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ABSTRACT Background Non–small cell lung cancer (NSCLC) is one of the cancers with the highest mortality and morbidity in the world. Circular RNAs (circRNAs) are newly identified players in carcinogenesis and development of various cancers. This study is aimed at exploring the functional effects and mechanism of circ_0028826 in the development of NSCLC. Methods Real‐time quantitative PCR (RT‐qPCR) was used to detect the expression levels of circ_0028826, IDH2 mRNA, and miR‐758‐3p. IDH2, Bcl2, Bax, and E‐cadherin protein levels were detected using a western blot. Cell Counting Kit‐8 (CCK‐8), 5‐ethynyl‐2′‐deoxyuridine (EdU), flow cytometry, wound healing, and transwell assays were used to assess the capacities of proliferation, apoptosis, migration, and invasion. Interaction between miR‐758‐3p and circ_0028826 or IDH2 was validated using a dual‐luciferase reporter assay. The role of circ_0028826 in vivo was checked based on a xenograft tumor model. Results Circ_0028826 was elevated in NSCLC, and its absence inhibited NSCLC cell proliferation, migration, invasion, and induced apoptosis. In terms of mechanism, circ_0028826 increased IDH2 expression by targeting miR‐758‐3p. In addition, circ_0028826 knockdown also regulated IDH2 by targeting miR‐758‐3p to inhibit tumor growth in vivo. Conclusion Circ_0028826 promoted the development of NSCLC via regulation of the miR‐758‐3p/IDH2 axis, providing a new strategy for NSCLC treatment.

Taking Chinese startups backed by venture capital (VC) in 1997–2017 as the sample, this study investigates the impact of VC background on chief executive officer (CEO) replacement in portfolio companies. The results show that (1) compared to foreign VC, domestic VC is more likely to replace the CEO of the portfolio companies. (2) Syndicate with domestic VC can overcome the disadvantage of foreign VC geographically distant from the portfolio companies, and domestic VC as coinvestors can effectively monitor portfolio companies, increasing the possibility of CEO replacement. Heterogeneity analysis shows that the positive effect of VC background on CEO replacement exists in the subgroup of VC geographically proximate to the portfolio companies, indicating that geographic proximity to the portfolio companies helps VC more easily grasp the development of the portfolio companies and more likely to replace CEO. This paper reveals the differences in the behavior of VC in replacing CEO during the postinvestment management process, highlights the critical role of geographical proximity, and provides important management insights for VC and entrepreneurs.

Target threat assessment is a key issue in the collaborative attack. To improve the accuracy and usefulness of target threat assessment in the aerial combat, we propose a variant of wavelet neural networks, MWFWNN network, to solve threat assessment. How to select the appropriate wavelet function is difficult when constructing wavelet neural network. This paper proposes a wavelet mother function selection algorithm with minimum mean squared error and then constructs MWFWNN network using the above algorithm. Firstly, it needs to establish wavelet function library; secondly, wavelet neural network is constructed with each wavelet mother function in the library and wavelet function parameters and the network weights are updated according to the relevant modifying formula. The constructed wavelet neural network is detected with training set, and then optimal wavelet function with minimum mean squared error is chosen to build MWFWNN network. Experimental results show that the mean squared error is , which is better than WNN, BP, and PSO_SVM. Target threat assessment model based on the MWFWNN has a good predictive ability, so it can quickly and accurately complete target threat assessment.

A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.