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Journal articles on the topic 'Cluster'

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Author: Grafiati
Published: 4 June 2021
Last updated: 1 August 2024

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1

Ahamad, Mohammed Gulam, Mohammed Faisal Ahmed, and Mohammed Yousuf Uddin. "Clustering as Data Mining Technique in Risk Factors Analysis of Diabetes, Hypertension and Obesity." European Journal of Engineering and Technology Research 1, no. 6 (July 27, 2018): 88–93. http://dx.doi.org/10.24018/ejeng.2016.1.6.202.

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This investigation explores data mining using open source software WEKA in health care application. The cluster analysis technique is utilized to study the effects of diabetes, obesity and hypertension from the database obtained from Virginia school of Medicine. The simple k-means cluster techniques are adopted to form ten clusters which are clearly discernible to distinguish the differences among the risk factors such as diabetes, obesity and hypertension. Cluster formation was tried by trial and error method and also kept the SSE as low as possible. The SSE is low when numbers of clusters are more. Less than ten clusters formation unable to yield distinguishable information. In this work each cluster is revealing quit important information about the diabetes, obesity, hypertension and their interrelation. Cluster 0: Diabetes ? Obesity ? Hypertension = Healthy patient, Cluster 1: Diabetes ? Obesity ? Hypertension = Healthy patient, Cluster2: Diabetes ? Obesity ? Hypertension = Obesity, Cluster3: Diabetes ? Obesity ? Hypertension = Patients with Obesity and Hypertension, Cluster4: Boarder line Diabetes ? Obesity ? Hypertension = Sever obesity, Cluster5: Obesity ? Hyper tension ? Diabetes = Hypertension, Cluster6: Border line obese ? Border line hypertension ? Diabetes = No serious complications, Cluster 7: Obesity ? Hypertension ? Diabetes= Healthy patients, Cluster 8: Obesity ? Hypertension ? Diabetes= Healthy patients, and Cluster 9: Diabetes ? Hyper tension ? Obesity = High risk unhealthy patients.
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2

Tri Gustiane, Indri, Martanto Martanto, and Tati Suprapti. "CLUSTERING HASIL CEK DARAH DIABETES LANSIA MENGGUNAKAN METODE K-MEANS DI POSBINDU KP. LEBAKJERO DESA CIHERANG." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 2 (April 24, 2024): 2125–29. http://dx.doi.org/10.36040/jati.v8i2.9281.

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Penelitian ini bertujuan untuk menganalisis hasil cek darah lansia yang menderita diabetes menggunakan metode K-Means. Diabetes adalah penyakit metabolic yang ditandai dengan tingginya kadar gula darah (hiperglikemia) yang disebabkan oleh kekurangan insulin atau tidak efektif insulin dalam mengatur metabolisme glukosa. Selain itu terdapat faktor-faktor lain menjadi penyebab terjadinya diabetes diantaranya seperti faktor keturunan, berat badan, usia, tekanan darah dan sebagainya. Diabetes penyakit kronis yang umumnya terjadi pada lansia dan membutuhkan pemantauan berkala untuk mengelola kondisi mereka. Dengan metode K-Means untuk mengelompokan lansia ke dalam kategori yang berbeda berdasarkan karakteristik darah mereka. Metode K-Means Clustering merupakan metode yang digunakan dalam data mining yang cara kerjanya mencari dan mengelompokan data yang mempunyai kemiripan karakteristik antara data satu dengan data lain yang telah diperol eh data yang memiliki kesamaan bukan data yang sama tetapi memiliki karakteristik yang sama, Dengan menerapkan metode K-Means Clustering dapat membantu pihak Posbindu Kp.Lebakjero Desa Ciherang. Penelitian ini akan di cluster menjadi Lansia yang memiliki penyakit Diabetes paling tinggi di Posbindu Kp.Lebakjero Desa Ciherang. Dalam Cluster tersebut atribut yang dipakai adalah Nama, Jenis Kelamin, Usia, dan Hasil Cek Darah. Hasil analisis dapat membantu petugas kesehatan dalam merancang intervensi yang lebih spesifik dan efektif untuk mengelola diabetes pada populasi lansia. Hasil penelitian K-Means Clustering dibantu hasil nilai DBI dengan -0.597, menjadi 6 cluster dimana hasil cluster0 57, cluster1 24, cluster2 30, cluster3 23, cluster4 44, cluster5 25 dan hasil paling optimal di cluster0 yaitu 57. Cluster0 dengan 57 lansia dimana hasil cluster adalah kp.lebakjero mempunyai lansia paling banyak dan mempunyai diabetes paling tinggi. Selain itu, penelitian ini juga untuk mencapai sesuatu hasil yang akurat terhadap data yang di hasilkan di Posbindu Kp.Lebakjero Desa Ciherang.
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3

Frontera, Jennifer A., Lorna E. Thorpe, Naomi M. Simon, Adam de Havenon, Shadi Yaghi, Sakinah B. Sabadia, Dixon Yang, et al. "Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: A prospective, observational study." PLOS ONE 17, no. 9 (September 29, 2022): e0275274. http://dx.doi.org/10.1371/journal.pone.0275274.

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Background Post-acute sequelae of COVID-19 (PASC) includes a heterogeneous group of patients with variable symptomatology, who may respond to different therapeutic interventions. Identifying phenotypes of PASC and therapeutic strategies for different subgroups would be a major step forward in management. Methods In a prospective cohort study of patients hospitalized with COVID-19, 12-month symptoms and quantitative outcome metrics were collected. Unsupervised hierarchical cluster analyses were performed to identify patients with: (1) similar symptoms lasting ≥4 weeks after acute SARS-CoV-2 infection, and (2) similar therapeutic interventions. Logistic regression analyses were used to evaluate the association of these symptom and therapy clusters with quantitative 12-month outcome metrics (modified Rankin Scale, Barthel Index, NIH NeuroQoL). Results Among 242 patients, 122 (50%) reported ≥1 PASC symptom (median 3, IQR 1–5) lasting a median of 12-months (range 1–15) post-COVID diagnosis. Cluster analysis generated three symptom groups: Cluster1 had few symptoms (most commonly headache); Cluster2 had many symptoms including high levels of anxiety and depression; and Cluster3 primarily included shortness of breath, headache and cognitive symptoms. Cluster1 received few therapeutic interventions (OR 2.6, 95% CI 1.1–5.9), Cluster2 received several interventions, including antidepressants, anti-anxiety medications and psychological therapy (OR 15.7, 95% CI 4.1–59.7) and Cluster3 primarily received physical and occupational therapy (OR 3.1, 95%CI 1.3–7.1). The most severely affected patients (Symptom Cluster 2) had higher rates of disability (worse modified Rankin scores), worse NeuroQoL measures of anxiety, depression, fatigue and sleep disorder, and a higher number of stressors (all P<0.05). 100% of those who received a treatment strategy that included psychiatric therapies reported symptom improvement, compared to 97% who received primarily physical/occupational therapy, and 83% who received few interventions (P = 0.042). Conclusions We identified three clinically relevant PASC symptom-based phenotypes, which received different therapeutic interventions with varying response rates. These data may be helpful in tailoring individual treatment programs.
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Akter, F., MZ Islam, A. Akter, SK Debsharma, A. Shama, and M. Khatun. "Genetic Diversity of Bacterial Blight Resistant Rice (Oryza sativa L.) Genotypes from INGER." Bangladesh Rice Journal 23, no. 2 (July 17, 2020): 59–64. http://dx.doi.org/10.3329/brj.v23i2.48248.

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Genetic diversity of 65 rice genotypes was studied from IRBBN (International Rice Bacterial Blight Nursery) of INGER (International Network for Genetic Evaluation of Rice) materials through Mahalanobis D2 statistic for grain yield and yield contributing characters. The genotypes were grouped into five clusters. The inter-cluster distances were higher than intra-cluster distances indicating wider genetic diversity among the genotypes of different clusters. The intra-cluster distances were lower in all the cases reflecting homogeneity of the genotypes within the clusters. The cluster III contained the highest number of genotypes (23) and the clusterv contained the lowest (8). The highest intra-cluster distance was noticed for the cluster I and lowest for cluster III. The highest inter-cluster distance was observed between cluster I and V, followed by cluster IV and V, cluster II andV and the lowest between cluster I and IV. Regarding inter-cluster distance, the genotypes of cluster V showed high genetic distance from all other clusters. The genotypes from cluster V could be hybridized with the genotypes of other clusters for producing transgressive segregants. Based on canonical vector analysis, panicle number per plant had maximum contribution towards genetic divergence. The highest cluster means for yield, grain/panicle and spikelet fertility were obtained from cluster V. The highest means for 1000 grain weight, second higher yield and the lowest growth duration were found in cluster II, while the lowest mean value for yield and 1000 grain weight and higher mean value for growth duration were found in cluster IV. The crosses between the genotypes/parents of cluster V and cluster II, cluster V and cluster I would exhibit high heterosis as well as higher level of yield potential. Therefore, more emphasis should be given for selection of the genotypes from clusters II and V for future breeding programme. Bangladesh Rice j. 2019, 23(2): 59-64
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Altomare, Michele, Andrea Chierici, Francesco Virdis, Andrea Spota, Stefano Piero Bernardo Cioffi, Shir Sara Bekhor, Luca Del Prete, et al. "Centralization of Major Trauma Influences Liver Availability for Transplantation in Northern Italy: Lesson Learned from COVID-19 Pandemic." Journal of Clinical Medicine 11, no. 13 (June 24, 2022): 3658. http://dx.doi.org/10.3390/jcm11133658.

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Background: During the COVID-19 pandemic, the centralization of patients allowed trauma and transplants referral centers to continue their routine activity, ensuring the best access to health care. This study aims to analyze how the centralization of trauma is linked with liver allocation in Northern Italy. Methods: Cluster analysis was performed to generate patient phenotype according to trauma-related variables. Comparison between clusters was performed to evaluate differences in damage control strategy procedures (DCS) performed and the 30-day graft dysfunction. Results: During the pandemic period, the centralization of major trauma has deeply impaired the liver procurement and allocation between the transplant centers in the metropolitan area of Milan (Niguarda: 22 liver procurement; other transplant centers: 2 organ procurement). Two clusters were identified the in Niguarda’s series: cluster 1 is represented by 17 (27.4%) trauma donors, of which 13 (76.5%) were treated with DCS procedures, and 4 (23.5%) did not; cluster 2 is represented by 45 trauma donors (72.6%), of which 22 (48.8%) underwent DCS procedures. A significant difference was found in the number of DCS procedures performed between clusters (3.18 ± 2.255 vs. 1.11 ± 1.05, p = 0.0001). Comparative analysis did not significantly differ in the number of transplanted livers (cluster1/cluster2 94.1%/95.6% p = 0.84) and the 30-day graft dysfunction rate (cluster1/cluster2 0.0%/4.8% p = 0.34). Conclusions: The high level of care guaranteed by first-level trauma centers could reduce the loss of organs suitable for donation, maintaining the good outcomes of transplanted ones, even in case of multiple organ injuries. The pandemic period underlined that the centralization of major trauma impairs the liver allocation between transplant centers.
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6

Botet, R., and R. Jullien. "Intrinsic anisotropy of clusters in cluster-cluster aggregation." Journal of Physics A: Mathematical and General 19, no. 15 (October 21, 1986): L907—L912. http://dx.doi.org/10.1088/0305-4470/19/15/008.

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7

Knospe, O., R. Schmidt, E. Engel, U. R. Schmitt, R. M. Dreizler, and H. O. Lutz. "Cluster-cluster collisions. III. Potential energy between clusters." Physics Letters A 183, no. 4 (December 1993): 332–37. http://dx.doi.org/10.1016/0375-9601(93)90466-d.

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8

Godara, Poonam, Shrawan Kumar, and Darvinder Kumar. "Evaluation of Genetic Variation in Indian mustard (Brassica Juncea L Czern and Coss) Using Multivariate Techniques." Journal of Agriculture Research and Technology 47, no. 03 (2022): 344–48. http://dx.doi.org/10.56228/jart.2022.47315.

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A set of 310 lines of Indian mustard (Brassica juncea L Czern and Coss) were analysed for cluster and principal component analysis (PCA). PCA identified four principal components which explained 65.13% of total variability among the 310 genotypes. Hierarchical cluster analysis grouped 310 genotypes into 3 clusters. Cluster1 included maximum number of 155 genotypes and clusters 3 had the lowest number of 43 genotypes. The grouping pattern of genotypes obtained by cluster analysis and PCA plots was almost similar.
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Baisya, Ritasman, Phani Kumar Devarasetti, Murthy G. S. R., and Liza Rajasekhar. "Autoantibody Clustering in Systemic Lupus Erythematosus–Associated Pulmonary Arterial Hypertension." Indian Journal of Cardiovascular Disease in Women - WINCARS 06, no. 02 (April 2021): 100–105. http://dx.doi.org/10.1055/s-0041-1732510.

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AbstractSystemic lupus erythematous–associated pulmonary arterial hypertension (SLE-PAH) is one of the important causes of mortality in lupus patients. Different autoantibodies are associated with SLE-PAH which can predict its future development. The objective of the study was to identify distinct autoantibody-based clusters in SLE-PAH patients and to compare demographic characters, clinical phenotypes, and therapeutic strategy across the clusters. Three distinct autoantibody clusters were identified using k-means cluster analysis in 71 SLE-PAH patients. Cluster1 had predominant Sm-RNP, Smith, SS-A association; cluster 2 had no definite autoantibody association; and cluster 3 was associated with nucleosome, histone, dsDNA, and ribosomal P protein. Patients in cluster 3 had a highly active disease while those in cluster 1 had significant cytopenia. Mean age and mean right ventricular systolic pressure (RVSP) were both high in cluster 2, indicating later-onset PAH in this group. This was the first autoantibody-based cluster analysis study in SLE-PAH patients in India which confirmed that autoantibodies did exist as clusters and the presence of definite autoantibodies can predict future development of pulmonary hypertension in these patients.
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Ramos-Almendares, Felipe, Mario Abadi, Hernán Muriel, and Valeria Coenda. "Intra-cluster Globular Clusters in a Simulated Galaxy Cluster." Astrophysical Journal 853, no. 1 (January 25, 2018): 91. http://dx.doi.org/10.3847/1538-4357/aaa1ef.

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11

Zhang, Xiaoli, Qiang Liu, Shengyang Niu, Chonghuai Liu, Xiucai Fan, Ying Zhang, Lei Sun, and Jianfu Jiang. "Varietal Differences Among the Fruit Quality Characteristic of 15 Spine Grapes (Vitis davidii Foëx)." HortScience 57, no. 10 (October 2022): 1282–88. http://dx.doi.org/10.21273/hortsci16702-22.

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Spine grape (Vitis davidii Foëx), an important wild grape species in South China, has gained attention because of its health-promoting effects and use in the wine industry. Fruit quality plays an important role in determining the quality of wine; however, a suitable evaluation system to monitor its fruit quality has not been established. The fruit quality characteristics (phenolics and aromas) of 15 spine grapes grown in China were evaluated using a combination of principal component and cluster analyses. The total sugar, organic acid, and phenolic content ranged from 81.80 to 154.89 mg·g−1, 8.02 to 15.48 mg·g−1, and 5.58 to 20.12 mg·g−1, respectively. The comprehensive assessment by principal component analysis revealed that ‘Red xiangzhenzhu’ had the highest quality and ‘Hongjiangci10’ and ‘Ziluolan’ the lowest quality. Cluster analysis using k-means grouped the cultivars into three clusters based on their quality: Cluster 1 grouped those with inferior quality (‘Hongjiangci09’, ‘Hongjiangci10’, ‘Hongjiangci11’, and ‘Hongjiangci07’, etc.), Cluster2 grouped those with average quality (‘Ciputao3#,’ ‘Ziluolan’, and ‘Xiangci4#’), and Cluster3 grouped those with superior quality (‘Red xiangzhenzhu’ and ‘Green xiangzhenzhu’). A combination of principal component analysis and cluster analysis provides a comprehensive and objective evaluation system for determining the quality of grape cultivars. This study is important for the systematic evaluation and utilization of spine grape resources.
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He, Yongshan, Yuanyuan Chen, Xuan Dai, and Shiyong Huang. "Dysregulation of Circadian Clock Genes Associated with Tumor Immunity and Prognosis in Patients with Colon Cancer." Computational and Mathematical Methods in Medicine 2022 (July 16, 2022): 1–19. http://dx.doi.org/10.1155/2022/4957996.

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Early research shows that disrupting the circadian rhythm increases the risk of various cancers. However, the roles of circadian clock genes in colorectal cancer, which is becoming more common and lethal in China, remained to be unclear. In conclusion, the present study has demonstrated that multiple CCGs were dysregulated and frequently mutated in CRC samples by analyzing the TCGA database. The higher expression levels of REV1, ADCYAP1, CSNK1D, NR1D1, CSNK1E, and CRY2 had a strong link with shorter DFS time in CRC patients, demonstrating that CCGs had an important regulatory role in CRC development. Moreover, 513 CRC tumor samples were divided into 3 categories, namely, cluster1 ( n = 428 ), cluster2 ( n = 83 ), and cluster 3 ( n = 109 ), based on the expression levels of the CCGs. Clinical significance analysis showed that the overall survival and disease-free survival of cluster 2 and cluster 3 were significantly shorter than those of cluster 1. The stemness scores in cluster 1 and cluster 2 were significantly higher than those of cluster 3 CRC samples. Clinically, we found that the C3 subtype had significantly higher percentage of T3/T4, N1/N2, and grades III and IV than groups C1 or C2. In addition, we reported that different CRC clusters had significantly different tumor-infiltrating immune cell signatures. Finally, pancancer analysis showed that higher expression of CSNK1D was correlated with shorter DFS time in multiple cancer types, such as COAD and LIHC, and was dysregulated in various cancers. In conclusion, we effectively developed a CCG-related predictive model and opened up new avenues for research into immune regulatory mechanisms and the development of immunotherapy for CRC.
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Schmid, Günter. "Metal clusters and cluster metals." Polyhedron 7, no. 22-23 (January 1988): 2321–29. http://dx.doi.org/10.1016/s0277-5387(00)86349-4.

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Assem, Ibrahim, Thomas Brüstle, and Ralf Schiffler. "Cluster-tilted algebras without clusters." Journal of Algebra 324, no. 9 (November 2010): 2475–502. http://dx.doi.org/10.1016/j.jalgebra.2010.07.035.

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15

Cheung, Man-Wai, Pierre-Philippe Dechant, Yang-Hui He, Elli Heyes, Edward Hirst, and Jian-Rong Li. "Clustering cluster algebras with clusters." Advances in Theoretical and Mathematical Physics 27, no. 3 (2023): 797–828. http://dx.doi.org/10.4310/atmp.2023.v27.n3.a5.

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16

Han, Yoo Na, and Insoo Oh. "A Study Utilzing Concept Map on Burnout Experience of Low-career Elementary School Counselors and the Factors Affecting Burnout." Korean Journal of Teacher Education 39, no. 2 (March 30, 2023): 117–46. http://dx.doi.org/10.14333/kjte.2023.39.2.06.

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Purpose: The purpose of this study is to explore the perceptions of low-career elementary schoolcounseling teachers on burnout experience and burnout factors through concept map method. Methods: From interviews with 10 low-career elementary school counselors, 25 statements on burnoutexperiences, 27 statements on factors affecting burnout were derived. Later, 20 low-career elementaryschool counselors including interview participants participated in the classification of statement similarity. The researcher used SPSS 29.0 to conduct multidimensional scale analysis and hierarchical clusteranalysis, and drew two conceptual diagrams by evaluating the similarity of each statement. Results: As a result, the experience of burnout turned out to be 5 clusters. Cluster 1 was found to be‘low efficacy and devotion in counseling’, Cluster 2 was ‘physical symptom’, Cluster 3 was ‘maximizationof negative emotions’, Cluster 4 was ‘difficulty in handling work’, and Cluster 5 was ‘depression-relatedpsychopathological symptoms’. The factors that promoted burnout were derived into 6 clusters. Cluster1 was found to be ‘lack of awareness of fellow teachers’, Cluster 2 was ‘lack of parental awareness’,Cluster 3 was ‘lack of awareness of school administrators’, Cluster 4 was found to be ‘lack of expertiseand competence’, Cluster 5 was ‘unestablished school counseling system’, and Cluster 6 was ‘schoolcounseling difficulty factors’. Conclusion: This study is meaningful in that it first studied the burnout of low-career professionalcounseling teachers in elementary schools and derived implications suitable for elementary school counselingthat have recently begun to activate. Based on the research contents, the limitations of this study andsuggestions for follow-up studies were discussed.
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Asakura, Kiyotaka, Yoshinao Yamazaki, Haruo Kuroda, Masafumi Harada, and Naoki Toshima. "A “Cluster-in-Cluster” Structure of the SiO2-Supported PtPd Clusters." Japanese Journal of Applied Physics 32, S2 (January 1, 1993): 448. http://dx.doi.org/10.7567/jjaps.32s2.448.

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Sholkamy, Mona Mostafa El. "Clusters, cluster policy and competitiveness: case of Dubai's free zones cluster." International Journal of Competitiveness 2, no. 2 (2022): 112. http://dx.doi.org/10.1504/ijc.2022.125751.

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El Sholkamy, Mona Mostafa. "Clusters, cluster policy and competitiveness: case of Dubai's free zones cluster." International Journal of Competitiveness 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijc.2022.10046157.

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20

Klyachko, D., and D. M. Chen. "Cluster shapes in STM images of isolate clusters and cluster materials." Surface Science 446, no. 1-2 (February 2000): 98–102. http://dx.doi.org/10.1016/s0039-6028(99)01110-3.

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21

Weisser, Martina, Sonja Tragl, and H. Jürgen Meyer. "From Cluster to Cluster: Structural Transformation Reactions Among Tungsten Chloride Clusters." Journal of Cluster Science 20, no. 1 (November 25, 2008): 249–58. http://dx.doi.org/10.1007/s10876-008-0221-6.

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22

ASSEM, IBRAHIM, VASILISA SHRAMCHENKO, and RALF SCHIFFLER. "CLUSTER AUTOMORPHISMS AND COMPATIBILITY OF CLUSTER VARIABLES." Glasgow Mathematical Journal 56, no. 3 (August 22, 2014): 705–20. http://dx.doi.org/10.1017/s0017089514000214.

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AbstractIn this paper, we introduce a notion of unistructural cluster algebras, for which the set of cluster variables uniquely determines the clusters, as well as the notion of weak unistructural cluster algebras, for which the set of cluster variables determines the clusters, provided that the type of the cluster algebra is fixed. We prove that, for cluster algebras of the Dynkin type, the two notions of unistructural and weakly unistructural coincide, and that cluster algebras of rank 2 are always unistructural. We then prove that a cluster algebra $\mathcal A$ is weakly unistructural if and only if any automorphism of the ambient field, which restricts to a permutation of cluster variables of $\mathcal A$, is a cluster automorphism. We also investigate the Fomin-Zelevinsky conjecture that two cluster variables are compatible if and only if one does not appear in the denominator of the Laurent expansions of the other.
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Farizon, B., M. Farizon, and M. J. Gaillard. "Multifragmentation after multi-ionization of hydrogen clusters in high energy cluster–atom and cluster–cluster collisions." International Journal of Mass Spectrometry 192, no. 1-3 (September 1999): 259–66. http://dx.doi.org/10.1016/s1387-3806(99)00091-3.

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24

Sai Prasanna, Kolli Naga Durga, C. Sarada, and L. Naram Naidu. "Genetic Divergence Studies in Brinjal (Solanum melongena L.). Genotypes." International Journal of Current Microbiology and Applied Sciences 12, no. 10 (October 10, 2023): 202–7. http://dx.doi.org/10.20546/ijcmas.2023.1210.022.

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Using the Mahalanobis D2 statistic, the genetic divergence among 32 genotypes of brinjal for 23 characters was examined. On the basis of the relative magnitude of the D2 values, the genotypes were divided into eight clusters. The largest of the eight clusters, cluster I, had 18 genotypes. It was followed by cluster V (five), cluster III (four), and the remaining clusters, which each had a single genotype. The largest intra-cluster distance (8277.51) was in cluster V, which was followed by cluster I (4488.09) and cluster III (3908.52). The remaining clusters had very small intra-cluster distances. The highest inter-cluster D2 value (23195.60) was found between clusters III and VI, while the lowest value (5777.13) was found between clusters IV and VI. In cluster VI, the mean value for almost all of the characteristics was greatest. Maximum genetic divergence was caused by traits including fruit weight, days to first harvest, number of fruits per cluster, and percentage of fruit set in pseudo short-styled flowers.
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Kumar, Rajendra, Ravi Kumar, Neeraj Kumar, and Nathi Ram. "Evaluation of genetic divergence in Barley (Hordeum vulgare L.) germplasms." International Journal of Agricultural Invention 1, no. 01 (June 30, 2016): 9–14. http://dx.doi.org/10.46492/ijai/2016.1.1.2.

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Thirty genotypes of wheat were evaluated for assessing genetic divergence among eleven different characters across one environment for exploitation in a breeding programme for improving yield of barley by using Mahalanobis D2 statistics. The germplasms were grouped into six clusters. Out of six clusters, cluster I had five genotypes, cluster II had two genotypes, cluster III had ten genotypes, cluster IV had two genotypes, cluster V had eight genotypes and cluster VI had three genotypes based on present investigation. The intra cluster distance among various clusters exhibited maximum intra cluster distance for cluster VI and lowest was recorded for cluster II. The maximum inter cluster distance was revealed between cluster I and V followed by cluster I and VI, cluster III and IV, cluster III and VI , cluster II and IV, cluster III and V and cluster II and V. The least inter cluster distance between clusters IV and VI followed by cluster IV and I and cluster I and II cluster. The phenotypic and genotypic path coefficient exhibited high positive direct contribution of harvest index, number of tillers per plant, number grain per spike and biological yield per plant.
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Wang, Juan, and Kwang Sook Sung. "A Study on the Discourse and Characteristics of Design Expression of NFT Virtual Fashion Design through China Social Media Big date Analysis." Korea Institute of Design Research Society 8, no. 2 (June 30, 2023): 335–52. http://dx.doi.org/10.46248/kidrs.2023.2.335.

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This study aims to analyze the discourse and design characteristics of ‘NFT virtual fashion’ through social media big data analysis. Data was collected using the keyword ‘NFT virtual fashion’ from Textom China, and the discourse of NFT virtual fashion was analyzed using CONCOR analysis. Cluster analysis was employed to examine the design characteristics of NFT virtual fashion. Four discourse groups emerged from the CONCOR analysis: Group 1 discussed the trends in NFT virtual fashion brands and its development, Group 2 explored the experiences and creation of NFT virtual fashion, Group 3 examined the market and investment trends of NFT virtual fashion in the fashion industry, while Group 4 focused on the subscribers desires and the presence of the sportswear brand Nike in NFT virtual fashion. Furthermore, eight design clusters were identified through cluster analysis, namely: Cluster 1 - ‘Boldness’, Cluster 2 - ‘Collaboration and High Quality’, Cluster 3 - ‘Artistry and Virtuality’, Cluster 4 - ‘Exaggeration and Technological Elements’, Cluster 5 - ‘Environmental Friendliness and Non-standardization’, Cluster6 - ‘Aesthetics and Interactivity’, Cluster 7 - ‘Non-contact’, and Cluster 8 - ‘Boundarylessness’. Finally, insights were drawn from the analysis of the discourse and design characteristics of NFT virtual fashion.
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Noviansyah, Moch Rosid, Ilyas Nuryasin, and Didih Rizki Chandranegara. "Pengembangan Sistem Pengelompokan Sekolah Menggunakan Metode Algoritma K-Means (Studi Kasus Dinas Pendidikan Kabupaten Malang)." Jurnal Repositor 4, no. 2 (May 10, 2022): 103–12. http://dx.doi.org/10.22219/repositor.v4i2.1323.

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Sebuah instansi pemerintah yang bergerak di bidang pendidikan khususnya pendidikan smp tentunya Dinas Pendidikan Kabupaten Malang yang merupakan instansi penting sebagai penggerak pendidikan di wilayah kabupaten malang untuk mengukur suatu mutu dan kualitas - kualitas sekolah yang ada di Dinas Kabupaten Malang yaitu berdasarkan standart penilaian pendidikan dan Standart pengelolaan yang berupa Nilai Ujian Nasional, Nilai Ujian Sekolah, dan Nilai Akreditasi Sekolah, namun demikian mengingat banyaknya jumlah Sekolah Menengah Pertama atau dalam kasus ini adalah SMP yang ada di Kabupaten Malang, nilai Ujian Nasional, Ujian Sekolah, serta Nilai Akreditasi Sekolah dari tiap tiap sekolah tidak seragam tentu pihak Dinas Kabupaten Malang akan kesulitan dalam mencari dan memilah tiap tiap sekolah berdasarkan karakteristik tersebut. Maka penelitian ini membahas tentang penerapan data mining menggunakan metode Algoritma K-Means untuk menghasilkan tampilan profil yang memiliki atribut sama , atribut atau parameter nilai yang digunakan adalah rata rata nilai dari setiap sekolah dari nilai Ujian Nasional, nilai Ujian Sekolah, serta nilai Akreditasi Sekolah, dengan menghasilkan cluster sejumlah 3 (k = 3) dengan cluster1 sebanyak 33 data , cluster2 sebanyak 56 data, cluster3 sebanyak 49 data. Hal ini menunjukan nilai SSE paling besar dengan jumlah cluster sebanyak 3 dimana dengan jumlah cluster tersebut yang paling ideal untuk melakukan clustering sekolah SMP berdasarkan data di Dinas Pendidikan Kabupaten Malang.
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28

Hilker, Michael. "Globular Cluster Systems in the Hydra I Galaxy Cluster." Symposium - International Astronomical Union 207 (2002): 281–86. http://dx.doi.org/10.1017/s0074180900223863.

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In this contribution, first results of deep VLT (V,I) photometry in the central region of the Hydra I galaxy cluster are presented. Many star clusters have been identified not only around several early-type galaxies, but also in the intra-cluster field, as far as 250 kpc from the cluster center. Outside the bulges of the central galaxies NGC 3311 and NGC 3309, the intra-cluster globular cluster system is dominated by blue clusters whose spatial distribution is similar to that of the (newly discovered) dwarf galaxies in Hydra I. The color distributions of globular clusters around NGC 3311 and NGC 3309 are multimodal, with a sharp blue peak and a slightly broader distribution of the red cluster population.
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Khalequzzaman, M., M. Z. Islam, Khaleda Akter, and M. K. Bashar. "GENETIC DIVERSITY IN LOCAL RAINFED LOWLAND RICE (Oryza sativa L.) IN BANGLADESH." Bangladesh Journal of Plant Breeding and Genetics 21, no. 1 (June 30, 2008): 49–54. http://dx.doi.org/10.3329/bjpbg.v21i1.17048.

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Genetic diversity of 40 traditional Bangladeshi rice genotypes was studied under rainfed lowland condition through Mahalanobis D2 statistic for grain yield and yield contributing characters. The genotypes were grouped into six clusters. The inter cluster distances were higher than the intra cluster distances indicating wider genetic diversity among the genotypes of different clusters. The intra cluster distances were lower in all the cases reflecting homogeneity of the genotypes within the clusters. The cluster VI contained the highest number of genotypes (12) and the cluster IV contained the lowest (2). The highest intra cluster distance was noticed for the cluster VI and the lowest for cluster IV. The highest inter cluster distance was observed between cluster III and IV followed by clusters IV and V, clusters I and IV, clusters IV and VI and the lowest between clusters II and VI. Regarding inter cluster distance, cluster IV showed high genetic distance from all other four clusters except cluster II. The genotypes from cluster IV could be hybridized with the genotypes of any of the four clusters for producing transgressive segregants. Days to flowering, duration and spikelets/panicle were mainly responsible for genetic divergence while considering mean values. Though filled grains/panicle and flag leaf area are the most positive contributors based on the latent vectors. The highest cluster means for yield and 1000 grain weight and other 2nd highest four yield contributing characters (viz. plant height, days to maturity, panicle length and flag leaf area) were obtained from cluster III. The highest flag leaf area, plant height, panicle length, filled grains per panicle and grain length were found in cluster V. Cluster IV had the lowest mean for yield and other moderate to low yield contributing characters. Therefore more emphasis should be given on cluster V and III for selecting genotypes as parents for crossing with the genotypes of cluster IV which would be used to produce new recombinants with desired traits. DOI: http://dx.doi.org/10.3329/bjpbg.v21i1.17048
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Islam, MZ, MA Siddique, ESMH Rashid, MS Ahmed, and M. Khalequzzaman. "Genetic Diversity in Sadajira Rice (Oryza sativa L.) Germplasm." Agriculturists 12, no. 1 (July 18, 2014): 26–32. http://dx.doi.org/10.3329/agric.v12i1.19576.

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Genetic diversity of 40 traditional Bangladeshi rice genotypes was studied under rainfed condition through Mahalanobis D2 statistic for grain yield and yield contributing characters. The genotypes were grouped into six clusters. The inter-cluster distances were higher than intra-cluster distances indicating wider genetic diversity among the genotypes of different clusters. The intra-cluster distances were lower in all the cases reflecting homogeneity of the genotypes within the clusters. The cluster VI contained the highest number of genotypes (18) and the clusters I and II contained the lowest (1). The highest intra-cluster distance was noticed for the cluster IV and lowest for cluster VI. The highest inter-cluster distance was observed between cluster I and IV, followed by cluster I and II, cluster I and III and the lowest between cluster III and IV. Regarding inter-cluster distance, the genotypes of cluster IV showed high genetic distance from all other clusters. The genotypes from cluster IV could be hybridized with the genotypes of other clusters for producing transgressive segregants. Based on canonical vector analysis, panicle number per plant and 1000 grain weight had maximum contribution towards genetic divergence. The highest cluster means for yield, filled grains panicle-1 and grain breadth were obtained from cluster VI. The highest means for plant height, flag leaf area, unfilled grains panicle-1, harvest index and the lowest growth duration were found in cluster I, while the lowest mean value for yield, grain length, 1000 grain weight, plant height and highest mean value for days to flowering and maturity were found in cluster II. The crosses between the genotypes/parents of cluster VI and cluster II, cluster VI and cluster V would exhibit high heterosis as well as higher level of yield potential. Therefore, more emphasis should be given for selection of the genotypes from clusters I, VI and V for future breeding program. DOI: http://dx.doi.org/10.3329/agric.v12i1.19576The Agriculturists 2014; 12(1) 26-32
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ŽIŽKA, Miroslav, and Natalie PELLONEOVÁ. "Do clusters with public support perform better? Case study of Czech cluster organizations." Administratie si Management Public 1, no. 33 (November 2019): 20–33. http://dx.doi.org/10.24818/amp/2019.33-02.

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Akter, N., H. Begum, MZ Islam, MA Siddique, and M. Khalequzzaman. "Genetic Diversity in Aus rice (Oryza sativa L.) Genotypes of Bangladesh." Bangladesh Journal of Agricultural Research 43, no. 2 (July 8, 2018): 253–66. http://dx.doi.org/10.3329/bjar.v43i2.37329.

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Genetic diversity in 31 traditional Bangladeshi Aus rice genotypes were studied under transplanted condition through Mahalanobis D2 statistic for grain yield and yield contributing characters. The genotypes were grouped into five clusters. The inter-cluster distances were higher than intra-cluster distances indicating wider genetic diversity among the genotypes of different clusters. The intra-cluster distances were lower in all the cases reflecting homogeneity of the genotypes within the clusters. The cluster II contained the highest number of genotypes (08) and the cluster I and III contained the lowest (05). The highest intra-cluster distance was noticed for the cluster II and the lowest for cluster IV. The highest inter-cluster distance was observed between cluster II and V followed by cluster III and V, cluster I and V and the lowest between cluster I and III. Regarding inter-cluster distance, the genotypes of cluster V showed high genetic distance from all other clusters. The genotypes from cluster V could be hybridized with the genotypes of other characters of other clusters for producing transgressive segregants. The highest cluster means for yield, effective tiller number and grain length, were obtained from cluster I; whereas the lowest mean value for yield, culm length, plant height and grain breadth were found in cluster II. Therefore, genotypes under cluster I, cluster II and cluster V might be selected for future breeding program as parents for crossing to produce new recombinants with desired traits.Bangladesh J. Agril. Res. 43(2): 253-266, June 2018
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33

Fauziyah, Wardah Muna, and Anneke Iswani Achmad. "Penerapan Analisis Cluster Hybrid untuk Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Indikator Kemiskinan Tahun 2022." Bandung Conference Series: Statistics 3, no. 2 (August 2, 2023): 566–74. http://dx.doi.org/10.29313/bcss.v3i2.8610.

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Abstract. Hybrid cluster analysis is a combination of hierarchical and non-hierarchical clusters, which has a goal as an alternative method. The advantage of hybrid cluster analysis is that it can determine k-clusters for the process of making non-hierarchical clusters through the results of making hierarchical cluster methods, which will produce the right k-clusters. With the advantages of the hybrid cluster analysis, this research will combine the single linkage method with k-means, then the ward method with k-means. The purpose of this study was to determine the grouping of districts/cities in West Java Province with the most optimal combination method based on poverty in 2022. Based on the results of hybrid cluster analysis using a combination of k-means clusters and single linkage, 4 clusters were obtained. While the results of hybrid cluster analysis using a combination of k-means clusters and the ward method obtained 3 clusters. Among the hybrid cluster analysis using a combination of k-means and single linkage clusters as well as a combination of k-means cluster and ward method, it can be said that the method with a combination of k-means and single linkage is the best or most optimal method with the smallest standard deviation ratio value of 88.38%. Abstrak. Analisis cluster hybrid merupakan kombinasi antara cluster hierarki dan non-hierarki, dimana memiliki tujuan sebagai metode alternatif. Kelebihan analisis cluster hybrid adalah dapat menentukan k-klaster untuk proses pembuatan cluster non-hierarki melalui hasil dari pembuatan cluster metode hierarki, dimana akan menghasilkan k-klaster yang tepat. Dengan kelebihan analisis cluster hybrid tersebut, maka dalam penelitian ini akan mengkombinasikan metode single linkage dengan k-means, kemudian ward method dengan k-means. Tujuan penelitian ini adalah dapat mengetahui pengelompokan Kabupaten/Kota di Provinsi Jawa Barat dengan metode kombinasi yang paling optimal berdasarkan kemiskinan tahun 2022. Berdasarkan hasil analisis cluster hybrid dengan menggunakan kombinasi cluster k-means dan single linkage diperoleh 4 cluster. Sedangkan hasil analisis cluster hybrid dengan menggunakan kombinasi cluster k-means dan ward method diperoleh 3 cluster. Diantara analisis cluster hybrid dengan menggunakan kombinasi cluster k-means dan single linkage serta kombinasi cluster k-means dan ward method, dapat dikatakan bahwa metode dengan kombinasi k-means dan single linkage merupakan metode terbaik atau yang paling optimal dengan nilai rasio simpangan baku terkecil yaitu sebesar 88,38%.
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34

Asha, A. B., Devaraju ., G. K. Latha, C. S. Ravi, and Chitra K. "Estimation of Genetic Divergence in Garden Pea (Pisum sativum L.) Germplasm." Environment and Ecology 41, no. 3A (August 2023): 1662–65. http://dx.doi.org/10.60151/envec/becm1320.

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Genetic diversity among thirty garden pea genotypes was worked out using Mahalanobis D2 statistics. Based on genetic distance, genotypes were grouped into ten clusters of the ten clusters, cluster I was the largest, having 11 genotypes, followed by cluster IV, cluster VIII, cluster III, cluster V and clusters II, VI, VII, IX and X, with one genotype in each. The average intra-cluster distance was maximum in cluster VIII (487.04) and minimum in cluster V (125.72). The maximum inter-cluster distance was observed between cluster VIII and cluster X (3439.67), indicating the genotypes in these clusters are more diverged and they can be used as parents in the hybridization program to obtain superior segregants. Cluster means for various traits revealed that cluster VIII was found superior for plant height at 60 DAS, number of branches per plant at 60 DAS, and number of pods per plant. Thus, genotypes from this cluster could be used as divergent parents to improve these particular characters. Pod yield per plant contributed the maximum (38.16 %) to the genetic diversity among the characters studied.
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35

Kładź-Postolska, Katarzyna. "THE ROLE OF CLUSTER INITIATIVES IN THE DEVELOPMENT OF CLUSTERS." Zeszyty Naukowe Wyższej Szkoły Humanitas Zarządzanie 20, no. 1 (March 31, 2019): 75–87. http://dx.doi.org/10.5604/01.3001.0013.2422.

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The terms in the title of the study, such as “cluster initiative” and “cluster” need to be differentiated as management of cluster initiatives serves the creation or development of clusters. The growth of clusters in regions is also supported by so-called “cluster organizations”. The aim of the study is to discuss the role of cluster initiatives in the development of clusters. The study presents the process of creating and managing cluster initiatives as well as examples of activities undertaken as part of cluster initiatives relevant for the creation and growth of cluster competitiveness.
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Khan, Nazmul Alam, Mohammad Saiful Islam, Saikat Hossain Bhuiyan, K. M. Mehedi Hasan, and Mustafa Kamrul Hasan. "Evaluation of yield contributing characters and cluster analysis of soybean genotypes." Algerian Journal of Biosciences 3, no. 1 (June 30, 2022): 027–32. http://dx.doi.org/10.57056/ajb.v3i1.52.

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A morphological divergence study among the twenty genotypes based on nine yield and yield contributing characters through the D2 statistic indicated the presence of substantial diversity by forming clusters with a wide range of inter-cluster distances. The soybean genotypes under investigation were divided into five clusters. Cluster I had the most genotypes, with 10, followed by clusters III and V, each with five and three genotypes. The relative divergence indicates how much each cluster varies from the others. Cluster I and Cluster III have the most significant order of divergence, followed by Cluster III and Cluster IV. The results revealed that the parents in these clusters are genetically heterogeneous. It's possible that a hybridization program obtained a significant heterotic response. Clusters I and II found the minimum inter-cluster distances, indicating limited genetic diversity. Cluster III had the maximum seed yield per plant cluster value. Individual performance was highest for the genotypes BINAsoybean-3, BINAsoybean-2, and Shohag for the trait seed yield per plant.
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37

Hasan, MJ, Umma Kulsum, MMH Rahman, MMH Chowdhury, and AZMKA Chowdhury. "Genetic diversity analysis of parental lines for hybrid development in rice (Oryza sativa L.)." Bangladesh Journal of Agricultural Research 37, no. 4 (April 2, 2013): 617–24. http://dx.doi.org/10.3329/bjar.v37i4.14386.

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Genetic divergence of 40 parental lines comprising 30 restorer and 10 maintainer lines were studied through Mohalanobis's D2 and principal component analysis for eleven characters. Genotypes were grouped into five different clusters. Cluster V comprised maximum number of genotypes (thirteen) followed by cluster I and II. The inter-cluster distance was maximum between clusters I and V (13.495) indicating wide genetic diversity between these two clusters followed by the distance between cluster I and 11 (9.489), cluster IV, and cluster V (8.969) and cluster I and cluster III (8.039). The minimum inter-cluster distance was observed between cluster II and cluster III (3.034) followed by cluster 111 and cluster IV (3.834) and cluster II and cluster V (4.945) indicating that the genotypes of these clusters were genetically close. The intra cluster distance in the entire five clusters was more or less low which indicated that the genotypes within the same cluster were closely related. Among the characters panicle weight contributed most for divergence in the studied parental lines. Difference in cluster means existed for almost all the characters studied. Highest mean value for number of effective tillers (7.8), days to 50% flowering (95.5), panicles/m2 (192.6), panicle weight (2.9), spikelet fertility (84.8), number of grains/panicle (177.8), days to maturity (123.6), and grain yield/plot (1065.5) were observed in cluster I indicated the parental lines fallen in this cluster having the genetic potentiality to contribute better for yield maximization of hybrid rice. DOI: http://dx.doi.org/10.3329/bjar.v37i4.14386 Bangladesh J. Agril. Res. 37(4): 617-624, December 2012
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38

Ali, Amir. "CLUSTERING DATA ANTROPOMETRI BALITA UNTUK MENENTUKAN STATUS GIZI BALITA DI KELURAHAN JUMPUT REJO SUKODONO SIDOARJO." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 7, no. 3 (December 18, 2020): 395–407. http://dx.doi.org/10.35957/jatisi.v7i3.530.

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Menurut data dari Buku Profil Kesehatan Kabupaten Sidoarjo tahun 2018 dimana di kabupaten Sidoarjo termasuk di kecamatan sukodono kelurahan jumput rejo pada tahun 2018, jumlah balita sebesar 175.393 dengan balita yang ditimbang 118.464. Dari hasil penimbangan dapat diketahui bahwa balita dengan status Bawah Garis Merah (BGM) sebesar 733 (0.6%) dengan rincian anak balita laki-laki 344 orang dan balita perempuan 388 orang. Hal ini mengindikasikan masih adanya balita dengan status gizi buruk. Pemantauan pertumbuhan fisik anak dilakukan dengan menggunakan parameter diantaranya ukuran antropometrik. Pada umumnya indeks antropometri yang digunakan yaitu berat badan menurut umur (BB/U), tinggi badan menurut umur (TB/U). Tujuan dari penelitian ini adalah untuk melakukan pengelompokan data antropometri balita dalam menentukan status gizinya di lingkungan Kelurahan Jumput Rejo Kecamatan Sukodono Kabupaten Sidoarjo. Metode yang digunakan menggunakan teknik data mining dengan algoritma K-Means. Penelitian ini menghasilkan 5 cluster dimana jumlah balita pada cluster1 = 37 balita dimana balita pada posyandu citra gading memerlukan perhatian khusus karena terdapat 11 balita yang menderita gizi buruk. Pada cluster2 jumlah balita yang mengalami gizi kurang sejumlah = 30 balita terutama balita pada posyandu puri sejahtera 3 yang jumlahnya 7 balita. Pada cluster3 terdapat 28 balita dimana cluster ini masuk pada cluster gizi baik terutama pada balita di posyandu surya asri 2 B sejumlah 7 orang. Sedangkan pada cluster 4 dimana masuk pada cluster gizi lebih berjumlah 33 balita dimana balita yang mengalami gizi lebih yaitu balita pada posyandu kedung 1. Serta pada cluster 5 yang merupakan cluster balita dengan obesitas terdapat sejumlah 22 balita terutama pada posyandu jumput rejo indah sejumlah 3 balita. Hasil pengelompokkan data antropometri balita di desa jumput rejo sukodono menunjukkan bahwa terdapat bayi dengan status gizi buruk sebanyak 37 balita, gizi kurang sebanyak 30 balita, gizi baik sebanyak 28 balita, gizi lebih sebanyak 33 balita dan 22 balita mengalami obesitas dari total 150 data antropometri balita. Kata Kunci : Clustering, algoritma, K-Means, Data Mining, Status Gizi
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39

Yen, Steffi X., Sabine Reffert, Elena Schilbach, Siegfried Röser, Nina V. Kharchenko, and Anatoly E. Piskunov. "Reanalysis of nearby open clusters using Gaia DR1/TGAS and HSOY." Astronomy & Astrophysics 615 (July 2018): A12. http://dx.doi.org/10.1051/0004-6361/201731905.

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Context. Open clusters have long been used to gain insights into the structure, composition, and evolution of the Galaxy. With the large amount of stellar data available for many clusters in the Gaia era, new techniques must be developed for analyzing open clusters, as visual inspection of cluster color-magnitude diagrams is no longer feasible. An automatic tool will be required to analyze large samples of open clusters. Aims. We seek to develop an automatic isochrone-fitting procedure to consistently determine cluster membership and the fundamental cluster parameters. Methods. Our cluster characterization pipeline first determined cluster membership with precise astrometry, primarily from TGAS and HSOY. With initial cluster members established, isochrones were fitted, using a χ2 minimization, to the cluster photometry in order to determine cluster mean distances, ages, and reddening. Cluster membership was also refined based on the stellar photometry. We used multiband photometry, which includes ASCC-2.5 BV, 2MASS JHKs, and Gaia G band. Results. We present parameter estimates for all 24 clusters closer than 333 pc as determined by the Catalogue of Open Cluster Data and the Milky Way Star Clusters catalog. We find that our parameters are consistent to those in the Milky Way Star Clusters catalog. Conclusions. We demonstrate that it is feasible to develop an automated pipeline that determines cluster parameters and membership reliably. After additional modifications, our pipeline will be able to use Gaia DR2 as input, leading to better cluster memberships and more accurate cluster parameters for a much larger number of clusters.
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40

D. Shashibhushan, C. V. Sameer Kumar, and Ravi Kiran Reddy Kondi. "Genetic diversity analysis of Pearl Millet germplasm by cluster analysis." emergent Life Sciences Research 08, no. 01 (2022): 70–74. http://dx.doi.org/10.31783/elsr.2022.817074.

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There are 40 genotypes of pearl millet that were gathered from ICRISAT in Hyderabad for the study. The experiment was performed in triplicate using Randomized Block Design. With an average D2 value of 398.08, the experimental material was divided into seven clusters, indicating the presence of diversity across the lines for the attributes being studied. Among different clusters the maximum cluster lines i.e., ten lines were observed in cluster V followed by cluster IV, cluster III, cluster I, cluster II, cluster VII, and cluster VI. Cluster V has the greatest intra cluster distance, followed by Cluster II, Cluster IV, and Cluster I. As a result, within these clusters, selection might be based on the greatest mean for desirable characteristics. The relative divergence shows the degree to which each cluster differs. The highest order of divergence was observed in cluster VIII and cluster X, followed by cluster V and cluster VIII. The results revealed that the parents are genetically heterogeneous in these clusters. The high heterotic response may have been achieved when used in a hybridization programme. Cluster VI and Cluster VII had the shortest inter-cluster distance, indicating low genetic diversity. Plant height had the highest cluster value in cluster VIII and the lowest in cluster X, whereas phenological parameters like days to flowering and days to maturity had the highest cluster value in cluster II. Days to flowering were the most important factor in genetic divergence, followed by the number of panicle length, fodder yield per plot, and productive tillers per plant.
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41

Peng, Dong Liang, K. Sumiyama, H. Yamada, Takehiko Hihara, and T. Uchida. "Preparation of Magnetically Soft, Highly-Densified Fe Cluster-Assembled Films by Impact Cluster Deposition." Advanced Materials Research 11-12 (February 2006): 595–98. http://dx.doi.org/10.4028/www.scientific.net/amr.11-12.595.

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Highly-densified Fe cluster-assembled films were obtained at room temperature by an energetic cluster deposition. Fe clusters were produced using a plasma-gas-condensation (PGC)-type cluster deposition apparatus with a high cluster productivity. Ionized clusters in a cluster beam were electrically accelerated and directly deposited onto a substrate together with neutral clusters from the same cluster source. By increasing the impact energy of the ionized clusters up to about 1 eV/atom, the obtained cluster-assemblies have packing fractions higher than 0.8 without any serious size change, and result in a soft magnetic behavior up to a frequency range of few hundred MHz.
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42

Kulheri, Anil, R. S. Sikarwar, Kiran Jakhar, and S. S. Rajput. "Genetic Divergence Study in Groundnut Genotypes (Arachishypogaea L.)." International Journal of Environment and Climate Change 13, no. 9 (August 1, 2023): 2512–17. http://dx.doi.org/10.9734/ijecc/2023/v13i92485.

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Employing the Mahalanobis D2 statistic, a divergence study was performed on 36 groundnut genotypes and 13 clusters were identified. Indicating that the genotypes of these groups may be more divergent from one another, the maximum inter-cluster distance (D) was found between cluster II and cluster XIII (315.56), followed by cluster III and cluster IX (289.56), cluster III and cluster XII (289.21), and cluster XII and cluster XIII (297.11). The genotypes in the mentioned clusters showed a significant difference in the means for traits that contribute significantly to yield, indicating that the genotypes in these clusters make ideal parents in a hybridization programme to produce transgressive segregants and improve groundnut.
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43

Zhang, Xue Fen, and Fang Zhen Li. "Cluster-to-Cluster Relay Schemes in Wireless Sensor Network." Advanced Materials Research 179-180 (January 2011): 728–33. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.728.

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Cooperative communications is a promising technology to improve the performance of wireless sensor networks. To ensure scalability sensor networks are often partitioned into clusters, each managed by a cluster head. most of the previous research focused on the optimal communication of sensors in one cluster, very little attention has been paid to the efficiency of cooperation among the clusters .In this paper, we consider cluster-to-cluster cooperative communications where cluster head node relay data packets to nearby cluster head nodes using cooperative communications, and propose two cooperative transmission scheme and analysis on the throughput and outage probability. Finally, we demonstrate the applicability of our results through numerical examples.
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Siddique, M. A., M. Khalequzzaman, M. Z. Islam, M. S. Ahamed, and E. S. M. H. Rashid. "GENETIC DIVERSITY IN LOCAL BORO RICE (Oryza sativa L.) GENOTYPES OF BANGLADESH." Bangladesh Journal of Plant Breeding and Genetics 26, no. 1 (June 30, 2013): 19–24. http://dx.doi.org/10.3329/bjpbg.v26i1.19980.

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Genetic diversity in 40 traditional boro rice genotypes was studied under irrigated condition through Mahalanobis D2 statistic for grain yield and yield contributing characters. The genotypes were grouped into five clusters. The inter-cluster distances were higher than intra-cluster distances indicating wider genetic diversity among the clusters. The intra-cluster distances were lower in all the cases reflecting homogeneity of the genotypes within the clusters. The cluster IV contained the highest number of genotypes (22) and the cluster II and V contained the lowest (1). The highest intra-cluster distance was noticed for the cluster III and lowest for cluster II and V. The highest inter-cluster distance was observed between cluster II and III followed by cluster II and V, cluster I and II and the lowest between cluster I and IV. Regarding inter-cluster distance, the genotypes of cluster II showed high genetic distance from all other clusters. The genotypes from cluster II could be hybridized with the genotypes of other clusters for producing transgressive segregants. Based on positive value of vector 1 and vector 2, panicle/hill had maximum contribution towards genetic divergence. The highest cluster means for yield, flag leaf length and grain length breadth ratio were obtained from cluster IV. The highest 1000-grain weight, shortest growth duration and grain breadth were found in cluster II while the lowest mean value for yield, flag leaf length, filled grains and 1000 grain weight were found in cluster V. Therefore, more emphasis should be given on cluster II, III and cluster IV for selecting genotypes as parents for crossing with the genotypes of cluster V which would be used to produce new recombinants with desired traits.DOI: http://dx.doi.org/10.3329/bjpbg.v26i1.19980
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KUMAWAT, ANAND. "Genetic divergence analysis of soybean (Glycine Max L.) genotypes using mahalanobis multivariate analysis." Annals of Plant and Soil Research 26, no. 1 (February 1, 2024): 172–74. http://dx.doi.org/10.47815/apsr.2024.10348.

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Wilk's criterion was used to group the 25 genotypes into three different clusters based on the D2 values. Cluster III was the biggest with 11 genotypes, followed by cluster II with 8 genotypes and cluster I with 6 genotypes. Cluster mean was the highest for days to maturity in cluster II (92.96) and lowest for number of primary branches/plant in cluster III (3.45). Using cluster means genetic diversity analysis reveals genetic backgrounds and interactions of germplasm and manages crop primary pools. The highest inter-cluster distance was observed between cluster II and I, followed by cluster III and II (3.142), and cluster III and I (2.913). This indicates wide diversity between genotypes in these clusters, which can be exploited to generate transgressive segregants. The highest intra-cluster distance was found for cluster I, followed by cluster II (1.961), and cluster III (1.913). It is suggested that genetic materials belonging to these clusters may be used as parents for hybridization programmes to develop desirable variety.
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46

Nurjaman, Deden, Teguh Husodo, Erri Noviar Megantara, Herri Y. Hadikusumah, and Indri Wulandari. "Relationship of Bird Diversity and Plant Composition Inside The Area Campus Green Space of Universitas Padjadjaran Jatinangor, Sumedang West Java." Biosaintifika: Journal of Biology & Biology Education 10, no. 3 (December 19, 2018): 500–509. http://dx.doi.org/10.15294/biosaintifika.v10i3.13543.

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Padjadjaran University (UNPAD) Jatinangor is currently conducting green Campus program. To support the program, a study of biota living in it, as one of the benchmarks of good or bad environmental conditions, is needed. The green space of Jatinangor Campus is divided into two clusters namely Cluster I green space (Campus Forest) and green space Cluster II (Campus Non Forest). The objective of the research was to know the relationship between diversity of birds with diversity of plants in the green space of Cluster I (Campus Forest) and Cluster II (Campus Non Forest) UNPAD Campus Jatinangor as one of the parameters of successful development of green Campus. This research is descriptive-explorative with census method on bird species and plant composition from green spaces of Cluster I (Campus Forest) and Cluste II (Campus Non Forest) Campus UNPAD Jatinangor. From the observations in Cluster I, we identified 46 species of birds and 77 species of plants, whereas in Cluster II, we identified 32 species of birds and 74 types of plants. The number of bird species is directly proportional to the number of plant species from Cluster I and Cluster II green spaces. From this study it was concluded that the diversity of tree species supports the diversity of bird species.
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47

Utami, Putri Yulidsf, Sahid Agustian Hudjimartsu, Tiara Aurilia Viona, and Hulwana Sharfina. "Optimasi Parameter Algoritma DBSCAN untuk Mendeteksi Titik Panas Kebakaran Hutan dan Lahan." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 9, no. 3 (December 22, 2023): 355. http://dx.doi.org/10.26418/jp.v9i3.61714.

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Pencegahan terjadinya kebakaran hutan dan lahan dapat dilakukan dengan menyediakan informasi terkait titik panas dan karakteristik wilayah yang berpotensi munculnya titik panas. Tujuan penelitian ini untuk mengelompokkan data titik panas menjadi beberapa cluster berdasarkan density menggunakan algoritma DBSCAN. Untuk mendapatkan hasil cluster terbaik dilakukan optimasi parameter DBSCAN yaitu nilai epsilon dan minpts. terbaik Berdasarkan beberapa kali hasil iterasi terbentuk nilai epsilon 10 dan minpts minimal titik dalam satu cluster adalah 5. Nilai ini merupakan nilai terbaik dikarenakan membentuk cluster minim noise. Cluster terbentuk berdasarkan nilai LST dan NBR. Berdasarkan hasil penelitian cluster terbanyak yaitu pada tahun 2019 dan tahun 2022 terbentuk 3 cluster. Cluster 0 merupakan cluster yang memiliki nilai LST tinggi atau suhu permukaan tinggi dan memiliki nilai NBR tinggi atau luas terbakar tinggi. Cluster1 merupakan cluster yang memiliki nilai LST tinggi atau suhu permukaan tinggi dan memiliki nilai NBR sedang atau luas terbakar sedang. Sedangkan Cluster2 merupakan cluster yang memiliki nilai LST sedang atau suhu permukaan sedang dan memiliki nilai NBR sedang atau luas terbakar sedang. Kombinasi nilai LST dan NBR menunjukkan area tersebut pernah mengalami kebakaran hutan dan lahan. Evaluasi performa clustering algoritma DBSCAN dilakukan menggunakan Silhouette coefficient. Hasil evaluasi tahun 2017 nilai Silhouette coefficient yaitu 0,773 termasuk strong structure. Tahun 2018 nilai Silhouette coefficient yaitu 0,722 termasuk strong structure. Tahun 2019 terbentuk 3 cluster nilai Silhouette coefficient cluster 0 yaitu 0,875, nilai Silhouette coefficient cluster 1 yaitu 0,802 nilai Silhouette coefficient cluster 2 yaitu 0,876 termasuk pada kategori strong structure. Tahun 2020 terbentuk 2 cluster nilai Silhouette coefficient cluster 0 yaitu 0,637, nilai Silhouette coefficient cluster 1 yaitu 0,649 termasuk pada kategori Medium Structure. Tahun 2021 nilai Silhouette coefficient yaitu 0,714 termasuk strong structure. Tahun 2022 nilai cluster 0 Silhouette coefficient yaitu 0,802, nilai Silhouette coefficient cluster 1yaitu 0,811, dan nilai Silhouette coefficient cluster 2 yaitu 0,712 termasuk pada kategori strong structure.
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48

Malviya, Nupur, K. Kumar, and DK Upadhyay. "Genetic divergence in Indian mustard (Brassica juncea L. Czern. & Coss." Bangladesh Journal of Botany 50, no. 1 (March 27, 2021): 37–44. http://dx.doi.org/10.3329/bjb.v50i1.52669.

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Genetic divergence was carried out in 15 Indian mustard varieties/strains, 4 testers and 60 F1s developed by crossing 15 lines with 4 testers. Based on D2 values between the pair of genotypes, 19 parental lines were grouped into five clusters in E1 (timely sown) & E2 (late sown) condition. In E1, cluster II was the largest having 8 genotypes followed by cluster I and IV with four genotypes. Whereas, in E2 cluster I was largest having 9 genotypes followed by cluster V which had four genotypes. In E1 the intra-cluster distance ranged from 0.00 (cluster V) to 581.64 (cluster IV). Cluster IV and V were the most diverse with inter-cluster distance of 7169.70 followed by cluster II and V (4829.11). Whereas, in E2 the intra-cluster distance ranged from 0.00 (cluster IV) to 296.03 (cluster V). Clusters IV and V were the most diverse with inter-cluster distance of 1894.16. The 60 F1s were grouped into eight clusters in E1 and E2. In E1, the maximum number of genotypes were grouped in clusters I and VI (12) and the intra-cluster distance ranged from 111.59 to 318.07. The maximum inter-cluster distance was observed between cluster IV and VIII (3554.76). Whereas, in E2 maximum number of genotypes grouped in cluster III (15) and intra-cluster distance ranged from 0.00 to 351.31. The maximum inter-cluster distance (D2) was observed between cluster V and VIII (43829.30).
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Kale, B. H., S. B. Sarode, S. G. Gawai, and S. L. Haloli. "Study on genetic diversity in Amaranthus (Amaranthus hypochondriacus L.)." INTERNATIONAL JOURNAL OF PLANT SCIENCES 19, no. 1 (January 15, 2024): 32–36. http://dx.doi.org/10.15740/has/ijps/19.1/32-36.

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Grain amaranth is a protein rich psuedocereal, assumes an important position in terms of quality breeding activities. Systematic characterization, evaluation and utilization of the potential genotype in the crop are prerequisite to any crop improvement. Genetic diversity study was conducted in one hundred fifty genotypes using Mahalanobis D2 Statistics during Kharif 2017 at College of Agriculture Badnapur. Based on D2 values, 150 genotypes were grouped into XII clusters. In the present investigation, the cluster II was with the highest number of genotypes (62) followed by cluster I (33), clusters VI (25), cluster VI (16), IX (3), X (3) and cluster III, V, VII, VIII, XI , XII had single genotype. The intra cluster distance (D) range from 4.74 to 48.64. The maximuminter cluster distance (D= 48.64) was observed between cluster XII and IX cluster, followed by cluster XI and IX (D= 46.25), cluster XI and cluster IV (D= 45.43), cluster IX and cluster VIII (D= 43.92), cluster IX and cluster II (D= 39.03) indicating that the genotypes falling in these clusters were highly divergent from each other implying large amount of diversity within and between groups, which could be exploited in breeding programmes. As crosses between genotypes belonging to the clusters with maximum inter cluster distance, may give high heterotic response resulting in better recombinants.
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50

Pandey, R. M., and Rekha Singh. "Genetic divergence in grain amaranth (Amaranthus hypochondriacus L.)." Genetika 43, no. 1 (2011): 41–49. http://dx.doi.org/10.2298/gensr1101041p.

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Extent and magnitude of genetic divergence for 14 characters among 98 genotypes of grain amaranth (Amaranthus hypochondriacus L.) were determined for the purpose of identifying more diverse parents which are expected to engender maximum variability. Based on genetic divergence D2 statistics, genotypes were grouped into 18 clusters in which cluster I contained maximum number of genotypes (42), Cluster II (11), Cluster III (7), Cluster IV and V (5 in each case) and Cluster VI has 4 genotypes. Cluster VII, VIII, IX, X have (3 in each), cluster XI, XII, XIII, XIV (2 in each) and clusters XV, XVI, XVII and XVIII (1in each case). The clustering pattern revealed that there was no relationship between genetic divergence and eco-geographical region. Intra cluster values ranged from 0.00 to 141.86 and cluster XI is most diverse group. The inter cluster values ranged from 133.08 to 1214.59. Maximum divergence was noticed between clusters VIII and XI (1214.59) followed by clusters XI and XV (982.16) and clusters II and XI (938.89). The diverse clusters derived could be used in hybridization programme to generate wide range of transgressive segregants in population to develop high yielding grain amaranth varieties.
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