Search

Your search keyword '"CLUSTER"' showing total 22,042 results
Start Over Descriptor "CLUSTER"
22,042 results on '"CLUSTER"'

38. Pattern Detection in Abnormal District Heating Data

Author

Mbiydzenyuy, Gideon, Sundell, Håkan, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, Giesselbach, Sven, editor, Pardalos, M. Panos, editor, and Umeton, Renato, editor

Published
2025
Full Text
View/download PDF

39. Port Charging Station Load Prediction Method Based on EEMD-DBN-LSTM and PSO-k-Density Dual Model

Author

Wang, Heng, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Wen, Fushuan, editor, Liu, Haoming, editor, Wen, Huiqing, editor, and Wang, Shunli, editor

Published
2025
Full Text
View/download PDF

40. Identifying Core Loads Clusters of Industrial Parks Considering Electricity Consumption Similarity

Author

Yufei, Wang, Qingqing, Wu, Bo, Zhang, Leyan, Qu, Weijun, Hu, Yihan, Liu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Xue, Yusheng, editor, Zheng, Yuping, editor, and Gómez Expósito, Antonio, editor

Published
2025
Full Text
View/download PDF

42. Combination of SKU in POD Assignment in Robotic Mobile Fulfillment Systems

Author

Pratiwi, Dinda Tria, Chou, Shuo-Yan, Suparno, Kurniati, Nani, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, and Tang, Loon Ching, editor

Published
2025
Full Text
View/download PDF

43. Optimal Location Based Fuzzy Logic Model for Underwater Communications

Author

Agrawal, Jaishree, Kalaiarasan, C., Agarwal, Trapty, Ojha, Ananta, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Kumar, Amit, editor, Gunjan, Vinit Kumar, editor, Senatore, Sabrina, editor, and Hu, Yu-Chen, editor

Published
2025
Full Text
View/download PDF

44. The Efficient Link Prediction Technique for Energy Conservation of Community in Wireless Sensor Network

Author

Pandey, Sunil Kumar, Sharma, Manisha, Tiwari, Rajesh, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Kumar, Amit, editor, Gunjan, Vinit Kumar, editor, Senatore, Sabrina, editor, and Hu, Yu-Chen, editor

Published
2025
Full Text
View/download PDF

45. CO-PROTECT: Greek Cluster for Cooperative and Interoperable Crisis and Disaster Management Solutions

Author

Gkotsis, Ilias, Diagourtas, Dimitrios, Zafeiropoulos, Nikolaos, Katsarou, Victoria, Katsaros, Dimitris, Galani, Theodora, Bliziotis, Dimitris, Ouzounoglou, Eleftherios, Akhgar, Babak, Series Editor, Gkotsis, Ilias, editor, Kavallieros, Dimitrios, editor, Stoianov, Nikolai, editor, Vrochidis, Stefanos, editor, and Diagourtas, Dimitrios, editor

Published
2025
Full Text
View/download PDF

48. Simplifying and Characterizing DAGs and Phylogenetic Networks via Least Common Ancestor Constraints: Simplifying and Characterizing DAGs and Phylogenetic Networks: A. Lindeberg, M. Hellmuth.

Author

Lindeberg, Anna and Hellmuth, Marc

Abstract

Rooted phylogenetic networks, or more generally, directed acyclic graphs (DAGs), are widely used to model species or gene relationships that traditional rooted trees cannot fully capture, especially in the presence of reticulate processes or horizontal gene transfers. Such networks or DAGs are typically inferred from observable data (e.g., genomic sequences of extant species), providing only an estimate of the true evolutionary history. However, these inferred DAGs are often complex and difficult to interpret. In particular, many contain vertices that do not serve as least common ancestors (LCAs) for any subset of the underlying genes or species, thus may lack direct support from the observable data. In contrast, LCA vertices are witnessed by historical traces justifying their existence and thus represent ancestral states substantiated by the data. To reduce unnecessary complexity and eliminate unsupported vertices, we aim to simplify a DAG to retain only LCA vertices while preserving essential evolutionary information. In this paper, we characterize LCA -relevant and lca -relevant DAGs, defined as those in which every vertex serves as an LCA (or unique LCA) for some subset of taxa. We introduce methods to identify LCAs in DAGs and efficiently transform any DAG into an LCA -relevant or lca -relevant one while preserving key structural properties of the original DAG or network. This transformation is achieved using a simple operator “ ⊖ ” that mimics vertex suppression. [ABSTRACT FROM AUTHOR]

Published
2025
Full Text
View/download PDF

49. Cluster analysis of digital competencies among professors in higher education.

Author

Chahuán-Jiménez, Karime, Lara-Yergues, Eduardo, Garrido-Araya, Dominique, Salum-Alvarado, Elena, Hurtado-Arenas, Paulina, and Rubilar-Torrealba, Rolando

Subjects
CLUSTER analysis (Statistics), K-means clustering, EDUCATORS, COLLEGE teachers, DIGITAL literacy, HIGHER education, EFFECTIVE teaching
Abstract

Purpose: This research focuses on the diagnosis and clustering of professor higher education in relation to digital competencies, based on different levels of digital competency development. Methods: The methodology employed in this study involved an Ordinary Least Squares (OLS) regression analysis and cluster analysis using K-means clustering algorithm, considering the Silhouette score, based on the responses obtained through a questionnaire DigComEdu framework, and adjusted according to the experts who conducted a validity analysis. Results: The findings indicate that, for the sample professor who answers the questionnaire voluntarily and confidentially, considering margin of error of 5%, a confidence level of 95%, and a response distribution of 50%, corresponding to 314 professors, with a Crombach's alpha of 0.56, there is no relation between the variables of investigation, age, gender, academic degree, academic hierarchy, and years in academy and the level of digital competencies among professors. Regarding the clustering analysis, specifically using the K-means clustering algorithm, four distinct clusters are identified based on the questionnaire scores, aligning with findings from Silhouette score and Quadratic error by number of clusters. Discussion: This research reveals that professors in higher education span all four levels of competency as defined by the DigComEdu model, primarily falling within the intermediate levels of digital competencies. Clustering analysis further provides insights for the implementation of enhancement and development policies, with the aim of guiding professors toward more complex digital activities, ultimately achieving the highest level of digital competencies. This, in turn, fosters improved teaching practices and, consequently, enhances the teaching experience. [ABSTRACT FROM AUTHOR]

Published
2025
Full Text
View/download PDF

50. Identifying Key Traits for Screening High-Yield Soybean Varieties by Combining UAV-Based and Field Phenotyping.

Author

Yang, Chen, Yang, Guijun, Wang, Haorang, Li, Simeng, Zhang, Jiaoping, Pan, Di, Ren, Pengting, Feng, Haikuan, and Li, Heli

Abstract

The breeding of high-yield varieties is a core objective of soybean breeding programs, and phenotypic trait-based selection offers an effective pathway to achieve this goal. The aim of this study was to identify the key phenotypic traits of high-yield soybean varieties and to utilize these traits for screening high-yield soybean varieties. In this study, the UAV (unmanned aerial vehicle)- and field-based phenotypic data were collected from 1923 and 1015 soybean breeding plots at the Xuzhou experimental site in 2022 and 2023, respectively. First, the soybean varieties were grouped by using a self-organizing map and K-means clustering to investigate the relationships between various traits and soybean yield and to identify the key ones for selecting high-yield soybean varieties. It was shown that the duration of canopy coverage remaining above 90% (Tcc90) was a critical phenotypic trait for selecting high-yield varieties. Moreover, high-yield soybean varieties typically exhibited several key phenotypic traits such as rapid development of canopy coverage (Tcc90r, the time when canopy coverage first reached 90%), prolonged duration of high canopy coverage (Tcc90), a delayed decline in canopy coverage (Tcc90d, the time when canopy coverage began to decline below 90%), and moderate-to-high plant height (PH) and hundred-grain weight (HGW). Based on these findings, a method for screening high-yield soybean varieties was proposed, through which 87% and 72% of high-yield varieties (top 5%) in 2022 and 2023, respectively, were successfully selected. Additionally, about 9% (in 2022) and 10% (in 2023) of the low-yielding (bottom 60%) were misclassified as high-yielding. This study demonstrates the benefit of high-throughput phenotyping for soybean yield-related traits and variety screening and provides helpful insights into identifying high-yield soybean varieties in breeding programs. [ABSTRACT FROM AUTHOR]

Published
2025
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results