is 7.06 times higher than that of pure ZIS. Furthermore, it demonstrated excellent cycling stability. This work elucidates the synergistic role of the hollow photothermal structure in enhancing solar energy utilization and catalytic reaction kinetics, providing a new strategy for designing efficient solar-driven hydrogen production systems. Full article

The substantial impact of the heating and cooling of the construction sector on global warming necessitates a focus on effective thermal insulation solutions to mitigate high CO₂ emissions. Thus, the development of efficient low-temperature thermochemical energy storage (TCES) materials offers a promising approach to improve thermal regulation. This study explores the morphological, physicochemical, and thermal properties of a silicon composite (PDMS foam) filled with calcium L-lactate (CaL) (0–70 wt.%) for the core sandwich thermopassive regulation of buildings. Furthermore, CaL was incorporated into a composite form to improve the handling and processability of the final sandwich material, as CaL is available in powder form. The results demonstrated that the filler is entirely confined within the polymer matrix (FTIR and ESEM). Additionally, the CaL-PDMS composites showed fully reversible dehydration/hydration abilities over a water vapor hydration–dehydration cycle within a temperature range suitable for low-temperature TCES, with no performance loss due to salt confinement. Regarding the energy density, the 70 wt.% CaL-PDMS composites achieved a value up to 955 MJ/m³, making it an excellent candidate for low-temperature energy storage in the construction sector as compared to other similar composites. These findings contribute to the development of new thermopassive regulation techniques for building materials. Full article