Objective: To estimate the national prevalence and direct incremental expenditures of musculoskeletal disorders (MSD's) using the 2007 Medical Expenditure Panel Survey data. <br>Methods: A retrospective database analysis was conducted and individuals with MSD's (ICD-9-CM codes 274.00; 710.00-738.00) were identified. Dependent variables were total health care and other service category expenditures. The study utilized descriptive and regression analyses. <br>Results: In 2007, the national prevalence of MSD's was 33 million with incremental costs of $886.49 per person. The inpatient expenditures ($33,461.85) were the highest cost component in MSD's and the predictors of total health care expenditures were age, marital status, and presence of the disease condition. <br>Conclusion: The systematic assessment of MSD's and their associated incremental costs to the society is essential in increasing the awareness of decision makers to implement intervention strategies that are effective in lowering the disease incidence and in reducing the overall cost of disease management. / Mylan School of Pharmacy and the Graduate School of Pharmaceutical Sciences / Pharmacy Administration / MS / Thesis

Real-world learning tasks present a range of issues for learning systems. Learning tasks can be complex and the training data noisy. When operating as part of a larger system, there may be limitations on available memory and computational resources. Learners may also be required to provide results from a stream. This thesis investigates the problem of incremental, relational learning from imperfect data with constrained time and memory resources. The learning process involves incremental update of a theory when an example is presented that contradicts the theory. Contradictions occur if there is an incorrect theory or noisy data. The learner cannot discriminate between the two possibilities, so both are considered and the better possibility used. Additionally, all changes to the theory must have support from multiple examples. These two principles allow learning from imperfect data. The Minimum Description Length principle is used for selection between possible worlds and determining appropriate levels of additional justification. A new encoding scheme allows the use of MDL within the framework of Inductive Logic Programming. Examples must be stored to provide additional justification for revisions without violating resource requirements. A new algorithm determines when to discard examples, minimising total usage while ensuring sufficient storage for justifications. Searching for revisions is the most computationally expensive part of the process, yet not all searches are successful. Another new algorithm uses a notion of theory stability as a guide to occasionally disallow entire searches to reduce overall time. The approach has been implemented as a learner called NILE. Empirical tests include two challenging domains where this type of learner acts as one component of a larger task. The first of these involves recognition of behavior activation conditions in another agent as part of an opponent modeling task. The second, more challenging task is learning to identify objects in visual images by recognising relationships between image features. These experiments highlight NILE'S strengths and limitations as well as providing new n domains for future work in ILP.

This paper deals with the problem of shipping multiple commodities from a single vendor to a single buyer. Each commodity is assumed to be constantly consumed at the buyer, and periodically replenished from the vendor. Furthermore, these replenishments are restricted to happen at discrete time instants, e.g., a certain time of the day or a certain day of the week. At any such time instant, transportation cost depends on the shipment quantity according to certain discount scheme. Specifically, we consider two transportation quantity discount schemes: LTL (less-than-truckload) incremental discount and TL (truckload) discount. For each case, we develop MIP (mixed integer programming) mathematical model whose objective is to make an integrated replenishment and transportation decision such that the total system cost is minimized. We also derive optimal solution properties and give numerical studies to investigate the problem. / Singapore-MIT Alliance (SMA)

Thesis (MComm (Industrial Psychology))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: It is well documented that intelligence (g, or general cognitive ability) is one of the best predictors of job and training performance (Ree, Earles & Teachout, 1994; Schmidt & Hunter, 1998). However, research evidence suggests that its predictive validity can be incremented by measures of personality and motivation. In this study, measures of general cognitive ability, training motivation and personality were administered to South African Army trainee soldiers (N = 108) to investigate the ability of the measures to predict training performance criteria. Hierarchical multiple regression was used to investigate the relationship between the predictor composites and two composites of training performance. Multiple correlations of .529 (p < .01) and .378 (p < .05) were obtained for general soldiering training proficiency and core technical training proficiency respectively. Findings reveal different prediction patterns for the two criteria, as general cognitive ability contributed to significantly predicting the criterion of general soldiering training performance, but not core technical training proficiency. Similarly, training motivation and openness to experience were not found to predict general soldiering training proficiency, but predicted core technical training proficiency. Therefore, the results indicate that the addition of motivation to a model already containing measures of general cognitive ability does add incremental validity; R2 increased from .051 to .109 (p < .05). Adding personality to a model already containing general cognitive ability and motivation also explains additional variance; R2 increased from .109 to .143, although this change was marginal (p = .055). Furthermore, evidence of interaction between intelligence and training motivation was found when predicting training performance, as motivation influenced performance only for individuals with lower intelligence scores. The implications of the results are discussed and areas for further research are highlighted. / AFRIKAANSE OPSOMMING: Verskeie studies toon aan dat intelligensie (g, of algemene kognitiewe vermoë) een van die beste voorspellers is van prestasie ten opsigte van werk en opleiding (Ree, Earles & Teachout, 1994; Schmidt & Hunter, 1998). Navorsingsbewyse dui egter ook aan dat hierdie voorspellingsgeldigheid verhoog kan word deur die toevoeging van metings van persoonlikheid en motivering. In die huidige studie, is metings van algemene kognitiewe vermoë, opleidingsmotivering en persoonlikheid afgeneem op soldate onder opleiding in the Suid Afrikaanse Leër (N = 108). Die doel hiermee was om te bepaal tot watter mate hierdie metings saam opleidingsprestasie voorspel. Hiërargiese meervoudige regressie-ontleding was gebruik om die verband tussen die voorspellersamestellings en twee opleidingprestasiekriteria te bepaal. Meervoudige korrelasies van .529 (p <. 01) en .378 (p < .05) was onderskeidelik verkry vir Algemene Krygsopleidingsprestasie (GSTP) en Tegniese Korpsopleidingsprestasie (CTTP), onderskeidelik. Die resultate toon verder verskillende voorspellingspatrone vir hierdie twee kriteriummetings. Eerstens, het algemene kognitiewe vermoë beduidend bygedra tot die voorspelling van GSTP, maar nié tot CTTP nie. Verder het opleidingsmotivering en persoonlikheid (oopheid tot ervaring) nie GSTP voorspel nie, maar wél CTTP. Met ander woorde, die resultate dui aan dat die toevoeging van motivering tot ‘n model wat reeds metings van algemene kognitiewe vermoë bevat, wel inkrementele geldigheid tot gevolg het; R2 het toegeneem vanaf .051 tot .109 (p < .05). Die toevoeging van persoonlikheid tot ‘n model wat reeds algemene kognitiewe vermoë en motivering bevat, verklaar ook addisionele variansie; R2 het toegeneem vanaf .109 tot .143, alhoewel hierdie inkrementering slegs marginaal (p = .055) was. Laastens, is bewyse van ‘n interaksie-effek tussen intelligensie en opleidingsmotivering gevind in die voorspelling van opleidingsprestasie. Daar is bevind dat motivering prestasie slegs beïnvloed het vir individue met laer intelligensietellings. Die implikasies van die resultate word bespreek en areas vir verdere navorsing word aangedui.

La Gestion de Connaissance et de l’innovation sont des thèmes à forte importance dans l’actualité, surtout parce que ces deux sujets sont liés et ils influencent la performance des entreprises. L’objectif de cette recherche est d’analyser le lien entre gestion de la connaissance et de l’innovation à partir de trois industries de produit simple dans le pôle industriel de Barcarena, état du Pará, au Brésil. On veut évaluer à la fois la relation de la Gestion de Connaissance dans ces entreprises et leur capacité d’innovation, pour comprendre l’influence des connaissances pour la capacité innovatrice, surtout l’innovation incrémentale. Pour cela, on se servira de modèles d’analyse qui prennent en compte des facteurs tels quels la culture, le leadership, la technologie, les ressources humaines et les processus. Notre approche méthodologique est qualitative. On prit comme base théorique les concepts et la littérature autour de la Gestion de Connaissance et de l’innovation. L’axe de l’industrie fut choisi par son importance économique dans la région où la recherche a été développée. Nous avons interviewé 24 employés chez Imerys S.A., Hydro Alunorte S.A. et Alubar S.A. Cette recherche a mis en relief le rôle du leader dans l’innovation incrémentale, l’importance du sens d’urgence pour la culture corporative innovante. De plus, selon les résultats les entreprises qui mieux gèrent les connaissances ont plus de possibiliter d’innover. / The management of knowledge and innovation are the themes to strong importance today, especially because these two topics are linked and they influence the performance of firms. The objective of this research is to analyze the link between knowledge management and innovation from three industries of simple product in the industrial pole of Barcarena city, Pará State, Brazil. We want to evaluate both the relationship of Knowledge Management in these companies and their innovative capacity, to understand the influence of knowledge for the innovative capacity, especially incremental innovation. For this, we will use models of analysis which take into account factors such as culture, leadership, technology, human resources and process. Our methodological approach is qualitative. We choose as basic concepts and theoretical literature about the Knowledge Management and Innovation. The axis of the industry was chosen by its economic importance in the region where the research has been developed. We interviewed 24 employees at Imerys S.A., Hydro Alunorte S.A. and Alubar S.A. This research has highlighted the role of the leader in the incremental innovation, the importance of the sense of urgency for the corporate culture innovative. In addition, according to the results of the companies that better manage the knowledge have more possibiliter to innovate.

abstract: Data-driven applications are becoming increasingly complex with support for processing events and data streams in a loosely-coupled distributed environment, providing integrated access to heterogeneous data sources such as relational databases and XML documents. This dissertation explores the use of materialized views over structured heterogeneous data sources to support multiple query optimization in a distributed event stream processing framework that supports such applications involving various query expressions for detecting events, monitoring conditions, handling data streams, and querying data. Materialized views store the results of the computed view so that subsequent access to the view retrieves the materialized results, avoiding the cost of recomputing the entire view from base data sources. Using a service-based metadata repository that provides metadata level access to the various language components in the system, a heuristics-based algorithm detects the common subexpressions from the queries represented in a mixed multigraph model over relational and structured XML data sources. These common subexpressions can be relational, XML or a hybrid join over the heterogeneous data sources. This research examines the challenges in the definition and materialization of views when the heterogeneous data sources are retained in their native format, instead of converting the data to a common model. LINQ serves as the materialized view definition language for creating the view definitions. An algorithm is introduced that uses LINQ to create a data structure for the persistence of these hybrid views. Any changes to base data sources used to materialize views are captured and mapped to a delta structure. The deltas are then streamed within the framework for use in the incremental update of the materialized view. Algorithms are presented that use the magic sets query optimization approach to both efficiently materialize the views and to propagate the relevant changes to the views for incremental maintenance. Using representative scenarios over structured heterogeneous data sources, an evaluation of the framework demonstrates an improvement in performance. Thus, defining the LINQ-based materialized views over heterogeneous structured data sources using the detected common subexpressions and incrementally maintaining the views by using magic sets enhances the efficiency of the distributed event stream processing environment. / Dissertation/Thesis / Ph.D. Computer Science 2011

O presente trabalho enfoca o estudo de transdutores indutivos de deslocamento linear. Dentre os diversos dispositivos dessa natureza, procurou-se desenvolver um estudo mais aprofundado do transdutor indutivo diferencial com núcleo de esferas. O desenvolvimento experimental, com a construção de quatro protótipos, levou ao projeto de um transdutor com características adequadas ao uso industrial. / The present work focuses on the study of inductive transducers of linear displacement. Among the several devices of that nature, a more detailed study of the inductive differential transducer was attempted with core of spherical balls. The experimental development of four prototypes resulted in a transducer with characteristics adequate for industrial use to be designed.

Até os dias de hoje, diversos algoritmos de mineração de modelos de processos já foram propostos para extrair conhecimento a partir de logs de eventos. O conhecimento que tais algoritmos são capazes de obter incluem modelos de processos de negócio, assim como aspectos da estrutura organizacional, como atores e papéis. A mineração de processos pode se beneficiar de uma estratégia incremental, especialmente quando as informações sobre um ou mais processos de negócio presentes no código fonte de um sistema de informação são logicamente complexas (diversas ramificações e atividades paralelas e/ou alternativas). Neste cenário, são necessárias muitas execuções da aplicação para a coleta de um grande conjunto de dados no arquivo de log, a fim de que o algoritmo de mineração possa descobrir e apresentar o processo de negócio completo. Outra situação que torna necessária a mineração incremental é a constante evolução dos processos de negócio, ocasionada geralmente por alterações nas regras de negócio de uma ou mais aplicações. Neste caso, o log pode apresentar novos fluxos de atividades, ou fluxos alterados ou simplesmente fluxos que não são mais executados. Estas mudanças devem ser refletidas no modelo do processo a fim de garantir a sincronização entre a aplicação (processo executado) e o modelo. A mineração incremental de processos pode ainda ser útil quando se faz necessária a extração gradual de um modelo de processo completo, extraindo modelos parciais (fragmentos de processo com início e fim) em um primeiro passo e integrando conhecimento adicional ao modelo em etapas até a obtenção do modelo completo. Contudo, os algoritmos atuais de mineração incremental de processos não apresentam total efetividade quanto aos aspectos acima citados, apresentando algumas limitações. Dentre elas podemos citar a não remoção de elementos obsoletos do modelo de processo descoberto, gerados após a atualização do processo executado, e também a descoberta de informações da estrutura organizacional associada ao processo como, por exemplo, os atores que executam as atividades. Este trabalho propõe um algoritmo incremental para a mineração de processos de negócio a partir de logs de execução. Ele permite a atualização completa de um modelo existente, bem como o incremento de um modelo de processo na medida em que novas instâncias são adicionadas ao log. Desta forma, podemos manter ambos, modelo de processo e o processo executado sincronizados, além de diminuirmos o tempo total de processamento uma vez que apenas novas instâncias de processo devem ser consideradas. Por fim, com este algoritmo é possível extrair modelos com acurácia igual ou superior aqueles que podem ser extraídos pelos algoritmos incrementais atuais. / Even today, several process mining algorithms have been proposed to extract knowledge from event logs of applications. The knowledge that such algorithms are able to discovery includes business process models, business rules, as well as aspects of organizational structure, such actors and roles of processes. These process mining algorithms can be divided into two: non-incremental and incremental. The mining process can benefit from an incremental strategy, especially when information about the process structure available in the system source code is logically complex (several branches and parallel activities). In this scenario, its necessary several executions of the application, to collect a large set of log data, so that the mining algorithm can discover and present the complete business process. Another use case where incremental mining is usefull is during the changing structure of the process, caused by the change in the business logic of an application. In this case, the log may provide new traces of activities, modified traces or simply traces that are no longer running. These changes must be reflected in the process model being generated to ensure synchronization between the application and model. The incremental process mining can also be useful when it is necessary to extract a complete process model in a gradual way, extracting partial models (process fragments with begin and end) in a first step and integrating additional knowledge to the model in stages to obtain the complete model. However, existing incremental process mining algorithms are not effective to all aspects mentioned above. All of them have limitations with respect to certain aspects of incremental mining, such as deletion of elements in the process model (process model update). Additionally, most of them do not extract all the information present in the structure of the process, such as the actors who perform the activities. This paper proposes an incremental process mining algorithm from execution logs of information systems. The new algorithm allows the full update (adding and removing elements) of an existing model, as well as the increment of a process model as new records are added to the log. Thus, we can keep process models and process execution syncronized, while reducting the total processing time, since only new process instances must be processed. Finally, are expected the extraction of process models with similar or higher accuracy compared to current incremental mining algorithms.

Recentemente, houve um aumento rápido na criação e disponibilidade de repositórios de dados, o que foi percebido nas áreas de Mineração de Dados e Aprendizagem de Máquina. Este fato deve-se principalmente à rápida criação de tais dados em redes sociais. Uma grande parte destes dados é feita de texto, e a informação armazenada neles pode descrever desde perfis de usuários a temas comuns em documentos como política, esportes e ciência, informação bastante útil para várias aplicações. Como muitos destes dados são criados em fluxos, é desejável a criação de algoritmos com capacidade de atuar em grande escala e também de forma on-line, já que tarefas como organização e exploração de grandes coleções de dados seriam beneficiadas por eles. Nesta dissertação um modelo probabilístico, on-line e incremental é apresentado, como um esforço em resolver o problema apresentado. O algoritmo possui o nome DV-INBC e é uma extensão ao algoritmo INBC. As duas principais características do DV-INBC são: a necessidade de apenas uma iteração pelos dados de treino para criar um modelo que os represente; não é necessário saber o vocabulário dos dados a priori. Logo, pouco conhecimento sobre o fluxo de dados é necessário. Para avaliar a performance do algoritmo, são apresentados testes usando datasets populares. / Recently the fields of Data Mining and Machine Learning have seen a rapid increase in the creation and availability of data repositories. This is mainly due to its rapid creation in social networks. Also, a large part of those data is made of text documents. The information stored in such texts can range from a description of a user profile to common textual topics such as politics, sports and science, information very useful for many applications. Besides, since many of this data are created in streams, scalable and on-line algorithms are desired, because tasks like organization and exploration of large document collections would be benefited by them. In this thesis an incremental, on-line and probabilistic model for document classification is presented, as an effort of tackling this problem. The algorithm is called DV-INBC and is an extension to the INBC algorithm. The two main characteristics of DV-INBC are: only a single scan over the data is necessary to create a model of it; the data vocabulary need not to be known a priori. Therefore, little knowledge about the data stream is needed. To assess its performance, tests using well known datasets are presented.

Ingeniero Civil / El presente trabajo de título tiene como objetivo principal determinar analíticamente el mejor valor para el Factor de Sobrerresistencia (Ωo) a partir de 4 valores de prueba (Ωo=2,3,5 y 10.7). Esto se llevó a cabo mediante la evaluación del desempeño sísmico de una serie de modelos analíticos no lineales que representan a nivel macro los fenómenos ocurridos en edificios de acero con marcos arriostrados concéntricamente. Estos macro-modelos consisten en un sistema de estructura de masa y rigidez concentrada con rótulas plásticas, que permiten introducir la no-linealidad al sistema y modelar la resistencia de las estructuras a partir de la utilización de curvas momento-rotación. Para asegurar la correcta utilización de los macro-modelos, éstos debieron ser calibrados a partir de los modelos realizados para los edificios reales. Se desarrollaron 12 arquetipos con distintas alturas, sobrerresistencias y niveles de ductilidad, siguiendo la metodología de FEMA P695 (llámese Metodología). Cada uno de ellos se sometió a un set de 18 registros sísmicos de alta intensidad ocurridos en Chile utilizando el algoritmo de un Análisis Dinámico Incremental (IDA, por sus iniciales en inglés). Finalmente, se evalúo la aceptabilidad de cada uno de los valores estudiados de Ωo de acuerdo a los requerimientos de la Metodología. Como objetivo secundario se estudió una posible relación entre el factor de sobrerresistencia y la ductilidad del sistema (µT), junto con analizar posibles desventajas al implementar altos factores de sobrerresistencia debido a una potencial reducción de la ductilidad total de la estructura. De los resultados, se recomienda la utilización de Ωo=2.0 para los edificios de acero estudiados con R=5. Además, para los niveles de sobrerresistencia analizados se determinó que, para Ωo>5.0, la potencial reducción de ductilidad podría deteriorar el desempeño sísmico del edificio. Estas conclusiones aplican para edificios dentro del rango de características estudiado, con altura de hasta 21[m].