In order to explore the relationship between the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) and the risk of rapid decline in kidney function, data from the China health and retirement longitudinal study (CHARLS) conducted in 2011 and 2015 was utilized. It includes 4 055 participants aged 40 and above with a baseline estimated glomerular filtration rate (eGFR) of at least 60 mL/(min·1.73m²), calculated using serum creatinine and cystatin C levels. Rapid kidney function decline is defined as a decrease in eGFR of 3 mL/(min·1.73m²·a). A multivariable logistic regression model was used to investigate the association between NHHR and the risk of rapid kidney function decline. Additionally, restricted cubic spline and threshold effect analyses were used to evaluate the dose-response relationship. The results show that over the 4-year follow-up, 447 participants (11.02%) experienced rapid decline in kidney function. In the fully adjusted multivariable logistic regression model, those in the highest NHHR group (T3) faced a 1.94-fold increased risk of rapid kidney function decline compared to the lowest NHHR group (T1) (OR=1.94,95%CI:1.46~2.61). As a continuous variable, each unit increase in NHHR is associated with a 1.21-fold increase in risk (OR=1.21, 95%CI:1.08~1.36). The restricted cubic spline analysis demonstrates a near logarithmic curve saturation effect between NHHR and rapid kidney function decline (P_nonlinearity<0.05). It is concluded that with NHHR ≤3.52, the risk increases as NHHR rises, while NHHR >3.52 marks a saturation point. In conclusion, among middle-aged and elderly individuals, a higher NHHR is linked to a greater risk of rapid decline in kidney function, displaying a nonlinear relationship.

Machine learning technology is a hot research topic at present. It is widely used in various prediction, recognition and classification tasks with its strong learning ability and high versatility. The application of machine learning in computational structural mechanics was discussed, with emphasis on its role in material property prediction, structural damage analysis, improvement of traditional methods, constitutive equation establishment and differential equation solving. Through literature review, the advantages of machine learning algorithms such as neural networks, support vector machines and random forests in improving computational efficiency and design process optimization were summarized. It is pointed out that the combination of machine learning and classical computing methods provides a new way to solve engineering problems. Future research will focus on algorithm optimization, model improvement and interdisciplinary technology integration.

The efficiency, precision, and automation of fruit and vegetable picking are realized through the integration of multiple mechanical arms in multi-mechanical arm cooperative picking technology, effectively addressing the high costs and low efficiency associated with traditional manual picking methods. The research progress in multi-mechanical arm cooperative picking technology was summarized, and the framework of the multi-mechanical arm cooperative picking system was comprehended. In light of the decision-making challenges in cooperative picking, the cooperative methods and task allocation within cooperative picking task planning was analyzed, and the collision detection, obstacle avoidance strategies, and path planning techniques utilized in cooperative picking with multiple robotic arms was reviewed. The future development direction of multi-mechanical arm cooperative picking technology is outlined, with a proposed development trend that envisions the combination of machine and agronomy, human-machine collaboration, decision-making big models, and multi-algorithm fusion.

Recently, the vessel-shaped aquacultural farm has received much attention from the academia and industry because of its importance in promotion and sustainability of marine fishery. Polluted surroundings unsuitable for fish growth are always introduced by traditional multiple point mooring system. Therefore, single point mooring system is more suitable for the aquacultural farm. In recent years, mooring scheme optimization and performance assessment was frequently investigated in most existing literature, while studies on design of the single point mooring system are rare. Based on the requirement of reducing hull reconstruction, an external turret single point mooring system was proposed, which is composed of the turret device, the mooring anchor, the truss structure and the bearings. And their working principle were also clearly illustrated. Then, three dimensional potential theory was applied to obtain the hydrodynamic coefficients of the ship by use of the software AQWA. Finally, in order to verify the positioning ability of the mooring system under both operational and survival conditions, the direction of wind, wave and current was combined according to the rules, and time-domain coupling analysis of the farm-mooring system was carried out. From the numerical results, it can be seen that yaw, relatively large sway and roll motions will be induced due to different direction of the wind, current and wave. The mooring line tension is adequate and safe under all conditions, indicating that this proposed single point mooring system exhibits good positioning performance.

The systematic construction of a biomechanical analysis of the full swing technique is considered essential to addressing the core issues and resolving technical problems from their root causes. Targeted special physical training is a powerful guarantee for the full utilization of technical skills. The research findings on the golf full swing technique were reviewed, its biomechanical characteristics were discussed and summarized. By further analyzing the strengths and weaknesses of full swing techniques in players of different genders and skill levels, rational recommendations for physical training were proposed, providing valuable insights for optimizing athletes' full swing techniques and enhancing the level of scientific training. During the full swing, the limbs follow the principle of proximal-to-distal motion, and muscle contractions adhere to the stretch-shortening cycle principle. Weight transfer is rationally adjusted based on specific swing patterns, and the terminal joint release effect is strengthened, contributing to the maximization of clubhead speed. In the downswing, the peak angular velocities of turnk and hip axial rotations, along with the timing and the peak speed of wrist release, are identified as the primary factors influencing clubhead speed. These factors also represent the key technical differences between male and female players. Strength and conditioning is regarded as a crucial pathway for improving full swing performance. Golfers are advised to focus on developing specific physical qualities, including upper limb muscle strength and explosiveness, lower limb muscle strength and explosiveness, as well as core stability and rotational power.

The explosive popularity of the new generation of artificial intelligence technologies will profoundly impact the risk experience of perceptual subjects within risk societies. Factor analysis and multiple indicators and multiple causes (MIMIC) model were used to study 12 risk scenarios of generative AI, besides four indicators reflecting the public's risk perception and five dimensions affecting the public's risk perception were explored. The results show that the public's perception of the risks of generative AI can be reflected by expectations of safety, technology, user and corporate regulatory. The public's risk perception is affected by its subjective evaluation of technology risks, macro risks, equity risks, subject risks and application risks, among which both equity risks and macro risks have the most significant impact. It shows that the public's risk perception of generative artificial intelligence is mainly characterized by “self-oriented” and “precautionary”. On this basis, the public's risk perception of generative artificial intelligence from the perspectives of history and culture, risk communication and technology governance was analyzed further, and corresponding countermeasures was put forward.

Considering the high-temperature thermosetting chemical issues in the manufacturing process of composite tensile armor layers, the curing kinetics of T700/epoxy prepregs were explored. Through differential scanning calorimetry (DSC) analysis and the Starink method, the autocatalytic reaction curing kinetic parameters were accurately calculated. Then a curing kinetic model was established. It has been shown by experimental results that the reaction rate of the prepreg is significantly increased at higher heating rates. After the peak is reached, the reaction rate is decreased more rapidly, resulting in a lower average final reaction heat. The apparent activation energy of the curing reaction for this prepreg is 77.04 kJ/mol, and high consistency with the experimental data is exhibited by the constructed curing kinetic model.

In order to address the issues of low accuracy and high missed detection rates in existing pavement crack detection algorithms, an improved pavement crack detection algorithm based on YOLOv8n, named YOLO-CD (YOLO-crack detection), has been proposed. The scale sequence feature fusion (SSFF) module and triple feature encoder (TFE) module from the ASF-YOLO architecture were utilized by the YOLO-CD algorithm to enhance the detection performance for multi-scale cracks and the perception capability of target features. Additionally, the coordinate attention(CA) mechanism was introduced at the end of the backbone network and in the neck network, with positional information embedded into channel attention, thereby strengthening the extraction capability of crack features. Furthermore, an additional P2 small object detection layer was added on top of the original three output layers of YOLOv8n, increasing the multi-scale receptive field of the network, allowing both global and local context information to be captured simultaneously, thereby improving the detection capability for small cracks in complex scenes. The original YOLOv8n detection head was replaced by the DyHead detection head, achieving the integration of scale, spatial, and task attention mechanisms, and further enhancing the network’s detection performance for cracks. Experimental results show that in the self-built PD-Dataset, the mAP50 of the improved YOLO-CD algorithm is increased by 4.1% compared to the original YOLOv8n algorithm. In the public dataset RDD2020, the mAP50 of the improved YOLO-CD algorithm is increased by 1.5% compared to the original YOLOv8n algorithm. Moreover, the algorithm’s detection speed is found to reach 89.9 frames/s, meeting the real-time requirements of pavement crack detection.

In order to solve the problem of automatic sorting of garbage, an intelligent sorting method for household garbage was designed based on the artificial intelligence computing platform Jetson NANO and YOLOv8 algorithm. Firstly, the lightweight YOLOv8 multi-target detection algorithm was used to classify garbage into four categories and achieve accurate recognition in the presence of occlusion. Secondly, the CoreXY coordinate transformation algorithm was used to determine the optimal angle for mechanical claw grasping and achieve precise garbage grasping. Finally, based on TensorRT optimization technology, the lightweight YOLOv8 multi-objective detection algorithm was deployed on the NANO hardware platform, completing the overall construction of the four major categories of garbage intelligent sorting system based on YOLOv8-NANO. The results show that in the self-made garbage test consisting of four major categories and a total of 13 sub categories, the sorting system can effectively achieve detection of single and multiple targets. The detection mAP0.5:0.95 value is 97.3%, and the target detection speed reaches 30.6 frames/s. The fastest garbage sorting speed could reach 6 pieces/min.

As essential components in power conversion modules, rectifiers are extensively utilized in power supply systems such as inverters, where their operational reliability directly influences the overall system performance. In order to enhance the reliability of rectifiers, it is critical to conduct lifespan predictions for sensitive components, particularly rectifier diodes. A predictive model was proposed that employs an improved grey wolf optimization (GWO) algorithm to optimize the hyperparameters of a simple recurrent unit (SRU) network. Initially, a power cycling accelerated aging test was performed on the diode, followed by an analysis of its characteristic parameters, with forward voltage drop identified as the primary aging indicator. Subsequently, the improved GWO algorithm was applied to optimize SRU hyperparameters—such as learning rate, number of hidden layers, and iteration count—thereby establishing a hybrid predictive model. Finally, the model was trained and validated using aging test data, with predictive accuracy compared against alternative models. The results show that the proposed model achieves superior predictive accuracy, and the data-driven predictive approach enhances the precision of diode lifespan estimation compared to conventional analytical modeling methods, thereby contributing to enhanced operational reliability of rectifiers.