884 resultados para Consultant Selection, Decision Support System, Design Science Research Methodology
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Design Manual
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The use of simple and multiple correspondence analysis is well-established in socialscience research for understanding relationships between two or more categorical variables.By contrast, canonical correspondence analysis, which is a correspondence analysis with linearrestrictions on the solution, has become one of the most popular multivariate techniques inecological research. Multivariate ecological data typically consist of frequencies of observedspecies across a set of sampling locations, as well as a set of observed environmental variablesat the same locations. In this context the principal dimensions of the biological variables aresought in a space that is constrained to be related to the environmental variables. Thisrestricted form of correspondence analysis has many uses in social science research as well,as is demonstrated in this paper. We first illustrate the result that canonical correspondenceanalysis of an indicator matrix, restricted to be related an external categorical variable, reducesto a simple correspondence analysis of a set of concatenated (or stacked ) tables. Then weshow how canonical correspondence analysis can be used to focus on, or partial out, aparticular set of response categories in sample survey data. For example, the method can beused to partial out the influence of missing responses, which usually dominate the results of amultiple correspondence analysis.
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Se analiza el buscador Science Research en el contexto de la e-ciencia y de la búsqueda federada en comparación con la indización y la recolección. Se pone en relación también con otros buscadores académicos, especialmente Scirus y Google Scholar. Se realiza un análisis de los diversos componentes de Science Research y un estudio comparativo de obtención de resultados.
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In this thesis, we study the use of prediction markets for technology assessment. We particularly focus on their ability to assess complex issues, the design constraints required for such applications and their efficacy compared to traditional techniques. To achieve this, we followed a design science research paradigm, iteratively developing, instantiating, evaluating and refining the design of our artifacts. This allowed us to make multiple contributions, both practical and theoretical. We first showed that prediction markets are adequate for properly assessing complex issues. We also developed a typology of design factors and design propositions for using these markets in a technology assessment context. Then, we showed that they are able to solve some issues related to the R&D portfolio management process and we proposed a roadmap for their implementation. Finally, by comparing the instantiation and the results of a multi-criteria decision method and a prediction market, we showed that the latter are more efficient, while offering similar results. We also proposed a framework for comparing forecasting methods, to identify the constraints based on contingency factors. In conclusion, our research opens a new field of application of prediction markets and should help hasten their adoption by enterprises. Résumé français: Dans cette thèse, nous étudions l'utilisation de marchés de prédictions pour l'évaluation de nouvelles technologies. Nous nous intéressons plus particulièrement aux capacités des marchés de prédictions à évaluer des problématiques complexes, aux contraintes de conception pour une telle utilisation et à leur efficacité par rapport à des techniques traditionnelles. Pour ce faire, nous avons suivi une approche Design Science, développant itérativement plusieurs prototypes, les instanciant, puis les évaluant avant d'en raffiner la conception. Ceci nous a permis de faire de multiples contributions tant pratiques que théoriques. Nous avons tout d'abord montré que les marchés de prédictions étaient adaptés pour correctement apprécier des problématiques complexes. Nous avons également développé une typologie de facteurs de conception ainsi que des propositions de conception pour l'utilisation de ces marchés dans des contextes d'évaluation technologique. Ensuite, nous avons montré que ces marchés pouvaient résoudre une partie des problèmes liés à la gestion des portes-feuille de projets de recherche et développement et proposons une feuille de route pour leur mise en oeuvre. Finalement, en comparant la mise en oeuvre et les résultats d'une méthode de décision multi-critère et d'un marché de prédiction, nous avons montré que ces derniers étaient plus efficaces, tout en offrant des résultats semblables. Nous proposons également un cadre de comparaison des méthodes d'évaluation technologiques, permettant de cerner au mieux les besoins en fonction de facteurs de contingence. En conclusion, notre recherche ouvre un nouveau champ d'application des marchés de prédiction et devrait permettre d'accélérer leur adoption par les entreprises.
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Summary
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The Iowa State Profile Tool is a comprehensive, high-level assessment of Iowa’s progress toward a balanced long-term care system – a system that relies less on institutional services and provides greater opportunities for the in-home and community-based services that most people prefer. This report includes long-term support for people of all ages and disability types and is based on a variety of state and federal data sources and interviews with public and private leaders in Iowa’s long-term care system.
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Abstract
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The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).
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Reaaliaikainen, ennakoiva kunnonvalvonta on erittäin tärkeä osa modernin tehtaan tai tuotantolinjan toimintaa. Diplomityön teettäjä haluaa edelleen kehittää akustiseen emissioon perustuvaa kunnonvalvonta järjestelmäänsä, jotta siitä olisi enemmän hyötyä asiakkaalle. Diplomityö sisältää johdannonakustiseen emissioon ja akustisiin emissio sensoreihin. Työn tavoitteena oli kehittää päätöksentekojärjestelmä, jota käytettäisiin työn teettäjän valmistamien sensoreiden antaman tiedon automatisoituun analysointiin. Työssä on vertailtu kolmea eri ohjelmistotoimittajaa ja heidän ohjelmiaan, ja tehty ehdotus hankittavasta ohjelmistosta. Lisäksi työssä on kehitetty ohjeita, joiden avulla ohjelmisto ohjelmoidaan tuottamaan reaaliaikaista tietoa ja huolto-ohjeita sen käyttäjille. Lisäksi työssä annetaan ehdotuksia kunnonvalvonta- ja päätöksentekojärjestelmän edelleen kehittämiseen.
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The driving forces of technology and globalization continuously transform the business landscape in a way which undermines the existing strategies and innovations of organizations. The challenge for organizations is to establish such conditions where they are able to create new knowledge for innovative business ideas in interaction between other organizations and individuals. Innovation processes continuously need new external stimulations and seek new ideas, new information and knowledge locating more and more outside traditional organizational boundaries. In several studies, the early phases of the innovation process have been considered as the most critical ones. During these phases, the innovation process can emerge or conclude. External knowledge acquirement and utilization are noticed to be important at this stage of the innovation process giving information about the development of future markets and needs for new innovative businessideas. To make it possible, new methods and approaches to manage proactive knowledge creation and sharing activities are needed. In this study, knowledge creation and sharing in the early phases of the innovation process has been studied, and the understanding of knowledge management in the innovation process in an open and collaborative context advanced. Furthermore, the innovation management methods in this study are combined in a novel way to establish an open innovation process and tested in real-life cases. For these purposes two complementary and sequentially applied group work methods - the heuristic scenario method and the idea generation process - are examined by focusing the research on the support of the open knowledge creation and sharing process. The research objective of this thesis concerns two doctrines: the innovation management including the knowledge management, and the futures research concerning the scenario paradigm. This thesis also applies the group decision support system (GDSS) in the idea generation process to utilize the converged knowledge during the scenario process.
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En este trabajo se investiga la coherencia y confiabilidad de estimaciones de funciones de densidad de probabilidad (FDP) subjetivas de rendimientos de cultivos realizadas por un amplio grupo de agricultores. Se utilizaron tres técnicas de elicitación diferentes: el método de estimación de FDP en dos pasos, la distribución Triangular y la distribución Beta. Los sujetos entrevistados ofrecieron estimaciones para los valores puntuales de rendimientos de cultivos (medio, máximo posible, más frecuente y mínimo posible) y para las FDP basadas en la estimación de intervalos. Para evaluar la persistencia, se utilizaron los conceptos de persistencia temporal y persistencia metodológica. Los resultados son interesantes para juzgar la adecuación de las técnicas de estimación de probabilidades subjetivas a los sistemas de ayuda en la toma de decisiones en agricultura.
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En este trabajo se investiga la persistencia de las estimaciones puntuales subjetivas de rendimientos en cultivos anua- les realizadas por un amplio grupo de agricultores. La persistencia en el tiempo es una condición necesaria para la co- herencia y la confiabilidad de las estimaciones subjetivas de variables aleatorias. Los sujetos entrevistados estimaron valores puntuales de rendimientos de cultivos anuales (rendimientos medio, mayor, mínimo y más frecuente). Se han encontrado diferencias relativas poco importantes en todas las variables, excepto en los rendimientos mínimos, donde existe una alta dispersión. Los resultados son interesantes para estimar la adecuación de las técnicas de estimación de probabilidades subjetivas para ser utilizadas en los sistemas de ayuda en la toma de decisiones en agricultura.