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Journal articles on the topic 'Clusters'
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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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Lee, Chang Hyung, and Douglas G. Steigerwald. "Inference for Clustered Data." Stata Journal: Promoting communications on statistics and Stata 18, no. 2 (June 2018): 447–60. http://dx.doi.org/10.1177/1536867x1801800210.
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In this article, we introduce clusteff, a community-contributed command for checking the severity of cluster heterogeneity in cluster–robust analyses. Cluster heterogeneity can cause a size distortion leading to underrejection of the null hypothesis. Carter, Schnepel, and Steigerwald (2017, Review of Economics and Statistics 99: 698–709) develop the effective number of clusters to reflect a reduction in the degrees of freedom, thereby mirroring the distortion caused by assuming homogeneous clusters. clusteff generates the effective number of clusters. We provide a decision tree for cluster–robust analysis, demonstrate the use of clusteff, and recommend methods to minimize the size distortion.
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Ye, Di, Yenchun Jim Wu, and Mark Goh. "Hub firm transformation and industry cluster upgrading: innovation network perspective." Management Decision 58, no. 7 (March 26, 2020): 1425–48. http://dx.doi.org/10.1108/md-12-2017-1266.
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PurposeThis research paper examines how hub firm transformation and restructuring of network partnerships shape the development of industrial clusters in China.Design/methodology/approachQuestionnaire data were collected from 210 managers (response rate 70.9 percent) from the manufacturing industrial clusters in Eastern China.FindingsThe results inform that a cluster’s hub firm transformation influences the evolution of the cluster. Though the hub firm may possess transformation capabilities, the cluster is likely to be weakened if network partnerships and resource synergy are not formed amongst the cluster members.Research limitations/implicationsThis paper, in examining the individual- and firm-level attributes of orchestration capability and their interactions, sheds light on the firm level and inter-firm level relationships between resources and innovation in an industrial cluster.Practical implicationsTo facilitate learning and the upgrading of firms within an industry cluster and promote a cluster’s innovation network, policymakers can initiate preferential policy measures to cultivate support to strategically transform a cluster’s hub firm, thus fostering cluster network growth.Originality/valueThe paper studies the evolution of clusters by investigating the hub firm transformation and member firm interaction. Focusing on the inter-firm network interactions lends a richer understanding of the nuances of the evolution of industrial clusters in Asia.
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Razminiene, Kristina. "Circular economy in clusters' performance evaluation." Equilibrium 14, no. 3 (September 30, 2019): 537–59. http://dx.doi.org/10.24136/eq.2019.026.
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Research background: The value of clusters in developing advanced technology products and services as well as promoting regional growth is acknowledged by many policymakers and researchers (Lee et al., 2012). Hence, clusters are identified as enablers of the circular economy and resource efficiency in this study. Companies aim to enhance competencies and create competitive advantages in global competition and this can be achieved through pulling from a common and accessible pool of resources, information and demand for innovation which means that companies can profit from belonging to a cluster.
Purpose of the article: The main aim of the article is to overview the scientific literature that addresses the circular economy, identify clusters and their role in the circular economy and suggest how small and medium enterprises could engage in a circular economy through clusters' performance development.
Methods: Bibliometric literature analysis enables identifying the latest trends in scientific articles regarding a circular economy and clusters. The analytical hierarchy process (AHP) allows for composing the scheme of the cluster’s competitive advantage within circular economy.
Findings & Value added: The findings suggest that resource efficiency is considered to be one of the most important ambitions and clusters can work as enablers of a circular economy for small and medium enterprises (SMEs), gaining a competitive advantage at the same time. Clusters can encourage and provide conditions in which SMEs would turn to a circular economy. The scheme of Cluster's competitive advantage proposed by the author can help cluster's coordinators, policymakers and all the concerned parties to verify the importance of clusters' involvement in the circular economy.
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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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Rines, Kenneth J., and Margaret J. Geller. "Redshift Survey of 12 Moderate-redshift Clusters." Research Notes of the AAS 6, no. 12 (December 22, 2022): 277. http://dx.doi.org/10.3847/2515-5172/acad05.
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Abstract We conducted a redshift survey of 12 X-ray selected clusters in the redshift range z = 0.35–0.50 with MMT/Hectospec. The redshift surveys confirm that these clusters are massive systems. There are no massive groups or clusters projected along the line of sight that might contaminate the observed cluster X-ray properties significantly. We identify 25–79 members per cluster and refine the estimates of each cluster’s mean redshift. We include the 8004 new redshifts in Data Behind the Figure.
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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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Ruomu, Li, Vincent Yip, and Paweł Dobrzański. "Clusters – Typology and Public Policy." Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu 67, no. 3 (2023): 38–51. http://dx.doi.org/10.15611/pn.2023.3.05.
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Clusters, especially high-tech, are important economic phenomena in modern society. As the archetype, Silicon Valley drives governments over the world to initiate their own cluster. Likewise, the cluster policy has also become a major policy tool in the urban planning field. Based on approaches to initiate and promote clusters, the current cluster policy can be divided into three models, the American model, the Asian model, and the European one. This paper focuses on the sustainability of clusters, reviews the literature about high-tech clusters, cluster policy, and the developments of selected clusters, including Silicon Valley, Silicon Fen, Hsinchu Science Park, Singapore Science Park and One-North, and Akademgorodok; discusses their developments and challenges, and aims to explore the role of government and possible approaches. Based on the data analysis, the research concluded that governments are essential to a cluster’s sustainability. The approach of mixing bottom-up and top-down intervention might be the final direction for all countries, and the only difference lies in timing, which varies with local resources, social environments, and especially culture. For governments, its role should be identifying the right timing to implement the adaption in policy, coordinate, and lead the battle for sustainability in practice.
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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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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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Tiongco, Maria A., Enrico Vesperini, and Anna Lisa Varri. "Early dynamical evolution of rotating star clusters in a tidal field." Monthly Notices of the Royal Astronomical Society 512, no. 2 (March 11, 2022): 1584–97. http://dx.doi.org/10.1093/mnras/stac643.
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ABSTRACT In order to explore how the early internal rotational properties of star clusters are affected by the external potential of their host galaxies, we have run a suite of N-body simulations following the early dynamical evolution and violent relaxation of rotating star clusters embedded in a tidal field. Our study focuses on models for which the cluster’s rotation axis has a generic orientation relative to the torque of the tidal field. The interaction between the violent relaxation process, angular momentum of the cluster, and the external torque creates a complex kinematic structure within the cluster, most prominently a radial variation in the position of the rotation axis, along both the polar and azimuthal directions. We also examine the cluster’s velocity dispersion anisotropy and show that the projected anisotropy may be affected by the variation of the rotation axis directions within the cluster; the combination of projection effects and the complex kinematical features may result in the measurement of tangential anisotropy in the cluster’s inner regions. We also characterize the structural properties of our clusters as a function of their initial rotation and virial ratio and find that clusters may develop a triaxial morphology and a radial variation of the minor axis not necessarily aligned with the rotation axis. Finally, we examine the long-term evolution of these complex kinematic features.
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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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Papadakis, Stamatios, Julie Vaiopoulou, Eirini Sifaki, Dimitrios Stamovlasis, and Michail Kalogiannakis. "Attitudes towards the Use of Educational Robotics: Exploring Pre-Service and In-Service Early Childhood Teacher Profiles." Education Sciences 11, no. 5 (April 27, 2021): 204. http://dx.doi.org/10.3390/educsci11050204.
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The introduction of STEM education, and specifically the implementation of educational robotics (ER), has drawn researchers’ attention and has shown that teachers play a crucial role in leading this innovation. The present study concerns in-service and pre-service early childhood teachers, focusing on their perceptions and attitudes about ER use in daily teaching practice. The data were collected via a questionnaire (N = 201) and explored using latent class analysis, which detected distinct clusters/profiles of participants based on their pattern of responses. Two clusters were identified: Cluster1 was relatively homogeneous, including those who share a positive attitude towards ER, while Cluster2 was heterogeneous, comprising participants with inconsistent responses and expressing negative and skeptical thinking. The cluster memberships were associated with external covariates, such as age, years of teaching experience, and variables measuring their technological competencies. The results showed that teaching experience and age were negatively associated with cluster1-membership, while educational robotics knowledge was positively associated. The findings are interpretable, and the implications for education are discussed considering the current literature.
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Rangwal, Geeta, R. K. S. Yadav, Alok Durgapal, D. Bisht, and D. Nardiello. "Astrometric and photometric study of NGC 6067, NGC 2506, and IC 4651 open clusters based on wide-field ground and Gaia DR2 data." Monthly Notices of the Royal Astronomical Society 490, no. 1 (September 21, 2019): 1383–96. http://dx.doi.org/10.1093/mnras/stz2642.
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ABSTRACT We present an analysis of three southern open star clusters NGC 6067, NGC 2506, and IC 4651 using wide-field photometric and Gaia DR2 astrometric data. They are poorly studied clusters. We took advantage of the synergy between Gaia DR2 high precision astrometric measurements and ground-based wide-field photometry to isolate cluster members and further study these clusters. We identify the cluster members using proper motions, parallax and colour–magnitude diagrams. Mean proper motion of the clusters in μαcosδ and μδ is estimated as −1.90 ± 0.01 and −2.57 ± 0.01 mas yr−1 for NGC 6067, −2.57 ± 0.01, and 3.92 ± 0.01 mas yr−1 for NGC 2506 and −2.41 ± 0.01 and −5.05 ± 0.02 mas yr−1 for IC 4651. Distances are estimated as 3.01 ± 0.87, 3.88 ± 0.42, and 1.00 ± 0.08 kpc for the clusters NGC 6067, NGC 2506, and IC 4651, respectively, using parallaxes taken from Gaia DR2 catalogue. Galactic orbits are determined for these clusters using Galactic potential models. We find that these clusters have circular orbits. Cluster radii are determined as 10 arcmin for NGC 6067, 12 arcmin for NGC 2506, and 11 arcmin for IC 4651. Ages of the clusters estimated by isochrones fitting are 66 ± 8 Myr, 2.09 ± 0.14 Gyr, and 1.59 ± 0.14 Gyr for NGC 6067, NGC 2506, and IC 4651, respectively. Mass function slope for the entire region of cluster NGC 2506 is found to be comparable with the Salpeter value in the mass range of 0.77–1.54 M⊙. The mass function analysis shows that the slope becomes flat when one goes from halo to core region in all the three clusters. A comparison of dynamical age with cluster’s age indicates that NGC 2506 and IC 4651 are dynamically relaxed clusters.
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Thongprayoon, Charat, Janina Paula T. Sy-Go, Voravech Nissaisorakarn, Carissa Y. Dumancas, Mira T. Keddis, Andrea G. Kattah, Pattharawin Pattharanitima, et al. "Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia." Diagnostics 11, no. 11 (November 15, 2021): 2119. http://dx.doi.org/10.3390/diagnostics11112119.
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Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.
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Bhawsar, Pragya. "Assessing advantage cluster: the case of Pithampur auto industry." Emerald Emerging Markets Case Studies 11, no. 2 (August 31, 2021): 1–28. http://dx.doi.org/10.1108/eemcs-10-2020-0365.
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Learning outcomes The learning outcomes of this paper will help students in understanding the dynamics of the formation of industry clusters and the benefits associated with industry clusters. The case will give stimulus towards the cluster competition. Case overview/synopsis The case describes the dilemma of a potential investor of a tyre company that wants to diversify its product line and is searching for a new strategic location. The investor is thoughtful about the Pithampur auto industry cluster for its upcoming investment. The case demonstrates how Pithampur has transformed into an “industry cluster” and the benefits it provides to firms in it. However, Pithampur is not the only auto industry cluster in India, clusters like Chakan-Pune is in competition with Pithampur for attracting investments. This is a cause of worry for the cluster’s stakeholders. The case projects amalgamation of concerns of the stakeholders of the clusters and those of potential investors in evaluating and benchmarking it with other clusters for a competitive future. Complexity academic level Suitable for both undergraduate and post-graduate students (MBA students). Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS: 11: Strategy.
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Cole, S. K., and K. Liu. "Metastable decay of photoionized niobium clusters: Clusters within a cluster?" Journal of Chemical Physics 89, no. 2 (July 15, 1988): 780–89. http://dx.doi.org/10.1063/1.455201.
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Kim, J. W., H. I. Kim, and Y. J. Sohn. "Red-giant branch morphology of metal-poor globular clusters in the Galactic bulge." Proceedings of the International Astronomical Union 3, S245 (July 2007): 363–64. http://dx.doi.org/10.1017/s1743921308018139.
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AbstractUsing the (J − K, K) color-magnitude diagrams (CMDs) of 16 metal-poor globular clusters in the Galactic bulge, we investigate the morphological properties of their red-giant branch (RGB), comparing with those of metal-rich clusters in the Galactic bulge and metal-poor clusters in the Galactic halo. The RGB morphological parameters, such as colors at fixed magnitudes, magnitudes at a fixed color, the RGB slope, and a difference of color indices at two fixed magnitudes have been derived from the near-IR CMDs for each cluster. Metal-poor Galactic bulge clusters follow the previous empirical relations between colors at fixed magnitudes and magnitudes at a fixed color of the RGB and the cluster's metallicity. However, the RGB slope and the color difference parameters of some bulge clusters deviate slightly from the previous empirical linear relations for the other globular clusters, implying that the metal-poor bulge clusters may have different formation origin from the other globular clusters in the Galaxy.
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Tarter, Michael E., and Stefano Poni. "A Vitis vinifera Cluster's Wing-related Structural Characteristics and Their Associations with Yield and Berry Composition." HortScience 45, no. 8 (August 2010): 1270–77. http://dx.doi.org/10.21273/hortsci.45.8.1270.
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The hypotheses considered in this article concern the basic question, besides bearing a wing, in what ways do wing-bearing and non-wing-bearing clusters differ? Vines sampled at midseason were again selected at harvest. Each weight of a Vitis vinifera cluster sampled at midseason was multiplied by the number of clusters on the vine from which the cluster had been selected. Correlation coefficients between this quantity and the sampled vine's yield at harvest differed significantly in the sense that coefficients determined solely from the subset of sampled clusters on which a wing (a lateral arm originating from the peduncle and separate from the main body of the cluster) was present were found to be larger than coefficients determined from all sampled clusters. To shed light on distinguishing characteristics of clusters that bore wings, the weights of clusters that had been sampled at midseason were studied. Despite being weighed after the removal of their wings, clusters that had wings were found to be significantly heavier than clusters (sampled at the same midseason date) that had never had wings. Box and whisker plots were constructed to assess this finding as well to study the relationships between a Vitis vinifera rachis' (a cluster's principal axis) weight, length, and diameter and wing absence or presence. For each of the five vineyard blocks that we studied, the median rachis midseason diameters of wing-bearing clusters exceeded the median rachis diameters of non-wing-bearing clusters. Concerning ‘Cabernet Sauvignon’ Vitis vinifera clusters that had wings, it was also found that the late-season differences between the median soluble solids concentrations (°Brix) of wing-borne berries and the median °Brix of non-wing-borne berries were inappreciable.
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Reid, Neil, and Bruce W. Smith. "Assessing the Success of an Industrial Cluster." International Journal of Applied Geospatial Research 3, no. 3 (July 2012): 21–36. http://dx.doi.org/10.4018/jagr.2012070102.
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Industrial clusters have received considerable attention as a regional development strategy. While their efficacy has been debated by academics, clusters have become popular among practitioners. Despite clusters’ acceptance, there have been few attempts to measure their success or their impact on constituent firms. This paper outlines and discusses the metrics developed to evaluate the success of the northwest Ohio greenhouse cluster. The cluster was launched in 2004 to help the industry become more competitive though collaborative problem solving. In identifying success metrics, the authors were cognizant of the fact that they had to reflect the cluster’s objectives and goals. Thus metrics that measured the impact of branding and marketing efforts, reducing energy costs, and increasing collaboration among cluster stakeholders were developed. The work reported in this paper is only the beginning phases of a longer-term, on-going effort to track the progress and success of the northwest Ohio greenhouse cluster.
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Thongprayoon, Charat, Panupong Hansrivijit, Michael A. Mao, Pradeep K. Vaitla, Andrea G. Kattah, Pattharawin Pattharanitima, Saraschandra Vallabhajosyula, et al. "Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia." Diseases 9, no. 3 (August 1, 2021): 54. http://dx.doi.org/10.3390/diseases9030054.
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Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster’s key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. Results: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. Conclusion: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach.
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de Grijs, Richard, Chengyuan Li, and Aaron M. Geller. "The dynamical importance of binary systems in young massive star clusters." Proceedings of the International Astronomical Union 12, S316 (August 2015): 222–27. http://dx.doi.org/10.1017/s1743921315009096.
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AbstractCharacterization of the binary fractions in star clusters is of fundamental importance for many fields in astrophysics. Observations indicate that the majority of stars are found in binary systems, while most stars with masses greater than 0.5M⊙ are formed in star clusters. In addition, since binaries are on average more massive than single stars, in resolved star clusters these systems are thought to be good tracers of (dynamical) mass segregation. Over time, dynamical evolution through two-body relaxation will cause the most massive objects to migrate to the cluster center, while the relatively lower-mass objects remain in or migrate to orbits at greater radii. This process will globally dominate a cluster’s stellar distribution. However, close encounters involving binary systems may disrupt ‘soft’ binaries. This process will occur more frequently in a cluster’s central, dense region than in its periphery, which may mask the effects of mass segregation. Using high resolution Hubble Space Telescope observations, combined with sophisticated N-body simulations, we investigate the radial distributions of the main-sequence binary fractions in massive young Large Magellanic Cloud star clusters. We show that binary disruption may play an important role on very short timescales, depending on the environmental conditions in the cluster cores. This may lead to radial binary fractions that initially decline in the cluster centers, which is contrary to the effects expected from dynamical mass segregation.
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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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Trachenko, M. B., and O. D. Gaisha. "Evaluation of the Effectiveness of Financing Industrial Clusters." Russian Economic Journal, no. 5 (November 2019): 36–47. http://dx.doi.org/10.33983/0130-9757-2019-5-36-47.
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The article is solving an actual problem — development of a system of indicators to evaluate the effectiveness of financing industrial clusters in Russia. The article analyzes the cluster models of Russian and foreign authors, identifies their strengths and weaknesses. A universal information model of the cluster was developed, reflecting the interaction of the participants among themselves and with external stakeholders of the cluster development. The developed model has three control loops: internal cluster stakeholders, cluster, cluster's region. Each has the specificity of the movement of inventory and cash flows, information interaction in the implementation of cluster policy, and reflects the interests of various stakeholders of industrial clusters. The model lays the groundwork to justify a three-tier system of indicators to evaluate the effectiveness of financing industrial clusters. The subsystems of the indicators of the impact of the industrial cluster on the regional economy, of the indicators of the industrial cluster development and the subsystem of the indicators of the financial condition of enterprises participating in the industrial cluster are highlighted in the proposed system. The study used the methods of bibliographic and logical analysis, synthesis and systems approach, mathematical methods of statistical data processing. The developed system of indicators for assessing the effectiveness of financing industrial clusters can be used to conduct current and subsequent monitoring of financing the implementation of cluster programs, to prepare decisions on the allocation of budgetary funds by state and municipal authorities, and to potential investors to determine the most promising investment instruments.
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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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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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Ishchenko, Maryna, Peter Berczik, and Margarita Sobolenko. "Milky Way globular clusters on cosmological timescales." Astronomy & Astrophysics 683 (March 2024): A146. http://dx.doi.org/10.1051/0004-6361/202347990.
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Context. The present epoch of the Gaia success gives us a possibility to predict the dynamical evolution of our Solar System in the global Galactic framework with high precision. Aims. We statistically investigated the total interaction of globular clusters with the Solar System during six billion years of look-back time. We estimated the gravitational influence of globular clusters’ flyby onto the Oort cloud system. Methods. To perform the realistic orbital dynamical evolution for each individual cluster, we used our own high-order parallel dynamical N-body φ-GPU code that we developed. To reconstruct the orbital trajectories of clusters, we used five external dynamical time variable galactic potentials selected from the IllustrisTNG-100 cosmological database and one static potential. To detect a cluster’s close passages near the Solar System, we adopted a simple distance criteria of below 200 pc. To take into account a cluster’s measurement errors (based on Gaia DR3), we generated 1000 initial positions and velocity randomisations for each cluster in each potential. Results. We found 35 globular clusters that have had close passages near the Sun in all the six potentials during the whole lifetime of the Solar System. We can conclude that at a relative distance of 50 pc between a GC and the SolS, we obtain on average ∼15% of the close passage probability over all six billion years, and at dR = 100 pc, we get on average ∼35% of the close passage probability over all six billion years. The globular clusters BH 140, UKS 1, and Djorg 1 have a mean minimum relative distance to the Sun of 9, 19, and 17 pc, respectively. We analysed the gravitational energetic influence on the whole Oort cloud system from the closest selected globular cluster flyby. We generally found that a globular cluster with a typical mass above a few times 105 M⊙ and with deep close passages in a 1–2 pc immediately results in the ejection more than ∼30% of particles from the Oort cloud system. Conclusions. We can assume that a globular cluster with close passages near the Sun is not a frequent occurrence but also not an exceptional event in the Solar System’s lifetime.
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Webb, Jeremy J., and Alison Sills. "The initial properties of young star clusters in M83." Monthly Notices of the Royal Astronomical Society 501, no. 2 (December 12, 2020): 1933–39. http://dx.doi.org/10.1093/mnras/staa3832.
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ABSTRACT The initial sizes and masses of massive star clusters provide information about the cluster formation process and also determine how cluster populations are modified and destroyed, which have implications for using clusters as tracers of galaxy assembly. Young massive cluster populations are often assumed to be unchanged since cluster formation; therefore, their distributions of masses and radii are used as the initial values. However, the first few hundred million years of cluster evolution does change both cluster mass and cluster radius, through both internal and external processes. In this paper, we use a large suite of N-body cluster simulations in an appropriate tidal field to determine the best initial mass and initial size distributions of young clusters in the nearby galaxy M83. We find that the initial masses follow a power-law distribution with a slope of −2.7 ± 0.4 , and the half-mass radii follow a lognormal distribution with a mean of 2.57 ± 0.04 pc and a dispersion of 1.59 ± 0.01 pc. The corresponding initial projected half-light radius function has a mean of 2.7 ± 0.3 pc and a dispersion of 1.7 ± 0.2 pc. The evolution of the initial mass and size distribution functions is consistent with mass-loss and expansion due to stellar evolution, independent of the external tidal field and the cluster’s initial density profile. Observed cluster sizes and masses should not be used as the initial values, even when clusters are only a few hundred million years old.
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Song, Jinfeng, Xiaojiang Long, Yanjun Hao, Jun Zhu, and Yundong Guo. "Ab initio calculations on structural and electronic transport properties of six-atom GaN clusters." International Journal of Modern Physics B 31, no. 29 (November 7, 2017): 1750222. http://dx.doi.org/10.1142/s0217979217502228.
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The structural and electronic transport properties of GaxNy ([Formula: see text]) clusters are investigated in the framework of density functional theory (DFT). To get their most stable structures, a strategy of particle swarm optimization (PSO) algorithm is adopted. It is found that the most stable cluster’s binding energy and HOMO–LUMO gap energy decrease with Ga atom’s number in cluster increasing. The electronic transport properties of the clusters connected with two Al(100) electrodes are obtained by a method of combining nonequilibrium Green’s function (NEGF) with DFT. Equilibrium conductance of all six-atom GaN cluster is low (less than 0.65 G0), and Ga2N4 has the highest one (0.635 G0). Significant negative differential resistance (NDR) phenomenon is observed in configurations with cluster Ga2N4, Ga3N3 and Ga5N1, and these three clusters have almost the same current value in voltage region from 0.8 V to 1.3 V.
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Zvereva, M., and S. Avdyukhina. "Recognition of emotional and expressive movements (gestures) and self-esteem of adolescents with affective disorders." European Psychiatry 65, S1 (June 2022): S425. http://dx.doi.org/10.1192/j.eurpsy.2022.1078.
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Introduction Successful adolescence depends on ability to correctly understand emotionally expressive gestures, especially symbolic (same meaning for everyone) and expressive (individual understanding). Presence of an internal mismatch in adolescent’s self-esteem between what he shows in society and what he really feels can lead to difficulties in forming an adequate adult self-esteem. Objectives Adolescents with affective disorders (F31) -12, normal adolescents - 32. Ages 12-17. Methods Recognition of emotionally expressive movements: postures&gestures (gestures-test), direct self-esteem by Dembo-Rubinstein test and indirect self-esteem by color attitude test by Etkind. Results The Mann-Whitney test showed significant differences between samples in terms of self-esteem gap - “mind” (U=270,000, p<0.37), “character” (U=279,000, p<0.20), “happiness” (U=288,000, p<0.01 ), gestures-test “symbolic” (U=301,000, p<0.003), “expressive” (U=292,000, p<0.007), “emotions” (U=109,000, p<0.028). Cluster analysis divided each of groups into two distinct clusters. Normal: Cluster1 small self-esteem gap, good gesture recognition, negative pole of emotions prevails. Cluster2 small self-esteem gap, worse gesture recognition, pole of emotions is closer to positive. Affective: Cluster1 large self-esteem gap in “mind”, good gesture recognition. Cluster2 large self-esteem gap in “character”, good gesture recognition and bright negative pole of emotions. Conclusions Gestures recognition in normal group is significantly higher than in affective disorder group. Normal adolescents clusters are distinguished by change in gaps throughout self-esteem and pole of emotional recognition. Affective disorder clusters differ by significant gap in one of self-esteem parameters, as well as in the degree of emotional recognition. Those with the largest “character” gap are more likely to attribute negative emotions to gestures than those with larger “mind” gap. Disclosure No significant relationships.
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de Andres, Daniel, Weiguang Cui, Florian Ruppin, Marco De Petris, Gustavo Yepes, Ichraf Lahouli, Gianmarco Aversano, Romain Dupuis, and Mahmoud Jarraya. "Mass Estimation of Planck Galaxy Clusters using Deep Learning." EPJ Web of Conferences 257 (2022): 00013. http://dx.doi.org/10.1051/epjconf/202225700013.
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Galaxy cluster masses can be inferred indirectly using measurements from X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical observations. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation of the cluster masses from the Planck PSZ2 catalog of galaxy clusters using a machine-learning method. We train a Convolutional Neural Network (CNN) model with the mock SZ observations from The Three Hundred (the300) hydrodynamic simulations to infer the cluster masses from the real maps of the Planck clusters. The advantage of the CNN is that no assumption on a priory symmetry in the cluster’s gas distribution or no additional hypothesis about the cluster physical state are made. We compare the cluster masses from the CNN model with those derived by Planck and conclude that the presence of a mass bias is compatible with the simulation results.
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Thongprayoon, Charat, Carissa Y. Dumancas, Voravech Nissaisorakarn, Mira T. Keddis, Andrea G. Kattah, Pattharawin Pattharanitima, Tananchai Petnak, et al. "Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements." Journal of Clinical Medicine 10, no. 19 (September 27, 2021): 4441. http://dx.doi.org/10.3390/jcm10194441.
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Background: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. Methods: We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster’s key features. We assessed the association of the clusters with mortality. Results: In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. Conclusion: Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.
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Anders, Peter, Uta Fritze –. v. Alvensleben, and Richard de Grijs. "Young Star Clusters: Progenitors of Globular Clusters!?" Highlights of Astronomy 13 (2005): 366–68. http://dx.doi.org/10.1017/s1539299600015987.
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AbstractStar cluster formation is a major mode of star formation in the extreme conditions of interacting galaxies and violent starbursts. Young clusters are observed to form in a variety of such galaxies, a substantial number resembling the progenitors of globular clusters in mass and size, but with significantly enhanced metallicity. From studies of the metal-poor and metal-rich star cluster populations of galaxies, we can therefore learn about the violent star formation history of these galaxies, and eventually about galaxy formation and evolution. We present a new set of evolutionary synthesis models of our GALEV code, with special emphasis on the gaseous emission of presently forming star clusters, and a new tool to compare extensive model grids with multi-color broad-band observations to determine individual cluster masses, metallicities, ages and extinction values independently. First results for young star clusters in the dwarf starburst galaxy NGC 1569 are presented. The mass distributions determined for the young clusters give valuable input to dynamical star cluster system evolution models, regarding survival and destruction of clusters. We plan to investigate an age sequence of galaxy mergers to see dynamical destruction effects in process.
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BAER, M., G. BOUSQUET, P. M. DINH, F. FEHRER, P. G. REINHARD, and E. SURAUD. "DYNAMICS OF METAL CLUSTERS IN RARE GAS CLUSTERS." International Journal of Modern Physics B 21, no. 13n14 (May 30, 2007): 2439–48. http://dx.doi.org/10.1142/s0217979207043798.
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We investigate the dynamics of Na clusters embedded in Ar matrices. We use a hierarchical approach, accounting microscopically for the cluster's degrees of freedom and more coarsely for the matrix. The dynamical polarizability of the Ar atoms and the strong Pauli-repulsion exerted by the Ar -electrons are taken into account. We discuss the impact of the matrix on the cluster gross properties and on its optical response. We then consider a realistic case of irradiation by a moderately intense laser and discuss the impact of the matrix on the hindrance of the explosion, as well as a possible pump probe scenario for analyzing dynamical responses.
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Jung, Dooseok Escher, Daniela Calzetti, Matteo Messa, Mark Heyer, Mattia Sirressi, Sean T. Linden, Angela Adamo, et al. "Universal Upper End of the Stellar Initial Mass Function in the Young and Compact LEGUS Clusters." Astrophysical Journal 954, no. 2 (August 31, 2023): 136. http://dx.doi.org/10.3847/1538-4357/aceb5c.
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Abstract We investigate the variation in the upper end of the stellar initial mass function (uIMF) in 375 young and compact star clusters in five nearby galaxies within ∼5 Mpc. All the young stellar clusters (YSCs) in the sample have ages ≲ 4 Myr and masses above 500 M ⊙, according to standard stellar models. The YSC catalogs were produced from Hubble Space Telescope images obtained as part of the Legacy ExtraGalactic UV Survey (LEGUS) Hubble treasury program. They are used here to test whether the uIMF is universal or changes as a function of the cluster’s stellar mass. We perform this test by measuring the Hα luminosity of the star clusters as a proxy for their ionizing photon rate, and charting its trend as a function of cluster mass. Large cluster numbers allow us to mitigate the stochastic sampling of the uIMF. The advantage of our approach relative to previous similar attempts is the use of cluster catalogs that have been selected independently of the presence of Hα emission, thus removing a potential sample bias. We find that the uIMF, as traced by the Hα emission, shows no dependence on cluster mass, suggesting that the maximum stellar mass that can be produced in star clusters is universal, in agreement with previous findings.
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MANUKYAN, Izabella. "FORMATION AND MANAGEMENT OF REGIONAL AGRI-FOOD CLUSTERS IN DEVELOPING COUNTRIES: CASE OF “AGROTRANSILVANIA” (ROMANIA)." Management of Sustainable Development 13, no. 2 (December 1, 2021): 34–40. http://dx.doi.org/10.54989/msd-2021-0014.
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Business clusters are considered to be more applicable in countries classified as developed. However, in modern times developing countries take risks of establishing business clusters and get the expected economic and social rewards. The work observes peculiarities of regional agri-food cluster formation and management in developing countries in the frames of the Romanian “AgroTransilvania” cluster. Based on the empirical analysis and the “CIPM” (“Cluster Initiative Performance Model”) technique, author conducts an expert assessment and finds out how the cluster’s members combine cooperation and competition in order to achieve outstanding results. The research paper encompasses practice-oriented models, including a diamond model for the Romanian agri-food market, a target board of the observed cluster, as well as a map of its members.
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Resmi, J., and I. Sreelathakumary. "Studies on Genetic Divergence in Bitter Gourd (Momordica charantia L.)." Journal of Horticultural Sciences 7, no. 2 (December 31, 2012): 152–55. http://dx.doi.org/10.24154/jhs.v7i2.367.
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Genetic divergence study was conducted on 33 bitter gourd genotypes for twenty characters. These genotypes were grouped into five clusters irrespective of geographic divergence, indicating no parallelism between geographic and genetic diversity. Cluster-I was the largest comprising 11 genotypes, followed by Clusters-III and V with 10 genotypes each. Clusters-II and IV comprised one genotype each. As regards cluster means, Clusters-II and IV performed better in most of the biometric characters studied. Maximum inter-cluster distance was observed in Clusters-III and IV, followed by Clusters-II and III, and clusters-I and IV. Intra-cluster distance was highest in Cluster I.
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RAYNER, D. M., L. LIAN, S. A. MITCHELL, and P. A. HACKETT. "GAS-PHASE CHEMISTRY OF MOLYBDENUM CLUSTERS." Surface Review and Letters 03, no. 01 (February 1996): 675–78. http://dx.doi.org/10.1142/s0218625x96001212.
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The kinetics of reactions of molybdenum clusters, Mo n, n=1–25, in the pressure range 0.4–4 Torr, and temperature range 270–380 K, have been investigated using a large-bore, He-buffered, fast-flow reactor equipped with a laser-vaporization source for the production of clusters. The reactor is designed to make kinetic measurements on neutral metal clusters in the gas phase under well-defined pressures and temperatures. We discuss a new version of the instrument in which LIF techniques, used previously to monitor atoms and dimers, are replaced by laser ionization, time-of-flight mass spectrometry (TOFMS) in order to monitor larger clusters. The new version of the reactor has been tested against known reactions of Ti atoms. Examples of the reactor’s performance are taken from studies performed on Mon cluster reactivity. In particular we summarize some results on the dissociative chemisorption of molecular nitrogen, where large cluster-size effects are found. In some cases a negative-temperature dependence of the kinetics indicates the involvement of a precursor bound state and leads to conclusions concerning the shape of the potential-energy surface and how subtle changes associated with the cluster’s geometric structure might profoundly alter reaction rates.
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Thongprayoon, Charat, Voravech Nissaisorakarn, Pattharawin Pattharanitima, Michael A. Mao, Andrea G. Kattah, Mira T. Keddis, Carissa Y. Dumancas, et al. "Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering." Medicina 57, no. 9 (August 30, 2021): 903. http://dx.doi.org/10.3390/medicina57090903.
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Background and Objectives: Despite the association between hyperchloremia and adverse outcomes, mortality risks among patients with hyperchloremia have not consistently been observed among all studies with different patient populations with hyperchloremia. The objective of this study was to characterize hyperchloremic patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters. Materials and Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,394 hospitalized adult patients with admission serum chloride of >108 mEq/L. We calculated the standardized mean difference of each variable to identify each cluster’s key features. We assessed the association of each hyperchloremia cluster with hospital and one-year mortality. Results: There were three distinct clusters of patients with admission hyperchloremia: 3237 (28%), 4059 (36%), and 4098 (36%) patients in clusters 1 through 3, respectively. Cluster 1 was characterized by higher serum chloride but lower serum sodium, bicarbonate, hemoglobin, and albumin. Cluster 2 was characterized by younger age, lower comorbidity score, lower serum chloride, and higher estimated glomerular filtration (eGFR), hemoglobin, and albumin. Cluster 3 was characterized by older age, higher comorbidity score, higher serum sodium, potassium, and lower eGFR. Compared with cluster 2, odds ratios for hospital mortality were 3.60 (95% CI 2.33–5.56) for cluster 1, and 4.83 (95% CI 3.21–7.28) for cluster 3, whereas hazard ratios for one-year mortality were 4.49 (95% CI 3.53–5.70) for cluster 1 and 6.96 (95% CI 5.56–8.72) for cluster 3. Conclusions: Our cluster analysis identified three clinically distinct phenotypes with differing mortality risks in hospitalized patients with admission hyperchloremia.
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ROSENBERG, ARNOLD L., and RON C. CHIANG. "HETEROGENEITY IN COMPUTING: INSIGHTS FROM A WORKSHARING SCHEDULING PROBLEM." International Journal of Foundations of Computer Science 22, no. 06 (September 2011): 1471–93. http://dx.doi.org/10.1142/s0129054111008829.
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Heterogeneity complicates the use of multicomputer platforms. Can it also enhance their performance? How can one measure the power of a heterogeneous assemblage of computers ("cluster"), in absolute terms (how powerful is this cluster) and relative terms (which cluster is more powerful)? Is a cluster that has one super-fast computer and the rest of "average" speed more/less powerful than one all of whose computers are "moderately" fast? If you can replace just one computer in a cluster with a faster one, should you replace the fastest? the slowest? A result concerning "worksharing" in heterogeneous clusters provides a highly idealized, yet algorithmically meaningful, framework for studying such questions in a way that admits rigorous analysis and formal proof. We encounter some surprises as we answer the preceding questions (perforce, within the idealized framework). Highlights: (1) If one can replace only one computer in a cluster by a faster one, it is (almost) always most advantageous to replace the fastest one. (2) If the computers in two clusters have the same mean speed, then the cluster with the larger variance in speed is (almost) always more productive (verified analytically for small clusters and empirically for large ones.) (3) Heterogeneity can actually enhance a cluster's computing power.
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Pallero, Diego, Facundo A. Gómez, Nelson D. Padilla, Yannick M. Bahé, Cristian A. Vega-Martínez, and S. Torres-Flores. "Too dense to go through: the role of low-mass clusters in the pre-processing of satellite galaxies." Monthly Notices of the Royal Astronomical Society 511, no. 3 (November 17, 2021): 3210–27. http://dx.doi.org/10.1093/mnras/stab3318.
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ABSTRACT We study the evolution of satellite galaxies in clusters of the c-eagle simulations, a suite of 30 high-resolution cosmological hydrodynamical zoom-in simulations based on the eagle code. We find that the majority of galaxies that are quenched at z = 0 (≳80${{\ \rm per\ cent}}$) reached this state in a dense environment (log10M200[M⊙] ≥13.5). At low redshift, regardless of the final cluster mass, galaxies appear to reach their quenching state in low-mass clusters. Moreover, galaxies quenched inside the cluster that they reside in at z = 0 are the dominant population in low-mass clusters, while galaxies quenched in a different halo dominate in the most massive clusters. When looking at clusters at z > 0.5, their in situ quenched population dominates at all cluster masses. This suggests that galaxies are quenched inside the first cluster they fall into. After galaxies cross the cluster’s r200 they rapidly become quenched (≲1 Gyr). Just a small fraction of galaxies ($\lesssim 15{{\ \rm per\ cent}}$) is capable of retaining their gas for a longer period of time, but after 4 Gyr, almost all galaxies are quenched. This phenomenon is related to ram pressure stripping and is produced when the density of the intracluster medium reaches a threshold of $\rho _{\rm ICM}\, \sim 3 \times 10 ^{-5}$ nH (cm−3). These results suggest that galaxies start a rapid-quenching phase shortly after their first infall inside r200 and that, by the time they reach r500, most of them are already quenched.
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Livernois, Alexander R., Enrico Vesperini, Anna Lisa Varri, Jongsuk Hong, and Maria Tiongco. "Long-term evolution of multimass rotating star clusters." Monthly Notices of the Royal Astronomical Society 512, no. 2 (March 10, 2022): 2584–93. http://dx.doi.org/10.1093/mnras/stac651.
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ABSTRACT We investigate the long-term dynamical evolution of the internal kinematics of multimass rotating star clusters. We have performed a set of N-body simulations to follow the internal evolution of clusters with different degrees of initial rotation and have explored the evolution of the rotational velocity, the degree of energy equipartition, and anisotropy in the velocity distribution. Our simulations show that (1) as the cluster evolves, the rotational velocity develops a dependence on the stellar mass with more massive stars characterized by a more rapid rotation and a peak in the rotation curve closer to the cluster centre than low-mass stars; (2) the degree of energy equipartition in the cluster’s intermediate and outer regions depends on the component of the velocity dispersion measured; for more rapidly rotating clusters, the evolution towards energy equipartition is more rapid in the direction of the rotational velocity; (3) the anisotropy in the velocity distribution is stronger for massive stars; and (4) both the degree of mass segregation and energy equipartition are characterized by spatial anisotropy; they have a dependence on both R and z, correlated with the flattening in the spatial variation of the cluster’s density and velocity dispersion, as shown by 2D maps of the mass segregation and energy equipartition on the (R–z) meridional plane.
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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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Casas, Madeline C., Ky Putnam, Adam B. Mantz, Steven W. Allen, and Taweewat Somboonpanyakul. "Optical Photometric Indicators of Galaxy Cluster Relaxation." Astrophysical Journal 967, no. 1 (May 1, 2024): 14. http://dx.doi.org/10.3847/1538-4357/ad41de.
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Abstract The most dynamically relaxed clusters of galaxies play a special role in cosmological studies as well as astrophysical studies of the intracluster medium (ICM) and active galactic nucleus feedback. While high-spatial-resolution imaging of the morphology of the ICM has long been the gold standard for establishing a cluster’s dynamical state, such data are not available from current or planned surveys, and thus require separate, pointed follow-up observations. With optical and/or near-IR photometric imaging, and red-sequence cluster finding results from those data, expected to be ubiquitously available for clusters discovered in upcoming optical and millimeter-wavelength surveys, it is worth asking how effectively photometric data alone can identify relaxed cluster candidates, before investing in, e.g., high-resolution X-ray observations. Here we assess the ability of several simple photometric measurements, based on the redMaPPer cluster finder run on Sloan Digital Sky Survey data, to reproduce X-ray classifications of dynamical state for an X-ray selected sample of massive clusters. We find that two simple metrics contrasting the bright central galaxy (BCG) to other cluster members can identify a complete sample of relaxed clusters with a purity of ∼40% in our data set. Including minimal ICM information in the form of a center position increases the purity to ∼60%. However, all three metrics depend critically on correctly identifying the BCG, which is presently a challenge for optical red-sequence cluster finders.
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Dicker, Simon R., Karen Perez Sarmiento, Brian Mason, Tanay Bhandarkar, Mark J. Devlin, Luca Di Mascolo, Saianeesh Haridas, et al. "Sensitive 3 mm Imaging of Discrete Sources in the Fields of Thermal Sunyaev–Zel’dovich Effect–Selected Galaxy Clusters." Astrophysical Journal 970, no. 1 (July 1, 2024): 84. http://dx.doi.org/10.3847/1538-4357/ad4e35.
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Abstract In this paper, we present the results of a blind survey for compact sources in 243 Galaxy clusters that were identified using the thermal Sunyaev–Zel'dovich effect (tSZ). The survey was carried out at 90 GHz using MUSTANG2 on the Green Bank Telescope and achieved a 5σ detection limit of 1 mJy in the center of each cluster. We detected 24 discrete sources. The majority (18) of these correspond to known radio sources, and of these, five show signs of significant variability, either with time or in spectral index. The remaining sources have no clear counterparts at other wavelengths. Searches for galaxy clusters via the tSZ strongly rely on observations at 90 GHz, and the sources found have the potential to bias mass estimates of clusters. We compare our results to the Websky simulation that can be used to estimate the source contamination in galaxy cluster catalogs. While the simulation shows a good match to our observations at the clusters’ centers, it does not match our source distribution further out. Sources over 104″ from a cluster’s center bias the tSZ signal high, for some of the sources found, by over 50%. When averaged over the whole cluster population, the effect is smaller but still at a level of 1%–2%. We also discovered that unlike previous measurements and simulations, we see an enhancement of source counts in the outer regions of the clusters and fewer sources than expected in the centers of this tSZ-selected sample.
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Henriksen, Mark J., and Prajwal Panda. "Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile." Astrophysical Journal Letters 961, no. 2 (January 25, 2024): L36. http://dx.doi.org/10.3847/2041-8213/ad1ede.
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Abstract We use 3D k-means clustering to characterize galaxy substructure in the A2146 cluster of galaxies (z = 0.2343). This method objectively characterizes the cluster’s substructure using projected position and velocity data for 67 galaxies within a 2.305 Mpc circular region centered on the cluster's optical center. The optimal number of substructures is found to be four. Four distinct substructures with rms velocity typical of galaxy groups or low-mass subclusters, when compared to cosmological simulations of galaxy cluster formation, suggest that A2146 is in the early stages of formation. We utilize this disequilibrium, which is so prevalent in galaxy clusters at all redshifts, to construct a radial mass distribution. Substructures are bound but not virialized. This method is in contrast to previous kinematical analyses, which have assumed virialization, and ignored the ubiquitous clumping of galaxies. The best-fitting radial mass profile is much less centrally concentrated than the well-known Navarro–Frenk–White profile, indicating that the dark-matter-dominated mass distribution is flatter pre-equilibrium, becoming more centrally peaked in equilibrium through the merging of the substructure.
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Stejskal, Jan, and Petr Hajek. "COMPETITIVE ADVANTAGE ANALYSIS: A NOVEL METHOD FOR INDUSTRIAL CLUSTERS IDENTIFICATION." Journal of Business Economics and Management 13, no. 2 (April 5, 2012): 344–65. http://dx.doi.org/10.3846/16111699.2011.620154.
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Many regions and their representatives decide on the amount of support they will provide to industrial clusters, their births, and their existing phase based on the public budget. Nowadays the efficiency of public spending must be provided. There are many examples showing situations where industrial clusters were cancelled when public support was limited or no longer available. Through the use of a special diagnostic method, one can find out if the industrial cluster is able to rise and also be viable without massive public budget support. The suggestion of a new method for industrial cluster identification is the aim of this paper. The Porter's diamond model of the cluster's competitiveness environment is the substance of the novel method.
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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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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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