Controlling factors of heavy metal(loid) accumulation in rice: Main and interactive effects.

Identifying the key determinants of heavy metal(loid) accumulation in rice and quantifying their contributions are critical for precise prediction of heavy metal(loid) concentrations in rice and the formulation of effective pollution control strategies. The accumulation of heavy metal(loid)s in rice can be influenced by both natural and anthropogenic factors, which may interact with each other. However, distinguishing the independent roles (main effects) from interactive effects and quantifying their impacts separately pose challenges. To address this knowledge gap, we employed TreeExplainer-based SHAP and random forest algorithms in this study to quantitatively estimate the primary influencing factors and their main and interactive effects on heavy metal(loid)s in rice. Our findings reveal that soil cadmium (SCd) and rice cultivation time (C_TIME) were the primary contributors to rice cadmium (RCd) and rice arsenic (RAs), respectively. Soil lead (SPb) and sampling distances from roads significantly contributed to rice lead (RPb). Additionally, we identified significant interactive effects of SCd and C_TIME, C_TIME and RCd, and RCd and rice variety on RCd, RAs, and RPb, respectively, emphasizing their significance. These insights are pivotal in improving the accuracy of heavy metal(loid) concentration predictions in rice and offering theoretical guidance for the formulation of pollution control measures.

Multi-Objective Optimization of a Long-Stroke Moving-Iron Proportional Solenoid Actuator.

In this study, the performance of a long-stroke moving-iron proportional solenoid actuator (MPSA) was improved by combining numerical simulations and experiments. A finite element model of the MPSA was developed; its maximum and mean relative absolute errors of electromagnetic force were 4.3% and 2.3%, respectively, under typical work conditions. Seven design parameters including the cone angle, cone length, depth of the inner hole of the coil skeleton, cone width of the armature, inner cone diameter, and initial position of the moving-iron core were selected for developing the model, and the coefficient of the variation in electromagnetic force, nominal acceleration, 95% of the maximum stable output electromagnetic force, and corresponding response time were used as the performance indicators. The constraint relation between each performance indicator and the influence of each design parameter on the performance indicators were revealed using the uniform Latin hypercube experiment design, correlation analysis, and the main effect analysis method. A multi-objective optimization mathematical model of the MPSA was developed by combining traditional surrogate and machine learning models. The Pareto solution set was obtained using the nondominated sorting genetic algorithm II (NSGA-II), and three decision schemes with different attitudes were determined using the Hurwicz multi-criteria decision-making method. The results showed that a strong contradiction exists among the 95% of the maximum stable output electromagnetic force and its corresponding response time and the coefficient of the variation in electromagnetic force. The cone angle considerably influenced the performance indicators. Compared with the initial design, the coefficient of the variation in electromagnetic force was reduced by 54.08% for the positive decision, the corresponding response time was shortened by 15.65% for the critical decision, and the corresponding acceleration was enhanced by 10.32% for the passive decision. Thus, the overall performance of the long-stroke MPSA effectively improved.