Search Results (10,514)

Transportation hubs serve as critical convergence points for traffic, information, and energy flows. However, their energy systems are characterized by high consumption randomness, significant power flow fluctuations, and geographically dispersed source and load nodes. These features pose challenges for integrating distributed renewable energy and often lead to high energy cost issues. Additionally, accommodating distributed photovoltaic (PV) is further complicated by grid corridors and high investment expenditure. To address these issues, this paper proposes a two-stage optimization model for a multi-node interconnected architecture for transportation hubs. In the first stage, a greedy algorithm determines a fixed connection topology, considering distance constraints and connection port limits to ensure engineering feasibility. The second employs linear programming to optimize real-time power allocation. This approach precomputes connection relationships, significantly reducing evaluation time and enabling efficient processing of operational data from multiple nodes. A case study confirms that the proposed method can increase PV consumption by 38.71%, with optimization evaluated on a millisecond scale. By inputting node generation, load, and distance data in prescribed format, the model outputs actionable planning results for flexible interconnection projects. Full article

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities. Full article

This paper proposes a method based on interval linear robust optimization to address the potential impacts of multiple uncertainties on the operational security of Regional Integrated Energy Systems (RIESs). The model considers the uncertainty in user loads and renewable energy outputs and determines the value ranges of related parameters through statistical analysis to characterize the boundaries of these uncertainties. To transform the stochastic disturbances into a solvable problem, the model introduces energy balance constraints under the worst-case scenario, ensuring that the system remains feasible under extreme conditions. The research framework integrates Nash bargaining theory, demand response mechanisms, and tiered carbon trading policies, constructing a cooperative game model for RIESs to minimize the overall operation cost of the alliance while providing a reasonable revenue distribution scheme. This approach aims to achieve fairness and sustainability in regional cooperation. Simulation results show that the method can effectively reduce the collaborative operation cost and improve the fairness of revenue distribution. To address potential issues of information misreporting and dishonesty in real-world scenarios, the model introduces an adjustable fraud factor in the revenue distribution process to characterize the strategy deviations of participants. Even under potential fraud risks, the mechanism can maintain an optimal revenue structure and lead the participants toward a stable fraud equilibrium, thereby enhancing the robustness and reliability of the overall collaboration. Full article

To achieve carbon neutrality goals, large amounts of renewable energy sources (RESs) are being integrated into power systems. In particular, high penetration of distributed photovoltaic (PV) makes distribution networks highly stochastic, calling for dynamic distribution network reconfiguration (DNR). Existing DNR approaches can be broadly categorized into model-driven optimization-based methods and learning-based methods, with deep reinforcement learning (DRL) being a representative paradigm for fast online decision-making. Existing DNR models typically belong to mixed-integer linear programming, which requires solution methods such as deep reinforcement learning (DRL). However, existing methods commonly struggle to account for human factors, i.e., the time-varying preferences of distribution network operators in DRL decisions. To this end, this paper proposes a natural language-driven, human-in-the-loop DNR framework, which combines a DRL base policy for hour-level dynamic reconfiguration with a large language model (LLM)-based instruction supervision layer. Based on this human-in-the-loop framework, commands from operators in natural language are translated into online adjustments of safety-screened DRL switching actions. Therefore, the framework demonstrates the fast, model-free decision capability of DRL while providing an explicit and interpretable interface for incorporating temporary and context-dependent operator requirements without retraining. Case studies on IEEE 16-bus and 33-bus distribution networks show that the proposed framework reduces network losses, improves voltage profiles, and limits switching operations. It also achieves markedly higher compliance with operator instructions than a conventional model-based method and a pure DRL baseline. These results highlight a viable path to embedding natural language guidance into the data-driven operation of active distribution networks. Full article

Accurate cloud identification is crucial for understanding the rapid evolution of weather systems, improving the accuracy of short-term forecasts, and ensuring aviation safety. Compared with traditional cloud image recognition methods, deep learning technology has advantages such as automatic learning of complex features, high-precision recognition, and strong robustness in changing environments, providing more reliable and detailed cloud information. This study utilized 256 cloud image observation data points collected by an all-sky imager from 3 to 30 November 2023, at the Tunchang County Meteorological Bureau in Hainan Province (19°21′N, 110°06′ E). A Convolutional Neural Network (CNN) model was employed for cloud image recognition. The results show that in terms of cloud recognition, the constructed CNN model achieved an accuracy rate, recall rate, and ${\mathrm{F}}_1$ score of 100%, 91%, and 95%, respectively, for clear skies and stratus clouds, cumulus clouds, and cirrus clouds, with an average recognition accuracy rate of 95%. In terms of cloud cover detection, when comparing the Normalized Red Blue Ratio (NRBR) and K-Means clustering algorithm with the system’s built-in monitoring results, the NRBR method performed optimally in cloud region segmentation, with cloud cover estimates closer to the actual distribution. In summary, deep learning technology demonstrates higher accuracy and strong robustness in all-sky cloud image recognition. Full article

To address the technical bottlenecks commonly encountered with real-time quantitative PCR instruments, such as insufficient ramp rates and uneven chamber temperature distribution, this study proposes an innovative design scheme for a temperature control system that incorporates key hot air circulation and temperature field regulation technologies. By combining the PCR instruments’ working principles and structural characteristics, the failure mechanisms associated with the temperature control system are systematically analyzed, and a reliability-oriented thermodynamic analysis model is constructed to clarify the functional positioning of core components and to systematically test the airflow uniformity, temperature dynamics, and nucleic acid amplification efficiency. An integrated fixture for airflow rectifier and cruciform frames is designed, which enables precise quantitative characterization of the system temperature uniformity, ramp rates, and amplification efficiency on a multi-condition comparison platform. Through modeling analysis combined with experimental validation, the thermal performance differences among various heating chamber structures are compared, leading to a multidimensional optimization of the temperature control system. The test results demonstrate outstanding core performance metrics for the optimized system: the up ramp reaches 7.5 ± 0.1 °C/s, the down ramp reaches 13.5 ± 0.1 °C/s, and the steady-state temperature deviation is only ±0.1 °C. The total duration for 35 PCR cycles is recorded at 16.3 ± 0.6 min, with a nucleic acid amplification efficiency of 98.9 ± 0.2%. The core performance metrics comprehensively surpass those of mainstream global counterparts. The developed temperature control system is well-suited for practical applications such as rapid detection, providing critical technological support for the iterative upgrade of nucleic acid amplification techniques while laying a solid foundation for the engineering development of high-performance PCR instruments. Full article

In order to meet the urgent needs of refined geological disaster risk assessment at a county scale, and in view of the shortcomings of existing methods in the aspects of sample dependence, rainfall time-varying differences, and vulnerability quantification, this study takes Linxiang City as an example, integrates multi-source data such as geology, geography, meteorology, remote sensing, and field survey, and explores practical methods. A random forest (RF) model was implemented for geological hazard susceptibility mapping, and its hyper-parameters were tuned using Bayesian optimization. Based on a statistical analysis of the frequency of historical disaster events, a risk classification of rainfall in the flood season and non-flood season was evaluated. A vulnerability simplification method based on the value and exposure of disaster-bearing bodies was proposed. Finally, rapid risk assessment was achieved by matrix superposition. The results showed that the model had high accuracy (AUC = 0.903). The use of field survey risk types effectively enhanced the susceptibility sample set and verified the accuracy of risk assessment. The risk factor in the flood season and non-flood season was significantly different, and the very-high- and high-risk areas in the flood season were mainly distributed in the shallow metamorphic rock mountainous area in the east of Yanglousi Town and the granite residual soil area in the south of Zhanqiao Town, the latter of which was highly consistent with the field survey results. This study demonstrated value in terms of sample enhancement, model optimization, consideration of time-varying rainfall, and vulnerability simplification. The evaluation results can provide direct support for the construction of a “point–area dual control” system for geological disasters in Linxiang City, and the methodological framework can also provide a practical reference for risk evaluation in other counties. Full article

In new distribution systems with high penetration of renewable energy, inverter-based sources exhibit significant differences in fault characteristics compared to traditional power sources due to the absence of a constant electromotive force and their operation under nonlinear control links, rendering conventional fault current calculation methods inadequate. To address these challenges, a full-time-domain analysis-based method for modelling and calculating fault transient characteristics is proposed. First, a dynamic model of inverter-based sources accounting for current loop saturation effects is established, and phase plane analysis is employed to resolve nonlinear control regions. On this basis, a full-time-domain fault current calculation method is proposed, wherein the steady-state operating point after a fault is determined by iteratively solving the network node voltage equations. By integrating control strategies and derived transient differential equations, the fault current expression across the full-time-domain scope is formulated. Furthermore, a multi-objective optimization control strategy is proposed to achieve effective fault current suppression, and an improved Simulated Annealing-Particle Swarm Optimization (SA-IPSO) hybrid algorithm is adopted for efficient solution. Finally, SIMULINK-based simulation experiments validate the accuracy and effectiveness of the proposed method in transient characteristic analysis and current suppression. Full article

The combined challenges of water scarcity and inefficient nitrogen use pose substantial barriers to sustainable agricultural development. Optimizing the coordinated regulation of water and nitrogen resources in fruit trees is essential for promoting water-saving agriculture in drylands. To establish a water and nitrogen collaborative management model for efficient resource utilization, this study conducted a 3-year field experiment examining different irrigation amount (W1: 4500 m³·ha⁻¹, W2: 6000 m³·ha⁻¹, and W3: 7500 m³·ha⁻¹) and nitrogen application rates (N1: 200 kg·ha⁻¹, N2: 300 kg·ha⁻¹, and N3: 400 kg·ha⁻¹), coupled with ¹⁵N isotopic labeling, to evaluate the impact of water and nitrogen regulation on the following: (i) the spatial distribution patterns of water and nitrogen in the root zone soil, (ii) dynamic characteristics of water and nitrogen across organs, and (iii) ¹⁵N absorption and utilization. The findings revealed that 20–80 cm depth was the key zone for water and nitrogen absorption by roots of pear. The W2 treatment met the optimal irrigation requirement for young pear tree roots and exhibited the optimal dynamic characteristics of water and nitrogen among the newly formed organs. At the end of the growth period, N3 treatment had the highest nitrogen content and the root system was the main organ for nitrogen absorption and storage. Water-saving irrigation coupled with optimized nitrogen application synergistically enhanced the nitrogen accumulation efficiency in fragrant pear. The W2N2 treatment exhibited the highest ¹⁵N absorption and utilization rate (40.79%), effectively promoting nitrogen absorption and assimilation, reducing nitrogen losses, and offering valuable insights for advancing sustainable practices in the fruit and forestry industries. Full article

This study investigates the two-dimensional (2D) steady-state frictional contact behavior of functionally graded piezoelectric material (FGPM) coatings under a high-speed rigid cylindrical punch. An electromechanical coupled contact model considering inertial effects is established, while a layered model is employed to simulate arbitrarily varying material parameters. Based on piezoelectric elasticity theory, the steady-state governing equations for the coupled system are derived. By utilizing the transfer matrix method and the Fourier integral transform, the boundary value problem is converted into a system of coupled Cauchy singular integral equations of the first and second kinds in the frequency domain. These equations are solved semi-analytically, using the least squares method combined with an iterative algorithm. Taking a power-law gradient distribution as a case study, the effects of the gradient index, relative sliding speed, and friction coefficient on the contact pressure, in-plane stress, and electric displacement are systematically analyzed. Furthermore, the contact responses of FGPM coatings with power-law, exponential, and sinusoidal gradient profiles are compared. The findings provide a theoretical foundation for the optimal design of FGPM coatings and for enhancing their operational reliability under high-speed service conditions. Full article

High-penetration integration of distributed photovoltaics (PV) into distribution networks introduces significant challenges regarding voltage limit violations and fluctuations. To address these issues, this manuscript proposes a hierarchical coordinated voltage control strategy for medium- and low-voltage distribution networks utilizing a cloud-edge collaboration architecture. The research methodology involves constructing a multi-objective optimization model at the cloud layer to minimize network losses and voltage deviations, solved via an improved Honey Badger Algorithm (HBA). Simultaneously, at the edge layer, a multi-mode coordinated control strategy incorporating Virtual Synchronous Generator (VSG) technology is developed to provide fast reactive power support and inertial response. Through simulation analysis on an IEEE 33-node test system, the findings demonstrate that the proposed strategy significantly mitigates voltage fluctuations and enhances the hosting capacity of distributed energy resources. The study concludes that the cloud-edge framework effectively decouples control time-scales, ensuring both global economic operation and local transient stability. These results are significant for advancing the resilient operation of active distribution networks with high renewable penetration. Full article

Chitosan–gelatin (CG) hybrid hydrogels are widely recognized for their biocompatibility and suitability for soft tissue engineering, wound dressings, and biomedical coatings. Despite this promise, conventional CG systems often exhibit limited mechanical strength, restricted durability, and uncontrolled swelling, which can reduce their clinical relevance. In this study, we introduce an enhanced soft hydrogel platform reinforced with niobium pentoxide (Nb₂O₅) nanoparticles and chemically crosslinked using glutaraldehyde, with citric acid serving as a dissolution medium and processing aid. Three hydrogel variants (G1, G2 and G3) were prepared by adjusting nanoparticle concentration and subsequently evaluated through structural, morphological, swelling, gel-fraction, and rheological analyses. SEM imaging revealed that increasing Nb₂O₅ content produced notable architectural transitions—from smooth porous matrices to nanoparticle-distributed, heterogenous pore structures. XRD, FTIR, and Raman spectroscopy confirmed the structural retention of Nb₂O₅ and its effective interaction with the polymer network. Swelling and gel-fraction measurements demonstrated improved network stability in nanoparticle-loaded systems, with G2 providing the most desirable balance between swelling capacity (298%) and gel fraction (91%). Rheological studies further identified G2 as the most stable and elastic composition, exhibiting strong shear-thinning behavior and high structural recovery. Overall, G2 emerges as the optimal formulation for future biomedical development. Full article

Accurate and energy-efficient localization of autonomous underwater vehicles (AUVs) remains a fundamental challenge due to the complex, bandwidth-limited, and highly dynamic nature of underwater acoustic environments. This paper proposes a fully adaptive deep reinforcement learning (DRL)-driven localization framework for AUVs operating in Underwater Acoustic Sensor Networks (UAWSNs). The localization problem is formulated as a Markov Decision Process (MDP) in which an intelligent agent jointly optimizes beacon selection and transmit power allocation to minimize long-term localization error and energy consumption. A hierarchical learning architecture is developed by integrating four actor–critic algorithms, which are (i) Twin Delayed Deep Deterministic Policy Gradient (TD3), (ii) Soft Actor–Critic (SAC), (iii) Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and (iv) Distributed DDPG (D2DPG), enabling robust learning under non-stationary channels, cooperative multi-AUV scenarios, and large-scale deployments. A round-trip time (RTT)-based geometric localization model incorporating a depth-dependent sound speed gradient is employed to accurately capture realistic underwater acoustic propagation effects. A multi-objective reward function jointly balances localization accuracy, energy efficiency, and ranging reliability through a risk-aware metric. Furthermore, the Cramér–Rao Lower Bound (CRLB) is derived to characterize the theoretical performance limits, and a comprehensive complexity analysis is performed to demonstrate the scalability of the proposed framework. Extensive Monte Carlo simulations show that the proposed DRL-based methods achieve significantly lower localization error, lower energy consumption, faster convergence, and higher overall system utility than classical TD3. These results confirm the effectiveness and robustness of DRL for next-generation adaptive underwater localization systems. Full article

To achieve an optimal balance of mechanical properties in low-cost alloy systems, this study tailored a heterogeneous bimodal structure in dilute Mg-0.4Al-0.3Ca-0.2Mn-xSn alloys (x = 0, 0.1 wt.%) and systematically investigated the critical role of trace Sn microalloying during hot extrusion. Mg-0.4Al-0.3Ca-0.2Mn-xSn alloys were fabricated via melting, homogenization, and subsequent hot extrusion at 320 °C. Trace Sn addition induced the formation of uniformly distributed CaMgSn phases within the homogenized matrix, facilitating a synergistic enhancement of strength and ductility. Specifically, the extruded alloys exhibited a characteristic bimodal grain structure consisting of coarse un-dynamic recrystallized (unDRXed) grains and fine dynamic recrystallized (DRXed) grains. Sn microalloying effectively refined the DRXed grains from 2.66 μm to 2.11 μm and significantly boosted the elongation (EL) from 12.9% to 26.3% while maintaining an Ultimate Tensile Strength (UTS) of 274 MPa. The Sn-containing secondary phases served as potent sites for particle-stimulated nucleation (PSN), thereby promoting the DRX process and reducing the texture intensity from 20.89 to 9.99. Overall, the superior strength-ductility synergy is primarily governed by the formation of the heterogeneous bimodal structure, where trace Sn facilitates grain refinement and texture weakening through PSN mechanisms, providing a robust strategy for the design of high-performance dilute magnesium alloys. Full article

This paper studies a novel movable antenna (MA)-aided Cell-Free Massive MIMO system to leverage the corresponding spatial degrees of freedom (DoFs) for improving the performance of distributed wireless networks. We aim to maximize the ergodic sum capacity by jointly optimizing the MA positions and the transmit covariance matrix based on statistical channel state information (CSI). To address the non-convex stochastic optimization problem, we propose a novel Constrained Stochastic Successive Convex Approximation (CSSCA) framework, enhanced with a robust slack-variable mechanism to handle non-convex antenna spacing constraints and ensure iterative feasibility. Numerical results show that the considered MA-enhanced system can significantly improve the ergodic capacity compared to fixed-antenna cell-free systems and that the proposed algorithm exhibits robust convergence behavior. Full article