Developing an effective task offloading strategy has been a focus of research to improve the task processing speed of IoT devices in recent years. Some of the reinforcement learning-based policies can improve the dependence of heuristic algorithms on models through continuous interactive exploration of the edge environment; however, when the environment changes, such reinforcement learning algorithms cannot adapt to the environment and need to spend time on retraining. This paper proposes an adaptive task offloading strategy based on meta reinforcement learning with task latency and device energy consumption as optimization targets to overcome this challenge. An edge system model with a wireless charging module is developed to improve the ability of IoT devices to provide service constantly. A Seq2Seq-based neural network is built as a task strategy network to solve the problem of difficult network training due to different dimensions of task sequences. A first-order approximation method is proposed to accelerate the calculation of the Seq2Seq network meta-strategy training, which involves quadratic gradients. The experimental results show that, compared with existing methods, the algorithm in this paper has better performance in different tasks and network environments, can effectively reduce the task processing delay and device energy consumption, and can quickly adapt to new environments.

In the operation of the machine, the tribological properties of the lubricant are apt to deteriorate with temperature elevation. Therefore, it is of great significance to seek a lubricant additive with good high-temperature tribological properties. In this study, the tribological properties of nano magnesium silicate hydroxide (MSH) as a lubricant additive were investigated at 40–200 °C and the results show that the sliding surface lubricated by PAO10 with MSH as the additive can nearly achieve zero wear. The worn surface was characterized by the Scanning electron microscope, Raman spectrometer, and X-ray photoelectron spectrometer. The analysis results show that the electrostatic adsorption between [Si₄O₁₀]⁴⁻ released from MSH and steel surface contributes to the formation of SiO₂ film; whereas ion exchange reaction between the phase transformation product of MSH in the contact zone and steel surface results in the chemical bonding Si-O-Fe, and the existence of the bonding facilitate the formation of the protective tribofilm with strong adhesion at high temperature.

In this study, the tribological properties of the mixture of nano magnesium silicate hydroxide (MSH) and micro carbon sphere (CS) are studied and a superior synergistic anti-wear performance is exhibited under 600 N and 600 RPM. For determining the anti-wear mechanism, an easy two-step experimental method is innovatively used. It's proved that the decomposition of MSH is the key to exhibit the synergistic anti-wear effect with a-C. Results show that the H⁺ protons released by MSH decomposition can effectively convert Fe₂O₃ to Fe₃O₄ on worn surfaces. Besides, MSH also helps the formed a-C layer exist in low H content with stronger adhesion on iron surface and the detached H atoms from C-H further facilitate the reduction of Fe₂O₃. It's believed that the critical role of lamellate clay in synergy with amorphous carbon (a-C) as lubricant additive is determined and a basis for the further anti-wear mechanism study of various a-C-modified clay composite is provided.

Constrained multi-objective optimization problems (CMOPs) widely exist in scientific research and industrial applications. In Type IV CMOPs, where the constrained Pareto front (CPF) is significantly separated from the unconstrained Pareto front (UPF) by large infeasible barriers, traditional single-population evolutionary algorithms often suffer from severe search reachability difficulties. Moreover, while existing dual-population coevolutionary frameworks can exploit auxiliary populations to provide global guidance for obstacle crossing, they typically adopt a constant knowledge transfer intensity, which may introduce negative transfer and interfere with fine-grained CPF convergence in later evolutionary stages. To address these challenges, this paper proposes a Dual-Population Constrained Multi-Objective Evolutionary Algorithm with Adaptive Knowledge Migration (ADCMO). The algorithm constructs a main–auxiliary dual-population coevolutionary framework: the main population pursues feasible convergence under the original constraints, while the auxiliary population explores the unconstrained objective landscape to maintain global awareness. A linearly decaying migration control factor is introduced to dynamically regulate the intensity of cross-population knowledge transfer. Specifically, a dual-defense mechanism is established by simultaneously controlling the auxiliary participation ratio in mating pool construction and the auxiliary offspring injection scale in environmental selection, thereby achieving the synergistic effect of enhanced obstacle crossing in early evolution and progressive interference suppression in later stages. Extensive experiments on two benchmark suites comprising 23 test problems and ten representative real-world constrained multi-objective optimization problems demonstrate that ADCMO shows clear advantages on several large-barrier Type IV-like CMOPs, especially on the LIR-CMOP suite, while maintaining feasible and competitive behavior on most remaining instances. Ablation studies further verify the non-negligible contributions of the auxiliary population, the adaptive migration factor, and the dual-defense mechanism to the overall performance.