Search Results (53,269)

To achieve global carbon-neutrality goals, magnetic levitation (maglev) technologies offer a promising pathway toward sustainable, energy-efficient transportation systems. In this study, a comprehensive methodology was developed to analyse and optimise the levitation performance of high-temperature superconducting (HTS) maglev systems. Several permanent magnet guideway (PMG) configurations were compared, and an optimised PMG Halbach array design was identified that enhances flux concentration and significantly improves levitation performance. To accurately model the electromagnetic interaction between the HTS bulk and the external magnetic field, finite element models based on the H-formulation were established in both two dimensions (2D) and three dimensions (3D). An HTS maglev demonstrator was built using YBCO bulks, and an experimental platform was constructed to measure levitation force. While the 2D model offers fast computation, it shows deviations from the measurements due to geometric simplifications, whereas the 3D model predicts levitation forces for the cylindrical bulk with much higher accuracy, with errors remaining below 10%. The strong agreement between experimental measurements and the 3D simulation across the entire force–height cycle confirms that the proposed model reliably reproduces the electromagnetic coupling and resulting levitation forces in HTS maglev systems. The paper provides a practical and systematic reference for the optimal design and experimental validation of HTS bulk-based maglev systems. Full article

Convolutional Neural Networks (CNNs) generally suffer from excessive computational overhead, high resource consumption, and complex network structures, which severely restrict the deployment on microprocessor chips. Existing related accelerators only have an energy efficiency ratio of 2.32–6.5925 GOPs/W, making it difficult to meet the low-power requirements of embedded application scenarios. To address these issues, this paper proposes a low-power and high-energy-efficiency CNN accelerator architecture based on a central processing unit (CPU) and an Application-Specific Integrated Circuit (ASIC) heterogeneous computing architecture, adopting an operator-fused systolic array algorithm with the YOLOv5n target detection network as the application benchmark. It integrates a 2D systolic array with Conv-BN fusion technology to achieve deep operator fusion of convolution, batch normalization and activation functions; optimizes the RISC-V core to reduce resource usage; and adopts a locking mechanism and a prefetching strategy for the asynchronous platform to ensure operational stability. Experiments on the Nexys Video development board show that the architecture achieves 20.6 GFLOPs of computational performance, 1.96 W of power consumption, and 10.46 GOPs/W of energy efficiency ratio, which is 58–350% higher than existing mainstream accelerators, thus demonstrating excellent potential for embedded deployment. Full article

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article

The intensification of shrimp aquaculture is crucial for global food security, but poses significant environmental challenges. This review critically assesses the strengths and bottlenecks of two main treatment paradigms: in situ systems, chiefly biofloc technology (BFT), and advanced ex situ systems, such as recirculating aquaculture systems (RASs), constructed wetlands (CWs), and membrane bioreactors (MBRs). Although BFT enables nutrient recycling, it suffers from nitrate accumulation and a high energy demand. Likewise, ex situ technologies can achieve a high treatment efficiency, but contend with high costs, large footprints, or membrane fouling. In this review, we propose the strategic integration of microalgae, representing a universal and synergistic solution for overcoming these disparate bottlenecks. We dissect how a microalgal co-culture can simultaneously remove nitrate and reduce the aeration costs in BFT systems. Furthermore, we explore how microalgae-based units can serve as efficient polishing steps for RASs, enhance the performance of CWs, and mitigate fouling in MBRs. This review delves into the fundamental mechanisms of the microalgal–bacterial symbiosis that underpins these enhancements. Finally, we highlight the valorization of the resulting algal biomass as a high-value aquafeed ingredient, which can transform waste management into a value-creation opportunity. This review aims to provide a comprehensive roadmap for developing next-generation, microalgae-enhanced aquaculture systems. Full article

The capability of wind turbines (WTs) to provide frequency response is crucial for future power systems with high wind power penetration. Existing strategies primarily focus on mitigating initial and secondary frequency drop (SFD), often overlooking the adverse effects of rotor speed recovery on turbine safety and sustained grid support. Moreover, the lack of a dynamic linkage between the frequency support stage (FSS) and speed recovery stage (SRS) impedes multi-objective coordination encompassing initial drop suppression, SFD mitigation, and rapid rotor speed recovery. To address these gaps, this paper proposes a two-stage coordinated control strategy. In the FSS, the frequency regulation coefficients (K_p and K_d) are adaptively adjusted based on available kinetic energy, ensuring its rational release. Subsequently, the switching timing from FSS to SRS is optimized using these coefficients to suppress the SFD and accelerate recovery. Finally, a fuzzy logic-based PI controller dynamically governs the SRS to restore rotor speed efficiently while further alleviating the SFD. Simulation results confirm the effectiveness of the proposed strategy under two wind speeds. It improves the initial frequency nadir by up to 0.197 Hz over no frequency control, reduces the secondary frequency drop by as much as 0.106 Hz compared to the stepwise method, and accelerates rotor speed recovery by over 30%, quantitatively validating its superior coordinated performance. Full article

Accurate short-term forecasting of solar photovoltaic (PV) power output is essential for efficient grid integration and energy management, especially given the widespread global adoption of PV systems. To address this research gap, the present study introduces a scalable, interpretable ensemble learning model of PV power prediction with respect to a large PVOD v1.0 dataset, which encompasses more than 270,000 points representing ten PV stations. The proposed methodology involves data preprocessing, feature engineering, and a hybrid ensemble model consisting of Random Forest, XGBoost, and CatBoost. Temporal features, which included hour, day, and month, were created to reflect the diurnal and seasonal characteristics, whereas feature importance analysis identified global irradiance, temperature, and temporal indices as key indicators. The hybrid ensemble model presented has a high predictive power, with an R² = 0.993, a Mean Absolute Error (MAE) = 0.227 kW, and a Root Mean Squared Error (RMSE) = 0.628 kW when applied to the PVOD v1.0 dataset to predict short-term PV power. These findings were achieved on standardized, multi-station, open access data and thus are not in an entirely rigorous sense comparable to previous studies that may have used other datasets, forecasting horizons, or feature sets. Rather than asserting numerical dominance over other approaches, this paper focuses on the real utility of integrating well-known tree-based ensemble techniques with time-related feature engineering to derive real, interpretable, and computationally efficient PV power prediction models that can be used in smart grid applications. This paper shows that a mixture of conventional ensemble methods and extensive temporal feature engineering is effective in producing consistent accuracy in PV forecasting. The framework can be reproduced and run efficiently, which makes it applicable in the integration of smart grid applications. Full article

Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results. This study introduces a physics-informed combinatorial digital twin for value-optimized coking, aimed at improving energy efficiency and environmental performance through adaptive quality forecasting. The approach builds a modular library of 32 candidate equations grouped into eight quality parameters and links them via cross-parameter dependencies. A two-level optimization scheme is applied: a genetic algorithm selects the best model combination, while a secondary loop tunes parameters under a multi-objective fitness function balancing accuracy, interpretability, and computational cost. Validation on five clustered operating regimes (industrial patterns augmented with noise-perturbed synthetic data) shows that optimal model ensembles outperform single best models, achieving typical cluster errors of ~7–13% NMAE. The developed digital twin framework enables accurate prediction of coke quality parameters that are critical for its energy applications, such as volatile matter and sulfur content, which serve as direct proxies for estimating the net calorific value and environmental footprint of coke as a fuel. Full article

Amidst the ongoing global energy crisis, environmental deterioration, and the exacerbation of climate change, the development of renewable energy, particularly solar energy, has become a central topic in the global energy transition. This study investigates a solar photovoltaic thermal (PVT) heat pump system that utilizes an expanded honeycomb-channel PVT module to enhance the comprehensive utilization efficiency of solar energy. A simulation platform for the solar PVT heat pump system was established using Aspen Plus software (V12), and the system’s performance impact mechanisms and engineering applications were researched. The results indicate that solar irradiance and the circulating water temperature within the PVT module are the primary factors affecting system performance: for every 100 W/m² increase in solar irradiance, the coefficient of performance for heating (COP_h) increases by 13.7%, the thermoelectric comprehensive performance coefficient (COP_co) increases by 14.9%, and the electrical efficiency of the PVT array decreases by 0.05%; for every 1 °C increase in circulating water temperature, the COP_h and COP_co increase by 11.8% and 12.3%, respectively, and the electrical efficiency of the PVT array decreases by 0.03%. In practical application, the system achieves an annual heating capacity of 24,000 GJ and electricity generation of 1.1 million kWh, with average annual COP_h and COP_co values of 5.30 and 7.60, respectively. The Life Cycle Cost (LCC) is 13.2% lower than that of the air-source heat pump system, the dynamic investment payback period is 4–6 years, and the annual carbon emissions are reduced by 94.6%, demonstrating significant economic and environmental benefits. This research provides an effective solution for the efficient and comprehensive utilization of solar energy, utilizing the low-global-warming-potential refrigerant R290, and is particularly suitable for combined heat and power applications in regions with high solar irradiance. Full article

In this study, an Integrated Energy System (IES) with hydrogen refinement within a tiered carbon trading mechanism (TCTM) is presented to improve energy efficiency and support decarbonization. To address uncertainties in the IES, a distributionally robust optimization (DRO) approach, employing a fuzzy set framework with Kernel Density Estimation (KDE) to construct error distributions and specify output ranges for renewable energy (RE), is proposed. Latin hypercube sampling (LHS) and K-means clustering are, respectively, applied to generate original and representative scenarios. Subsequently, case studies are performed to evaluate advantages of the presented model. The results indicate that hydrogen refinement within the TCTM framework has substantial benefits for the IES. Specifically, the proposed scenario integrates hydrogen blending combustion (HBC) with synthetic methane, demonstrating significant economic and carbon benefits, with cost reductions of 7.3%, 7.1%, and 4.3% and carbon emission reductions of 6%, 3%, and 2.4% compared to scenarios with no hydrogen utilization, HBC only, and synthetic methane only, respectively. In contrast, to exclude carbon trading and include fixed-price trading, the TCTM achieves a 3.5% and 1.1% reduction in carbon emissions, respectively. Finally, a comprehensive sensitivity analysis is performed, examining factors such as the ratio of hydrogen blending, price, and growth rate of carbon trading. Full article

We present a rapid, energy-efficient, and ecofriendly route for the synthesis of alkaline earth titanate perovskites—CaTiO₃, SrTiO₃, and BaTiO₃—using an affordable, commercially available CO₂ laser engraver, commonly found in makerspaces and small-scale workshops. The method involves direct laser irradiation of compacted pellets composed of low-cost, abundant, and non-toxic precursors: TiO₂ and alkaline earth carbonates (CaCO₃, SrCO₃, BaCO₃). CaTiO₃ and BaTiO₃ were synthesized with phase purities exceeding 97%, eliminating the need for conventional high-temperature furnaces or prolonged thermal treatments. X-ray diffraction (XRD) coupled with Rietveld refinement confirmed the formation of orthorhombic CaTiO₃ (Pbnm), cubic SrTiO₃ (Pm3⁻m), and tetragonal BaTiO₃ (P4mm). Raman spectroscopy independently corroborated the perovskite structures, revealing vibrational fingerprints consistent with the expected crystal symmetries and Ti–O bonding environments. All samples contained only small amounts of unreacted anatase TiO₂, while BaTiO₃ exhibited a partially amorphous fraction, attributed to the sluggish crystallization kinetics of the Ba–Ti system and the rapid quenching inherent to laser processing. Transmission electron microscopy (TEM) revealed nanoparticles with average sizes of 50–150 nm, indicative of localized melting followed by ultrafast solidification. This solvent-free, low-energy, and highly accessible approach, enabled by widely available desktop laser systems, demonstrates exceptional simplicity, scalability, and sustainability. It offers a compelling alternative to conventional ceramic processing, with broad potential for the fabrication of functional oxides in applications ranging from electronics to photocatalysis. Full article

Future lunar south pole missions face dual challenges of highly variable illumination and rugged terrain that directly constrain rover mobility and energy sustainability. To address these issues, this study proposes a dynamic illumination-constrained spatio-temporal A* (DIC3D-A*) path-planning algorithm that jointly optimizes terrain safety and illumination continuity in polar environments. Using high-resolution digital elevation model data from the Lunar Reconnaissance Orbiter Laser Altimeter, a 1300 m × 1300 m terrain model with 5 m/pixel spatial resolution was constructed. Hourly solar visibility for November–December 2026 was computed based on planetary ephemerides to generate a dynamic illumination dataset. The algorithm integrates slope, distance, and illumination into a unified heuristic cost function, performing a time-dependent search in a 3D spatiotemporal state space. Simulation results show that, compared with conventional A* algorithms considering only terrain or distance, the DIC3D-A* algorithm improves CSDV by 106.1% and 115.1%, respectively. Moreover, relative to illumination-based A* algorithms, it reduces the average terrain roughness index by 17.2%, while achieving shorter path length and faster computation than both the Rapidly-exploring Random Tree Star and Deep Q-Network baselines. These results demonstrate that dynamic illumination is the dominant environmental factor affecting lunar polar rover traversal and that DIC3D-A* provides an efficient, energy-aware framework for illumination-adaptive navigation in upcoming missions such as Chang’E-7. Full article

The emulsification of a molten fusible metal alloy in a liquid epoxy matrix with its subsequent curing is a novel way to create a highly concentrated phase-change material. However, numerous challenges have arisen. The high interfacial tension between the molten metal and epoxy resin and the difference in their viscosities hinder the stretching and breaking of metal droplets during stirring. Further, the high density of metal droplets and lack of suitable surfactants lead to their rapid coalescence and sedimentation in the non-cross-linked resin. Finally, the high differences in the thermal expansion coefficients of the metal alloy and cross-linked epoxy polymer may cause cracking of the resulting phase-change material. This work overcomes the above problems by using nanosilica-induced physical gelation to thicken the epoxy medium containing Wood’s metal, stabilize their interfacial boundary, and immobilize the molten metal droplets through the creation of a gel-like network with a yield stress. In turn, the yield stress and the subsequent low-temperature curing with diethylenetriamine prevent delamination and cracking, while the transformation of the epoxy resin as a physical gel into a cross-linked polymer gel ensures form stability. The stabilization mechanism is shown to combine Pickering-like interfacial anchoring of hydrophilic silica at the metal/epoxy boundary with bulk gelation of the epoxy phase, enabling high metal loadings. As a result, epoxy shape-stable phase-change materials containing up to 80 wt% of Wood’s metal were produced. Wood’s metal forms fine dispersed droplets in epoxy medium with an average size of 2–5 µm, which can store thermal energy with an efficiency of up to 120.8 J/cm³. Wood’s metal plasticizes the epoxy matrix and decreases its glass transition temperature because of interactions with the epoxy resin and its hardener. However, the reinforcing effect of the metal particles compensates for this adverse effect, increasing Young’s modulus of the cured phase-change system up to 825 MPa. These form-stable, high-energy-density composites are promising for thermal energy storage in building envelopes, radiation-protective shielding, or industrial heat management systems where leakage-free operation and mechanical integrity are critical. Full article

Laser Wireless Power Transmission (LWPT), as a revolutionary energy supply technology, holds broad application prospects in areas such as drone endurance, space solar energy transmission, and power supply in remote regions. The core efficiency of this technology primarily depends on the energy concentration and uniformity of the light spot at the receiving end. Through systematic simulation analysis, this paper studies the spot uniformity and energy transmission efficiency of Gaussian beams, vortex beams, and flat-topped beams under different atmospheric conditions (turbulence intensity, visibility) and transmission distances. By quantitatively analyzing key indicators such as light spot non-uniformity and power density within the bucket, the advantages and disadvantages of the three beam types are comprehensively evaluated. The results indicate that the flat-topped beam is the optimal choice for short-distance laser energy transfer under favorable atmospheric conditions, while the vortex beam exhibits the best overall performance and robustness in medium and strong turbulence transmission environments. This study provides a theoretical basis for beam selection in different application scenarios. Full article

Carbon emission reduction in the construction industry is pivotal for global carbon emission reduction, yet the lack of coordination mechanisms within the sector limits its effectiveness. This study examines the Yangtze River Economic Belt from 2010 to 2022, capturing the spatial and temporal evolution characteristics and key influencing factors of carbon emission efficiency in the construction industry (CEECI) to achieve coordinated emission reduction. Using the super-efficiency Slack-Based Measure (SBM) model and the Malmquist–Luenberger (ML) index, the study analyzes changes in CEECI, revealing significant regional variations: downstream, midstream, and upstream regions demonstrated average values of 1.10, 1.00, and 0.68, respectively. Resource redundancy is a major issue affecting CEECI, with energy redundancy rates exceeding 20%. The ML index indicates continuous improvement in CEECI, with technological change (TC) contributing the most to this improvement, as shown by index decomposition. Spatial analysis using Moran’s index (Moran’s I) revealed significant positive spatial autocorrelation, with distinct “high-high” (H-H) and “low-low” (L-L) clustering patterns, suggesting that regions with high CEECI positively influence their neighbors. Finally, we built a spatial econometric model to identify key influencing factors, including industrialization level, construction industry production level, energy consumption structure, human resources, and internal innovation levels, which directly or indirectly impact CEECI to varying degrees. These findings highlight the importance of regional coordination and targeted policy interventions to enhance carbon emission efficiency in the construction industry, addressing resource redundancy and leveraging technological advancements to contribute to global carbon reduction goals. Full article

Hierarchical Federated Learning (HFL) alleviates the trade-off between communication overhead and privacy protection in mobile scenarios via multi-level aggregation and mobility consideration. However, its idealized convex loss assumption and full-dimension parameter upload deviate from real-world non-convex tasks and edge channel constraints, causing excessive energy consumption, high communication cost, and compromised convergence that hinder practical deployment. To address these issues in mobile/UAV networks, this paper proposes an energy-efficient optimization scheme for HFL under non-convex loss, integrating a dynamically adjustable partial-dimension model upload mechanism. By screening key update dimensions, the scheme reduces uploaded data volume. We construct a total energy minimization model that incorporates communication/computation energy formulas related to upload dimensions and introduces an attendance rate constraint to guarantee learning performance. Using Lyapunov optimization, the long-term optimization problem is transformed into single-round solvable subproblems, with a step-by-step strategy balancing minimal energy consumption and model accuracy. Simulation results show that compared with the original HFL algorithm, our proposed scheme achieves significant energy reduction while maintaining high test accuracy, verifying the positive impact of mobility on system performance. Full article