Capacitated Vehicle Routing Problem (CVRP) is a critical challenge in logistics optimization, which directly impact operational costs and service efficiency. While quantum differential evolution (QDE) algorithm offers potential advantages in solving combinatorial optimization problems, its application in CVRP is still limited due to the premature convergence, poor search capability and stagnation. To address these limitations, a novel multi-strategy QDE algorithm with cooperative co-evolution (CC) framework and hybrid local search strategy, namely MSCFLQDE is proposed to effectively solve the CVRP. Firstly, a new multi-population strategy with CC framework is designed to solve each sub-CVRP for enabling parallel optimization and preserving global constraints. Then an adaptive differential mutation mechanism is developed to balance the exploration and exploitation and accelerate the convergence. Thirdly, a new quantum rotation mode with the sorting coding rule is designed to adjust the search direction and reduce stagnation. In the later stage, a hybrid local search strategy is proposed to dynamically eliminate the redundant nodes and intersections. Finally, the experiment results on the five CVRPs (set A, set B, set P, set E, and set G) demonstrate that the MSCFLQDE has better search ability, higher convergence and stronger stability by comparing with the state-of-the-art algorithms(such as CCDE, CCDE-D, CCDE-R, CCDE-S, HGS, BILA, AGA-ES and TAMLS and so on), which achieves 5.53% shorter distances for P51_K10 by comparing with AGA-ES.

MOTIVATION: Unsupervised clustering of single-cell RNA sequencing (scRNA-seq) data holds the promise of characterizing known and novel cell type in various biological and clinical contexts. However, intrinsic multi-scale clustering resolutions poses challenges to deal with multiple sources of variability in the high-dimensional and noisy data. RESULTS: We present ClusterMatch, a stable match optimization model to align scRNA-seq data at the cluster level. In one hand, ClusterMatch leverages the mutual correspondence by canonical correlation analysis and multi-scale Louvain clustering algorithms to identify cluster with optimized resolutions. In the other hand, it utilizes stable matching framework to align scRNA-seq data in the latent space while maintaining interpretability with overlapped marker gene set. Through extensive experiments, we demonstrate the efficacy of ClusterMatch in data integration, cell type annotation, and cross-species/timepoint alignment scenarios. Our results show ClusterMatch's ability to utilize both global and local information of scRNA-seq data, sets the appropriate resolution of multi-scale clustering, and offers interpretability by utilizing marker genes. AVAILABILITY AND IMPLEMENTATION: The code of ClusterMatch software is freely available at https://github.com/AMSSwanglab/ClusterMatch.

Precisely calling chromatin loops has profound implications for further analysis of gene regulation and disease mechanisms. Technological advances in chromatin conformation capture (3C) assays make it possible to identify chromatin loops in the genome. However, a variety of experimental protocols have resulted in different levels of biases, which require distinct methods to call true loops from the background. Although many bioinformatics tools have been developed to address this problem, there is still a lack of special introduction to loop-calling algorithms. This review provides an overview of the loop-calling tools for various 3C-based techniques. We first discuss the background biases produced by different experimental techniques and the denoising algorithms. Then, the completeness and priority of each tool are categorized and summarized according to the data source of application. The summary of these works can help researchers select the most appropriate method to call loops and further perform downstream analysis. In addition, this survey is also useful for bioinformatics scientists aiming to develop new loop-calling algorithms.

Background articles are crucial for readers to grasp the context of news stories fully. However, existing approaches of background article search tend to apply a single ranking method to all types of search topics. In this paper, we focus on exploring search topics on news articles by classifying them into two types:time-sensitive andnon-time-sensitive. To verify whether or not these two types of search topics can benefit from different retrieving methods, we examined a suite of strategies such as document expansion, query rewriting, and semantic re-ranking. Moreover, the relationship between background articles and topics is verified by the two strategies of document expansion (specificity and diversity). The experimental results demonstrate that the optimal usage of the aforementioned strategies is indeed different between the two types of search topics. Furthermore, our in-depth analysis of topics and search results verified that: time-sensitive topics benefit from background articles that can provide more specific knowledge, while non-time-sensitive topics benefit from diversified retrieved documents.

Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.

Static synchronous compensator combined with battery energy storage system-STATCOM/BESS, can regulate four-quadrant active and reactive power, which is an ideal scheme to solve problems of wind farm integration. Multilevel converter is the key technology for STATCOM/BESS. The advantages of cascaded multilevel converter are analyzed and the structure of cascaded STATCOM/BESS is described. Hybrid power control strategy is proposed to compensate active and reactive power of wind farm comprehensively. The reference current of STATCOM/BESS is determined according to the requirements of active power smoothness and voltage control. The control strategy can coordinate charge or discharge of batteries with reactive power compensation of STATCOM, and balance the batteries capacity of H-bridges. The proposed control strategy is validated by simulation on the wind power system using cascaded STATCOM/BESS as the compensation device. Simulation results show that the cascaded STATCOM/BESS can effectively improve the characteristics of wind farm integration and provide dynamic support to the grid.

Control and operation of a DC microgrid under various operating conditions are investigated in this paper. A wind turbines based DC microgrid configuration is used to accurately describe different operation modes firstly. For a DC mcrogrid system, an abnormal DC voltage caused by power fluctuations can disrupt normal operation or even cause the whole system to collapse. Therefore, to improve the system stability, this paper proposes a hierarchical coordinated control strategy according to the DC voltage variation range. Under any condition, there must be at least one DC terminal being responsible for DC voltage control. On this basis, the control method for each power electronic converter is described under different control levels. Finally, the validity of the proposed hierarchical control strategy is demonstrated by simulations on a DC microgrid with wind turbines using MATLAB/Simulink in different operation modes, considering various operating conditions, such as variations of wind speed and load, AC grid fault, and load shedding.