Factory aquaculture faces a difficult situation due to its high running costs, with one of the main contributing factors being the high energy consumption of aquaculture workshops. Accurately predicting the power load of recirculating aquaculture systems (RAS) is critical to optimizing energy use, reducing energy consumption, and promoting the sustainable development of factory aquaculture. Adequate data can improve the accuracy of the prediction model. However, there are often missing and abnormal data in actual data detection. To solve this problem, this study uses a time-series convolutional network–temporal sequence generation adversarial network (TCN-TimeGAN) to synthesize multivariate RAS data and train a long short-term memory (LSTM) network on the original and generated data to predict future electricity loads. The experimental results show that the data generated based on the improved TCN-TimeGAN provide more comprehensive coverage of the original data distribution, with a lower discriminative score (0.2419) and a lower predictive score (0.0668) than the conventional TimeGAN. Using the generated data for prediction, the R² reached 0.86, which represents a 19% improvement over the ARIMA model. Meanwhile, compared to LSTM and GRU without data augmentation, the mean absolute error (MAE) was reduced by 1.24 and 1.58, respectively. The model has good prediction performance and generalization ability, which benefits the RAS energy saving, production planning, and the long term sustainability of factory aquaculture.

The hulls of marine vehicles are generally very effective at attenuating airborne acoustic noise generated by their powertrains. However, conventional hull designs are generally not very effective at attenuating wide-band low-frequency noise. Meta-structure concepts offer an opportunity for the design of laminated hull structures tailored to address this concern. This research proposes a novel meta-structure laminar hull concept using periodic layered Phononic crystals to optimize the sound insolation performance on the air–solid side of the hull structure. The acoustic transmission performance is evaluated using the transfer matrix, the acoustic transmittance, and the tunneling frequencies. The theoretical and numerical models for a proposed thin solid-air sandwiched meta-structure hull indicate ultra-low transmission within a 50-to-800 Hz frequency band and with two predicted sharp tunneling peaks. The corresponding 3D-printed sample experimentally validates the tunneling peaks at 189 Hz and 538 Hz, with 0.38 and 0.56 transmission magnitudes, respectively, with the frequency band between those values showing wide-band mitigation. The simplicity of this meta-structure design provides a convenient way to achieve acoustic band filtering of low frequencies for marine engineering equipment and, accordingly, an effective technique for low-frequency acoustic mitigation.

Selective multi-directional sound projection across a wide-band low mid-frequency range remains a significant challenge in acoustic applications, primarily due to the manipulation of long wavelengths within compact acoustic meta-lens. This study introduces a novel directional acoustic beaming theoritical method, specifically a duplexcavity four-lobe valve-shaped acoustic metalens (DFVAM). By manipulating geometric parameters, the DFVAM effectively controls downstream acoustic in a directional sound projection fashion that enhances sound wave propagation across multiple wide frequency bands. Experimental results validate substantial sound focusing at frequencies of 4500-4800 Hz, 7200-7600 Hz, 9700-10300 Hz, and 11400-11600 Hz, achieving sound beam lengths between 12-21 λ, cross-sectional areas ranging from 0.7-2 λ, and FWHM values of 0.140 m to 0.218 m. This meta-lens offers efficient acoustic modulation and focusing across various frequency ranges, presenting promising applications in acoustic directional transmission, sound energy collecting and acoustic levitation.

Based on multi-objective genetic algorithm, naca4415 airfoil (NACA airfoil is a series of airfoils developed by the National Aeronautical Advisory Committee of the United States) is used as the original airfoil to optimize the blade airfoil of low water speed tidal current turbine, The lift drag ratio of the generator airfoil is improved (lift drag parameter is one of the key factors to determine the low-speed water kinetic energy conversion efficiency of the airfoil), the power generation efficiency of the turbine airfoil is improved, and the s airfoil is spliced on the basis of the optimized airfoil. The results show that the lift drag ratio performance of the optimized airfoil at the designed angle of attack is improved compared with that of the unoptimized airfoil, and the lifting range increases with the increase of the angle of attack. The pressure coefficient of the optimized airfoil decreases and the flow velocity increases. The pressure coefficient of the assembled S-shaped airfoil and the S-shaped airfoil have good bi-directional energy harvesting performance at the optimal attack angle of 12° to 15 °

Action quality assessment (AQA) is vital in sports science and clinical rehabilitation, but existing methods suffer from data noise sensitivity, weak physical interpretability, and limited generalization across body types. This study proposes a novel AQA paradigm for standing long jump to address these limitations. A methodological framework based on kinematic reconstruction and physical deviation analysis is developed. Ideal trajectories derived from forward kinematics establish a unified evaluation benchmark. Three deviation features (Peak Deviation, Cumulative Deviation Integral, Phase-Weighted Deviation) are computed between ideal and experimental samples, then input into a Random Forest Classifier for automatic grading. Experimental results demonstrate the proposed method significantly outperforms traditional dynamic-time-warping-based templates in accuracy and robustness. Ablation studies confirm kinematic trajectory benchmarks are superior to raw or averaged trajectories. Model decisions show strong consistency with physical characteristics, verifying interpretable assessment capability. This work shifts AQA from “data similarity comparison” to “physical rationality evaluation”, offering a new perspective for vision-based automated motion analysis.

In order to make the ocean energy generator system realize the maximum power under various conditions, this paper proposes a control strategy based on linear active disturbance rejection control algorithm. The output power is mainly affected by the spin-speed of the generator in a nonlinear manner, and this phenomenon is originated from the tip speed ratio state of the turbine blades. This tip speed ratio associated maximum power can be achieved through a coupled control system that adjusts the spin-speed of generator elaborately. The state equations of ocean power generation system are transformed into a standard form to fit the linear active disturbance rejection control. Finally, the simulation model of the system is established by using Matlab/Simulink platform, with a series of input signals for different working conditions. The results verify that the maximum power under control can be effectively attained by a quick tracking of the target speed.