Basic Cluster Research Articles

Structures of importin-α bound to the wild-type and an internal deletion mutant of the bipartite nuclear localization signal of HIF-1α

Hypoxia-inducible factor 1 (HIF-1) is a heterodimeric transcription factor that plays an important role as a master regulator of oxygen homeostasis. The activity of HIF-1 is regulated in part by dynamic intracellular trafficking of its α subunit (HIF-1α) that can shuttle between the nucleus and cytoplasm. It has been shown that nuclear localization of HIF-1α requires a variant of classic nuclear localization signal (NLS) and that an internal deletion of the amino acid residues (residues 724–751) in the NLS almost abolish the nuclear localization. Here we report the X-ray crystal structure of the nuclear import adaptor importin-α1 bound to the wild-type HIF-1α NLS at 1.8 Å resolution and of importin-α1 bound to the Δ724-751 mutant of the HIF-1α NLS at 1.9 Å resolution. In the wild-type structure, two basic clusters in the HIF-1α NLS made extensive interactions with importin-α1 on two sites (the major site and the minor site). In the mutant structure, the NLS residues still interacted extensively with the major site on importin-α1, but the interactions with the minor site were not observed. The structural data, together with computational analyses of binding free energies, indicate that the loss of the minor-site interactions inhibit nuclear accumulation of HIF-1α.

Methods for stable multimeric molecular dynamics simulation and modeling of cardiac SK2 channel function.

Small conductance Ca2+-activated K+ (SK) channels are unique since they are gated solely by changes in intracellular Ca2+ and are known to be upregulated in heart failure. SK channels are regulated via the ubiquitous Ca2+ sensing protein, calmodulin (CaM). Currently there are no structures for SK1-3 channels bound to CaM, thus, significant need for a method to generate stable SK channel models to assess conduction and membrane interactions exists. Recent cryo-EM structures with nearly complete tetrameric SK4-CaM complex (PDB: 6CNM, 6CNN, 6CNO) allows the generation of nearly full-length SK2-CaM homology models (SK2-CaM). Molecular dynamic simulations (MD) performed on SK2-CaM models generated with established methods, however, critical loops and subunits were unstable due to multimeric and size of complex. We have developed a method for each state of SK4-CaM to be used as an input with output being 1μs+ stable MD simulation ofSK2-CaM complex. This process includes many novel approaches using Rosetta, NAMD, and Amber along with specific regional restraints during specific steps; the procedure can differ for various states. MD designed to assess stability and conduction of the relevant SK4-CaM PDBs listed above and comparing results to the similar MD simulations of the final SK2-CaM models, we propose potential mechanisms for ion conduction in SK2 and SK4. In addition, basic residue clustering was detected near the lower leaflet headgroup of the membrane, implying anionic lipid binding locations. SK2-CaM was also stable in all 3 tested states with POPC membrane and varying concentrations of anionic lipids, allowing for the exploration of binding/interacting mechanisms. Using our expertise on the SK2-CaM complex as a template, we propose a new protocol that can be used to create stable MD simulations of homology models for SK1-3 using SK4 as an input.

Fitness function X-means for prolonging wireless sensor networks lifetime

<span lang="EN-US">X-means and k-means are clustering algorithms proposed as a solution for prolonging wireless sensor networks (WSN) lifetime. In general, X-means overcomes k-means limitations such as predetermined number of clusters. The main concept of X-means is to create a network with basic clusters called parents and then generate (</span><em><span lang="EN-US">j</span></em><span lang="EN-US">) number of children clusters by parents splitting. X-means did not provide any criteria for splitting parent’s clusters, nor does it provide a method to determine the acceptable number of children. This article proposes fitness function X-means (FFX-means) as an enhancement of X-means; FFX-means has a new method that determines if the parent clusters are worth splitting or not based on predefined network criteria, and later on it determines the number of children. Furthermore, FFX-means proposes a new cluster-heads selection method, where the cluster-head is selected based on the remaining energy of the node and the intra-cluster distance. The simulation results show that FFX-means extend network lifetime by 11.5% over X-means and 75.34% over k-means. Furthermore, the results show that FFX-means balance the node’s energy consumption, and nearly all nodes depleted their energy within an acceptable range of simulation rounds. </span>

Structure and productivity of the phytoplankton community in the southwestern Kara Sea in early summer

As a result of research conducted in July 2018 in the southwestern part of the Kara Sea, it was revealed that during the period of the beginning of the seasonal ice shift, the pelagic phytocen was at the stage of ice/marginal bloom, which was realized within a single spatiotemporal field. Distinctive features of the organization of the community of pelagic microalgae were a characteristic set of dominants and a mosaic structure of the pelagial. The mosaic pattern of marine phytoplankton is determined by the interaction between physical environmental factors and the biological activity of microalgae populations. In this case, the pelagic phytocene was a network of short-lived basins limited by local frontal sections with the predominant development of individual species/species complexes against the background of the functioning of the basic cluster of early spring cryophilic diatoms.

Consensus graph learning-based self-supervised ensemble clustering

目的 随着实际应用场景中海量数据采集技术的发展和数据标注成本的不断增加，自监督学习成为海量数据分析的一个重要策略。然而，如何从海量数据中抽取有用的监督信息，并该监督信息下开展有效的学习仍然是制约该方向发展的研究难点。为此，提出了一个基于共识图学习的自监督集成聚类框架。方法 框架主要包括3个功能模块。首先，利用集成学习中多个基学习器构建共识图；其次，利用图神经网络分析共识图，捕获节点优化表示和节点的聚类结构，并从聚类中挑选高置信度的节点子集及对应的类标签生成监督信息；再次，在此标签监督下，联合其他无标注样本更新集成成员基学习器。交替迭代上述功能块，最终提高无监督聚类的性能。结果 为验证该框架的有效性，在标准数据集（包括图像和文本数据）上设计了一系列实验。实验结果表明，所提方法在性能上一致优于现有聚类方法。尤其是在MNIST-Test （modified national institute of standards and technology database）上，本文方法实现了97.78%的准确率，比已有最佳方法高出3.85%。结论 该方法旨在利用图表示学习提升自监督学习中监督信息捕获的能力，监督信息的有效获取进一步强化了集成学习中成员构建的能力，最终提升了无监督海量数据本质结构的挖掘性能。;Objective Clustering is focused on machine learning-related data segmentation for multiple datasets. Its applications are in relevant to such domains like image segmentation and anomaly detection. In addition，to simplify complex tasks optimize its performance，clustering is used in data preprocessing tasks of those are data sub-blocks segmentation， pseudo-labels generation，and abnormal points-removal. Self-supervised learning has become an essential technique for massive data analysis. However，it is challenged to extract effective supervision information and analyze the input data. Method A consensus graph learning based self-supervised ensemble clustering（CGL-SEC）framework is developed. It consists of three main modules：1）to construct the consensus graph based on several ensemble components（i. e. ，the basic clustering methods）. 2）to extract the supervision information by learning the consensus graph representation，and 3）its node clustering results，where the subset of nodes with the high-confidence are selected as labeled samples. To optimize the ensemble components and the corresponding consensus graph，t basic clustering methods are re-trained in related the option of samples-labeled and other samples-unlabeled. The final clustering results can be optimized iteratively until the learning process converges. Result A series of experiments are carried out on benchmarks，including both image and textual datasets. Especially，CGL-SEC is 3. 85% over baseline in terms of clustering evaluation metric on themodified national institute of standards and technology database（MNIST-Test）. First，to optimize data representation and cluster assignment at the same time，deep embedding clustering can be focused on data itself as the supervision information and auto-encoder with the reconstruction loss is pre-trained. The soft cluster assignment of features-embedded is then calculated，and the KL（Kullback-Leibler）divergence is minimized between the soft cluster assignment and the auxiliary target distribution. To improve the performance of the model further，following deep clustering network（DCN）can use hard clustering instead of soft allocation，and local constraints are applied by improved deep embedding clustering（IDEC）. The pseudo-label strategy is implemented as a self-supervised learning method that uses the prediction results of the neural network as the label to simulate the supervision information compared to using data itself as the supervision information. Deepcluster-based K-means clustering is used to generate pseudo-labels to guide the training of convolutional networks. However，the generated pseudo-labels have lower confidence and are prone to trivial solutions in the initial stage of network training. Deep embedding clustering with data augmentation（DEC-DA）and MixMatch-based prediction of data-enhanced samples are used as the supervision information of the original data，which improves the accuracy of the supervision information to a certain extent，but this method is difficult to extend to text and other fields. Deep adaptive clustering-based high-confidence pseudo-label subsets-selected are iteratively trained the network in the prediction results，but lowconfidence samples-involved data distribution information is ignored. Pseudo-semi-supervised clustering votes are used to select a subset of high-confidence pseudo-labels，and all samples are used to train semi-supervised neural network. Although the ensemble strategy can improve the confidence of the pseudo-label，the voting strategy is concerned of category representation only without the feature representation of the sample itself，which can reduce the clustering performance in some cases. The ensemble learning is regarded as a representative machine learning method that reflects the ability of group intelligence，whereas a learning method can improve the overall prediction performance via multiple base learners training and their coordinated prediction results. In pseudo-label-based clustering tasks，it can coordinate multiple base learners to obtain high-confidence pseudo-labels. However，the effectiveness of the supervision information acquisition is still to be resolved. The category information of the sample is considered for current pseudo-label-based ensemble clustering method only when the label is captured and some effective information are ignored like the feature representation of the sample itself and the clustering structure between samples. Conclusion Graph neural network is composed of content information of nodes and the structural information between nodes at the same time. To design a self-supervised ensemble clustering method based on consensus graph representation learning，it is required to make full use of sample features and relationships between samples in ensemble learning. To obtain higher confidence pseudo-labels as supervised information and improve the performance of self-supervised clustering，it is necessary to mine global and local information at the same time. We illustrate a learnable data ensemble representation through graph neural network. The confidence of pseudo-labels is improved，and the entire model is trained in self-supervision iteratively. To be summarized：1）Commonly-used consensus graph learning-integrated clustering framework is developed，which can use multi-level information like clusteringintegrated sample characteristics and category structure. 2）Self-supervision method is proposed，which uses graph neural network to mine the global and local information of the consensus graph，and high-confidence pseudo-labels are obtained as supervised information. 3）Experiments are demonstrated that the consensus graph learning ensemble clustering method has its potentials on image and text datasets.

An ensemble hierarchical clustering algorithm based on merits at cluster and partition levels

Ensemble clustering has emerged as a combination of several basic clustering algorithms to achieve high quality final clustering. However, this technique is challenging due to the complexities in primary clusters such as overlapping, vagueness, instability and uncertainty. Typically, ensemble clustering uses all the primary clusters into partitions for consensus, where the merits of a cluster or a partition can be considered to improve the quality of the consensus. In general, the robustness of a partition may be poorly measured, while having some high-quality clusters. Inspired by the evaluation of cluster and partition, this paper proposes an ensemble hierarchical clustering algorithm based on the cluster consensus selection approach. Here, the selection of a subset of primary clusters from partitions based on their merit level is emphasized. Merit level is defined using the development of Normalized Mutual Information measure. Clusters of basic clustering algorithms that satisfy the predefined threshold of this measure are selected to participate in the final consensus. In addition, the consensus of the selected primary clusters to create the final clusters is performed based on the clusters clustering technique. In this technique, the selected primary clusters are re-clustered to create hyper-clusters. Finally, the final clusters are formed by assigning instances to hyper-clusters with the highest similarity. Here, an innovative criterion based on merit and cluster size for defining similarity is presented. The performance of the proposed algorithm has been proven by extensive experiments on real-world datasets from the UCI repository compared to state-of-the-art algorithms such as CPDM, ENMI, IDEA, CFTLC and SSCEN.

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

AbstractEnsemble clustering is an efficient unsupervised learning technique that has attracted a lot of attention. The purpose of this technique is to aggregate the results of several basic clustering algorithms in order to create a better clustering. This is not only possible, but has been developed with many techniques in recent years. However, there are still challenges such as similarity measure, agreement function and how to implement clustering algorithms. To address these challenges, this article proposes an ensemble clustering algorithm based on the consistency cluster consensus approach and the MapReduce model. In addition, the proposed algorithm is equipped with a new membership similarity measure. The consistency cluster consensus is defined as a new agreement function for the consensus of the results of the basic clustering methods. Besides, the proposed similarity measure consists of two factors: one is cluster similarity and the another is membership similarity. The process of the proposed ensemble clustering method is summarized in four steps. In the first step, partitions are generated by applying a number of individual clustering algorithms to the data. The second step is to convert the primary clusters from the partitions to a binary representation. The third step is to identify the candidate primary clusters for the consensus, where a subset with highest similarity between the clusters is emphasized. In the fourth step, the consistency cluster consensus is performed through a new agreement function. In addition, we use the MapReduce model to implement basic clustering algorithms to reduce computational complexity. Our method is evaluated on several real‐world datasets and their performance was compared with other clustering ensemble methods, such as TWCE and RDPC‐DSS. Experiments show that our method is more efficient and accurate and is 7% to 15% better than the best method compared.

Regulating clustering and assortativity affects node centrality in complex networks

The limitations of classical node centralities such as degree, closeness, betweenness and eigenvector are rooted in the network topology structure. For a deeper understanding, we regulate the basic network topology structure clustering and assortative coefficient to study the effect on these four classical node centralities. To observe the structural diversity of the complex network, we firstly construct two types of the growing scale-free networks with tunable clustering coefficient and assortative coefficient respectively, and simulate three types of null models on ten real networks to adjust cluster and assortativity. The results indicate that the impact of varying cluster and assortativity on node centrality in complex networks is obvious. We should pay more attention to the network topology when selecting node centralities as identifying the significance or influence of nodes in complex networks.

DCE-IVI: Density-based clustering ensemble by selecting internal validity index

As each clustering algorithm cannot efficiently partition datasets with arbitrary shapes, the thought of clustering ensemble is proposed to consistently integrate clustering results to obtain better division. Most of ensemble research employs a single algorithm with different parameters to clustering. And this can be easily integrated, however it is hardly to divide complex datasets. Other available methods integrate different algorithms, it can divide datasets from different aspects, but fail to take outliers into account, which produces negative effects on the partition results. In order to solve these problems, we clustering datasets with three different density-based algorithms. The innovation of this paper is described as: (1) by setting dynamic thresholds, lower frequency evidence in the co-association matrix is gradually deleted to obtain multiple reconstructed matrices; (2) these reconstructed matrices are analyzed by hierarchical clustering to obtain basic clustering results; (3) an internal validity index is designed by the compactness within clusters and the correlation between clusters, which is used to select the final clustering result. By this innovation, the clustering effect is significantly improved. Finally, a series of experiments are designed, and the results verify the improvement and effectiveness of the proposed technique (DCE-IVI).

Security Analysis on Blockchain-Powered Mobile APPs Connected with In-Vehicle Networks by Context-Based Reverse Engineering

The controller area network (CAN) bus for interconnection of electronic control units (ECUs) plays a highly important role in modern intelligent vehicles. To facilitate the CAN Bus accessing to vehicle control or diagnosis, a number of mobile APPs are designed and published by automobile manufacturers to support driving and vehicle-based social network, and some are realized through the in-vehicle infotainment (IVI) middleware. Blockchain technologies are also mature for automobiles to interact service information with the whole industry. Unfortunately, there is a serious threat of command leakage from these mobile APPs, and the reverse engineering (RE) can be exploited by hackers. Previous work has researched this threat by an automatic reverse engineering tool on both automotive android and IOS APPs. However, in such common tool, APP itself-related contexts, including the feature information of CAN Bus commands, vehicle application functions, and control diagnostic protocols, are overlooked, which might be utilized to promote the reverse engineering recall. In this paper, we propose a context-based reverse engineering approach to find deep hidden commands for further revealing security threats for blockchain-powered mobile automotive APPs. For the reverse engineering, we design a context model of four-order tensor to organize multidimensional contexts and establish a continuous updating mechanism. Based on the context model, we further develop two basic analysis algorithms, max-compute (A) and clustering (A), to perform the analysis of CAN Bus commands. Extensive experiments are conducted, and we evaluate it by two metrics, recovered ratio and correctness ratio. Experimental results and the case studied on the familiar APP Carly validate the effectiveness of our approach and reveal the threat of command leakage.

A multiple hierarchical clustering ensemble algorithm to recognize clusters arbitrarily shaped

As a research hotspot in ensemble learning, clustering ensemble obtains robust and highly accurate algorithms by integrating multiple basic clustering algorithms. Most of the existing clustering ensemble algorithms take the linear clustering algorithms as the base clusterings. As a typical unsupervised learning technique, clustering algorithms have difficulties properly defining the accuracy of the findings, making it difficult to significantly enhance the performance of the final algorithm. AGglomerative NESting method is used to build base clusters in this article, and an integration strategy for integrating multiple AGglomerative NESting clusterings is proposed. The algorithm has three main steps: evaluating the credibility of labels, producing multiple base clusters, and constructing the relation among clusters. The proposed algorithm builds on the original advantages of AGglomerative NESting and further compensates for the inability to identify arbitrarily shaped clusters. It can establish the proposed algorithm’s superiority in terms of clustering performance by comparing the proposed algorithm’s clustering performance to that of existing clustering algorithms on different datasets.

Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks.

In cognitive radio sensor networks, single clustering protocol cannot simultaneously satisfy the various requirements of time-triggered and event-driven traffic, as a result, different kinds of clustering protocols are designed to serve them separately. In addition, for event-driven traffic, the long delay incurred by clustering and searching for available routes after events results in poor timeliness of information transmission. In order to solve above problems, a traffic-driven ions motion optimization-based clustering routing protocol (TD-IMOCRP) is proposed in this paper. For the first time, time-triggered and event-driven traffic can be served by a single clustering protocol. To be specific, ions motion optimization algorithm is leveraged to automatically determine the optimal number of clusters and form basic clustering structure. In this case, time-triggered traffic can be periodically served. Priority-based schedule and corresponding frame structure are designed to ensure priority delivery of event-driven information. The clustering architecture built for time-triggered traffic is leveraged, and there is no cluster construction and route selection after emergent events. Only the CRSNs nodes which discover emergent events and corresponding CHs participate in data transmission, which means that TD-IMOCRP covers fewer nodes, especially when the sink is located at the corner. Therefore, it can help reduce node energy consumption and delay. Simulation results demonstrate that compared with representative event-driven clustering protocols, TD-IMOCRP can decrease the average number of covered nodes and the total energy consumption by more than 66.3% and 25%, respectively. In addition, when serving time-triggered traffic, TD-IMOCRP can achieve almost the same performance as its basic version IMOCRP which is better than majority of current time-triggered clustering protocols. In a word, TD-IMOCRP can guarantee in-time delivery of event-driven information while guaranteeing its performance of serving time-triggered traffic.

Molecular dissection of condensin II-mediated chromosome assembly using in vitro assays.

In vertebrates, condensin I and condensin II cooperate to assemble rod-shaped chromosomes during mitosis. Although the mechanism of action and regulation of condensin I have been studied extensively, our corresponding knowledge of condensin II remains very limited. By introducing recombinant condensin II complexes into Xenopus egg extracts, we dissect the roles of its individual subunits in chromosome assembly. We find that one of two HEAT subunits, CAP-D3, plays a crucial role in condensin II-mediated assembly of chromosome axes, whereas the other HEAT subunit, CAP-G2, has a very strong negative impact on this process. The structural maintenance of chromosomes ATPase and the basic amino acid clusters of the kleisin subunit CAP-H2 are essential for this process. Deletion of the C-terminal tail of CAP-D3 increases the ability of condensin II to assemble chromosomes and further exposes a hidden function of CAP-G2 in the lateral compaction of chromosomes. Taken together, our results uncover a multilayered regulatory mechanism unique to condensin II, and provide profound implications for the evolution of condensin II.

Characteristic density peak clustering algorithm for taxi hot spots detection

Density peak clustering can be used in identifying high-density regions for urban hot spots detection. The distance matrix of each two position points needs to be calculated in the existing density peak clustering methods which causes inefficient clustering when processing large-scale data, and the traditional two-dimensional decision map cannot identify the coincident points. Thus, characteristic density peak clustering algorithm is proposed to avoid the influence of noise. At first, the location feature points and support index are defined to simulate the original locations. The number of feature points is adjusted by parameters to make density peak clustering no longer sensitive to the amount of data to simplify the complexity to be solved. And then, the local density and the distance between the clustering centers of the feature points are proposed to construct three-dimensional decision map. Finally, the clustering center, basic clustering points, and noise data points are determined using the three-dimensional decision map combined with the support index of the feature points. Experiments are performed on real data set and the prototype system to verify that the method can significantly improve time efficiency while clustering accuracy is maintained.

A Hybrid Clustering Method Based on the Several Diverse Basic Clustering and Meta-Clustering Aggregation Technique

In hybrid clustering, several basic clustering is first generated and then for the clustering aggregation, a function is used in order to create a final clustering that is similar to all the basic clustering as much as possible. The input of this function is all basic clustering and its output is a clustering called clustering agreement. However, this claim is correct if some conditions are met. This study has provided a hybrid clustering method. This study has used the basic k-means clustering method as a basic cluster. Also, this study has increased the diversity of consensus by adopting some measures. Here, the aggregation process of the basic clusters is done by the meta-clustering technique, where the primary clusters are re-clustered to form the final clusters. The proposed hybrid clustering method has the advantages of k-means, its high speed, as well as it does not have its major weaknesses, the inability to detect non-spherical and non-uniform clusters. In the empirical studies, we have evaluated the proposed hybrid clustering method with other up-to-date and robust clustering methods on the different datasets and compared them. According to the simulation results, the proposed hybrid clustering method is stronger than other clustering methods.

Involvement of a Cluster of Basic Amino Acids in Phosphorylation-Dependent Functional Repression of the Ceramide Transport Protein CERT.

Ceramide transport protein (CERT) mediates ceramide transfer from the endoplasmic reticulum to the Golgi for sphingomyelin (SM) biosynthesis. CERT is inactivated by multiple phosphorylation at the serine-repeat motif (SRM), and mutations that impair the SRM phosphorylation are associated with a group of inherited intellectual disorders in humans. It has been suggested that the N-terminal phosphatidylinositol 4-monophosphate [PtdIns(4)P] binding domain and the C-terminal ceramide-transfer domain of CERT physically interfere with each other in the SRM phosphorylated state, thereby repressing the function of CERT; however, it remains unclear which regions in CERT are involved in the SRM phosphorylation-dependent repression of CERT. Here, we identified a previously uncharacterized cluster of lysine/arginine residues that were predicted to be located on the outer surface of a probable coiled-coil fold in CERT. Substitutions of the basic amino acids in the cluster with alanine released the SRM-dependent repression of CERT activities, i.e., the synthesis of SM, PtdIns(4)P-binding, vesicle-associated membrane protein-associated protein (VAP) binding, ceramide-transfer activity, and localization to the Golgi, although the effect on SM synthesis activity was only partially compromised by the alanine substitutions, which moderately destabilized the trimeric status of CERT. These results suggest that the basic amino acid cluster in the coiled-coil region is involved in the regulation of CERT function.

Multiobjective Deep Clustering and its Applications in Single-cell RNA-seq Data

Single-cell RNA sequencing is a transformative technology that enables us to study the heterogeneity of the tissue at the cellular level. Clustering is used as the key computational approach to group cells under the transcriptome profiles from single-cell RNA-seq data. However, accurate identification of distinct cell types is facing the challenge of high dimensionality, and it could cause uninformative clusters when clustering is directly applied on the original transcriptome. To address such challenge, an evolutionary multiobjective deep clustering (EMDC) algorithm is proposed to identify single-cell RNA-seq data in this study. First, EMDC removes redundant and irrelevant genes by applying the differential gene expression analysis to identify differentially expressed genes across biological conditions. After that, a deep autoencoder is proposed to project the high-dimensional data into different low-dimensional nonlinear embedding subspaces under different bottleneck layers. Then, the basic clustering algorithm is applied in those nonlinear embedding subspaces to generate some basic clustering results to produce the cluster ensemble. To lessen the unnecessary cost produced by those clusterings in the ensemble, the multiobjective evolutionary optimization is designed to prune the basic clustering results in the ensemble, unleashing its cell type discovery performance under three objective functions. Multiple experiments have been conducted on 30 synthetic single-cell RNA-seq datasets and six real single-cell RNA-seq datasets, which reveal that EMDC outperforms eight other clustering methods and three multiobjective optimization algorithms in cell type identification. In addition, we have conducted extensive comparisons to effectively demonstrate the impact of each component in our proposed EMDC.

Global Research Status and Trends in Hair Follicle Stem Cells: a Bibliometric Analysis

Hair follicle stem cells (HFSCs) are derived from the bulge region and are important autologous stem cell sources. Bibliometric is a statistical method that quantitatively analyses the research papers concerned about one special topic. This study aims to estimate the research status and trends of HFSCs worldwide by bibliometric analyses. Data were obtained from the Web of Science by searching keywords related to HFSCs. Publication distributions stratified by countries/regions, institutions, journals, and authors were systematically assessed. The frequency of keywords was assessed, and bibliometric mapping was employed to describe the development of HFSC research. A total of 458 publications that met our screening criteria were included in this study, consisting of 423 (92.4%) articles and 35 (7.6%) reviews. The United States of America (USA) ranked first in the number of publications at 146 (31.9%), followed by China at 130 (28.4%), which is consistent with the rank of the H-index. Author keywords were classified into three clusters, namely, basic study, applied study, and biomarker; average publication time of keywords in applied study cluster is later than basic study cluster. The keywords "bulge", "nestin", and "skin" are the top three most frequent keywords in basic studies; "differentiation", "proliferation", and "alopecia" are the top three most frequent keywords in applied studies. With respect to the latest research hotspots, "apoptosis" and "tissue engineering" are relatively new keywords. The USA and China were the most productive countries for research on HFSCs. The focus of keywords gradually shifted from basic study to applied study. Research on the differentiation/proliferation of HFSCs and the role of HFSCs in alopecia have been recent research focuses. Apoptosis and tissue engineering are recommended as promising research hotspots. Our study provides profound insights into the research history, current status, and future trend of HFSCs.

Three‐stage improved algorithm based on clustering decomposition and its application in drone demand and task allocation

This paper proposes a three-stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK-iD-N), aiming at the optimization task allocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual-objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK-D) with neighbourhood insertion algorithm into task allocation, to get high-quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK-iD-N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK-D to get better solutions of the iK-iD-N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K-iD-N) that iK- iD-N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions.

Research on driving fatigue detection based on basic scale entropy and MVAR-PSI

In long-term continuous driving, driving fatigue is the main cause of traffic accidents. Therefore, accurate and rapid detection of driver mental fatigue is of great significance to traffic safety. In our study, the electroencephalogram (EEG) signals of subjects were preprocessed to remove interference signals. The Butterworth band-pass filter is used to extract the EEG signals of α and β rhythms, and then the basic scale entropy of α and β rhythms is used as driving fatigue characteristics. In addition, combined with the fast multiple autoregressive (MVAR) model and phase slope index (PSI), short-term data is used to accurately estimate the effective connectivity of EEG signals between different channels, and analyzed the causality flow direction in the left and right prefrontal regions of drivers at different driving stages. Further comprehensive analysis of the driver’s driving fatigue state in the continuous driving phase. Finally, the correlation coefficient value between the parameter pairs (basic scale entropy, clustering coefficient, global efficiency) is calculated. The results showed that the causality flow outflow degree of prefrontal lobe decreased during the transition from sober driving state to tired driving state. The left and right prefrontal lobes were the source of causality in sober driving state, and gradually became the target of causality with the occurrence of driving fatigue. The results showed that when transitioning from a waking state to a fatigued driving state, the causal flow direction out-degree value of the prefrontal cortex on a declining curve, and the left and right prefrontal cortex exhibited the causal source in the awake driving state, which gradually changed into the causal target along with the occurrence of driving fatigue. The three parameters of basic scale entropy, clustering coefficient and global efficiency are used as driving fatigue characteristics, and every two parameters have strong correlation. It shows that the combination of basic scale entropy and MVAR-PSI method can effectively detect the driver’s long-term driving fatigue state in continuous driving mode.