Journal articles: 'Cluster design'

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Abdulhamid, Mohanad, and Mwalimu Muthami. "Design of Raspberry PI Parallel Processor." Scientific Bulletin 25, no. 2 (December 1, 2020): 63–74. http://dx.doi.org/10.2478/bsaft-2020-0009.

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AbstractAs data centers have increased in size, there has been a need to create clusters out of cheaper, more affordable commodity parts that can easily be replaced upon failure, and that create more affordable data centers overall. However, such large clusters are still outside of feasibility for individuals and small businesses. It is a worthwhile exercise to see if much smaller clusters could be created for such applications, and to compare their performance / price measure to that of the previous traditional data centers. For this paper, such a cluster is created using Raspberry Pis which are small-sized, single-board computers. A data sharing model is built in Python using message passing interface (MPI) that ran on the cluster of the four Raspberry Pis. So as to evaluate the performance of the system, some greedy algorithms are created. During the implementation process, previously unknown skills, including how to create a cluster, programming the infrastructure are learnt.

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Huang, He, and Hui Xiao. "Internet Industry Cluster Design Based on PDE Mathematical Model." Applied Mechanics and Materials 539 (July 2014): 959–63. http://dx.doi.org/10.4028/www.scientific.net/amm.539.959.

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The industrial cluster is formed by the common competitiveness elements of enterprise group. Under the cluster environment, common technology and common customer as well as distribution channel are composition of cluster development performance mode. On the basis of the parabolic PDE cluster development model, and combined with Internet industrial cluster analysis of virtual platform, the Internet structure industrial cluster analysis system is designed. In order to verify the validity and reliability of the model and system, this paper takes the cluster development of machining as an example to carry on the research for the system performance, which can get the virtual grid node and stress distribution of cluster processing center, finally we can obtain the industrial cluster investment and performance relationship table, to provide the theoretical guidance for the development of industrial clusters.

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Madrigal, Ana Maria. "Cluster allocation design networks." Bayesian Analysis 2, no. 3 (September 2007): 557–89. http://dx.doi.org/10.1214/07-ba222.

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Gracey, Fergus, Donna Malley, Adam P. Wagner, and Isabel Clare. "Characterising neuropsychological rehabilitation service users for service design." Social Care and Neurodisability 5, no. 1 (February 4, 2014): 16–28. http://dx.doi.org/10.1108/scn-09-2013-0034.

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Purpose – Needs of people following acquired brain injury vary over their life-course presenting challenges for community services, especially for those with “hidden” neuropsychological needs. Characterisation of subtypes of rehabilitation service user may help improve service design towards optimal targeting of resources. This paper aims to characterise a neuropsychologically complex group of service users. Design/methodology/approach – Preliminary data from 35 participants accepted for a holistic neuropsychological rehabilitation day programme were subject to cluster analysis using self-ratings of mood, executive function and brain injury symptomatology. Findings – Analysis identified three clusters significantly differentiated in terms of symptom severity (Cluster 1 least and Cluster 2 most severe), self-esteem (Clusters 2 and 3 low self-esteem) and mood (Cluster 2 more anxious and depressed). The three clusters were then compared on characteristics including age at injury, type of injury, chronicity of problems, presence of pre-injury problems and completion of rehabilitation. Cluster 2 were significantly younger at time of injury, and all had head injury. Research limitations/implications – Results suggest different subgroups of neuropsychological rehabilitation service user, highlighting the importance of early identification and provision of rehabilitation to prevent deterioration, especially for those injured when young. Implications for design of, and research into, community rehabilitation service design for those with “hidden disability” are considered. Originality/value – The paper findings suggests that innovative conceptual frameworks for understanding potentially complex longer term outcomes are required to enable development of tools for triaging and efficient allocation of community service resources.

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Radeva, Irina. "Multi-Criteria Models for Clusters Design." Cybernetics and Information Technologies 13, no. 1 (March 1, 2013): 18–33. http://dx.doi.org/10.2478/cait-2013-0003.

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Abstract The paper describes economic agents’ integration in clusters on a predefined technological network. The process is divided and directed by three multi-criteria models. The first one allows selection of economic agents. The second one aims at definition of alternative cluster designs. The third model evaluates the risk of the clusters. The process and models are tested on nineteen economic agents.

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Zhou, Xin, Xiaomei Liao, Lauren M. Kunz, Sharon-Lise T. Normand, Molin Wang, and Donna Spiegelman. "A maximum likelihood approach to power calculations for stepped wedge designs of binary outcomes." Biostatistics 21, no. 1 (August 1, 2018): 102–21. http://dx.doi.org/10.1093/biostatistics/kxy031.

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Summary In stepped wedge designs (SWD), clusters are randomized to the time period during which new patients will receive the intervention under study in a sequential rollout over time. By the study’s end, patients at all clusters receive the intervention, eliminating ethical concerns related to withholding potentially efficacious treatments. This is a practical option in many large-scale public health implementation settings. Little statistical theory for these designs exists for binary outcomes. To address this, we utilized a maximum likelihood approach and developed numerical methods to determine the asymptotic power of the SWD for binary outcomes. We studied how the power of a SWD for detecting risk differences varies as a function of the number of clusters, cluster size, the baseline risk, the intervention effect, the intra-cluster correlation coefficient, and the time effect. We studied the robustness of power to the assumed form of the distribution of the cluster random effects, as well as how power is affected by variable cluster size. % SWD power is sensitive to neither, in contrast to the parallel cluster randomized design which is highly sensitive to variable cluster size. We also found that the approximate weighted least square approach of Hussey and Hughes (2007, Design and analysis of stepped wedge cluster randomized trials. Contemporary Clinical Trials 28, 182–191) for binary outcomes under-estimates the power in some regions of the parameter spaces, and over-estimates it in others. The new method was applied to the design of a large-scale intervention program on post-partum intra-uterine device insertion services for preventing unintended pregnancy in the first 1.5 years following childbirth in Tanzania, where it was found that the previously available method under-estimated the power.

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MERZLIKINA, G. S., and I. V. PSHENICHNIKOV. "Forming of an innovative design of industry clusters as basis of preventive crisis regional management." Strategic decisions and risk management, no. 1-2 (June 29, 2017): 42–49. http://dx.doi.org/10.17747/2078-8886-2017-1-2-42-49.

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Significant institutional weaknesses of the formation of the management structure of an industry cluster are revealed, the possibility of engineering new structures of governance based on the principles of innovative management is identified. Feature of industrial clusters is the need and necessity of innovation that permeates all structures of the cluster management and all the processes of cluster management and, in turn, creates the preconditions for the formation of business entities the ability to successfully overcome a crisis situation. The purpose of the work is to develop the improved construction of innovational sectorial clustering with the use of effective centralized system of management of cluster business processes, based on diffusion of innovational technologies and their production embodiment. A typical structure of sectorial cluster management does not allow determining goals, tasks, tools, and mechanisms of innovational development of sectorial cluster’s enterprises. Drawbacks of the typical structure of sectorial cluster management are as follows: lack of representation of the scheme of cooperation between cluster members and external environment, format of development of interrelations between cluster’s elements in the sphere of information exchange, movement of material flows and financial resources, lack of formed areas of responsibility and matrices of competences of cluster members in various scenarios of development of external environment.The “structural imbalance” in the system of management of sectorial cluster is determined, which consists in elaboration of production and functional structure and “fuzziness” of responsibility for cluster activities efficiency. The use of the improved innovational construction of sectorial cluster management will eliminate structural imbalance in the process of management of sectorial clusters and increase effectiveness of their activities under the conditions of turbulent development of external environment.

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JENA, P., S. N. KHANNA, and B. K. RAO. "DESIGNING CLUSTERS AS SUPERELEMENTS." Surface Review and Letters 03, no. 01 (February 1996): 993–99. http://dx.doi.org/10.1142/s0218625x96001789.

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Atomic clusters formed by agglomeration of atoms constitute a new state of matter with novel properties which depend uniquely on their size, shape, dimensionality, and composition. If clusters could be designed in such a way that they can retain their structure when assembled into a material form, it is possible to envision a new class of solids with clusters as building blocks. These cluster-assembled materials would exhibit rather uncommon properties, thus enlarging the frontiers of material science. Self-consistent calculations have been carried out to study the effect of composition and geometry on the stability and reactivity of clusters. An understanding of the role that atomic and electronic structure play on cluster-cluster interaction would enable us to design cluster materials. This paper deals with the design guidelines, as well as properties of cluster-assembled crystals. It is shown that clusters designed to mimic the properties of known atoms can be viewed as superelements and thus help extend the limits of the current periodic table.

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Harling, Guy, Rui Wang, Jukka-Pekka Onnela, and Victor De Gruttola. "Leveraging contact network structure in the design of cluster randomized trials." Clinical Trials 14, no. 1 (October 24, 2016): 37–47. http://dx.doi.org/10.1177/1740774516673355.

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Background: In settings like the Ebola epidemic, where proof-of-principle trials have provided evidence of efficacy but questions remain about the effectiveness of different possible modes of implementation, it may be useful to conduct trials that not only generate information about intervention effects but also themselves provide public health benefit. Cluster randomized trials are of particular value for infectious disease prevention research by virtue of their ability to capture both direct and indirect effects of intervention, the latter of which depends heavily on the nature of contact networks within and across clusters. By leveraging information about these networks—in particular the degree of connection across randomized units, which can be obtained at study baseline—we propose a novel class of connectivity-informed cluster trial designs that aim both to improve public health impact (speed of epidemic control) and to preserve the ability to detect intervention effects. Methods: We several designs for cluster randomized trials with staggered enrollment, in each of which the order of enrollment is based on the total number of ties (contacts) from individuals within a cluster to individuals in other clusters. Our designs can accommodate connectivity based either on the total number of external connections at baseline or on connections only to areas yet to receive the intervention. We further consider a “holdback” version of the designs in which control clusters are held back from re-randomization for some time interval. We investigate the performance of these designs in terms of epidemic control outcomes (time to end of epidemic and cumulative incidence) and power to detect intervention effect, by simulating vaccination trials during an SEIR-type epidemic outbreak using a network-structured agent-based model. We compare results to those of a traditional Stepped Wedge trial. Results: In our simulation studies, connectivity-informed designs lead to a 20% reduction in cumulative incidence compared to comparable traditional study designs, but have little impact on epidemic length. Power to detect intervention effect is reduced in all connectivity-informed designs, but “holdback” versions provide power that is very close to that of a traditional Stepped Wedge approach. Conclusion: Incorporating information about cluster connectivity in the design of cluster randomized trials can increase their public health impact, especially in acute outbreak settings. Using this information helps control outbreaks—by minimizing the number of cross-cluster infections—with very modest cost in terms of power to detect effectiveness.

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Lujun, Shang. "Cluster." ACM SIGPLAN Notices 26, no. 1 (January 1991): 57–74. http://dx.doi.org/10.1145/122203.122208.

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Crespi, Catherine M., Weng Kee Wong, and Sheng Wu. "A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes." Clinical Trials 8, no. 6 (November 2, 2011): 687–98. http://dx.doi.org/10.1177/1740774511423851.

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Background and Purpose Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. Methods We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. Results Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. Limitations The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. Conclusions The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.

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Jasim, Abbas F., Hao Wang, and Thomas Bennert. "Evaluation of Clustered Traffic Inputs for Mechanistic-Empirical Pavement Design: Case Study in New Jersey." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 13, 2019): 332–48. http://dx.doi.org/10.1177/0361198119853557.

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Truck traffic is one of the significant inputs in design and analysis of pavement structures. This paper focuses on comprehensive cluster analysis of truck traffic in New Jersey for implementation of mechanistic-empirical pavement design. Multiple year traffic data were collected from a large number of weigh-in-motion stations across New Jersey. Statistical analysis was first conducted to analyze directional and temporal (yearly) variations of traffic data. Hierarchical cluster analysis was conducted and three optimum clusters were found for axle load spectra (single, tandem, tridem), vehicle class distribution, and axle/truck ratio, respectively. Road functional classifications were employed to identify different clusters as no common geographic trend could be perceived. The results illustrate that the predicted performance using the site-specific traffic data is comparable with that using the traffic cluster for the selected 10 sites. Among four different traffic inputs, the cluster traffic inputs generated the closest predictions of pavement life as compared with those using site-specific traffic input and the default traffic inputs yielded the highest error. It is recommended to use traffic clusters in mechanistic-empirical pavement design when site-specific data is unavailable.

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Zhan, Denghuang, Liang Xu, Yongdong Ouyang, Richard Sawatzky, and Hubert Wong. "Methods for dealing with unequal cluster sizes in cluster randomized trials: A scoping review." PLOS ONE 16, no. 7 (July 29, 2021): e0255389. http://dx.doi.org/10.1371/journal.pone.0255389.

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In a cluster-randomized trial (CRT), the number of participants enrolled often varies across clusters. This variation should be considered during both trial design and data analysis to ensure statistical performance goals are achieved. Most methodological literature on the CRT design has assumed equal cluster sizes. This scoping review focuses on methodology for unequal cluster size CRTs. EMBASE, Medline, Google Scholar, MathSciNet and Web of Science databases were searched to identify English-language articles reporting on methodology for unequal cluster size CRTs published until March 2021. We extracted data on the focus of the paper (power calculation, Type I error etc.), the type of CRT, the type and the range of parameter values investigated (number of clusters, mean cluster size, cluster size coefficient of variation, intra-cluster correlation coefficient, etc.), and the main conclusions. Seventy-nine of 5032 identified papers met the inclusion criteria. Papers primarily focused on the parallel-arm CRT (p-CRT, n = 60, 76%) and the stepped-wedge CRT (n = 14, 18%). Roughly 75% of the papers addressed trial design issues (sample size/power calculation) while 25% focused on analysis considerations (Type I error, bias, etc.). The ranges of parameter values explored varied substantially across different studies. Methods for accounting for unequal cluster sizes in the p-CRT have been investigated extensively for Gaussian and binary outcomes. Synthesizing the findings of these works is difficult as the magnitude of impact of the unequal cluster sizes varies substantially across the combinations and ranges of input parameters. Limited investigations have been done for other combinations of a CRT design by outcome type, particularly methodology involving binary outcomes—the most commonly used type of primary outcome in trials. The paucity of methodological papers outside of the p-CRT with Gaussian or binary outcomes highlights the need for further methodological development to fill the gaps.

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Frate, Guido Francesco, Andrea Baccioli, Elena Lucchesi, and Lorenzo Ferrari. "ORC Optimal Design through Clusterization for Waste Heat Recovery in Anaerobic Digestion Plants." Applied Sciences 11, no. 6 (March 19, 2021): 2762. http://dx.doi.org/10.3390/app11062762.

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Waste heat recovery (WHR) systems through organic rankine cycles (ORCs) in anaerobic digestion plants may improve cogeneration efficiency. Cogeneration unit power output, flue gas temperature, and mass flow rate are not constant during the day, and the thermal load requested by digesters shows seasonal variations. For this reason, a proper design of the ORC is required. In this study, a design methodology is proposed, based on the clustering of the boundary conditions expected during one year of operation and the anaerobic digestion plant operation. The design has to be a compromise between part-load operation and nominal power rating. In this study, the ORC design boundary conditions were partitioned into four representative clusters with a different population, and the centroid of each cluster was assumed as a potential representative boundary condition for the cycle design. Four different ORC designs, one for each cluster, were defined through an optimization problem that maximized the cycle net power output. ORC designs were compared to those resulting from the seasonal average boundary conditions. The comparison was made based on the ORC off-design performance. Part-load behavior was estimated by implementing a sliding-pressure control strategy and the annual production was therefore calculated. ORC off-design was studied through a detailed Aspen HYSYS simulation. Simulations showed that the power output of each design was directly connected to the cluster population. The design obtained from the most populated cluster generated 10% more energy than that from a system designed by taking into account only the year average conditions.

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Kongsin, Tanongsak, and Sakon Klongboonjit. "Machine components clustering with DSM and repeating method : case study of a soil mixing machine." MATEC Web of Conferences 192 (2018): 01037. http://dx.doi.org/10.1051/matecconf/201819201037.

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In this study, components of the machine are analyzed to group all components into modular groups with a case study of a soil mixing machine. The study begins by creating a design structure matrix of all components. Next, the design structure matrix is transferred into a distance matrix of all components with Jaccard method. After that, the equation of complete linkage must be applied to change the distance matrix to a tree dendrogram for showing the relationship of machine components and dependent coefficient. With this tree dendrogram, six clusters are arranged:- the 1st cluster has 8 modules at the lowest dependent coefficient, the 2nd cluster has 7 modules, the 3rd cluster has 6 modules, the 4th cluster has 5 modules, the 5th cluster has 4 modules, and the 6th cluster has 2 modules at the highest dependent coefficient. Finally, the 1st cluster with 8 modules is considered to be the most proper cluster for this soil mixing machine by applying the repeating method to analyze all six clusters.

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Grøndahl, Vigdis Abrahamsen, and Liv Berit Fagerli. "Nursing home care quality: a cluster analysis." International Journal of Health Care Quality Assurance 30, no. 1 (February 13, 2017): 25–36. http://dx.doi.org/10.1108/ijhcqa-12-2015-0145.

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Purpose The purpose of this paper is to explore potential differences in how nursing home residents rate care quality and to explore cluster characteristics. Design/methodology/approach A cross-sectional design was used, with one questionnaire including questions from quality from patients’ perspective and Big Five personality traits, together with questions related to socio-demographic aspects and health condition. Residents (n=103) from four Norwegian nursing homes participated (74.1 per cent response rate). Hierarchical cluster analysis identified clusters with respect to care quality perceptions. χ2 tests and one-way between-groups ANOVA were performed to characterise the clusters (p<0.05). Findings Two clusters were identified; Cluster 1 residents (28.2 per cent) had the best care quality perceptions and Cluster 2 (67.0 per cent) had the worst perceptions. The clusters were statistically significant and characterised by personal-related conditions: gender, psychological well-being, preferences, admission, satisfaction with staying in the nursing home, emotional stability and agreeableness, and by external objective care conditions: healthcare personnel and registered nurses. Research limitations/implications Residents assessed as having no cognitive impairments were included, thus excluding the largest group. By choosing questionnaire design and structured interviews, the number able to participate may increase. Practical implications Findings may provide healthcare personnel and managers with increased knowledge on which to develop strategies to improve specific care quality perceptions. Originality/value Cluster analysis can be an effective tool for differentiating between nursing homes residents’ care quality perceptions.

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Rozes, L., G. Fornasieri, and C. Sanchez. "Design of Nanohybrids from Well Defined NanoBuilding Blocks." Advances in Science and Technology 45 (October 2006): 752–58. http://dx.doi.org/10.4028/www.scientific.net/ast.45.752.

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Titanium-oxo clusters are employed as inorganic nanobuilding blocks in order to obtain new organic-inorganic hybrid materials. Nanobuilding blocks are well-defined preformed entities which allow a better control of the inorganic domains for the elaboration of hybrid nanocomposites. The oxo-alcoxo cluster Ti16O16(OEt)32 presents a shell of labile ethoxy groups which can be selectively exchanged with preservation of the oxo-core. Both the kinetics of the solvolysis and the number of substituted titanium atoms are strongly dependent on the nature of the reactants. The post-modification of the cluster lead to the elaboration of new oxo-alcoxo clusters Ti16O16(OEt)32- x(OR)x (R : alkyl, phenyl… groups) and when polymerizable ligands, such as methacrylate or styrenic ligands, are introduced at the surface of the nanobrick, new functional nanobuilding blocks can be obtained. These functional clusters are then copolymerized with organic monomers leading to 3D networks where the inorganic nano-fillers are covalently linked to the organic polymer and act as very effective nano-cross-linker. These nanobricks can be considered as good models to correlate the structure of hybrid materials and their physical properties especially their mechanical, thermal, and optical properties.

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Fatemi, Zahra, and Elena Zheleva. "Minimizing Interference and Selection Bias in Network Experiment Design." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 176–86. http://dx.doi.org/10.1609/icwsm.v14i1.7289.

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Current approaches to A/B testing in networks focus on limiting interference, the concern that treatment effects can "spill over" from treatment nodes to control nodes and lead to biased causal effect estimation. Prominent methods for network experiment design rely on two-stage randomization, in which sparsely-connected clusters are identified and cluster randomization dictates the node assignment to treatment and control. Here, we show that cluster randomization does not ensure sufficient node randomization and it can lead to selection bias in which treatment and control nodes represent different populations of users. To address this problem, we propose a principled framework for network experiment design which jointly minimizes interference and selection bias. We introduce the concepts of edge spillover probability and cluster matching and demonstrate their importance for designing network A/B testing. Our experiments on a number of real-world datasets show that our proposed framework leads to significantly lower error in causal effect estimation than existing solutions.

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SATO, Mitsuo. "Cluster Design Using Zeolite Lattice Space." Hyomen Kagaku 14, no. 4 (1993): 209–14. http://dx.doi.org/10.1380/jsssj.14.209.

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Lang, Willis, Stavros Harizopoulos, Jignesh M. Patel, Mehul A. Shah, and Dimitris Tsirogiannis. "Towards energy-efficient database cluster design." Proceedings of the VLDB Endowment 5, no. 11 (July 2012): 1684–95. http://dx.doi.org/10.14778/2350229.2350280.

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Khan, M. G. M., Mahmood A. Rashid, and Sushita Sharma. "An optimal multivariate cluster sampling design." Communications in Statistics - Theory and Methods 49, no. 24 (June 6, 2019): 6121–28. http://dx.doi.org/10.1080/03610926.2019.1626430.

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Eriksson, Lennart, Erik Johansson, Martin MÃ¼ller, and Svante Wold. "Cluster-based Design in Environmental QSAR." Quantitative Structure-Activity Relationships 16, no. 5 (1997): 383–90. http://dx.doi.org/10.1002/qsar.19970160506.

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Lintang Trenggonowati, Dyah, Asep Ridwan, and Zunika DwiAnggraini. "Design of a small and medium industry cluster in cilegon city with supply chain system approach." MATEC Web of Conferences 218 (2018): 04021. http://dx.doi.org/10.1051/matecconf/201821804021.

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Future industrial development strategies, derived from current ideas, that is industrial development through a cluster approach. Cilegon city is a city with many petrochemical industries and other, it should be a potential that can give a positive impact for the welfare of Cilegon city community, the researchers want to design the cluster of the petrochemical derivative of IKM with supply chain system approach. The objective of the research is to design the development of a petrochemical derivative cluster of IKM in Cilegon, to determine the factors that influence the development of SMEs clusters, and to design improvement scenarios in the development of SMEs clusters with dynamic system simulations. Based on the results of SMEs cluster design, the stakeholders in the design are government, big companies, and business actors. Types of products to be produced are plastic packaging, plastic bottles, and plastic bags. The location of the right cluster of IKM development is located in Grogol sub-district. Raw materials used by petrochemical derivatives can be obtained from factories producing polyethylene in Grogol sub-district such as PT Lotte Chemical Titan. Factors that have the significant effect on SMEs cluster development are the factors of government support and local business power factor. The simulation result shows that the second alternative proposal is considered reliable enough to face the fluctuating market demand. From the simulation results obtained the average production of 228,513,6914 psc, the average production stock 821,742.4 and the average sales of 200,000 psc.

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Fan, Li Xian, Yong Zhao Xu, and Hong Tao Li. "Design and Realization of Virtual Computer Cluster System." Advanced Materials Research 271-273 (July 2011): 1313–17. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1313.

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Virtualization technology has revolted the computer technology. In the paper, we present our virtual computer cluster. The components of computer cluster have been improved by virtualization technology from the actual computer server to virtual server instance. Virtual computer cluster includes front-end machine and the cluster nodes. The front-end machine can be physical machines or virtual machines; the cluster node can also be virtual or physical machine node. The front-end machine of the cluster communicates with the cluster nodes through physical or virtual network adapter. The front-end machine connects with the cluster management software in each node so as to monitor and control every physical or virtual node within the cluster. A single virtual computer cluster extends its resources with adding physical or virtual resources including computing resources, storage resources and etc. within secure and stabile occasions with special needs. The technology can be widely applied to cluster and parallel computing demand but with lower-cost system design scenarios.

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Gbolagade, M. D., R. G. Jimoh, K. A. Gbolagade, and O. V. Mejabi. "Design of Hybridized Wireless Sensor Network using K-Means Clustering and Genetic Algorithm." Circulation in Computer Science 2, no. 5 (June 20, 2017): 1–6. http://dx.doi.org/10.22632/ccs-2017-251-96.

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Prolonging the network lifetime in wireless sensor networks (WSNs), Clustering has been recognized has one of the significant methods in achieving this, It entails grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CH’s accept data from relevant cluster’s nodes and forward the aggregate data to base station. A main challenge in WSNs is the selection of appropriate cluster heads. This work proposes a system that is efficient, scalable and load balanced. The proposed scheme combines two known algorithms namely k-means clustering and genetic algorithms based on the weaknesses identified in the two. The simulated data is obtained through the enhancement of clustering by the cluster head (base station) that helps in locating the nearest node that is important in the data transfer instead of transferring to a node that is not necessary, thereby wasting time and resources. The obtained simulation results indicate that this approach is efficient and last longer in elongating the battery life time than the conventional method by 60%.

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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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Zhou, Liang, Jian-jun Luo, Tiago Nogueira, and Klaus Schilling. "Orbit design and control method for satellite clusters and its applications to NetSat project." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 8 (March 9, 2017): 1559–70. http://dx.doi.org/10.1177/0954410017696109.

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In this paper, an optimal orbit design and control method for satellite clusters is presented and the method is applied to the four pico/nano-satellite system NetSat. Firstly, the relative motion based on relative eccentricity/inclination vectors is reviewed. Then, an optimal orbit design method for the cluster flight is developed, and the initial states of satellites in the cluster are derived using a graphical representation of the relative eccentricity/inclination vectors combined with a constrained nonlinear programming method. Next, a novel satellite cluster feedback control method is designed, and an optimal three-maneuver strategy is proposed, which could maintain the satellites in a bounded relative motion, satisfying the tight fuel consumption constraints and fuel balance for a long operational lifetime. Finally, the feasibility and effectiveness of the optimal orbit design and control for satellites cluster and its application to NetSat project are verified through numerical simulations.

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Grosbøl, Preben, and Horacio Dottori. "Comparing ALMA, VLT, and HST data for Massive, Young Clusters in Grand-Design Spirals." Proceedings of the International Astronomical Union 12, S316 (August 2015): 141–42. http://dx.doi.org/10.1017/s1743921316007080.

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AbstractA population of young, massive stellar cluster complexes with near-infrared (NIR) colors indicating high extinction (i.e. Av ~ 7m) was identified on HAWK-I/VLT images of several nearby, grand-design spiral galaxies. Models suggest that they are very young cluster complexes still embedded in a dust/gas envelope which will be expelled after 5-7 Myr. This type of very young, embedded clusters are not seen in optical studies using HST data.A detailed comparison of HST and HAWK-I images was done to better understand the discrepancy between the optical and NIR detection of stellar clusters in nearby galaxies. More than 70% of the NIR clusters are located close to dust lanes which would make an optical detection difficult. A comparison of the ALMA CO(1-0)-map of NGC 4321 and the young, massive clusters shows that 60% of them have CO emission within 2“ indicating a correlation between giant molecular clouds and formation of massive clusters.

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Wu, Jianjun, Xiao Feng, Huidang Zhang, and Wei Lv. "Design and Analysis of Clustering Routing Protocol in Wireless Sensor Network of Frozen Food." International Journal of Online Engineering (iJOE) 13, no. 12 (December 11, 2017): 37. http://dx.doi.org/10.3991/ijoe.v13i12.7893.

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<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;">In order to address the issues of uneven energy dissipation and uniform cluster coverage in wireless sensor networks (WSNs) of frozen food, we developed a load balancing and uniform coverage clustering (LBUCC) algorithm to find an efficient way to generate clusters. Considering node density and the coverage radius of cluster heads, the nearest neighbor clustering algorithm was adopted to cluster the sensor network. On the basis of the number of neighbors and the residual energy of nodes, the LBUCC algorithm ensures the equal distribution of cluster head responsibility among sensor nodes and performs well in periodic data gathering with selected cluster head. As the storage and distribution of frozen food is frequent, the clustering strategy was proposed for dynamic topology in this paper. The LBUCC algorithm was compared with LEACH-C and DHAC algorithms which are well-known in using centralized control algorithm to select cluster head. The simulation results demonstrate that the LBUCC algorithm has longer network lifetime and uniform coverage than the clustering protocols LEACH-C and DHAC do.</span>

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Moerbeek, Mirjam. "The cluster randomized crossover trial: The effects of attrition in the AB/BA design and how to account for it in sample size calculations." Clinical Trials 17, no. 4 (March 19, 2020): 420–29. http://dx.doi.org/10.1177/1740774520913042.

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Background/Aims: This article studies the effect of attrition in the cluster randomized crossover trial. The focus is on the two-treatment two-period AB/BA design where attrition occurs during the washout period. Attrition may occur at either the subject level or the cluster level. In the latter case, clusters drop out entirely and provide no measurements in the second period. Subject attrition can only occur in the cohort design, where each subject receives both treatments. Cluster attrition can also occur in the cross-sectional design, where different subjects are measured in the two time periods. Furthermore, this article explores two different strategies to account for potential levels of attrition: increasing sample size and replacing those subjects who drop out by others. Methods: The statistical model that takes into account the nesting of subjects within clusters, and the nesting of repeated measurements within subjects is presented. The effect of attrition is evaluated on the basis of the efficiency of the treatment effect estimator. Matrix algebra is used to derive the relation between efficiency, the degree of attrition, cluster size and the intraclass correlations: the within-cluster within-period correlation, the within-cluster between-period correlation and (in the case of a cohort design) the within-subject correlation. The methodology is implemented in two Shiny Apps. Results: Attrition in a cluster randomized crossover trial implies a loss of efficiency. Efficiency decreases with an increase of the attrition rate. The loss of efficiency due to attrition of subjects in a cohort design is largest for small number of subjects per cluster-period, but it may be repaired to a large degree by increasing the number of subjects per cluster-period or by replacing those subjects who drop out by others. Attrition of clusters results in a larger loss of efficiency, but this loss does not depend on the number of subjects per cluster-period. Repairing for this loss requires a large increase in the number of subjects per cluster-period. The methodology of this article is illustrated by an example on the effect of lavender scent on dental patients’ anxiety. Conclusion: This article provides the methodology of exploring the effect of attrition in cluster randomized crossover trials, and to repair for attrition. As such, it helps researchers plan their trial in an appropriate way and avoid underpowered trials. To use the methodology, prior estimates of the degree of attrition and intraclass correlation coefficients are needed. It is advocated that researchers clearly report the estimates of these quantities to help facilitate planning future trials.

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Tan, Yan-Xi, Xue Yang, Bei-Bei Li, and Daqiang Yuan. "Rational design of a flu-type heterometallic cluster-based Zr-MOF." Chemical Communications 52, no. 94 (2016): 13671–74. http://dx.doi.org/10.1039/c6cc08191f.

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The first heterometallic cluster-based Zr-MOF with flu-type topology was rationally designed by the cooperative assembly of [Cu₄I₄(Ina)₄]⁴⁻ and [Zr₆(μ₃-OH)₈(OH)₈]⁸⁺ clusters.

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Murpratomo, Eldwin Ilham, Amelia Kurniawati, and Hilman Dwi Anggana. "Course Design Based on Students’ English Skill Cluster: A Case Study in a University Language Center." Jurnal METRIS 21, no. 02 (December 1, 2020): 67–71. http://dx.doi.org/10.25170/metris.v21i02.2488.

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The English Proficiency Test (EPrT) is a prediction test for English as a Foreign Language (TOEFL), which is a prerequisite for graduation at XYZ University. The Language Center provides a course for EPrT preparation. The course posttest data shows that only 74% of students met the graduation prerequisites. This study aims to develop an English course design based on the students’ English skill cluster. This study uses the K-Means clustering approach to classify the students based on English skills. The respondents are 397 students who joined the EPrT preparation course in October and November 2018. The 397 students are distributed into 3 clusters, which are 174 students in cluster 1, 116 students in cluster 2, and 107 students in cluster 3. Cluster 1 consists of students with the score below average. Cluster 2 consists of students with the total score above average, but the components score is below average. Cluster 3 consists of students with pre-test total score below average, but the post-test score are above average. Therefore, the EPrT preparation course is suggested to have different levels, instead of one level as now. The course materials are designed to be suitable for students’ initial English skills at each level.

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Zhang, Yalin, Yunzhong Song, and Shumin Fei. "Consensus Design for Heterogeneous Battery Energy Storage Systems with Droop Control Considering Geographical Factor." Applied Sciences 10, no. 2 (January 20, 2020): 726. http://dx.doi.org/10.3390/app10020726.

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This paper proposes a hierarchical control strategy to coordinate battery energy storage devices based on a multi-agent system. The heterogeneous nature of the battery volume is paid much more attention in designing the proportional protocol of the consensus controller. Besides that, a cluster algorithm based on Minimum Spanning Tree (MST) is suggested to represent geographical factor, and on account of that, each Battery Energy Storage System (BESS) is classified into its specific cluster zone. Further, an active leader is assigned to be in charge of information from the external side in every cluster. The consensus algorithm reconciles all clusters in a step-by-step way. Energy level, voltage, frequency and active/reactive power sharing of every BESS can reach consensus by an information exchange within and among clusters respectively. Further, a virtual leader is taken into the active leader role in directing frequency and voltage to the reference values. To verify the consensus algorithm, a modified IEEE 57-bus is employed for time-domain simulations in an islanded mode and all BESSs are working in a discharge model.

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Rahul, V. P., Bharat Bhushan, Ravi Kumar, and Ram Krishna. "Genetic divergence analysis of yield and its contributing traits in tall field Pea (Pisum sativum L.)." International Journal of Agricultural Invention 2, no. 01 (June 30, 2017): 60–63. http://dx.doi.org/10.46492/ijai/2017.2.1.14.

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The experimental material consisting of forty advance genotypes including two checks (Rachna and Shikha) was evaluated in Randomized Block Design with three replications for eleven characters by using Mahalanobis’s D2 statistic. The clustering pattern of genotypes showed no definite relationship between genetic divergence and geographical distribution of genotypes. The genotypes were grouped into seven clusters. The maximum ten lines were put cluster IV, it had maximum intra-cluster distance while inter-cluster distance was highest between I and II. Cluster means indicated that none of the clusters was superior for all the characters studies; however hybridization between genotypes belonging to different cluster (viz. Ist and IInd) is suggested for development of superior genotypes in field pea.

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Corrigan, Neil, Michael J. G. Bankart, Laura J. Gray, and Karen L. Smith. "Changing cluster composition in cluster randomised controlled trials: design and analysis considerations." Trials 15, no. 1 (2014): 184. http://dx.doi.org/10.1186/1745-6215-15-184.

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Takkala, Rohit Reddy, and Chris Chu. "CHIP: Clustering Hotspots in Layout Using Integer Programming." Journal of Electrical and Computer Engineering 2019 (January 17, 2019): 1–12. http://dx.doi.org/10.1155/2019/9430593.

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Clustering algorithms have been explored in recent years to solve hotspot clustering problems in integrated circuit design. With various applications in design for manufacturability flow such as hotspot library generation, systematic yield optimization, and design space exploration, generating good quality clusters along with their representative clips is of utmost importance. With several generic clustering algorithms at our disposal, hotspots can be clustered based on the distance metric defined while satisfying some tolerance conditions. However, the clusters generated from generic clustering algorithms need not achieve optimal results. In this paper, we introduce two optimal integer linear programming formulations based on triangle inequality to solve the problem of minimizing cluster count while satisfying given constraints. Apart from minimizing cluster count, we generate representative clips that best represent the clusters formed. We achieve a better cluster count for both formulations in most test cases as compared to the results published in the literature in the ICCAD 2016 contest benchmarks as well as the reference results reported in the ICCAD 2016 contest website.

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Adsul, Hemant, and Bachubhai Monpara. "Genetic divergence analysis in indigenously developed Indian soybean (Glycine max L. Merrill) germplasm." Genetika 46, no. 2 (2014): 401–9. http://dx.doi.org/10.2298/gensr1402401a.

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Knowledge of the naturally occurring diversity helps to identify diverse groups of soybean genotypes that can be useful for the breeding program. Therefore, this study aims to identify traits that influence the soybean genotypes in cluster formation using D2 analysis. Hundred genotypes of soybean were studied for 15 characters in randomized block design with three replications and grouped into fifteen clusters. The cluster I was the largest with 55 genotypes followed by cluster III containing 17 genotypes and cluster IV containing 16 genotypes. The remaining clusters were solitary with single genotype each. The inter-cluster distance was the highest between clusters XIII and XIV (D=38.28) followed by clusters X and XII (D=33.64), XIII and XIV (D=32.71), III and XIV (D=32.06) and XII and XIV (D=31.65). Genotypes falling in these clusters may serve as potential parents for a hybridization programme. Pods per plant contributed the highest in manifestation of total genetic diversity. The presence of clear phenotypic and genotypic differences in the characters under consideration between or among clusters gives us an opportunity to bring about improvement through hybridization of genotypes between these clusters and subsequent selection in the segregating generations. The genotype JS (SH) 131 of cluster XIV, J 606 of cluster X, JS 46-75 of cluster V and Himso 1548 of cluster XV were identified as genetically diverse parents, which can be utilized for future crop improvement programme.

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Skokan, Karel, and Lucie Zotyková. "Evaluation of Business Cluster Performance During Its Lifecycle." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 62, no. 6 (2014): 1395–405. http://dx.doi.org/10.11118/actaun201462061395.

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Business clusters as geographic groupings of firms and other organizations and associated institutions are for more than two decades considered to be the efficient instrument in regional and industrial policies which enable to increase the innovation and competitiveness of firms and other institutions and regions. The assessment of their benefits and performance is an issue often discussed in professional literature with various approaches. The composition and structure of clusters develop during their lifecycle from establishment to maturity or decay and different parts of firms in cluster contribute to cluster results in a variety of ways. The main aim of paper is to design a methodology for evaluation of business cluster performance during its lifecycle based on cluster analysis and time series analysis of business clusters and to reveal which parts of clusters called sub-clusters exert the decisive impact on business indicators by which the performance may be measured and to predict the future development of cluster and its parts. The methodology is verified on the case of the Czech machinery cluster with the longest life time in the Czech Republic. The results show that development of cluster is significantly affected by its structure and by performance of firms in sub-clusters.

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Jaisakthi, K., and S. Nithyadhevi. "Location Privacy and Lifetime Maximization using Low Energy Adaptive Technique in WSN." International Journal of Advance Research and Innovation 3, no. 1 (2015): 48–51. http://dx.doi.org/10.51976/ijari.311508.

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In a sensor network, an important problem is to provide privacy to the event detecting sensor node and integrity to the data gathered by the node. In the privacy preserving location monitoring system for wireless sensor networks can be design to enable the system to provide high quality location monitoring services for system users, while preserving personal location privacy. Hybrid Cluster Structure (CS) used for sensor networks to improve the lifetime by using Low Energy Adaptive (LEA) protocol. The sensor nodes are organized into clusters. Within a cluster, nodes transmit data to cluster head (CH) without using Cluster Structure. All CHs are interconnected in CS mode to transmit data to sink. Hybrid Cluster Structure (CS) used for sensor networks. The sensor nodes are organized into clusters. Within a cluster, nodes transmit data to cluster head (CH) without using Cluster Structure.CH can be select by using stochastic algorithm. All CHs are interconnected in CS mode to transmit data to sink.

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Rahardjo, Setiamurti, and Andreas Handoyo. "ANALISIS KONSEP TEMATIK PADA TAMAN PERUMAHAN DI KOTA BARU PARAHYANGAN SEBAGAI DAYA TARIK BAGI ANAK-ANAK (The Thematic Concept Analysis of the Neighborhood Parks in Kota Baru Parahyangan as an Attraction for Children)." Tesa Arsitektur 13, no. 2 (December 30, 2015): 93. http://dx.doi.org/10.24167/tes.v13i2.642.

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Neighborhood parks are generally designed as esthetical elements. Nevertheless, some are intentionally designed to become recreational facilities to the residents, especially in a form of playground for children. Kota Baru Parahyangan (KBP) is a residential area with clusters, all of which are complemented by thematic park(s). With each cluster adopts a different theme for its park(s), the implementation of the thematic concept of the park may vary between one cluster and another. Therefore, this paper aims to analyze the implementation of the thematic concept in the neighborhood park design as an attraction for children. It also aims to bring out the park that has applied the this concept most successfully, as a reference for further park design and planning in the residential neighborhood context. The research is performed sequentially which includes: scoring the park facilities in 9 clusters that are already built through a quantitative method, followed by the discussion of the analysis through a qualitative method. As the result, Tatar (cluster) Jingganagara is found as the best cluster that has successfully integrated its thematic concept to the rest of the facilities in the park, and contributed good impacts to the cluster itself and the other clusters nearby.

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Hu, Lan-Xia, Fei Wang, Yao Kang, and Jian Zhang. "Structural Design of Zeolitic Cluster Organic Frameworks from Hexamethylentetramine and Copper-Halide Clusters." Crystal Growth & Design 16, no. 12 (November 9, 2016): 7139–44. http://dx.doi.org/10.1021/acs.cgd.6b01364.

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KOROGLU, Bilge. "Istanbul Jewelry Cluster: Innovation and Design Capacity." Ekonomik Yaklasim 23, no. 84 (2012): 81. http://dx.doi.org/10.5455/ey.34107.

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ZHOU, Meiyu. "Application of Cluster Model in Kansei Design." Chinese Journal of Mechanical Engineering 44, no. 07 (2008): 248. http://dx.doi.org/10.3901/jme.2008.07.248.

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Xu, Bing, Yuan Xiao, and Mohib Ur Rahman. "Enterprise level cluster innovation with policy design." Entrepreneurship & Regional Development 31, no. 1-2 (November 2, 2018): 46–61. http://dx.doi.org/10.1080/08985626.2018.1537146.

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Sitte, Joaquin, Tim Körner, and Ulrich Rückert. "Local cluster neural net analog VLSI design." Neurocomputing 19, no. 1-3 (April 1998): 185–97. http://dx.doi.org/10.1016/s0925-2312(97)00064-7.

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Lavrentiev, Vasily, Jiri Vacik, Hiroshi Naramoto, and Kazumasa Narumi. "Fullerene nanostructure design with cluster ion impacts." Journal of Alloys and Compounds 483, no. 1-2 (August 2009): 479–83. http://dx.doi.org/10.1016/j.jallcom.2008.08.114.

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Rietbergen, Charlotte, and Mirjam Moerbeek. "The Design of Cluster Randomized Crossover Trials." Journal of Educational and Behavioral Statistics 36, no. 4 (August 2011): 472–90. http://dx.doi.org/10.3102/1076998610379136.

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CHU, MEI-TAI, RAJIV KHOSLA, and KAH-HIN CHAI. "A CLUSTER ANALYSIS OF IC DESIGN INDUSTRY." International Journal of Innovation and Technology Management 11, no. 02 (March 25, 2014): 1450003. http://dx.doi.org/10.1142/s0219877014500035.

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IC design is upstream and innovation-oriented in semiconductor industry. This industry is emerging rapidly due to severe competition and shorter product life cycle. In this context, maintaining the leading edge among global competitors as well as overcoming operational challenges is critical to pursue sustainability. In order to enhance the achievements and improve service quality, IC design firms often endeavor to comprehend, if the inputs/investments have led to effective outputs/achievements. A holistic discussion on how successful IC design companies prevail can facilitate how different IC design firms utilize their particular attributes associated with organizational goals. The purpose of this research is to conduct a cluster analysis for global top 30 IC design firms through 15 selected variables to understand their characteristics. These 15 variables include inputs/investments and outputs/achievements collected from financial indicators primarily to examine their characteristics in each cluster. The factor analysis, cluster analysis, and discriminant analysis are adopted to identify the attributes of these companies. The findings first show that the preliminary 15 variables can be refined into four significant factors by their factor loadings using factor analysis. Second, five groups among 30 companies have been categorized in terms of cluster analysis. Third, the accuracy is 94.7% by the validation of discrimination analysis. The strengths and weaknesses of each single cluster are also discussed. The results can help IC design companies to pursue a more suitable business model and tackle unique improvements, respectively.

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Alimohamadi, Yousef, and Mojtaba Sepandi. "Considering the design effect in cluster sampling." Journal of Cardiovascular and Thoracic Research 11, no. 1 (February 17, 2019): 78. http://dx.doi.org/10.15171/jcvtr.2019.14.

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Katz, Joanne, and Scott L. Zeger. "Estimation of design effects in cluster surveys." Annals of Epidemiology 4, no. 4 (July 1994): 295–301. http://dx.doi.org/10.1016/1047-2797(94)90085-x.

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Journal articles on the topic 'Cluster design'

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