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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]

Efficiency evaluation is crucial for providing decision support in both target setting and competency recognition. In many practical applications, flexible measures that can be classified as either input or output often lead to inconsistent results due to distinct classifications. Conventional classification methods may identify many efficient decision making units (DMUs), which cannot provide us with the best alternative and reduces the discriminating power of associated models. In this study, we develop a novel integrated approach by identifying the single efficient DMU considering flexible measures. Specifically, our proposed approach guarantees the feasibility of the associated efficiency estimation model under relatively mild conditions. Moreover, the weight scheme and non-dominated classification result can be uniquely determined via a revised min-max computational procedure. We validate and illustrate the proposed approach by applying it to evaluate the efficiencies of Chinese commercial banks before and during the pandemic. Our findings suggest that the pandemic does have an impact on bank efficiency, and different banks respond differently to the pandemic shock. More specifically, the best-performing bank that stands out before and during the pandemic can be identified as a unique benchmark, and the others could learn from this alternative to mitigate the pandemic shock. [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]

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]

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]

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]

Identifying the sources of profit inefficiency is of great importance to practitioners and academics. In this paper, we tend to uncover profit drivers from the perspective of resource allocation. Specifically, we focus particularly on the DEA-based resource allocation approach with a possible financing strategy, which is significantly different from prior studies in the sense that it not only offers a flexible way to tackle the infeasibility problem associated with the potential conflicting constraints, but also provides a relatively more profitable allocation plan by taking a possible debt into account. First, we consider the situation in which each of DMUs allocates their resources independently, and develop a corresponding decentralised resource allocation DEA model concerning a possible financing strategy. In particular, a novel concept of profit to financing is defined to establish the linkage between potential profit gain and possible financing strategy. Second, we propose a centralised resource allocation DEA model to solve the problem in which all DMUs are controlled by a central authority, and provide a novel aggregate profit inefficiency decomposition method to uncover sources of the aggregate (or industry) profit inefficiency. Finally, an example of 20 fast-food restaurants is applied to illustrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Silencing the silencing suppressor genes of viruses by RNA interference (RNAi) has been demonstrated to be an efficient way to combat viral diseases in plants.Citrus tristeza virus(CTV) has three silencing suppressor genes,p20, p23, andp25. To silence them, the splicing by overlap extension by PCR (SOE-PCR) was applied in this study for construction of two RNAi vectors, pCAMBIA2301G-hp2520 (to targetp25andp20) and pCAMBIA2301G-hp252023 (to targetp25,p20andp23). FollowingAgrobacterium-mediated transformation, seven and one independent sweet orange transgenic lines were obtained for expressing the fusion transgenes,hp2520(hairpin RNA ofp25andp20) andhp252023(hairpin RNA ofp25, p20andp23), respectively. Challenging the transgenic plants by graft-inoculation with severe stem-pitting CTV strain CT14A showed that no transgenic line was immune to the strain. However, lower to much lower CTV titer was detected in two lines ofhp2520and the only line ofhp252023. Vegetative progenies of thehp252023-expressing line showed no stem-pitting symptoms 2 years post CT14A inoculation, although a low level of CT14A was still detectable in their leaves. Our results show that expressing the hpRNAs of CTV silencing suppressor genes conferred sweet orange with high tolerance to stem-pitting CTV. [ABSTRACT FROM PUBLISHER]

The electronic power transformer has many advantages, such as reactive power compensation, voltage regulation, harmonic suppression, and power factor correction. When a three-phase electronic power transformer with a star configuration is applied in the distribution network, three-phase load unbalance will cause three-phase DC voltage unbalance if the electronic power transformer controller generates improper asymmetric voltage signals, which may trigger over-voltage protection. The effects of three-phase load unbalance to the work condition of the electronic power transformer are analyzed, and a control algorithm is proposed to balance three-phase DC voltages in this article. Meanwhile, input stage control, isolation stage control, output stage control, and individual DC voltage balancing control are presented. Simulation and lab experimental results show that the electronic power transformer still works properly even under serious unbalanced load conditions. [ABSTRACT FROM AUTHOR]