Underwater wet-mateable connectors have been widely used to reduce the cost and the time of installation, maintenance and reconfiguration in many fields, such as the oil and gas (O&G) industry, offshore renewable energy (ORE), and undersea observatories. In the past few years, the authors’ group has made some efforts in developing wet-mateable connectors. This paper presents a methodology for designing and testing a wet-mateable electrical connector. First, an innovative wet-mateable electrical connector with dual-bladder pressure-balanced oil-filled (PBOF) technology is proposed. Second, the generalized equations of differential pressure are derived. Then, a procedure of thermal-electric-structure (TES) coupling simulation is proposed, and a series of finite element analysis (FEA) involving coupled multi-field problems is conducted, including thermal-electric coupling analysis, static structural analysis, and dynamic analysis. Finally, a prototype of the proposed connector is developed successfully, and its electrical performance is verified by the online test in a hydrostatic pressure environment with an ocean depth of 3000 m, which has reached the leading level in China. This paper is the first discloser on wet-mateable connectors in the aspects of design, theory, simulation and testing, which might be helpful to many ocean scientists in developing countries who are technically blocked or could not afford the high cost.

Unmanned surface pursuit is a complex and challenging engineering problem, especially when conducted by multiple intelligent surface vehicles (ISVs). To enhance the pursuit performance and facilitate strategic interaction during the target pursuit, this paper proposes a novel game theory-based maneuver planning method for pursuit ISVs. Firstly, a specific two-player zero-sum pursuit–evasion game (ZSPEG)-based target-pursuit model is formed. To ensure the vehicles reach a quick consensus, a target-guided relay-pursuit mechanism and the corresponding pursuit payoffs are designed. Meanwhile, under the fictitious play framework, the behavioral pattern and the strategies of the target could be fictitiously learned. Furthermore, mixed-strategy Nash equilibrium (MNE) is employed to determine the motions for the vehicles, the value of which is the best response in the proposed ZSPEG model. Finally, simulations verify the effectiveness of the above methods in multi-ISV surface pursuit.

Sand-based thermal energy storage systems represent a paradigm shift in sustainable energy solutions, leveraging Earth’s most abundant mineral resource through advanced nanocomposite engineering. This review examines sand-based phase change materials (PCM) systems with emphasis on integration with human-powered energy generation (HPEG). Silicon-based hierarchical pore structures provide multiscale thermal conduction pathways while achieving PCM loading capacities exceeding 90%. Carbon-based nanomaterial doping enhances thermal conductivity by up to 269%, reaching 3.1 W/m·K while maintaining phase change enthalpies above 130 J/g. This demonstrated cycling stability exceeds 1000 thermal cycles with <8% capacity degradation. Thermal energy storage costs reach ~$20 kWh⁻¹—60% lower than lithium-ion systems when normalized by usable heat capacity. Integration with triboelectric nanogenerators achieves 55% peak mechanical-to-electrical conversion efficiency for direct pathways, while thermal-buffered systems provide 8–12% end-to-end efficiency with temporal decoupling between intermittent human power input and stable electrical output. Miniaturized systems target off-grid communities, offering 5–10× cost advantages over conventional batteries for resource-constrained deployments. Levelized storage costs remain competitive despite efficiency penalties versus lithium-ion alternatives. Critical challenges, including thermal cycling degradation, energy-power density trade-offs, and environmental adaptability, are systematically analyzed. Future directions explore biomimetic multi-level pore designs, intelligent responsive systems, and distributed microgrid implementations.

This protocol presents a Gaussian Process Regression (GPR) optimization framework for human-powered electricity generation systems to address technological unemployment and renewable energy demands simultaneously. Seven participants performed 112 trials across 16 configurations combining four battery voltages (12 V, 24 V, 36 V, 48 V) with four electrical loads (10 W, 30 W, 50 W, 70 W) using a modified stationary bicycle generator. The GPR model incorporated automatic relevance determination (ARD) squared exponential kernels to map pedaling speed, pressure, voltage, and load parameters to generation efficiency while quantifying prediction uncertainty. Real-time data acquisition at 2 Hz captured mechanical and electrical parameters through Hall-effect sensors, strain gauges, and power analyzers with Modbus communication protocols. The optimized system achieved 80%-90% theoretical maximum efficiency with coefficient of variation (CoV) below 15%, compared to 60%-70% efficiency and 25%-35% variability in commercial systems. GPR predictions demonstrated R² = 0.713 with sub-10ms latency through sparse approximation techniques, enabling real-time control. Optimal operational parameters varied systematically: low loads required 150-180 revolutions per minute (RPM) at 50-70 N pressure, while higher loads peaked at 100-120 RPM with 100-150 N pressure. The 48 V configuration improved efficiency by 68% over 12 V baseline (measured as relative efficiency index, REI, normalized to baseline = 100%). Phase-specific monitoring identified fatigue onset at 35-40 min, triggering risk-aware control adjustments based on posterior uncertainty. The protocol establishes quantitative methods for balancing energy generation with physiological constraints, providing a reproducible framework for deploying human-powered systems in fitness facilities where break-even occurs at $0.18/kWh electricity pricing.