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Latin hypercube design (LHD) is one of the most frequently used sampling methods. However, most LHDs generate data samples in a manner that hinders computational efficiency and space-filling performance when high dimensions and large samples are involved. Therefore, a sequential recursive evolution Latin hypercube design (RELHD) is proposed in this article, which adopts a permutation inheritance algorithm to update and optimize the LHD. A recursive split algorithm is also proposed and used to enhance the computational efficiency by dividing the sample set into smaller subsets. Numerical experiments demonstrate that the space-filling quality of the RELHD compares well with the enhanced stochastic evolutionary algorithm (ESE) in complex problems with large samples and high dimensions, with RELHD having a significantly higher computational efficiency than ESE. Finally, the sequential approach of RELHD proves to be a more efficient strategy when dealing with sampling-based analysis problems. [ABSTRACT FROM AUTHOR]

Currently, the occurrence of toxic levels of metals in soils is a serious environmental issue worldwide. Phytoremediation is getting much attention to control metals soil pollution because it is economic and environmentally friendly. However, the methods used to detect metals in plants are not uniform and have depicted poor comparability of the research investigations. Therefore, the present overview is designed to discuss the possible chemical forms of metals in various environmental matrixes and the detection methods employed to identify the chemical forms of metals in plants. Moreover, the in situ and indirect methods to detect metals in plants have also been discussed herein. In addition, the pros and cons of the available techniques have also been critically analyzed and discussed. Finally, key points/challenges and future perspectives of these methods have been highlighted for the scientific community. Novelty statement In the current review, the possible chemical forms of metals in various environmental matrixes are discussed in detail. Various extraction agents and their efficiency for extracting metals from plants have been clearly illustrated. Further, all the available methods for analyzing the chemical forms of metals in plants have been compared. [ABSTRACT FROM AUTHOR]

Aircraft optimization design problems are mostly computationally intensive. These complicated problems probably contain mixed-variables, while most research has focused on continuous variables. This article sets up a mixed-variable surrogate-based optimization algorithm framework that includes a mixed-variable experiment design method and an inaccurate infilling method. The mixed-variable experiment design method combines improved successive local enumeration with enhanced stochastic evolution to deal directly with discrete variables. The inaccurate infilling method tends to find points with better fitness value and relatively low sample density so as to balance exploration and exploitation. Several numerical functions and a mixed-variable solid rocket motor performance matching design problem are solved using the modified surrogate-based optimization method. The results indicate that the proposed method is competitive compared with other heuristic algorithms and surrogate-based algorithms, and can deal with mixed-variable aircraft design problems effectively. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is regarded as one of the most effective techniques for supervised learning, while the Gaussian kernel SVM is widely utilized due to its excellent performance capabilities. To ensure high performance of models, hyperparameters, i.e. kernel width and penalty factor must be determined appropriately. This paper studies the influence of hyperparameters on the Gaussian kernel SVM when such hyperparameters attain an extreme value (0 or ∞). In order to improve computing efficiency, a parameter optimization method based on the local density and accuracy of Leave-One-Out (LOO) method are proposed. Kernel width of each sample is determined based on the local density needed to ensure a higher separability in feature space while the penalty parameter is determined by an improved grid search using the LOO method. A comparison with grid method is conducted to verify validity of the proposed method. The classification accuracy of five real-life datasets from UCI database are 0.9733, 0.9933, 0.7270, 0.6101 and 0.8867, which is slightly superior to the grid method. The results also demonstrate that this proposed method is computationally cheaper by 1 order of magnitude when compared to the grid method. [ABSTRACT FROM AUTHOR]

Sequential Approximate Optimization (SAO) has been widely used in engineering optimization design problems to improve efficiency. The infilling strategy is one of the critical techniques of the SAO, which is of paramount importance to the surrogate model accuracy and optimization efficiency. In this paper, an adaptive parallel infill strategy for surrogate-based single-objective optimization is proposed within a multi-objective optimization framework to balance exploration and exploitation during the optimization process. Within this method, an inaccurate Pareto Front is adopted to assist the infilling of the sampling points. The proposed SAO method with its adaptive parallel sampling strategy is tested on several numerical test cases and an engineering test case with the optimization results compared to state-of-the-art optimization algorithms. The results show that the proposed SAO with the adaptive parallel sampling strategy possesses excellent performance and better stability. [ABSTRACT FROM AUTHOR]

In this study, a novel extraction and safety evaluation method for heavy metals based on different functions of plants was proposed, and an edible plant (pea) was used as the research material to explore the feasibility of the novel method. Pea sprouts were cultured in cadmium (Cd) concentrations of 0, 1.0, 3.0, and 5.0 mg L−1, respectively. The Cd in pea sprouts was continuously extracted with 100 °C distilled water, 60% ethanol, 6% acetic acid, and simulated gastric juice. It was observed that highest amount of Cd (48.65–58.87%) was found in the extraction of roots with 6% acetic acid, followed by 100 °C distilled water (28.68–37.61%). While in stems, most of the Cd (70.73–85.39%) was extracted by 6% acetic acid. The recovery rate of the sequential chemical extraction technique employed in this experiment was between 93 and 106%. Compared with traditional methods, this study has its development potential in two aspects. First, it can determine which steps of sequential extractions of heavy metals in plants are the most harmful to humans. Secondly, corresponding measures can be taken to reduce heavy metals in vegetables used daily, such as soaking edible vegetables in vinegar for a short time. Novelty statement: In this study, a novel extraction and safety evaluation method for heavy metals based on different functions of plants was proposed, and an edible plant (pea) was used as the research material to explore the feasibility of the novel method. Compared with the commonly used extraction methods, the novel method is more reasonable and has greater development potential. [ABSTRACT FROM AUTHOR]