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Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Identifying the water inrush source accurately is a fundamental work and crucial for safe mining in coal mines. This study focuses on a coal mine in Gansu Province based on a comprehensive method of Piper trilinear diagram, hierarchical clustering and gray rational analysis. The water chemical types of underground drainage, fault fissure water and roof sandstone fissure water in the working face are all SO4-K·Na type and the water chemical types of ground is SO4·HCO3-Ca type. In the Piper trilinear diagram, the underground drainage water sample is far from other water sample points, indicating the underground drainage water may be mixed from multiple sources. Hierarchical clustering analysis shows that the underground drainage water is relatively close to the water chemistry of roof sandstone fissure water and fault fissure water. Finally, the gray rational analysis indicates that underground drainage water is better correlative with roof fissure water and fault fissure water. According to the aforementioned comprehensive method, the underground drainage water is mainly derived from roof sandstone water and fault fissure water, yet has little relations with surface water. It is speculated that it may be the roof water supply caused by strata disturbance during the mining process, as well as the pore water supply through the water conducting fracture zone. Therefore, the research and exploration of the roof aquifer and water conducting fracture zone will be of great importance in the future work of water prevention and control in the coal mine.

At present, the prediction accuracy of a single model is difficult to remain optimal with power fluctuation. To improve the stability of grid connected system operation and energy saving dispatching of power grid, this study proposes a dynamic learning combination short-term power prediction method based on k-sums hierarchical clustering. The weather types are divided into sunny day A1, cloudy day A2, and rainy day B through segmentation clustering using k-sums algorithm. The TCN (Temporal Convolutional Network) is used to extract the time series characteristics of data, and the GRU(Gate Recurrent Unit) structure of the fusion extraction time series characteristics module is established with GRU to achieve the effect of being sensitive to the time series characteristics. After dynamically combining the improved GRU structure with the SVM(Support Vector Machine), the Elastic Net algorithm is adopted to output the best weight value to obtain the final prediction value. The power data of photovoltaic power generation and corresponding meteorological data of a region in Jiangsu are used to verify the proposed method. The results show that the MAE(Mean Absolute Error) of the dynamic combination learning model is 1.888, and the RMSE(Root Mean Squared Error) is 2.403. [ABSTRACT FROM AUTHOR]

China has complex natural conditions and is rich in biodiversity. Based on the geographical distribution and species composition of terrestrial mammals, we explored the characteristics and geographic partitioning of mammal populations in different regions of China. We used a clustering algorithm, combined with the spatial distribution data and taxonomic characteristics of mammals, to geographically partition the terrestrial mammals in China. We found 10 zoogeographic regions of terrestrial mammals in China: Northeast region, North China region, Eastern grassland region, Western region, Northwest region, Qiangtang plateau region, Eastern Qinghai-Tibet Plateau region, Himalayan region, South China region, and Taiwan-Hainan region. We found a new geographical zoning pattern for terrestrial mammals in China, examined the variability and characteristics of species composition among different regions, and quantified the association between species distribution and environmental factors. We proposed a method of incorporating taxonomic information into cluster analysis, which provided a new idea for zoogeographic region studies, a new perspective for understanding species diversity, and a scientific basis for animal conservation and habitat planning. [ABSTRACT FROM AUTHOR]

Background With the increasing aging of China, the exploring of smart elderly care institutions development has become a research focus in the field of elderly care at home and abroad. Objective To establish an evaluation model for the intelligence level of elderly care institutions, and provide theoretical reference and basis for research work in related fields. Methods A smart evaluation index system for the intelligence level of elderly care institutions containing a total of 37 three-level indicators was constructed based on literature analysis and descriptive statistics; hierarchical clustering analysis was performed based on the variable data of each evaluation index to obtain a cluster pedigree; the hierarchical clustering method was proposed, and 43 top-end elderly care institutions in Shanghai were selected for empirical analysis of hierarchical clustering based on the "elbow method". Results Top-end elderly care institutions in Shanghai can be divided into three categories of smart, semi-smart and non-smart; more than 60% of the elderly care institutions are non-smart; more than 70% of the elderly care institutions do not have comprehensive intelligent equipment or have sound intelligent supervision system unable to provide in-depth psychological services. Conclusion The intelligent construction of elderly care institutions in Shanghai is still at a low level of development with a large upside. Finally, the endogenous and exogenous meanings of smart elderly care institutions proposed based on the literature review and empirical analysis results can provide reference for research in related fields.

Copyright of South China Fisheries Science is the property of South China Fisheries Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional group recommendation has such problems as ineffective predefined strategy, neglect of the interaction between users and projects, and failure to capture the migration of user preferences caused by the passage of time.In response to the above problems, a group recommendation model TAGR (time-attentive group recommendation) that integrates time series and attention mechanisms was proposed. Firstly, high similarity groups were divided through hierarchical clustering. Secondly, a time series model was introduced to capture the process of user preference transfer, obtain the interest preferences of user behavior at each moment, and aggregate the interest preferences at each moment as user preferences. Finally, with attention mechanism, user weights were obtained for preference fusion to represent group preferences, serving as the input of the recommendation model. By comparing with NCF, AGREE and other models on the Goodbook and MovieLens datasets, the proposed model TAGR has been significantly improved in both normalized discount cumulative gain and hit rate. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As an important non-engineering measure, the optimal operation of reservoir flood control can reduce the loss caused by flood disasters with little investment and plays a key role in flood control and disaster reduction. In this paper, considering the safety of the reservoirs themselves and the safety of the downstream flood control section, a multi-objective optimal model for real-time flood control operation of reservoirs is established with the objective function of minimizing the maximum flow of the downstream flood control section and minimizing the maximum water level of each reservoir. Based on the "filter operator", an improved multi-objective Lichtenberg algorithm (MOLA) is proposed which is used to solve the multi-objective model and the real-time flood control operation schemes of the reservoirs are obtained to enhance the practicability of operation solutions. Finally, a comprehensive screening method based on hierarchical clustering and the physical meaning of Pareto front is proposed to screen the scheduling schemes on the Pareto front and select limited ones for the scheduler, to increase the focus of decision-making. Taking the Shiguanhe flood control system of the Huaihe River Basin as an example, this paper applies the multi-objective optimal model for real-time flood control operation of reservoirs. The result shows that the proposed model based on the improved multi-objective Lichtenberg algorithm has high computational efficiency and strong applicability. The gradient analysis method is used to quantitatively analyze the mutual feedback relationship between the maximum water level of Nianyushan Reservoir, the maximum water level of Meishan Reservoir, and the maximum flow of Jiangjiaji flood control section. The results show that the water level change of Meis-han Reservoir has a more significant impact on the flow of Jiangjiaji section, and the Meishan Reservoir is a priority in flood risk control of Shiguanhe River Basin. The research results can provide decision-making support for real-time flood control operation of reservoirs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Clustering analysis plays a key role in machine learning,data mining and biological DNA information.Clustering algorithms can be categorized into flat clustering and hierarchical clustering.Flat clustering mostly divides the data set into K parallel communities without intersections,but the real communities have multi-level inclusion relations,so the hierarchical clustering algorithms can provide more elaborate analysis and better interpretability.Compared with flat clustering,the research progress of hierarchical clustering is slow.Aiming at the problem of hierarchical clustering,this paper surveys a large number of related papers in the aspects of the selection of cost functions,the evaluations of clustering results and the performance of clustering algorithms.The evaluation indices of clustering results mainly include modularity,Jaccard index,normalized mutual information,dendrogram purity,etc.Among the flat clustering algorithms,the classical algorithms include K-means algorithm,label propagation algorithm,DBSCAN algorithm,spectral algorithm and so on.The hierarchical clustering algorithms can be further classified into agglomerative clustering algorithms and split clustering algorithm.The split clustering algorithms involves dichotomy K-means algorithm,recursive sparsest cut algorithm,etc.Agglomerative clustering algorithms involves classical Louvain algorithm,BIRCH algorithm and more recent HLP algorithm,PERCH algorithm and GRINCH algorithm.This paper also further analyzes the advantages and disadvantages of these algorithms,and finally,the whole paper is summarized.

Aiming at the problems of high false detection rate, difficult to find unknown attack behavior and high cost of obtaining marked data in existing wireless network intrusion detection algorithms based on supervised learning, this paper proposes an unsupervised wireless network intrusion detection algorithm based on multiple perspectives hierarchical clustering. The algorithm is based on unsupervised learning, and does not need to manually mark a large number of wireless network data participating in classifier learning. It has the advantages of easy access to training datasets and detection of unknown types of attack behavior. At the same time, the algorithm introduces multiple perspectives cosine distance as the similarity measure between wireless network data objects in hierarchical clustering, which makes the clustering results more reasonable and the judgment of network data behavior more accurate, and reduces the false detection rate of intrusion detection to a certain extent. In this paper, Aegean WIFI intrusion dataset (AWID) is selected as the experimental dataset, and principal component analysis is used to reduce the dimension of the experimental dataset, which greatly reduces the time complexity of intrusion detection algorithm. Experimental results show that the proposed wireless network intrusion detection algorithm based on multiple perspectives hierarchical clustering has a significant improvement in detection rate, false detection rate and detection of unknown attack types compared with traditional wireless network intrusion detection algorithms.

Aiming at the problem of dynamic reconfiguration of distribution network with distributed generation (DG), a dynamic distribution networks reconfiguration scheme considering the time-varying property of DG and distribution network load was proposed.Firstly, according to the comprehensive similarity between different periods based on both load characteristics and optimal network structure, an improved hierarchical clustering method was used to divide the reconstruction interval into segments.On this basis, the genetic learning adaptive particle swarm optimization algorithm was proposed to realize the dynamic reconstruction with minimum network loss.To tackle the shortcomings such as the lack of speed dynamic adjustment strategy and ease to fall into local optimum in basic particle swarm optimization algorithm, a genetic learning scheme based on the optimal position of individual particles was proposed to enhance diversity and improve global search ability.Adaptive inertia weight and acceleration coefficients were introduced to meet the optimization requirements of different periods.Finally, a simulation was carried out through the IEEE 33-bus distribution system as an example to verify the effectiveness and superiority of the proposed method.

Mastering structured query language (SQL) is the key to learn the database technology. However, a lot of teaching practices show that some students may plagiarize when doing SQL exercises. Existing SQL plagiarism detection techniques either detect plagiarized submissions simply by matching the similarities of students' SQL submissions, or identify plagiarism problems by analyzing students' SQL submissions based on the simple coding features displayed in students' SQL codes, which fails to make good use of the rich coding features of students when they write SQL codes to achieve high-accuracy plagiarism detection. In view of this, this paper proposes an SQL plagiarism detection technique based on coding features of students, named SQL-Detector. SQL-Detector first extracts both of the exercise coding features of students for specific SQL exercises and the exercise generalization coding features of students based on their coding habits, so as to profile the students. Then, SQL-Detector identifies the plagiarism group by conducting a clustering analysis over the exercise coding features of all students. Finally, SQL-Detector determines the copiers and givers by comparing the consistency between students' exercise generalization coding features and his/her historical generalization coding features. Experimental evaluation is conducted by using the dataset collected from real classroom practices. Experimental results show that the plagiarism detection accuracy of the SQL-Detector technique is on average 14.0% higher than that of the state-of-the-art technique.

Research on the development mechanism of cascading failure and identification the critical line of evolution path is of great significance for exposing weak links in the power system and reducing the risk of cascading failures. For this reason, an identification method for critical lines of power grid based on fault chain clustering algorithm is proposed. Firstly, an improved direct current power flow simulator of power system separation (DCSS) cascading failure simulation model, which combines with the random chemistry (RC) method to efficiently generate fault chain set with detailed timing information is established. Then, the hierarchical clustering of the failure chain set is realized by using edit distance as the similarity index, and the classified fault chain set can not only reduce the difficulty of subsequent data mining, but also more comprehensively identify the weak links in different cascading failure evolution modes. Finally, taking the Matpower 2 383 node system as an example, the importance of the lines identified by various algorithms is evaluated through the reduction of the system risk level after the expansion of critical lines. The results show that the proposed method can better reduce the risk level of cascading failure, which proves the effectiveness of the proposed model and algorithm.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Donghua University (Natural Science Edition) is the property of Journal of Donghua University (Natural Science) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most existing risk assessments of Android applications directly assign weights to factors according to experience, and calculate security risks by counting the frequency statistics of few factors.A new method for risk assessment of Android applications is proposed, which can provide both quantitative and qualitative assessment.This method integrates multiple risk factors such as system permissions, API calls, the action properties of Intent Filter, and data flow.The risks of factors are assigned based on their risk classification and addition, and the weights of factor subsets are distributed based on hierarchical clustering.Experiments show that the assessment results can effectively reflect the real security risks of Android applications.

Most existing risk assessments of Android applications directly assign weights to factors according to experience, and calculate security risks by counting the frequency statistics of few factors. A new method for risk assessment of Android applications is proposed, which can provide both quantitative and qualitative assessment. This method integrates multiple risk factors such as system permissions, API calls, the action properties of Intent Filter, and data flow. The risks of factors are assigned based on their risk classification and addition, and the weights of factor subsets are distributed based on hierarchical clustering. Experiments show that the assessment results can effectively reflect the real security risks of Android applications.

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the field of tumor diagnosis, the AI-assisted diagnosis system can accurately distinguish tumor attributes and malignant tumor stages, thereby prolonging the survival time of patients. Taking breast tumor as an example, this paper proposes an artificial intelligence-assisted diagnosis model based on supervised learning to solve the problem of overfitting caused by the large amount of data in the feature extraction process. When extracting features, effective feature dimensionality reduction is achieved by introducing hierarchical clustering analysis, and the classified feature data is used as the feature input of the artificial neural network model, so as to realize the effective training of the classifier. The experimental results show that the accuracy and AUC value of the proposed algorithm are improved compared with the control algorithm, indicating that the model can not only solve the problem of overfitting caused by the description of massive feature regions, but also enhance the generalization of the artificial intelligence-aided diagnosis system. The ability to achieve high-precision differentiation of mammography-targeted breast tumors. [ABSTRACT FROM AUTHOR]

This study aims to detect the development of dry-shrinkage cracks on the farmland soil surface using Electrical Resistivity Technology (ERT). The Simultaneous Successive Linear Estimation (SimSLE) coupled with the hierarchical clustering was also used to invert the morphology of the dry-shrinkage cracks. The experimental and simulated images were then compared to analyze the crack morphology. It was found that the resistance of the soil around the cracks increased sharply, where there were some cracks in the soil, while the resistance of the soil without cracks still maintained a slowly increasing trend. This phenomenon was set as a reference for the formation of cracks. After that, the estimated resistivity was obtained by inversion using the SimSLE algorithm, further to represent the development state of soil shrinkage cracks. The evaluation of fracture images between the natural cracks and the inversion was made using the geometrical properties of Minkowski numbers Mk for the quantitative analysis, where Mk was used to describe the structure of fissures. Three parameters were utilized for the Minkowski number of fissures on the soil surface: the area A, the length L, and the Euler number E of the fissure, representing the fissure area, fissure length, and connectivity in the two-dimensional fissure structure, respectively. The Minkowski density of the fracture images and the simulated binary networks were quantified by the open-source code realized by MATLAB software. Three indicators were selected to evaluate, including the coefficient of determination (R²), the consistency index (IA), and the Root Mean Square Error (RMSE). The results show that the simulated images presented accurate to capture the shape and position of the fracture. The area, length, and Euler number density IA of the test and simulation images were all greater than 0.9, and the R² values were greater than 0.6, but the RMSE values were small. The morphological characteristics of dry-shrinkage cracks in farmland soil were more accurately to continuously update the partition mean and boundary after coupling hierarchical clustering. The simulation images were better after clustering and partitioning. The partition of hierarchical clustering from the resistivity was estimated to obtain via the inversion and calculation of the uniform resistivity as the initial input values in the traditional SimSLE algorithm. The Euclidean distance criterion was used to obtain the optimal number of clusters for the dataset of estimated values. Then, the mean of the estimates for each region was calculated to adjust the covariance matrix. Finally, the updated mean and covariance were used as the prior information for the next round of iterative calculation. As such, the computational accuracy was improved to identify the small-scale spatial variability within soil layers. After the introduction of clustering partition, the R² and IA of Minkowski density increased by 9.76%-18.5%, and 0.93%-1.47%, respectively, whereas, the RMSE was reduced by 12.1%-21.1%, indicating an improved accuracy of the prediction. Therefore, effective detection of soil fissures can greatly contribute to preventing the occurrence of cracks from the damaged integrity of the soil structure, even various engineering geological disasters. The finding can provide an ideal way to characterize the fractures morphologies using non-invasive detection. [ABSTRACT FROM AUTHOR]

In order to effectively display the hierarchical relationship of the decision result set, a multi-criteria decision-making method based on hierarchical clustering is constructed by adopting the theory and method of poset, combined with the weight order, that is, the preference order optimization hierarchical clustering analysis. By analyzing the loan data provided by commercial banks, through the change of the node level in the hasse diagram, potential risky customers can be found more accurately, and the risk can be avoided earlier. The results of study show that this method overcomes the drawback of traditional multi-criteria decision making research that only focuses on the optimal solution or satisfactory solution and ignores the disadvantages of the hierarchical structure between the understanding sets, and breaks through the bottleneck of the traditional method that relies too much on the weight value, and can provide better decision-making basis for decision-makers. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing amount of logs produced by nodes in CNGrid, traditional manual methods for abnormal log analysis can no longer meet the need of daily analysis. In order to analyze the log automatically and efficiently, a two-stage detection method is proposed in this paper. In the first stage, the log patterns are classified during preprocessing, then the principal component analysis is used for anomaly detection and the sequence of log types is defined as a log flow pattern. The abnormal flow patterns obtained from anomaly detection are extracted by the definition. Finally, the hierarchical clustering algorithm is used to simplify the results of the flow pattern and the results are saved. In the second stage, through the detection model and flow pattern obtained in the first stage, the log flow information can be monitored and analyzed in real time and the corresponding flow pattern can be matched. Finally, the experiment is carried out on real logs in CNGrid, and the results are visualized in real time. These greatly reduce the manual work of operations.

The flooded area detection method based on the fusion of optical and Synthetic Aperture Radar (SAR) images is applicable for all weather conditions and times. However, due to the large number of randomly distributed intensive speckle noise in SAR images, the conventional methods of detection often trigger high false alarm rates at flood-stricken zones. Inspired by the Fuzzy C-Means (FCM) clustering method, a hierarchical clustering algorithm (Hierarchical Fuzzy C-Means, H-FCM) is proposed in this paper. This method fuses the SAR image captured after the flood with the optical image captured before the flood. Based on the fused image, this method uses the proposed hierarchical clustering model to obtain the preliminary detection results of the flooded area. Additionally, the algorithm uses the proposed region-growing algorithm to obtain the river location before the flood and uses it as a spatial constraint for the preliminary detection results to further screen out suspected flooded areas and significantly improve detection performance. The experimental data used in this paper include the remote sensing images captured before and after the Gloucester floods in the United Kingdom in 1999, as well as the remote sensing images captured before and after the Nanchang floods in China in 2019. The effectiveness and validity of the H-FCM algorithm are also supported by comparison experiments.

Digital image inpainting provides an important basis for the scientific protection and restoration of ancient cave murals. At present, the complete extraction and reasonable restoration for disease regions of digital murals is an important topic of virtual restoration of cultural relics. This paper took the mural image of Maijishan Grottoes as the research object. The method analyzed characteristics and formation mechanism of crack diseases accompanied by point spalling firstly. Then the complete extraction of disease information was based on the principle of minimum intervention of cultural relic restoration. It used the local optimal hierarchical clustering with weighted average as similarity measurement to extract the disease information mask. Taking the mask image as the object, the reasonable restoration used curvature driven diffusion model with effective pixel estimation to achieve image restoration. This method makes the virtual restoration of Maijishan Grottoes more accurate and achieves the effect of minimum intervention principle. [ABSTRACT FROM AUTHOR]

Aiming at the ubiquitous network spammers on e-commerce platform, this paper proposed an algorithm considering network structure and time characteristics to detect the spammer groups in the comment network. The algorithm consisted of four steps: a) mining the target products that were vulnerahle to attack by the spammers based on the analysis of the structural characteristics of the comment network; b) this paper proposed an algorithm for mining the suspicious period when the spammer groups attacked the target product inspired by the "co-bursting phenomenon" ; c) this paper constructed the induced subgraph of target products-reviewers based on the data of target product in suspicious period, and applied hierarchical agglomerative clustering algorithm to generate candidate spammer groups on the subgraph; d) in order to filter out the normal users who shopped and commented during the suspicious period, this paper proposed a spammer groups purification method, and then classified the purified groups based on the behavior characteristics of the reviewers. The experimental results based on real data sets show that the proposed algorithm can accurately and efficiently detect the network spammer groups active on e-commerce websites. [ABSTRACT FROM AUTHOR]

Copyright of China Plastics / Zhongguo Suliao is the property of Journal Office of CHINA PLASTICS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at problems of time-consuming, labor-consuming and limitation in constructing coal mine accident ontology by manual means, an automatic construction method of coal mine accident ontology was proposed. Firstly, taking text of coal mine accident case as data source, BP neural network is used to automatically extract ontology concept. Then, hierarchical relationship and non-hierarchical relationship between ontology concepts are automatically extracted by adopting hierarchical clustering method and association rules method. Finally, ontology concepts, relationship between concepts and examples of coal mine accident are visualized by use of Protégé ontology editor, so as to obtain coal mine accident ontology. The coal mine accident ontology by automatic construction is more comprehensive and can provide strong support for coal mine safety production work.

In the research of code refactoring at the package level, in order to obtain the software structure of "high cohesion and low coupling", the hierarchical clustering algorithm is considered to be a better software clustering algorithm because of its simple and effective characteristics and high clustering accuracy. However, the time complexity of the hierarchical clustering algorithm is high, which is not conducive to processing large-scale software. The DBSCAN algorithm, on the other hand, has faster clustering speed but lower accuracy. Therefore, a software hierarchical clustering algorithm based on DBSCAN is proposed, which uses the classes generated by the DBSCAN algorithm to constrain the clustering space of the hierarchical clustering algorithm. This algorithm can keep the accuracy of the hierarchical clustering algorithm unchanged, and its time complexity lies between DBSCAN and the hierarchical clustering algorithm. The experimental results show that the algorithm can effectively divide the software reasonably, and prove that the performance of the algorithm is better than other common clustering algorithms through expert evaluation, module division metrics and algorithm running time comparison. [ABSTRACT FROM AUTHOR]

Objective: Atrial fibrillation (AF) is a disease of high heterogeneity, and the association between AF phenotypes and the outcome of different catheter ablation strategies remains unclear. Conventional classification of AF (e.g. according to duration, atrial size, and thromboembolism risk) fails to provide reference for the optimal stratification of the prognostic risks or to guide individualized treatment plan. In recent years, research on machine learning has found that cluster analysis, an unsupervised data-driven approach, can uncover the intrinsic structure of data and identify clusters of patients with pathophysiological similarity. It has been demonstrated that cluster analysis helps improve the characterization of AF phenotypes and provide valuable prognostic information. In our cohort of AF inpatients undergoing radiofrequency catheter ablation, we used unsupervised cluster analysis to identify patient subgroups, to compare them with previous studies, and to evaluate their association with different suitable ablation patterns and outcomes., Methods: The participants were AF patients undergoing radiofrequency catheter ablation at West China Hospital between October 2015 and December 2017. All participants were aged 18 years or older. They underwent radiofrequency catheter ablation during their hospitalization. They completed the follow-up process under explicit informed consent. Patients with AF of a reversible cause, severe mitral stenosis or prosthetic heart valve, congenital heart disease, new-onset acute coronary syndrome within three months prior to the surgery, or a life expectancy less than 12 months were excluded according to the exclusion criteria. The cohort consisted of 1102 participants with paroxysmal or persistent/long-standing persistent AF. Data on 59 variables representing demographics, AF type, comorbidities, therapeutic history, vital signs, electrocardiographic and echocardiographic findings, and laboratory findings were collected. Overall, data for the variables were rarely missing (<5%), and multiple imputation was used for correction of missing data. Follow-up surveys were conducted through outpatient clinic visits or by telephone. Patients were scheduled for follow-up with 12-lead resting electrocardiography and 24-hours Holter monitoring at 3 months and 6 months after the ablation procedure. Early ablation success was defined as the absence of documented AF, atrial flutter, or atrial tachycardia >30 seconds at 6-month follow-up. Hierarchical clustering was performed on the 59 baseline variables. All characteristic variables were standardized to have a mean of zero and a standard deviation of one. Initially, each patient was regarded as a separate cluster, and the distance between these clusters was calculated. Then, the Ward minimum variance method of clustering was used to merge the pair of clusters with the minimum total variance. This process continued until all patients formed one whole cluster. The "NbClust" package in R software, capable of calculating various statistical indices, including pseudo t 2 index, cubic clustering criterion, silhouette index etc, was applied to determine the optimal number of clusters. The most frequently chosen number of clusters by these indices was selected. A heatmap was generated to illustrate the clinical features of clusters, while a tree diagram was used to depict the clustering process and the heterogeneity among clusters. Ablation strategies were compared within each cluster regarding ablation efficacy., Results: Five statistically driven clusters were identified: 1) the younger age cluster ( n =404), characterized by the lowest prevalence of cardiovascular and cerebrovascular comorbidities but the highest prevalence of obstructive sleep apnea syndrome (14.4%); 2) a cluster of elderly adults with chronic diseases ( n =438), the largest cluster, showing relatively higher rates of hypertension, diabetes, stroke, and chronic obstructive pulmonary disease; 3) a cluster with high prevalence of sinus node dysfunction ( n =160), with patients showing the highest prevalence of sick sinus syndrome and pacemaker implantation; 4) the heart failure cluster ( n =80), with the highest prevalence of heart failure (58.8%) and persistent/long-standing persistent AF (73.7%); 5) prior coronary artery revascularization cluster ( n =20), with patients of the most advanced age (median: 69.0 years old) and predominantly male patients, all of whom had prior myocardial infarction and coronary artery revascularization. Patients in cluster 2 achieved higher early ablation success with pulmonary veins isolation alone compared to extensive ablation strategies (79.6% vs. 66.5%; odds ratio [OR]=1.97, 95% confidence interval [CI]: 1.28-3.03). Although extensive ablation strategies had a slightly higher success rate in the heart failure group, the difference was not statistically significant., Conclusions: This study provided a unique classification of AF patients undergoing catheter ablation by cluster analysis. Age, chronic disease, sinus node dysfunction, heart failure and history of coronary artery revascularization contributed to the formation of the five clinically relevant subtypes. These subtypes showed differences in ablation success rates, highlighting the potential of cluster analysis in guiding individualized risk stratification and treatment decisions for AF patients., Competing Interests: 利益冲突　所有作者均声明不存在利益冲突, (© 2024《四川大学学报（医学版）》编辑部 版权所有Copyright ©2024 Editorial Office of Journal of Sichuan University (Medical Sciences).)

China has complex natural conditions and is rich in biodiversity. Based on the geographical distribution and species composition of terrestrial mammals, we explored the characteristics and geographic partitioning of mammal populations in different regions of China. We used a clustering algorithm, combined with the spatial distribution data and taxonomic characteristics of mammals, to geographically partition the terrestrial mammals in China. We found 10 zoogeographic regions of terrestrial mammals in China: Northeast region, North China region, Eastern grassland region, Western region, Northwest region, Qiangtang plateau region, Eastern Qinghai-Tibet Plateau region, Himalayan region, South China region, and Taiwan-Hainan region. We found a new geographical zoning pattern for terrestrial mammals in China, examined the variability and characteristics of species composition among different regions, and quantified the association between species distribution and environmental factors. We proposed a method of incorporating taxonomic information into cluster analysis, which provided a new idea for zoogeographic region studies, a new perspective for understanding species diversity, and a scientific basis for animal conservation and habitat planning.

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Aiming at the diversity of keywords extracted from similar texts, and the fuzzy relationship between similarity and relevance, this paper proposed a text feature extraction method based on hierarchical clustering of words. The method took the similarity and relevance of words as the semantic distance under the premise that the same word between texts affects text similarity, and according to the difference of the semantic distance, introduced a hierarchical clustering method and gave different clustering weights. Finally, it obtained a vector space model with clustering weight, which took words and clusters as the feature unit. This paper introduced word2vec to train word vectors to obtain text similarity, and according to the algorithm characteristics of Skip-Gram + Huffman softmax model, used the point mutual information formula to accurately obtain the correlation between words. The text categorization experimental results show that the proposed method can improve the accuracy of text feature extraction, and more effectively than the currently popular method of using only similarity monolayer clustering and statistics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large search systems are especially necessary for quick response to user queries. At the same time, strict backend delay constraints must be observed when calculating the feature relevance of candidate documents. Feature selection can improve the machine learning efficiency. Considering the characteristics that most of the initial points of fast feature selection in ranking learning start from the single feature, which has the best ranking effect, this paper first proposes an algorithm of generating initial points of fast feature selection by hierarchical clustering, and applies the algorithm to two existing fast feature selection algorithms. In addition, a new method that makes full use of clustering features is proposed to deal with feature selection. Experiments on two standard datasets show that the proposed algorithm can obtain a smaller feature subset without affecting the accuracy and obtain the best ranking accuracy on a medium subset. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Community structure is one of the important characteristics of Complex Network, the community detection algorithms with hierarchical clustering makes good use of modularity to achieve community detection, hut its limitations lead to poor detection quality of networks which have complex structure of community and it may fail to identify communities smaller than a scale. This paper used local modularity to make up the lack of modularity on the basis of hierarchical clustering, it can avoid unreasonable result. The algorithm is tested on real-world data sets and artificial network, experimental results confirm that it has feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

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A novel approach to analyze DNS query behaviors was introduced.This approach embeds queried domains or querying hosts to vector space by deep learning mechan then the relationship between querying of domains or hosts was mapped to vector space operations.By processing two real campus network DNS log datasets,it is found that this method maintains relationships very well.After doing mension reduction and clustering analysis,researchers can not only easily explore hidden relationships intuitively,but also discover abnormal network events like botnet.

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In order to solve the problem of "loophole" tendency between clone code and non-clone code, this paper made a comparative analysis of different types of clones and non-clones based on "loopholes". First of all, it extracted the vulnerability of the software system code, used the clone detection tool to detect the software cloning code. Secondly, it extracted the clone and nonclone code which could produce "loophole" , and calculated the BOC vulnerability density and LOC vulnerability density of different clone types and non-clones separately. Finally, this paper compared and analyzed the vulnerability of type-1, pure type-2, pure-type3 clones and non-clones, and analyzed the results by Mann-Whitney test (WMM). The experimental results show that type-1 clones are more prone to "loopholes" , and that the cloning of pure type-3 clones is less likely to introduced vulnerabilities. The study also found that clone and non-cloned code are in a higher frequency of "loopholes" set. 'Ibis study adds to understanding of cloning features to help software designers and developers reduce the impact of code cloning on software. [ABSTRACT FROM AUTHOR]