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Cardiovascular disease (CVD) remains a major public health challenge in China, and dietary factors play a crucial role in its development. Understanding the trends and key dietary risk factors of diet-related CVD is essential for formulating effective prevention and control strategies. This study utilized data from the Global Burden of Disease (GBD) 2021 to conduct a comprehensive analysis of dietary risk factors associated with CVD in China over the period from 1990 to 2021. The analysis focused on various dietary components and their relationships with CVD-related mortality and disability-adjusted life years (DALYs). In 2021, the age-standardized mortality rate (ASMR) for diet-related CVD was 77.76 per 100,000 population, and the age-standardized DALY rate (ASDR) was 1,499.58 per 100,000 population. A diet high in sodium continued to be the leading dietary risk factor. Notably, diets high in processed meat, low in polyunsaturated fatty acids, low in whole grains, and high in sugar-sweetened beverages showed upward trends. Among these, the death and DALY rates associated with high sugar-sweetened beverage consumption demonstrated the most significant growth. The findings highlight the urgent need to implement ongoing preventive measures. Prioritizing strategies to reduce sodium intake, increase whole grain and fruit consumption, and limit the intake of processed meats and sugary beverages is crucial for mitigating the burden of CVD in China. Healthcare practitioners and policymakers can use these insights to develop targeted interventions and public health policies.

The purpose of this study was to ensure the compliance and safety of a robot’s movements during interactions with the external environment. This paper proposes a control strategy for learning variable impedance characteristics from multiple sets of demonstration trajectories. This strategy can adapt to the control of different joints by adjusting the parameters of the variable impedance control policy. Firstly, multiple sets of demonstration trajectories are aligned on the time axis using Dynamic Time Warping. Subsequently, the variance obtained through Gaussian Mixture Regression and a variable impedance strategy based on an improved Softplus function are employed to represent the variance as the variable impedance characteristic of the robotic arm, thereby enabling variable impedance control for the robotic arm. The experiments conducted on a self-designed robotic arm demonstrate that, compared to other variable impedance methods, the motion accuracy of the trajectories of joints 1 to 4 improved by 57.23%, 3.66%, 5.36%, and 20.16%, respectively. Additionally, a stiffness-variable segmented generalization method based on Dynamic Movement Primitive is proposed to achieve variable impedance control in various task environments. This strategy fulfills the requirements for compliance and safety during robot interactions.

Locomotion stability is essential for controlling quadruped robots and adapting them to unstructured terrain. We propose a control strategy with center-of-mass (CoM) dynamic planning for the stable locomotion of these robots. The motion trajectories of the swing legs are synchronized with the CoM of the robot. To implement the synchronous control scheme, we adjusted the swing legs to form a support triangle. The strategy is applicable to both static walk gait and dynamic trot gait. In the motion control processes of the robot legs, the distribution of the ground reaction forces is optimized to minimize joint torque and locomotion energy consumption. We also used an improved joint-torque controller with varied controller coefficients in the stance and swing phases. The simulation and experimental results demonstrate that the robot can complete omnidirectional locomotion in both walk and trot gaits. At a given locomotion speed, the stability margins for the robot during walking and trotting were 27.25% and 37.25% higher, respectively, than in the scheme without CoM planning. The control strategy with energy consumption optimization (ECO) reduced the energy consumption of the robot in walk and trot gaits by 11.25% and 13.83%, respectively, from those of the control scheme without ECO.

In this paper, a pseudostatic seismic coefficient evaluation method for slope dynamic stability analysis was explored with Yushu Airport Road 3# landslide as a typical engineering case, and the shaking table test and numerical calculation were performed during the exploration. The loading waveform was selected as Yushu wave, and the acceleration time-history of seismic waves was measured and analyzed, revealing the failure mode of slopes. Based on the rigid-body limit equilibrium theory, the instantaneous additional seismic forces of each block and the time-history landslide stability coefficient were calculated. According to the time-history of the landslide, dynamic stability coefficients were calculated. Subsequently, we proposed a pseudostatic seismic coefficient evaluation method and discussed the seismic coefficient slope dynamic stability analysis. The results showed that as the vibration frequency rose, the average acceleration and the residual displacement of the slope decreased, but the slope grew more dynamically stable. With the proposed method, we calculated the period of slope seismic action to be 0.126 s and the average maximum acceleration to be 0.156 g, which was close to the designed ground motion acceleration of 0.15 g. Besides, we calculated the safety factor of landslides under earthquake to be 0.93∼0.97, which was close to that obtained from the building code method and in accordance with the present seismic deformation and failure mode of landslides. Moreover, the results obtained from the method of nuclear power plant specification were relatively small compared to other specification methods. The research is significant because it provides a new idea for the evaluation of seismic landslide stability in practical engineering.

Adulteration of high-value oils such as olive and camellia oil poses serious challenges to market integrity and consumer safety. This study develops a comprehensive, model-free marker library for high-throughput detection of single and multivariate adulteration across nine vegetable oils (olive, camellia, sesame, rapeseed, flaxseed, soybean, peanut, industrial hemp seed, and sunflower seed oils) using untargeted metabolomics via UHPLC-Q-TOF-MS. We identified 34 characteristic markers, including 9 confirmed by reference standards, such as hydroxytyrosol in olive oil, camelliasaponins in camellia oil, and sesamin in sesame oil, which are uniquely present in specific oils and absent in others. The method enables reliable qualitative screening of adulteration at levels as low as 5% without dependence on chemometric models. Validation using binary and multicomponent blends confirmed its robustness and specificity. In commercial sample analysis, adulteration was detected in 16.0% of olive oils (4/25) and 12.7% of camellia oils (7/55), with results consistent with regulatory findings. This work establishes the first integrated marker library for simultaneous screening of nine vegetable oils, offering a standardized, high-throughput tool for large-scale market surveillance that bridges the gap between discovery-based omics and routine regulatory practice.

Aims. We investigated the changes of retinal structure in normal glucose tolerance (NGT), impaired glucose tolerance (IGT), diabetes mellitus (DM), and diabetic kidney disease (DKD) stages in Otsuka Long-Evans Tokushima Fatty (OLETF) rats. Methods. We assigned OLETF rats to four groups based on their OGTT results and 24 h urinary microalbumin (24 h UMA) levels: NGT, IGT, DM, and DKD groups. We observed the structural and the corresponding pathological changes and quantified the expression of HIF-1α, iNOS, NF-κB, VEGF, ICAM-1, and occludin in the retina. Results. Significant damage to the retinal structure, especially in retinal ganglion cells (RGCs), was observed in the IGT stage. The expression of HIF-1α, iNOS, NF-κB, VEGF, and ICAM-1 was significantly upregulated, while that of occludin was downregulated. Conclusion. Significant retinal neuropathy occurs in the IGT stage. Inflammation and hypoxia may damage the blood retina barrier (BRB), leading to diabetic retinopathy.

PurposeRobotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.Design/methodology/approachThis study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.FindingsExperiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.Originality/valueA new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

Reliable foot-to-ground contact state detection is crucial for the locomotion control of quadruped robots in unstructured environments. To improve the reliability and accuracy of contact detection for quadruped robots, a detection approach based on the probabilistic contact model with multi-information fusion is presented to detect the actual contact states of robotic feet with the ground. Moreover, a relevant control strategy to address unexpected early and delayed contacts is planned. The approach combines the internal state information of the robot with the measurements from external sensors mounted on the legs and feet of the prototype. The overall contact states are obtained by the classification of the model-based predicted probabilities. The control strategy for unexpected foot-to-ground contacts can correct the control actions of each leg of the robot to traverse cluttered environments by changing the contact state. The probabilistic model parameters are determined by testing on the single-leg experimental platform. The experiments are conducted on the experimental prototype, and results validate the contact detection and control strategy for unexpected contacts in unstructured terrains during walking and trotting. Compared with the body orientation under the time-based control method regardless of terrain, the root mean square errors of roll, pitch, and yaw respectively decreased by 60.07%, 54.73%, and 64.50% during walking and 73.40%, 61.49%, and 61.48% during trotting.

Objective: To establish a method for the simultaneous determination of seven alkaloids in Semen Nelumbinis and its products, the extraction technology and HPLC method were optimized by the single factor experiment. Methods: Firstly, the samples were extracted ultrasonically with 1% formic acid ethanol and purified by PXC SPE column. Then, the extracts and the purified liquid were taken after concentration with nitrogen for quantitative analysis of seven alkaloids by HPLC method. Next, the contents of alkaloids in five samples were determined. Results: The method was fully validated and the result showed that seven kinds of alkaloids had good linear relation in the corresponding range of mass concentration, r2 > 0.999, where the detection limit was 0.5–1.5 mg/kg, the quantification limit was 1.25–4.5 mg/kg, the recovery was 83.33–116.04%, and the RSD of detection method was 1.06–5.25% (n = 7). In five samples, the contents of Lotusine and Neferine were the highest, Berberine Hydrochloride was not detected. Conclusion: This method is simple, sensitive, accurate and reproducible, and it can realize the quantitative analysis and chemical separation of seven kinds of common alkaloids in Semen Nelumbinis and its products and provide a theoretical method for the simultaneous determination of alkaloids. The extraction yields of alkaloids in Semen Nelumbinis can be increased through the extraction process, which is optimized by a single factor experiment.

This study evaluates the capability of Vision-Language Models (VLMs) in image data annotation by comparing their performance on the CelebA dataset in terms of quality and cost-effectiveness against manual annotation. Annotations from the state-of-the-art LLaVA-NeXT model on 1000 CelebA images are in 79.5% agreement with the original human annotations. Incorporating re-annotations of disagreed cases into a majority vote boosts AI annotation consistency to 89.1% and even higher for more objective labels. Cost assessments demonstrate that AI annotation significantly reduces expenditures compared to traditional manual methods -- representing less than 1% of the costs for manual annotation in the CelebA dataset. These findings support the potential of VLMs as a viable, cost-effective alternative for specific annotation tasks, reducing both financial burden and ethical concerns associated with large-scale manual data annotation. The AI annotations and re-annotations utilized in this study are available on https://github.com/evev2024/EVEV2024_CelebA.