Integrating marine renewable energy with wireless charging, wave energy storage–based wireless power transfer (WES–WPT) systems provide a sustainable and low–maintenance power supply solution for autonomous underwater vehicles (AUVs) and distributed sensor nodes. Despite their potential, integrated WES–WPT systems face interrelated system–level challenges caused by stochastic wave intermittency, coil displacement, and seawater–induced eddy current losses. Although each issue can be partially mitigated by existing techniques, their simultaneous occurrence may lead to DC–link fluctuation, mutual–inductance variation, resonance detuning, and transmission energy degradation. To improve transmission energy robustness under marine operating conditions, this paper systematically reviews efficiency–optimization control strategies, including primary–side regulation, impedance matching, and active rectification, together with constant–current constant–voltage (CC–CV) charging methods. From a system–level perspective, the role of energy–storage buffering, second–order swing dynamics, and droop control is discussed to explain how front–end power regulation can stabilize the input interface of the downstream high–frequency WPT stage. Furthermore, a Comprehensive Marine Suitability Evaluation Matrix (CMSEM) is introduced within a Multi–Criteria Decision Analysis (MCDA) framework as a semi–quantitative tool for comparing the marine deployment suitability of different WPT topologies and control strategies. Based on the reviewed literature and illustrative scenario analysis, this work establishes a “modeling–perception–execution” roadmap for WES–WPT systems, emphasizing soft–sensing parameter identification, coordinated control, modular hardware design, and AI–assisted optimization. These findings provide a structured reference for developing robust marine energy nodes and future air–sea–subsea integrated power networks.
This study reports the rational design and fabrication of a novel multifunctional composite hydrogel by integrating copper tripeptide-1 (GHK-Cu) with cellulose nanocrystals (CNC). Fourier-transform infrared spectroscopy and scanning electron microscopy analyses verified the formation of a stable, physically crosslinked three-dimensional network between GHK-Cu and CNC. Mechanical characterization demonstrated outstanding flexibility, with an elongation at break reaching 1303
Microgrids (MGs) integrating renewable energy sources (RESs), plug-in hybrid electric vehicles (PHEVs), battery storage, and proton exchange membrane fuel cell-based combined heat and power (PEMFC-CHP) systems face increasing complexity due to uncertainty in both energy supply and demand, as well as dynamic electricity market prices. This paper proposes a comprehensive energy management strategy for renewable-enriched microgrids that simultaneously coordinate the dispatch of electric, thermal, and hydrogen energy vectors. The proposed system integrates photovoltaic (PV) and wind resources, a proton exchange membrane fuel cell combined heat and power unit (PEMFC-CHP), battery energy storage systems (BESS), plug-in hybrid electric vehicles (PHEVs), and a hydrogen production and storage subsystem. To address the inherent uncertainties in load demand, renewable generation, and market prices, a Monte Carlo Simulation (MCS)-based scenario framework is adopted. A constrained variant of the Harris Hawks Optimization (HHO) algorithm is introduced to solve the multi-objective optimization problem, minimizing total operational cost, carbon emissions, and load or storage violations. The optimization process enforces technical and economic constraints including power balance, storage capacity, thermal demand satisfaction, and hydrogen trading limits. The proposed framework is developed and simulated using MATLAB (R) software and validated on a modified 16-bus microgrid under multiple operational scenarios, ranging from uncontrolled PHEV charging to full vector coordination with PEMFC and CHP integration. Simulation results demonstrate that the proposed HHO-based energy management framework significantly outperforms benchmark algorithms in minimizing operational cost, emissions, and unmet energy demand. Case 6, which integrates smart PHEV charging with PEMFC-CHP coordination, achieves the most optimal performance-delivering the lowest cost (320 ), reduced emissions (520 kg CO2), and zero unmet load across all scenarios.
With the wide application of cloud and fog computing, fog devices deployed in scenarios such as the Internet of Vehicles (IoV) are faced with the need to share massive amounts of data, which makes it crucial to implement fine-grained data access control. However, data transmission between fog devices and vehicles in the IoV is highly susceptible to eavesdropping, tampering, and other attacks by malicious vehicles. Most of the existing attribute-based encryption schemes rely on periodic key updates to enable malicious user tracking and attribute revocation, but they cannot meet the demand for real-time security response in the IoV. For this reason, we propose traceable and revocable multi-authority attribute-based encryption with cloud and fog computing (TR-MAABE-CFC). We elaborate on the system model, rigorously define the concepts, and build the security model for TR-MAABE-CFC. Subsequently, we construct a specific implementation scheme for TR-MAABE CFC. Our proposed solution provides fine-grained access control mechanisms, enables the identification of malicious entities, and supports precise attribute revocation. It can be effectively put into practice in cloud and fog computing scenarios. Experimental findings show that our scheme demonstrates superior decryption efficiency and is highly compatible with intelligent transportation system scenarios, making it a viable solution for the IoV.
Building change detection (CD) from bitemporal remote sensing images aims to identify newly constructed, demolished, or modified buildings by comparing observations acquired at different times. A major limitation of existing supervised approaches is their strong dependence on pixelwise building and change annotations, which are costly and labor-intensive to obtain at scale. To address this challenge, we propose SemiBCD, a semi-supervised framework that integrates visual-language model (VLM) priors with consistency learning for building CD under limited supervision. A VLM pretrained on natural images is first employed to generate building pseudo-labels for remote sensing imagery. To mitigate domain shift and suppress noise in these pseudo-labels, we introduce an uncertainty-aware pseudo-label refinement (UAPLR) module that progressively improves pseudo-label reliability during training. Leveraging a small set of annotated change masks together with the refined pseudo-labels, we further design a dual consistency learning strategy, combining perturbation-based multiview consistency and temporal-hierarchical alignment to better exploit unlabeled data. Experiments on the LEVIR-CD and WHU-CD benchmarks, as well as the WHU Building dataset, demonstrate that SemiBCD consistently outperforms representative semi-supervised baselines under low-label regimes. The results indicate that incorporating VLM priors with uncertainty-aware refinement and consistency regularization provides an effective solution for reducing annotation dependence in building CD.
