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1
Rapley, Veronica Elizabeth. « Model-based adaptive cluster sampling ». Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433939.
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Lin, Dong. « Model-based cluster analysis using Bayesian techniques ». To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Tantrum, Jeremy. « Model based and hybrid clustering of large datasets / ». Thesis, Connect to this title online ; UW restricted, 2003. http://hdl.handle.net/1773/8933.
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BARBERIS, STEFANO. « New developments in Cluster-Weighted Modeling ». Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241157.
Texte intégralRésumé :
In questo lavoro vengono presentate due estensioni del modello Cluster Weighted (un modello mistura di regressioni che considera non solo la distribuzione condizionata ma anche la distribuzione marginale delle covariate).
La prima estensione proposta presenta il modello GAM-CWM. Si tratta di un modello molto flessibile, in grado di descrivere relazioni complesse tra una variabile risposta e un insieme di covariate in ogni componente della mistura. In questo modello proposto la classe dei Generalized Additive Models è stata inserita all’interno del framework CWM.
La seconda estensione, è relativa alla regressione beta che rappresenta l'approccio standard per modellare una variabile dipendente in [0,1]. In alcune situazioni, un problema che potrebbe sorgere è una conseguenza diretta della flessibilità della distribuzione beta, perché quando viene utilizzata come componente in un modello mistura potrebbe essere troppo flessibile a causa della grande varietà di forme (incluse le forme multimodali) che può assumere. In questo caso quindi può essere difficile capire e interpretare il significato di ciascun gruppo latente. Per questo motivo, abbiamo sviluppato un'estensione dei modelli mistura di beta focalizzandoci sul sottoinsieme della distribuzioni beta unimodali, con l'obiettivo di migliorare l'interpretazione di ciascun componente e quindi interpretare meglio il rispettivo cluster nella popolazione.
Infine, un pacchetto R in fase di sviluppo che sarà pubblicato sul CRAN implementa le metodologie proposte.
La stima di questi modelli viene eseguita tramite la massima verosimiglianza con l'algoritmo EM. Con dati simulati e reali esaminiamo le prestazioni, i limiti e i benefici confrontando i nuovi modelli proposti con modelli simili presentati e disponibili in letteratura.In this work two extensions of Cluster Weighted Models (a mixture model with random covariate) are presented for model-based clustering applications. The first presents the Generalized Additive Cluster Weighted Model. This is a very flexible model, able to capture complex relations between a response variable and a set of covariates in each mixture component introducing the Generalized Additive Model into the CWM framework. The second, is related to the beta regression that represents the standard approach to model a dependent variable with the range in the unit interval [0,1]. In some situations, a problem that could arise is a direct consequence of flexibility of the beta distribution, because when it is considered as a mixture component it may be too flexible due to the great variety of shapes (including multi-modal shapes) that can assume so that it may be difficult to understand easily the real meaning of each component. For this reason, we developed an extension of the beta mixture models focusing on the subset of unimodal beta distribution, with the aim of improving the interpretation of each mixture component and then identifying better the respective cluster in the population. Finally, an R package under development that will published on the CRAN implements the proposed methodologies. The estimation of these models is performed via maximum likelihood with EM algorithm. With simulated and real data we investigate the performances, limits and benefits comparing this model with other models related to it.
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Annakula, Chandravyas. « Hierarchical and partitioning based hybridized blocking model ». Kansas State University, 2017. http://hdl.handle.net/2097/35468.
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Master of ScienceDepartment of Computing and Information Sciences
William H. Hsu
(Higgins, Savje, & Sekhon, 2016) Provides us with a sampling blocking algorithm that enables large and complex experiments to run in polynomial time without sacrificing the precision of estimates on a covariate dataset. The goal of this project is to run the different clustering algorithms on top of clusters formed from above mentioned blocking algorithm and analyze the performance and compatibility of the clustering algorithms. We first start with applying the blocking algorithm on a covariate dataset and once the clusters are formed, we then apply our clustering algorithm HAC (Hierarchical Agglomerative Clustering) or PAM (Partitioning Around Medoids) on the seeds of the clusters. This will help us to generate more similar clusters. We compare our performance and precision of our hybridized clustering techniques with the pure clustering techniques to identify a suitable hybridized blocking model.
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Mohamed, Esha [Verfasser], et Ralf [Akademischer Betreuer] Münnich. « Design-based and model-based estimation in adaptive cluster sampling / Esha Mohamed ; Betreuer : Ralf Münnich ». Trier : Universität Trier, 2017. http://d-nb.info/1197807535/34.
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Malsiner-Walli, Gertraud, Sylvia Frühwirth-Schnatter et Bettina Grün. « Model-based clustering based on sparse finite Gaussian mixtures ». Springer, 2016. http://dx.doi.org/10.1007/s11222-014-9500-2.
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In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number of non-empty components visited during MCMC sampling. Specifying a shrinkage prior, namely the normal gamma prior, on the component means leads to improved parameter estimates as well as identification of cluster-relevant variables. After estimating the mixture model using MCMC methods based on data augmentation and Gibbs sampling, an identified model is obtained by relabeling the MCMC output in the point process representation of the draws. This is performed using K-centroids cluster analysis based on the Mahalanobis distance. We evaluate our proposed strategy in a simulation setup with artificial data and by applying it to benchmark data sets. (authors' abstract)
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Shaffer, Anne, Monica Whitehead, Molly Davis, Diana Morelen et Cynthia Suveg. « A Model‐Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior ». Digital Commons @ East Tennessee State University, 2018. https://doi.org/10.1111/famp.12326.
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In a diverse community sample of mothers (N = 108) and their preschool‐aged children (Mage = 3.50 years), this study conducted person‐oriented analyses of maternal emotion regulation (ER) based on a multimethod assessment incorporating physiological, observational, and self‐report indicators. A model‐based cluster analysis was applied to five indicators of maternal ER: maternal self‐report, observed negative affect in a parent–child interaction, baseline respiratory sinus arrhythmia (RSA), and RSA suppression across two laboratory tasks. Model‐based cluster analyses revealed four maternal ER profiles, including a group of mothers with average ER functioning, characterized by socioeconomic advantage and more positive parenting behavior. A dysregulated cluster demonstrated the greatest challenges with parenting and dyadic interactions. Two clusters of intermediate dysregulation were also identified. Implications for assessment and applications to parenting interventions are discussed.
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Whitehead, Monica R., Anne Shaffer, Molly Faye Davis, Diana M. Morelen et Cynthia Suveg. « A Model-Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior ». Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/745.
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Liu, Jinghui. « Approaches to improve the precision of similarity patterns and reproducibility for cluster analysis infinite mixture model based cluster analyses for gene expression data / ». Cincinnati, Ohio : University of Cincinnati, 2008. http://rave.ohiolink.edu/etdc/view.cgi?acc_num=ucin1211903300.
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Li, Qian. « Approaches to Find the Functionally Related Experiments Based on Enrichment Scores : Infinite Mixture Model Based Cluster Analysis for Gene Expression Data ». University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351.
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Al-Khalissi, Hayder Hekmat Sulman [Verfasser], et Mladen [Akademischer Betreuer] Berekovic. « Efficient Programming Model for OpenMP on Cluster-Based Many-Core System / Hayder Hekmat Sulman Al-Khalissi ; Betreuer : Mladen Berekovic ». Braunschweig : Technische Universität Braunschweig, 2015. http://d-nb.info/1175819271/34.
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LeBlanc, Lawrence Joseph. « CFD evaluation of cluster specific image based asthma lung features on particle transport and hygroscopic particle growth model validation ». Thesis, University of Iowa, 2017. https://ir.uiowa.edu/etd/5546.
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Aerosolized drug delivery to the human lungs for asthma treatment has long been studied and yet the relationship between the delivery efficacy and the inter-subject variability due to gender, age, and disease severity remains unclear. A recent imaging-based cluster analysis on a population of asthmatic patients identifies four clusters with distinct structural and functional characteristics. The use of cluster membership to explore inter-subject variability by investigating numerically the air flow and particle transport in representative subjects of the asthmatic clusters on inhalation drug delivery in asthma sub-populations is proposed. Large-eddy simulations using computed tomography (CT)-based airway models were performed with a slow and deep breathing profile corresponding to application of a metered dose inhaler. Physiologically consistent subject specific boundary conditions in peripheral airways were produced using an image registration technique and a resistance network compliance model. Particle simulations and final deposition statistics were calculated for particle sizes ranging from 1–8 μm. The results suggested an emphasis on the importance of airway constriction for regional particle deposition and prominent effects of local features in lobar, segmental, and sub-segmental airways on overall deposition patterns. Asthmatic clusters characterized by airway constriction had an increase in deposition efficiency in lobar, segmental, and sub-segmental airways. Local constrictions produced jet flows that impinged on distal bifurcations and resulted in large inertial depositions. Decreased right main bronchus (RMB) branching angle decreased the fraction of particles ventilated to the right upper lobe (RUL). Cluster-based computational fluid dynamics results demonstrate particle deposition characteristics associated with imaging based variables that could be useful for future drug delivery improvements.
One method for circumventing low deposition in small airways due to constriction in tracheobronchial airways is through hygroscopic growth of aerosols for inhalation. Hygroscopic materials have an affinity for water and can enlarge in size significantly as they traverse through respiratory tract. Hygroscopic growth has shown promise as a viable drug delivery method for decreasing deposition in the upper tracheobronchial region and increasing drug penetration and retention in small airways. Current models for hygroscopic growth models show promise in predicting steady state final diameter aerosol droplet sizes, but much uncertainty in predicting transient effects exists. This paper discusses in detail one such growth model and modifies it to include realistic spatial temperature and humidity variations associated with the lung. The growth model is simplified through grouping of terms and is then solved using MATLAB ODE 45 solver. The model is compared to experimentally acquired in vitro data for validation. The results do not show good agreement with the model, and suggests that additional factors exist that inhibit aerosol droplet growth from commencing immediately upon entering the respiratory tract like is assumed true in literature. This paper briefly hypothesizes for reasons for model and data disagreement and limitations of current growth models.
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Ngu, Hong Ming. « Agent-based modelling of worker interactions and related impacts on workplace dynamics ». Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/90738/1/Hong%20Ming_Ngu_Thesis.pdf.
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This study uses agent based modelling to simulate the worker interactions within a workplace and to investigate how the interactions can have impact on the workplace dynamics. Two new models (Bounded Confidence with Bias model and Relative Agreement with Bias model) are built based on the theoretical foundation of two existing models. A new factor, namely bias, is added into the new models which raises several issues to be studied.
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Kurin, Erik, et Adam Melin. « Data-driven test automation : augmenting GUI testing in a web application ». Thesis, Linköpings universitet, Programvara och system, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-96380.
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For many companies today, it is highly valuable to collect and analyse data in order to support decision making and functions of various sorts. However, this kind of data-driven approach is seldomly applied to software testing and there is often a lack of verification that the testing performed is relevant to how the system under test is used. Therefore, the aim of this thesis is to investigate the possibility of introducing a data-driven approach to test automation by extracting user behaviour data and curating it to form input for testing. A prestudy was initially conducted in order to collect and assess different data sources for augmenting the testing. After suitable data sources were identified, the required data, including data about user activity in the system, was extracted. This data was then processed and three prototypes where built on top of this data. The first prototype augments the model-based testing by automatically creating models of the most common user behaviour by utilising data mining algorithms. The second prototype tests the most frequent occurring client actions. The last prototype visualises which features of the system are not covered by automated regression testing. The data extracted and analysed in this thesis facilitates the understanding of the behaviour of the users in the system under test. The three prototypes implemented with this data as their foundation can be used to assist other testing methods by visualising test coverage and executing regression tests.
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Polat, Esra. « Spatio-temporal Crime Prediction Model Based On Analysis Of Crime Clusters ». Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608881/index.pdf.
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Crime is a behavior disorder that is an integrated result of social, economical and environmental factors. In the world today crime analysis is gaining significance and one of the most popular subject is crime prediction. Stakeholders of crime intend to forecast the place, time, number of crimes and crime types to get precautions. With respect to these intentions, in this thesis a spatio-temporal crime prediction model is generated by using time series forecasting with simple spatial disaggregation approach in Geographical Information Systems (GIS).
The model is generated by utilizing crime data for the year 2003 in Bahçelievler and Merkez Ç
ankaya police precincts. Methodology starts with obtaining clusters with different clustering algorithms. Then clustering methods are compared in terms of land-use and representation to select the most appropriate clustering algorithms. Later crime data is divided into daily apoch, to observe spatio-temporal distribution of crime. In order to predict crime in time dimension a time series model (ARIMA) is fitted for each week day, Then the forecasted crime occurrences in time are disagregated according to spatial crime cluster patterns. Hence the model proposed in this thesis can give crime prediction in both space and time to help police departments in tactical and planning operations.
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Frisk, Christoffer. « Automated protein-family classification based on hidden Markov models ». Thesis, Uppsala universitet, Bioinformatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-252372.
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The aim of the project presented in this paper was to investigate the possibility toautomatically sub-classify the superfamily of Short-chain Dehydrogenase/Reductases (SDR).This was done based on an algorithm previously designed to sub-classify the superfamily ofMedium-chain Dehydrogenase/Reductases (MDR). While the SDR-family is interesting andimportant to sub-classify there was also a focus on making the process as automatic aspossible so that future families also can be classified using the same methods.To validate the results generated it was compared to previous sub-classifications done on theSDR-family. The results proved promising and the work conducted here can be seen as a goodinitial part of a more comprehensive full investigation
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Kannamareddy, Aruna Sai. « Density and partition based clustering on massive threshold bounded data sets ». Kansas State University, 2017. http://hdl.handle.net/2097/35467.
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Master of ScienceDepartment of Computing and Information Sciences
William H. Hsu
The project explores the possibility of increasing efficiency in the clusters formed out of massive data sets which are formed using threshold blocking algorithm. Clusters thus formed are denser and qualitative. Clusters that are formed out of individual clustering algorithms alone, do not necessarily eliminate outliers and the clusters generated can be complex, or improperly distributed over the data set. The threshold blocking algorithm, a current research paper from Michael Higgins of Statistics Department on other hand, in comparison with existing algorithms performs better in forming the dense and distinctive units with predefined threshold. Developing a hybridized algorithm by implementing the existing clustering algorithms to re-cluster these units thus formed is part of this project. Clustering on the seeds thus formed from threshold blocking Algorithm, eases the task of clustering to the existing algorithm by eliminating the overhead of worrying about the outliers. Also, the clusters thus generated are more representative of the whole. Also, since the threshold blocking algorithm is proven to be fast and efficient, we now can predict a lot more decisions from large data sets in less time. Predicting the similar songs from Million Song Data Set using such a hybridized algorithm is considered as the data set for the evaluation of this goal.
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Toolo, Mpho. « Agriculture based clusters : a model to stimulate South Africa s rural small-scale farming sector ». Diss., University of Pretoria, 2015. http://hdl.handle.net/2263/52334.
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Unemployment in South Africa is a major problem and the marginal economic growth has not stimulated job creation. The problem is skewed to the rural communities. As the most labour absorptive sector, agriculture is best placed to help address this challenge. However, to become a viable globally competitive sector rural small-scale farmers need Government policy certainty and support; cluster creation is a means for small-scale farmers to be provided the required support.
An exploratory study was conducted on Western Cape Wine cluster members and relevant Government employees. This study sought to identify behaviours and enablers instrumental in this cluster s success; while the Government employees clarified South Africa s official readiness for cluster implementation.
The cluster abundantly evidenced : strong linkages, competition coupled with cooperation, connectedness among all the players as well as knowledge spill-over. These elements were enabled by historical support, primarily in research. The Government Departments, however, displayed paucity in these very behaviours, thus failing to maximise on efforts they made. Cluster members bemoaned the debilitating frequent changes in policy.
Unless this crucial enabler together with the provision of infrastructure for creating connected communities is provided by collaborating Government Departments, South Africa s small-scale farmers cannot enjoy the benefits of clustering.Mini Dissertation (MBA)--University of Pretoria, 2015.
vn2016
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted
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Liu, Yulin. « Urban transit quality of service : user perception and behaviour ». Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61517/1/Yulin_Liu_Thesis.pdf.
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Despite its potential multiple contributions to sustainable policy objectives, urban transit is generally not widely used by the public in terms of its market share compared to that of automobiles, particularly in affluent societies with low-density urban forms like Australia. Transit service providers need to attract more people to transit by improving transit quality of service. The key to cost-effective transit service improvements lies in accurate evaluation of policy proposals by taking into account their impacts on transit users. If transit providers knew what is more or less important to their customers, they could focus their efforts on optimising customer-oriented service. Policy interventions could also be specified to influence transit users’ travel decisions, with targets of customer satisfaction and broader community welfare. This significance motivates the research into the relationship between urban transit quality of service and its user perception as well as behaviour.
This research focused on two dimensions of transit user’s travel behaviour: route choice and access arrival time choice. The study area chosen was a busy urban transit corridor linking Brisbane central business district (CBD) and the St. Lucia campus of The University of Queensland (UQ). This multi-system corridor provided a ‘natural experiment’ for transit users between the CBD and UQ, as they can choose between busway 109 (with grade-separate exclusive right-of-way), ordinary on-street bus 412, and linear fast ferry CityCat on the Brisbane River. The population of interest was set as the attendees to UQ, who travelled from the CBD or from a suburb via the CBD. Two waves of internet-based self-completion questionnaire surveys were conducted to collect data on sampled passengers’ perception of transit service quality and behaviour of using public transit in the study area.
The first wave survey is to collect behaviour and attitude data on respondents’ daily transit usage and their direct rating of importance on factors of route-level transit quality of service. A series of statistical analyses is conducted to examine the relationships between transit users’ travel and personal characteristics and their transit usage characteristics. A factor-cluster segmentation procedure is applied to respodents’ importance ratings on service quality variables regarding transit route preference to explore users’ various perspectives to transit quality of service. Based on the perceptions of service quality collected from the second wave survey, a series of quality criteria of the transit routes under study was quantitatively measured, particularly, the travel time reliability in terms of schedule adherence. It was proved that mixed traffic conditions and peak-period effects can affect transit service reliability.
Multinomial logit models of transit user’s route choice were estimated using route-level service quality perceptions collected in the second wave survey. Relative importance of service quality factors were derived from choice model’s significant parameter estimates, such as access and egress times, seat availability, and busway system. Interpretations of the parameter estimates were conducted, particularly the equivalent in-vehicle time of access and egress times, and busway in-vehicle time. Market segmentation by trip origin was applied to investigate the difference in magnitude between the parameter estimates of access and egress times. The significant costs of transfer in transit trips were highlighted. These importance ratios were applied back to quality perceptions collected as RP data to compare the satisfaction levels between the service attributes and to generate an action relevance matrix to prioritise attributes for quality improvement.
An empirical study on the relationship between average passenger waiting time and transit service characteristics was performed using the service quality perceived. Passenger arrivals for services with long headways (over 15 minutes) were found to be obviously coordinated with scheduled departure times of transit vehicles in order to reduce waiting time. This drove further investigations and modelling innovations in passenger’ access arrival time choice and its relationships with transit service characteristics and average passenger waiting time. Specifically, original contributions were made in formulation of expected waiting time, analysis of the risk-aversion attitude to missing desired service run in the passengers’ access time arrivals’ choice, and extensions of the utility function specification for modelling passenger access arrival distribution, by using complicated expected utility forms and non-linear probability weighting to explicitly accommodate the risk of missing an intended service and passenger’s risk-aversion attitude.
Discussions on this research’s contributions to knowledge, its limitations, and recommendations for future research are provided at the concluding section of this thesis.
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Kohn, Maximilian-Benedikt Herwarth Detlef. « Speculative bubbles and contagion : analysis of volatility’s clusters during the DotCom bubble based on the dynamic conditional correlation model ». reponame:Repositório Institucional do FGV, 2015. http://hdl.handle.net/10438/14193.
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Reviewing the definition and measurement of speculative bubbles in context of contagion, this paper analyses the DotCom bubble in American and European equity markets using the dynamic conditional correlation (DCC) model proposed by Engle and Sheppard (2001) as an econometrical - and on the other hand the behavioral finance as an psychological explanation. Contagion is defined in this context as the statistical break in the computed DCCs as measured by the shifts in their means and medians. Even it is astonishing, that the contagion is lower during price bubbles, the main finding indicates the presence of contagion in the different indices among those two continents and proves the presence of structural changes during financial crisis.
Revendo a definição e determinação de bolhas especulativas no contexto de contágio, este estudo analisa a bolha do DotCom nos mercados acionistas americanos e europeus usando o modelo de correlação condicional dinâmica (DCC) proposto por Engle e Sheppard (2001) como uma explicação econométrica e, por outro lado, as finanças comportamentais como uma explicação psicológica. Contágio é definido, neste contexto, como a quebra estatística nos DCC’s estimados, medidos através das alterações das suas médias e medianas. Surpreendentemente, o contágio é menor durante bolhas de preços, sendo que o resultado principal indica a presença de contágio entre os diferentes índices dos dois continentes e demonstra a presença de alterações estruturais durante a crise financeira.
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De, Paris Renata. « An effective method to optimize docking-based virtual screening in a clustered fully-flexible receptor model deployed on cloud platforms ». Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2016. http://tede2.pucrs.br/tede2/handle/tede/7329.
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Conselho Nacional de Pesquisa e Desenvolvimento Cient?fico e Tecnol?gico - CNPq
O uso de conforma??es obtidas por trajet?rias da din?mica molecular nos experimentos de docagem molecular ? a abordagem mais precisa para simular o comportamento de receptores e ligantes em ambientes moleculares. Entretanto, tais simula??es exigem alto custo computacional e a sua completa execu??o pode se tornar uma tarefa impratic?vel devido ao vasto n?mero de informa??es estruturais consideradas para representar a expl?cita flexibilidade de receptores. Al?m disso, o problema ? ainda mais desafiante quando deseja-se utilizar modelos de receptores totalmente flex?veis (Fully-Flexible Receptor - FFR) para realizar a triagem virtual em bibliotecas de ligantes. Este estudo apresenta um m?todo inovador para otimizar a triagem virtual baseada em docagem molecular de modelos FFR por meio da redu??o do n?mero de experimentos de docagem e, da invoca??o escalar de workflows de docagem para m?quinas virtuais de plataformas em nuvem. Para esse prop?sito, o workflow cient?fico basedo em nuvem, chamado e-FReDock, foi desenvolvido para acelerar as simula??es da docagem molecular em larga escala. e-FReDock ? baseado em um m?todo seletivo sem param?tros para executar experimentos de docagem ensemble com m?ltiplos ligantes. Como dados de entrada do e-FReDock, aplicou-se seis m?todos de agrupamento para particionar conforma??es com diferentes caracter?sticas estruturais no s?tio de liga??o da cavidade do substrato do receptor, visando identificar grupos de conforma??es favor?veis a interagir com espec?ficos ligantes durante os experimentos de docagem. Os resultados mostram o elevado n?vel de qualidade obtido pelos modelos de receptores totalmente flex?veis reduzidos (Reduced Fully-Flexible Receptor - RFFR) ao final dos experimentos em dois conjuntos de an?lises. O primeiro mostra que e-FReDock ? capaz de preservar a qualidade do modelo FFR entre 84,00% e 94,00%, enquanto a sua dimensionalidade reduz em uma m?dia de 49,68%. O segundo relata que os modelos RFFR resultantes s?o capazes de melhorar os resultados de docagem molecular em 97,00% dos ligantes testados quando comparados com a vers?o r?gida do modelo FFR.
The use of conformations obtained from molecular dynamics trajectories in the molecular docking experiments is the most accurate approach to simulate the behavior of receptors and ligands in molecular environments. However, such simulations are computationally expensive and their execution may become an infeasible task due to the large number of structural information, typically considered to represent the explicit flexibility of receptors. In addition, the computational demand increases when Fully-Flexible Receptor (FFR) models are routinely applied for screening of large compounds libraries. This study presents a novel method to optimize docking-based virtual screening of FFR models by reducing the size of FFR models at docking runtime, and scaling docking workflow invocations out onto virtual machines from cloud platforms. For this purpose, we developed e-FReDock, a cloud-based scientific workflow that assists in faster high-throughput docking simulations of flexible receptors and ligands. e-FReDock is based on a free-parameter selective method to perform ensemble docking experiments with multiple ligands from a clustered FFR model. The e-FReDock input data was generated by applying six clustering methods for partitioning conformations with different features in their substrate-binding cavities, aiming at identifying groups of snapshots with favorable interactions for specific ligands at docking runtime. Experimental results show the high quality Reduced Fully-Flexible Receptor (RFFR) models achieved by e-FReDock in two distinct sets of analyses. The first analysis shows that e-FReDock is able to preserve the quality of the FFR model between 84.00% and 94.00%, while its dimensionality reduces on average 49.68%. The second analysis reports that resulting RFFR models are able to reach better docking results than those obtained from the rigid version of the FFR model in 97.00% of the ligands tested.
23
Jahanshahi, Kaveh. « Quantification of the influences of built-form upon travel of employed adults : new models based on the UK National Travel Survey ». Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/267841.
Texte intégralRésumé :
After decades of research, a host of analytical difficulties is still hindering our understanding of the influences of the built form on travel. The main challenges are (a) assembling good quality data that reflects the majority of the known influences and that supports continuous monitoring, and (b) making sense methodologically of the many variables which strongly intercorrelate. This study uses the UK national travel survey (NTS) data that is among the most comprehensive of its form in the world. The fact that it has rarely been used so far for this purpose may be attributable to the methodological difficulties. This dissertation aims to develop a new analytical framework based on extended structural equation models (SEMs) in order to overcome some of the key methodological difficulties in quantifying the influences of the built form on travel, and in addition to provide a means to continuously monitor any changes in the effects over time. The analyses are focused on employed adults, because they are not only the biggest UK population segment with the highest per capita travel demand, but also the segment that are capable of adapting more rapidly to changing land use, built form and transport supply conditions. The research is pursued through three new models. Model 1 is a path diagram coupled with factor analyses, which estimates continuous, categorical and binary dependent variables. The model estimates the influences on travel distance, time and trip frequency by trip purpose while accounting for self-selection, spatial sorting, endogeneity of car ownership, and interactions among trip purposes. The results highlight stark differences among commuters, particularly the mobility disadvantages of women, part time and non-car owning workers even when they live in the most accessible urban areas. Model 2 incorporates latent categorisation analyses in order to identify a tangible typology of the built form and the associated variations in impacts on travel. Identifying NTS variables as descriptors for tangible built form categories provides an improved basis for investigating land use and transport planning interventions. The model reveals three distinct built form categories in the UK with striking variations in the patterns of influences. Model 3 further investigates the variations across the built form categories. The resulting random intercept SEM provides a more precise quantification of the influences of self-selection and spatial sorting across the built form categories for each socioeconomic group. Four research areas are highlighted for further studies: First, new preference, attitude and behavioural parameters may be introduced through incorporating non-NTS behavioural surveys; Second, the new SEMs provide a basis for incorporating choice modelling where the utility function is defined with direct, indirect and latent variables; Third, conceptual and methodological developments – such as non-parametric latent class analysis, allow expanding the current model to monitor changes in travel behaviour as and when new NTS or non NTS data become available. Fourth, the robustness of the inferences regarding causal or directional influences may require further quantification through designing new panel data sets, building on the findings above.
24
PILLAI, Vinoshene. « Intravital two photon clcium imaging of glioblastoma mouse models ». Doctoral thesis, Scuola Normale Superiore, 2021. http://hdl.handle.net/11384/109211.
Texte intégral
25
Zhong, Shi. « Probabilistic model-based clustering of complex data ». Thesis, 2003. http://wwwlib.umi.com/cr/utexas/fullcit?p3116470.
Texte intégral
26
« Model-based clustering with network covariates by combining a modified product partition model with hidden Markov random field ». Thesis, 2012. http://library.cuhk.edu.hk/record=b5549146.
Texte intégralRésumé :
乘積型劃分模型最近被擴展為容許個體有協變量的隨機聚類模型，然而協變量受限與對個體性質的描述。隨著科技發展，於越來越多生物醫學或社會研究的聚類問題中，我們需要考慮聚類對象間兩兩關連的額外資料，如基因間的調節關係或人際關係中的社交網絡。為此我們提出一個基於模型的方法，綜合乘積型劃分模型的一種改型與隱馬可夫隨機場對有網絡和協變量信息的對象做聚類。統計推論以貝葉斯方法進行。模型計算以馬可夫鏈蒙地卡羅運算法則進行。為了使馬可夫鏈能更好地混和，使用循序分配合併分裂取樣器進行群體移動以減少困於區域性頂點的機會。為了測試本文提出的新方法的聚類性能，我們在兩個合成數據集上進行了模擬實驗。該實驗涵括多種類型的應變量，協變量網絡結構。結果顯示該方法在大部分實驗條件下都具有高正確聚類率。我們還將此返法應用於兩個真實數據集。第一個真實數據集利用學術期刊間相互引用的信息幫助對學術期刊的分門別類。第二個真實數據集合併酵母中基因的表達、轉錄因子結合位點和基因間的調控網絡信息，已對基因做詳細的功能分類。這兩個基於真實數據的實驗都給出諸多有意義的結果。
The product partition model was recently extended for the covariate-dependent random partition of subjects, where the covariates are limited to properties of individual subjects. For many clustering problems in biomedical or social studies, we often have extra clustering information from the pairwise association among subjects, such as the regulatory relationship between genes or the social network among people. Here we propose a model-based method for clustering with network information by combining a modified product partition model with hidden Markov random field. The Bayesian approach is used for statistical inference. Markov Chain Monte Carlo algorithms are used to compute the model. In order to improve the mixing of the chain, the Sequentially-Allocated Merge-Split Sampler is adapted to perform group moves as an eort to lower the chance of trapping in local modes.
The new method is tested on two synthesized data sets to evaluate its performance on different types of response variables, covariates and networks. The correct clustering rate is satisfactory under a wide range of conditions. We also applied this new method on two real data sets. The first real data set is the journal data, where the cross citation information among journals is used to groups journals to different categories. The second real data set involves the gene expression, motif binding and gene network of yeast, where the goal is to find detail gene functional groups. Both experiments yielded interesting results.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Fung, Ling Hiu.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2012.
Abstracts also in Chinese.
Abstract --- p.i
Acknowledgement --- p.iv
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Technical Background --- p.7
Chapter 2.1 --- Variable notation --- p.8
Chapter 2.2 --- Two exemplary models for the response variable --- p.10
Chapter 2.3 --- PPMx --- p.12
Chapter 2.3.1 --- PPM - definition and its equivalence to DPM --- p.12
Chapter 2.3.2 --- PPMx - extension with covariates --- p.15
Chapter 2.3.3 --- Posterior inference --- p.18
Chapter 2.4 --- HMRF --- p.19
Chapter 2.4.1 --- Definition --- p.19
Chapter 2.4.2 --- Constrained Dirichlet Process Mixture --- p.21
Chapter 3 --- Model-based Clustering with Network Covariates --- p.27
Chapter 3.1 --- Design of the model --- p.27
Chapter 3.2 --- The Bayesian MCNC model --- p.30
Chapter 3.3 --- MCMC computing --- p.31
Chapter 3.4 --- Performance evaluation criteria --- p.37
Chapter 4 --- Simulation study --- p.39
Chapter 4.1 --- Network --- p.39
Chapter 4.2 --- Covariates --- p.41
Chapter 4.3 --- The Phase model (M1) --- p.42
Chapter 4.4 --- The Normal model (M2) --- p.52
Chapter 4.5 --- Comparing correct clustering percentage and correct co-occurrence percentage --- p.62
Chapter 5 --- Real data --- p.68
Chapter 5.1 --- Journal cross-citation data --- p.68
Chapter 5.2 --- Gene Network of yeast data --- p.76
Chapter 6 --- Conclusions --- p.89
Chapter A --- p.91
Chapter A.1 --- Covariates --- p.91
Chapter A.1.1 --- Continuous covariates --- p.91
Chapter A.1.2 --- Categorical covariates --- p.94
Chapter A.1.3 --- Count covariates --- p.96
Chapter A.2 --- Phase model --- p.98
Chapter A.2.1 --- Prior specification --- p.99
Chapter A.2.2 --- Data generation --- p.99
Chapter A.2.3 --- Posterior estimation --- p.100
Chapter A.3 --- Normal model --- p.111
Chapter A.3.1 --- Prior specification --- p.111
Chapter A.3.2 --- Data generation --- p.112
Chapter A.3.3 --- Posterior estimation --- p.112
Chapter A.4 --- Journal dataset --- p.115
27
Huang, Hong-Chen, et 黃閎琛. « Model and Analysis of Clustered Machine-to-Machine Wireless Networks Based on the Poisson Cluster Process ». Thesis, 2017. http://ndltd.ncl.edu.tw/handle/74632781922750608843.
Texte intégralRésumé :
碩士國立臺灣大學
電信工程學研究所
105
In wireless sensor networks, the homogeneous Poisson Point Process (PPP) assumption holds when the sensor nodes are uniformly distributed in space. However, due to geographic factors, it may be common for sensor nodes to cluster around some region where the physical or environmental conditions often occur. Therefore, the PPP assumption does not provide an accurate model for the interference in these conditions. This motivates the need to characterize the SINR of wireless sensor networks when the nodes are clustered. Due to the cluster property, we use Poisson Cluster Process (PCP) to model the location of sensor nodes. In this thesis, we set the transmission power of each node the same, fading is modeled as Rayleigh. We consider two kinds of sensor networks. In the first one, the data collectors are randomly deployed and follow another PPP, which is independent to the PPP sensor nodes following. Combining some mathematical models from reference papers, we provide numerically integrable expression for the success probability and the average achievable rate, and some lower bounds. From both the analytic and simulation results, we found the performance of this setting of the position of each data collectors is bad and inefficient. This inspires us to deploy the data collectors on the center of the cluster distribution of the PCP, that is, the parent process in the PCP. In this scenario, the interference model and the probability density function of the distance between the transmitter and receiver proposed from the reference papers are not applicable. As a consequence, we analyze these mathematical formulas by ourselves and to the best of our knowledge we are the first to provide these formulas. We also provide numerically integrable expression for the success probability and the average achievable rate, and some lower bounds for this scenario. The results outperform the setting that data collectors are randomly deployed on these metrics we concerned.
28
CHIH, CHIU YEN, et 邱彥智. « Statistics-Based Evaluation of Soil Liquefaction Using Cluster Analysisand Logit Model ». Thesis, 2003. http://ndltd.ncl.edu.tw/handle/88619154630855198260.
Texte intégralRésumé :
碩士長榮大學
土地管理與開發學系碩士班
91
Taiwan is located at the boundary between the Philippine and the Eurasian Plates. Seismicity is extremely active on this island. Among the earthquake disasters, soil liquefaction has attracted a lot of attention lately. Due to the liquefaction of soils, there were many ground failures with the occurrence of earthquake. Lots of damages such as ground settlement, lateral spreading, building damage, and twisted lifelines were observed during the Chi-Chi earthquake of Taiwan in 1999. Therefore, in addition to appropriate planning and control, more accurate evaluation of liquefaction potential must be done before land development to protect the life and property of people. Geographic information system (GIS) in cooperated with modules of liquefaction evaluation based on binary Logit model which are written in FORTRAN langrage is employed in evaluation of liquefaction potential in this research. The proposed computer-aided system uses the powerful function of GIS in both spatial and non-spatial analysis. The field data in Yunlin gathered from the Chi-Chi earthquake is used to perform the verification of evaluation system. The result shows that this system works well and can be put to practice in land development.
29
Luo, Lung-Jin, et 羅隆晉. « Cluster-Based Multiple-Classifiers Model for Classification Prediction in Imbalanced Data ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/78462502199614709900.
Texte intégralRésumé :
碩士銘傳大學
資訊工程學系碩士班
98
In real data, the data distribution is imbalanced when the quantity of some classes is usually much less than other classes and it is called ‘Imbalance data’. The data in minority classes are quite important in research. In the classification technique of data mining, the training data quality is a critical factor which can influence the accuracy of the technique. However, traditional data mining classification technique is not effective on imbalance data. Hence, it is a quite significant goal to improve the performance of result on mining imbalance data. This paper will present an approach that filter majority class by neural networks classification model. The neural networks classification model will be used to filter majority class which was high probability forecast for majority class. After that, the ration of data in minority of classes will be increased, and the extent of imbalanced class distribution will be decreased. Then we made two different methods: Method 1 will use the cluster analysis to segment the data into multiple groups and the data in each group will be used to build the classification model; Method 2 will use the data to directly build the classification model instead of segment the data into multiple groups. At the same time, we will optimize the performance of the classification model. Finally, the different sampling technique will be used to select the data from well-handled dataset and classification model will be built up. The experiments show that our approach can increase the performance of traditional classification technique in imbalance dataset.
30
Araviashvili, Tamari. « Wine Tourism Cluster Model Based on Kettmeir and Santa Margherita Wine Group ». Master's thesis, 2019. https://hdl.handle.net/10216/126695.
Texte intégral
31
Chandra, Mohan Lakshmi Kanth. « Cluster based wireless sensor network security model using game theory and risk assessment ». 2007. http://digital.library.okstate.edu/etd/umi-okstate-2589.pdf.
Texte intégral
32
Chandra, Mohan Lakshmi Kanth. « Cluster based wireless sensor network security model using game theory and risk assessment ». 2008. http://digital.library.okstate.edu/etd/umi-okstate-2615.pdf.
Texte intégral
33
Hu, Wei-Che, et 胡維哲. « A dynamic data driven prediction model based on evolutionary algorithms and cluster computing ». Thesis, 2016. http://ndltd.ncl.edu.tw/handle/ce7726.
Texte intégralRésumé :
碩士中原大學
資訊管理研究所
104
In recent years, big data has become an important research topic. Such as the main reason for the following characteristics: high velocity in real-time data, distributed of complex data sources, integration of heterogeneous data and growth of data volumes. Therefore, in this complex information environment. It is a new challenge to achieve an efficient and accurate prediction. The concept of dynamic data-driven applications system (DDDAS) is a solution, to provide simulation and prediction capabilities, and expansion of the relevant application model. In the DDDAS framework, to find out the relationship between data instantly can help to improve the efficiency of DDDAS. In the past, the evolutionary algorithms have been widely used, it has been proven to be effective in solving the practical applications of optimization problems. But, with the advent of the rapid development of information and the big data. The size and complexity of these issues continues to expand. The traditional evolutionary algorithms can’t give a satisfactory answer within a reasonable time. Cluster computing is a parallel computing architecture. It combined with parallel computing, high-performance, distributed, and high availability capabilities through the network integration. In a dynamic data-driven concept. This architecture can be used to solve the operation with evolutionary algorithms on dynamic computing and dynamic resource allocation. In this study. We propose a dynamic data driven prediction model based on evolutionary algorithms and cluster computing. In this model, it will be added to the concept of dynamic data-driven application system. In a dynamic data environment, build a distributed evolutionary algorithms base on cluster computing architecture. To find out the relationship between the dynamic data and prediction target in time and make an efficient and accurate predictions.
34
Chen, Ting-Ting, et 陳婷婷. « A Study on Corporate Users’Acceptance of Digital Cluster Based on Technology Acceptance Model ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/21345511787126828901.
Texte intégralRésumé :
碩士國立中興大學
生物產業暨城鄉資源管理學系所
98
Digital cluster is a new business model for the cooperation building among enterprises, which combines the strategy of industrial cluster and application of information technology to fulfill the mission of cluster by the network platform of electronic commerce. The goal of digital cluster aims to introduce local features to the world and enhance the competitiveness by the cooperation among enterprises. In spite of the aims digital cluster desires to reach, the key to success in terms of whether digital cluster can strive for a long run lies on what enterprises see in digital cluster. The purpose of this research is to investigate and discuss the practical experiences and views of corporate users about the application of digital cluster based on technology acceptance model (TAM). By the means of questionnaire survey and based on the results of analyses of 176 valid samples, here conclude several findings as follows: (1)The viewpoints of corporate users about digital cluster: Most corporate users think digital cluster not only can increase the public exposure but also is helpful for promotion, though it cannot significantly enhance sale volume or low down the cost. Even it’s not very difficult for corporate users to learn how to use the platform of digital cluster, it is still not so flexible that the they cannot adjust its functions freely. More corporate users have positive perceptions on digital cluster, but the intention of application inclines to be low to some extents. Most of the enterprises indicate that they will keep participating in digital cluster in the future, but less frequent and proactive move will be taken place. (2)The status quo of corporate users’, application of digital cluster: The frequency and time of spending on digital cluster for corporate users are ranked on medium-low degree. For the application of platform functions of digital cluster, corporate users employ it to present information most frequently, then to interact with customers on the Internet, and less for the order taking or databank building of customers. (3)Analyzing the influential factors of acceptance based on TAM: The degree of how corporate users feel about digital cluster is critical to their continuous application in the future. To promote the usage of enterprises, it is necessary to first enhance the perceived usefulness of digital cluster.
35
Yang, Yu Chuang, et 莊楊裕. « Computer server sales forecasting using cluster-based forecasting model with different linkage strategies ». Thesis, 2015. http://ndltd.ncl.edu.tw/handle/41152233888692300027.
Texte intégralRésumé :
碩士健行科技大學
工業管理系碩士班
103
Sales forecasting is crucial for every company since it is an important task for manufacturing, inventory management and marketing. In this study, a computer server sales forecasting model using clustering method with support vector regression (SVR) and extreme learning machine (ELM) with different linkage strategies is proposed. The proposed scheme first uses k-means algorithm to partition the whole training sales data into several disjoint clusters. Then, for each group, the SVR and ELM is applied to construct forecasting model. Finally, for a given testing data, three linkage methods are used to find the cluster which the testing data belongs to and then employee the corresponding trained SVR model and ELM model to generate prediction result. A real data of computer server sales collected from a Taiwanese multinational electronics company is used as illustrative examples to evaluate the performance of the proposed model. Experimental results revealed that the proposed clustering-based sales forecasting scheme outperforms the single method and seasonal naive forecasting models and hence is an effective alternative for sales forecasting.
36
« Scalable model-based clustering algorithms for large databases and their applications ». 2002. http://library.cuhk.edu.hk/record=b6073478.
Texte intégralRésumé :
by Huidong Jin."August 2002."
Thesis (Ph.D.)--Chinese University of Hong Kong, 2002.
Includes bibliographical references (p. 193-204).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
37
Sithole, Sibusiso Clement. « Cluster-based economic development strategies : a model for the tourism industry in Kwazulu-Natal ». 2008. http://hdl.handle.net/10500/3162.
Texte intégralRésumé :
The purpose of this study was to explore how a potential model of a tourism industry cluster could be developed in KwaZulu-Natal (KZN). To undertake this task an extensive literature review of cluster-based economic strategies was done. This was followed by a synopsis of the tourism industry from a global and South African perspective in order to determine issues of competitiveness and their impact on provincial dynamics. The study employed a qualitative research design and focused on the tourism industry in KwaZulu-Natal as a case study. Major stakeholders in the industry were interviewed.
The main findings of the study are that KZN has the necessary preconditions for existence of a potential tourism cluster, and these conditions make it ripe for the cluster to be activated and developed. Activating and developing a cluster would bring home major benefits. The study highlighted various tools and mechanisms which could be used to analyse the province’s cluster map, and a model cluster map is also suggested based on contributions from different respondents. Using Porter’s Diamond Model, the competitiveness of the KZN tourism industry was assessed. Overall, it has been found that the province’s tourism industry possesses a mixture of resources and capabilities, which could be capitalised upon to developing the industry in future. However, glaring weaknesses are also exposed, which need to be dealt with urgently. In particular, crime and grime, together with the lack of tourism infrastructure to attract the high-end of the market, are seen as huge liability for the industry. A major contribution of this study is in identifying strategic management challenges that cluster studies have not addressed previously. The study also highlighted important critical success factors for cluster development and the drivers for change. The presence of some of these factors contributes to making the future prospect of the tourism industry in KZN to look bright. The study concludes by recommending that a tourism cluster be activated and developed in KZN and this process be led by an Independent Cluster Facilitator, who must be appointed by the Member of the Executive Committee responsible for Finance and Economic Development in the province in consultation with industry leaders.
38
Erar, Bahar. « Mixture model cluster analysis under different covariance structures using information complexity ». 2011. http://trace.tennessee.edu/utk_gradthes/968.
Texte intégralRésumé :
In this thesis, a mixture-model cluster analysis technique under different covariance structures of the component densities is developed and presented, to capture the compactness, orientation, shape, and the volume of component clusters in one expert system to handle Gaussian high dimensional heterogeneous data sets to achieve flexibility in currently practiced cluster analysis techniques. Two approaches to parameter estimation are considered and compared; one using the Expectation-Maximization (EM) algorithm and another following a Bayesian framework using the Gibbs sampler. We develop and score several forms of the ICOMP criterion of Bozdogan (1994, 2004) as our fitness function; to choose the number of component clusters, to choose the correct component covariance matrix structure among nine candidate covariance structures, and to select the optimal parameters and the best fitting mixture-model. We demonstrate our approach on simulated datasets and a real large data set, focusing on early detection of breast cancer. We show that our approach improves the probability of classification error over the existing methods.
39
Yu-MingHsu et 許佑銘. « Utilization of Cluster Analysis on the Sampling Selection of the Model-based Sampling Survey ». Thesis, 2016. http://ndltd.ncl.edu.tw/handle/4r7c49.
Texte intégralRésumé :
碩士國立成功大學
統計學系
104
For the prediction problem in survey sampling under a finite population, n sampling units are selected out of N population units and observed to predict the population quantity of interest. The optimal sampling strategies proposed by different authors in the past can be used to select the optimal sample with which the mean-square error can be minimized. However, the computational load can be very extensive, and the optimization algorithm is not easy to implement. Additionally, the exact population distribution has to be assumed. Two model-based sampling selection methods based on Cluster Analysis under a given sample size n are proposed in this article. Both design are better than SRSWOR in terms of given lower prediction mean-square error. These sampling methods do not require extensive computation nor exact population distribution to select the sampling units. Simulation study shows that they can be more effective than SRSWOR. An example on the utilization of the proposed sampling methods in practice is also presented.
40
Andrews, Jeffrey Lambert. « Model-based Learning : t-Families, Variable Selection, and Parameter Estimation ». Thesis, 2012. http://hdl.handle.net/10214/3879.
Texte intégralRésumé :
The phrase model-based learning describes the use of mixture models in machine learning problems. This thesis focuses on a number of issues surrounding the use of mixture models in statistical learning tasks: including clustering, classification, discriminant analysis, variable selection, and parameter estimation. After motivating the importance of statistical learning via mixture models, five papers are presented. For ease of consumption, the papers are organized into three parts: mixtures of multivariate t-families, variable selection, and parameter estimation.Natural Sciences and Engineering Research Council of Canada through a doctoral postgraduate scholarship.
41
PAN, PO-CHUN, et 潘柏君. « The Development Model of Local Culture Based Creative Cluster──Taking the Xiluo Traditional Street as Example ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/92h4f4.
Texte intégralRésumé :
碩士國立雲林科技大學
創意生活設計系
107
As the countries worldwide commute frequently, many cultures of the local downtown has tended to be consistent where many local cultures are affected by each other. In the case of the globalization emerging increasingly, countries and cities start to be aware of the importance of the local cultures and features, and developing the local cultures has become the important local new issue. This study takes cultural and creative industries which are located at Yanping old street, Xiluo downtown, Yunlin county as subject. The purpose is : 1) Analyzing the local cultural way which is used at the cultural and creative story on the Xiluo Traditional Street. 2) Analyzing the connective way which is used at the cultural and creative story on the Xiluo Traditional Street. 3) Concluding the cultural and creative story on the Xiluo Traditional Street how to use local culture to develop the cultural and creative industries. This study used document analysis to understand how Louyoung Cultural and Education Foundation, which is located at Xiluo downtown, Yunlin county to implement the old street regeneration campaign of Yanping old street, and they actually carry on participatory observation at Louyoung Cultural and Education Foundation. They interview the creative store on the old street, and carry out grounded theory to analyze. Through research result we can find that there are seven ways which creative stores use local culture: 1) Using text image to show the living style and local history. 2) Taking local history as the content of display. 3)Taking historic building as exhibition space and creative product. 4) Using industrial culture to offer guides, displays, foods, DIY event Services, teaching activities and crafts. 5) Appling aqueduct to plan a trip. 6)Using local plant material to work. 7) Using individual creation to make a theme park. This study find that the creative stores and the creative settlements are connected by following methods: organizing events and cooperating together, the work commissions among stores and settlements, joining certification and interacting with residents and stores. This study constructed the development pattern of Xiluo creative cluster and its can be divided into three level, the first level: the types of the creative store to use local culture. The second level: the stores inside the village link with network relationship. The third level: creative stores use local culture to produce the products, services and landscapes.
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Murray, Paula. « Mixtures of Skew-t Factor Analyzers ». Thesis, 2012. http://hdl.handle.net/10214/5274.
Texte intégralRésumé :
Model-based clustering allows for the identification of subgroups in a data set through the use of finite mixture models. When applied to high-dimensional microarray data, we can discover groups of genes characterized by their gene expression profiles. In this thesis, a mixture of skew-t factor analyzers is introduced for the clustering of high-dimensional data. Notably, we make use of a version of the skew-t distribution which has not previously appeared in mixture-modelling literature. Allowing a constraint on the factor loading matrix leads to two mixtures of skew-t factor analyzers models. These models are implemented using the alternating expectation-conditional maximization algorithm for parameter estimation with an Aitken's acceleration stopping criterion used to determine convergence. The Bayesian information criterion is used for model selection and the performance of each model is assessed using the adjusted Rand index. The models are applied to both real and simulated data, obtaining clustering results which are equivalent or superior to those of established clustering methods.
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Sung, Shao-Chung, et 宋少中. « Multicast with Intra-Cluster Device-to-Device (D2D) Data Sharing Algorithm by Relay Supported Scheme Based on Small World Model ». Thesis, 2015. http://ndltd.ncl.edu.tw/handle/r8j2ue.
Texte intégralRésumé :
碩士國立交通大學
電信工程研究所
103
Device-to-Device (D2D) communications help improve the performance of wireless multicast service in local area. In this thesis, we propose an improved intra-cluster D2D multicast algorithm to make data sharing realized among D2D users. In our algorithm, the data transmitting order is according to the data demand. In each data transmission process, we will choose a proper transmitter to multicast data in a cluster. Moreover, the relay scheme of data transmission is used in our algorithm, so as to get over transmission rate restriction that made by link quality. Greedy algorithm is used to select transmitter, but we add a parameter to make fairness among all the users. The small world model is concerned in this paper. Simulation shows that the proposed algorithm not only improves the fairness, but also decreases the transmission cost.
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Levchenko, Oleksandr, О. М. Левченко, Ilona Tsarenko et І. О. Царенко. « The Role of Universities in Cluster development of Countries’ Economy ». Thesis, 2017. http://dspace.kntu.kr.ua/jspui/handle/123456789/7169.
Texte intégralRésumé :
The paper deals with the issue of cluster development. We analyze and asses the linear relation between cluster development and independent variables such as university ranking, according to the Global Talent Competitiveness Index. The aim of this paper is to highlight the relationship between university ranking as an indicator of the evaluation of level of higher education of country and cluster development. For the purposes of this paper, the analysis of these indicators of 75 countries of the world. With regard to the aim, we have set the following hypothesis: we assume that there is a statistically significant correlation between the impact of university ranking and cluster development of country. We provide mentioned analysis by multidimensional regression analysis for the year 2017 of 75 countries of the world. The analysis confirmed that the cluster development has notable relationship with university ranking of country. У статті розглядається питання розвитку кластерів. Проаналізовано та оцінено лінійний зв'язок між розвитком кластеру та незалежними змінами, такими як рейтинг університетів, відповідно до Індексу глобальної конкурентоспроможності талантів. Метою даного дослідження є виявити наявність або відсутність кореляційного зв'язку між університетським рейтингом як індикатором оцінки рівня вищої освіти країни та розвитком кластеру. Для цілей цього дослідження було взято показники 75 країн світу. Відповідно до мети, визначено наступну гіпотезу: існує статистично значуща кореляція між впливом університетських рейтингів та кластерним розвитком країни. Авторську гіпотезу апробовано за допомогою багатовимірного регресійного аналізу показників 75 країн світу в 2017 році. Аналіз підтвердив, що розвиток кластерів має значний зв'язок з рівнем рейтингу університетів країни.
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Dang, Sanjeena. « Variational Approximations and Other Topics in Mixture Models ». Thesis, 2012. http://hdl.handle.net/10214/3876.
Texte intégralRésumé :
Mixture model-based clustering has become an increasingly popular data analysis technique since its introduction almost fifty years ago. Families of mixture models are said to arise when the component parameters, usually the component covariance matrices, are decomposed and a number of constraints are imposed. Within the family setting, it is necessary to choose the member of the family --- i.e., the appropriate covariance structure --- in addition to the number of mixture components. To date, the Bayesian information criterion (BIC) has proved most effective for this model selection process, and the expectation-maximization (EM) algorithm has been predominantly used for parameter estimation.
We deviate from the EM-BIC rubric, using variational Bayes approximations for parameter estimation and the deviance information criterion (DIC) for model selection. The variational Bayes approach alleviates some of the computational complexities associated with the EM algorithm. We use this approach on the most famous family of Gaussian mixture models known as Gaussian parsimonious clustering models (GPCM). These models have an eigen-decomposed covariance structure.
Cluster-weighted modelling (CWM) is another flexible statistical framework for modelling local relationships in heterogeneous populations on the basis of weighted combinations of local models. In particular, we extend cluster-weighted models to include an underlying latent factor structure of the independent variable, resulting in a novel family of models known as parsimonious cluster-weighted factor analyzers. The EM-BIC rubric is utilized for parameter estimation and model selection.
Some work on a mixture of multivariate t-distributions is also presented, with a linear model for the mean and a modified Cholesky-decomposed covariance structure leading to a novel family of mixture models. In addition to model-based clustering, these models are also used for model-based classification, i.e., semi-supervised clustering. Parameters are estimated using the EM algorithm and another approach to model selection other than the BIC is also considered.NSERC PGS-D
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Plappert, H., C. Hobson-Merrett, B. Gibbons, E. Baker, S. Bevan, M. Clark, S. Creanor et al. « Evaluation of a primary care-based collaborative care model (PARTNERS2) for people with diagnoses of schizophrenia, bipolar, or other psychoses : study protocol for a cluster randomised controlled trial ». 2003. http://hdl.handle.net/10454/18577.
Texte intégralRésumé :
YesCurrent NHS policy encourages an integrated approach to provision of mental and physical care for individuals with long term mental health problems. The 'PARTNERS2' complex intervention is designed to support individuals with psychosis in a primary care setting. The trial will evaluate the clinical and cost-effectiveness of the PARTNERS2 intervention. This is a cluster randomised controlled superiority trial comparing collaborative care (PARTNERS2) with usual care, with an internal pilot to assess feasibility. The setting will be primary care within four trial recruitment areas: Birmingham & Solihull, Cornwall, Plymouth, and Somerset. GP practices are randomised 1:1 to either (a) the PARTNERS2 intervention plus modified standard care ('intervention'); or (b) standard care only ('control'). PARTNERS2 is a flexible, general practice-based, person-centred, coaching-based intervention aimed at addressing mental health, physical health, and social care needs. Two hundred eligible individuals from 39 GP practices are taking part. They were recruited through identification from secondary and primary care databases. The primary hypothesis is quality of life (QOL). Secondary outcomes include: mental wellbeing, time use, recovery, and process of physical care. A process evaluation will assess fidelity of intervention delivery, test hypothesised mechanisms of action, and look for unintended consequences. An economic evaluation will estimate its cost-effectiveness. Intervention delivery and follow-up have been modified during the COVID-19 pandemic. The overarching aim is to establish the clinical and cost-effectiveness of the model for adults with a diagnosis of schizophrenia, bipolar, or other types of psychoses.
PARTNERS2 is funded by the National Institute for Health Research (NIHR) under its Programme Grant for Applied Research Programme (grant number: RP-PG- 200625). This research was also supported by the NIHR Collaboration for Leadership in Applied Health Research and Care South West Peninsula at the Royal Devon and Exeter NHS Foundation Trust.
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Plappert, H., C. Hobson-Merrett, B. Gibbons, E. Baker, S. Bevan, M. Clark, S. Creanor et al. « Evaluation of a primary care-based collaborative care model (PARTNERS2) for people with diagnoses of schizophrenia, bipolar, or other psychoses : study protocol for a cluster randomised controlled trial ». 2021. http://hdl.handle.net/10454/18577.
Texte intégralRésumé :
YesCurrent NHS policy encourages an integrated approach to provision of mental and physical care for individuals with long term mental health problems. The 'PARTNERS2' complex intervention is designed to support individuals with psychosis in a primary care setting. The trial will evaluate the clinical and cost-effectiveness of the PARTNERS2 intervention. This is a cluster randomised controlled superiority trial comparing collaborative care (PARTNERS2) with usual care, with an internal pilot to assess feasibility. The setting will be primary care within four trial recruitment areas: Birmingham & Solihull, Cornwall, Plymouth, and Somerset. GP practices are randomised 1:1 to either (a) the PARTNERS2 intervention plus modified standard care ('intervention'); or (b) standard care only ('control'). PARTNERS2 is a flexible, general practice-based, person-centred, coaching-based intervention aimed at addressing mental health, physical health, and social care needs. Two hundred eligible individuals from 39 GP practices are taking part. They were recruited through identification from secondary and primary care databases. The primary hypothesis is quality of life (QOL). Secondary outcomes include: mental wellbeing, time use, recovery, and process of physical care. A process evaluation will assess fidelity of intervention delivery, test hypothesised mechanisms of action, and look for unintended consequences. An economic evaluation will estimate its cost-effectiveness. Intervention delivery and follow-up have been modified during the COVID-19 pandemic. The overarching aim is to establish the clinical and cost-effectiveness of the model for adults with a diagnosis of schizophrenia, bipolar, or other types of psychoses.
PARTNERS2 is funded by the National Institute for Health Research (NIHR) under its Programme Grant for Applied Research Programme (grant number: RP-PG- 200625). This research was also supported by the NIHR Collaboration for Leadership in Applied Health Research and Care South West Peninsula at the Royal Devon and Exeter NHS Foundation Trust.
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Ludorf, Sebastian. « Besonderheiten von Produkten aus nachwachsenden Rohstoffen und deren Auswirkungen auf die Wahl effizienter Koordinationsformen in B2B-Geschäftsbeziehungen ». Doctoral thesis, 2015. http://hdl.handle.net/11858/00-1735-0000-0028-86A8-2.
Texte intégral
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Šedová, Michaela. « Odhad parametru při dvoufázovém stratifikovaném a skupinovém výběru ». Doctoral thesis, 2011. http://www.nusl.cz/ntk/nusl-299533.
Texte intégralRésumé :
Title: Parameter Estimation under Two-phase Stratified and Cluster Sampling Author: Mgr. Michaela Šedová Department: Department of Probability and Mathematical Statistics Supervisor: Doc. Mgr. Michal Kulich, Ph.D. Abstract: In this thesis we present methods of parameter estimation under two-phase stratified and cluster sampling. In contrast to classical sampling theory, we do not deal with finite population parameters, but focus on model parameter inference, where the ob- servations in a population are considered to be realisations of a random variable. However, we consider the sampling schemes used, and thus we incorporate much of survey sampling theory. Therefore, the presented methods of the parameter estimation can be understood as a combination of the two approaches. For both sampling schemes, we deal with the concept where the population is considered to be the first-phase sample, from which a sub- sample is drawn in the second phase. The target variable is then observed only for the subsampled subjects. We present the mean value estimation, including the statistical prop- erties of the estimator, and show how this estimation can be improved if some auxiliary information, correlated with the target variable, is observed for the whole population. We extend the method to the regression problem....
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Ramroop, Renuka Suekiah. « A qualitative study of the impact of organisational development interventions on the implementation of Outcomes Based Education ». Diss., 2004. http://hdl.handle.net/10500/1791.
Texte intégralRésumé :
Outcomes Based Education (OBE), has been, since its inception, fraught with problems. OBE in its very nature is complex. To fully embrace this method and ensure its success, schools must be able to make the necessary paradigm shift. This can only be achieved when schools receive relevant and empowering training, support and development. In other words, organisational development must be the key words. The aim of this study is to explore the impact of organisational development interventions on the implementation of OBE. The case study method was employed where it was realised that schools that received organisational development interventions, together with Outcomes Based Education, were able to implement this method with greater understanding, skill, and confidence.
The investigation recommends an organisational development design that could be used instead of the cascade model, and provides suggestions on what can be done to ensure a more successful implementation process.Educational Studies
M. Ed (Education Management)