Examinando por Materia "Cost effectiveness"
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Ítem Análisis de costo-efectividad de la Termoterapia, en comparación con el Glucantime, para el tratamiento de pacientes con Leishmaniasis cutánea en Colombia 2015(Universidad EAFIT, 2016) Cardona Arias, Jaiberth Antonio; Tamayo Plata, Mery PatriciaIntroducción: Leishmaniasis cutánea genera una elevada carga de la enfermedad en Colombia y los tratamientos disponibles presentan toxicidad sistémica, baja adherencia, contraindicaciones y alto costo -- Objetivo: Estimar la de costo-efectividad de la termoterapia, en comparación con el Glucantime, en pacientes con leishmaniasis cutánea de Colombia 2015 -- Métodos: Estudio de costo-efectividad desde la perspectiva institucional en 8.113 casos incidentes -- Se incluyeron datos de eficacia y seguridad terapéuticas, se realizó un costeo estándar y los desenlaces fueron los Años de Vida Ajustados por Discapacidad (DALYs) y el número de pacientes curados -- Las fuentes de información fueron el Sistema de Vigilancia en Salud Pública de Colombia, estudios de carga de la enfermedad y un metanálisis desarrollado por el investigador principal de este estudio -- Se estableció la costo-efectividad en términos incrementales y se evaluó la incertidumbre con el gráfico de tornado y simulaciones de Monte Carlo -- Resultados: La aplicación de termoterapia cuesta $1.530.444.433, el manejo de los efectos adversos $89.162.112 y el de las fallas terapéutica $915.460.665, mientras que en Glucantime fueron $8.333.121.916, $177.733.234 y $1.239.616.610, respectivamente -- Con Termoterapia cada DALY evitado cuesta $6.290.510 y cada paciente curado $214.835, en Glucantime $12.936.660 por DALY evitado y $258.231 por paciente curado -- En las simulaciones de Monte Carlo la termoterapia fue dominante para los DALYs evitados en el 67,9% de los casos y fue altamente costo-efectiva para los pacientes curados en un 72% -- Conclusión: En Colombia la termoterapia puede incluirse como una estrategia costo-efectiva para el manejo de la Leishmaniasis cutánea en, su incorporación en las guías de práctica clínica podría representar ahorros de aproximadamente 32 millones de pesos por cada DALY evitado y gastos de $352.830 por cada paciente curado adicional, en comparación con el uso del Glucantime; esto evidencia la relevancia de su incorporación en nuestro país y otros con patrones parasitológicos, clínicos y epidemiológicos similaresÍtem Automatic detection of building typology using deep learning methods on street level images(PERGAMON-ELSEVIER SCIENCE LTD, 2020-03-20) Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Mecánica AplicadaAn exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier LtdÍtem Automatic detection of building typology using deep learning methods on street level images(PERGAMON-ELSEVIER SCIENCE LTD, 2020-03-20) Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)An exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier LtdÍtem Automatic detection of building typology using deep learning methods on street level images(PERGAMON-ELSEVIER SCIENCE LTD, 2020-03-20) Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Universidad EAFIT. Departamento de Ingeniería de Producción; Materiales de IngenieríaAn exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier LtdÍtem Costeo del ciclo de vida de un activo : proyecto unidad constructiva(Universidad EAFIT, 2013) Pérez Gelves, Alvaro; Carrasquilla Franco, Eduardo; Uribe de Correa, Beatriz; Gómez Salazar, Elkin ArcesioLa empresa TN (Transmisor Nacional) requiere decidir si debe realizar una modernización o un cambio del activo eléctrico unidad constructiva (UC) para lograr una mayor rentabilidad por sus servicios de transmisión de energía, de acuerdo con la normatividad expedida por la Comisión de Regulación de Energía y Gas del Ministerio de Minas y Energía de Colombia (CREG), mediante la resolución CREG 011 de 2009 -- El proyecto consiste en elaborar un análisis para determinar la mejor alternativa de reemplazo o modernización del activo UC, basado en el cálculo de su edad económica -- Para tomar una decisión adecuada, la norma PAS 55, expedida por la British Standard, recomienda la utilización del modelo Life Cycle Costing (LCC), que determina cuál es el momento óptimo, en términos económicos, para realizar el reemplazo del activo o unidad constructiva, después de considerar los diferentes costos en los que se incurre durante su ciclo de vida: costos de capital (CAPEX), costos de operación (OPEX) y costos de eliminación -- Después de construir el flujo de caja a 30 años y analizar las alternativas: primera, mantener el activo UC en servicio; segunda, cambiar el activo a mitad de su vida útil, y tercera, modernizar el activo (upgrade) , se concluye, desde el punto de vista de evaluación de proyectos, que las tres alternativas son viables; sin embargo, con el indicador CAUE se determina que la mejor alternativa es mantener el activo UC en servicio, debido a que la edad económica del mismo es, como mínimo, de 25 añosÍtem Diseño de modelo 360 para determinación de precios en proyectos de empresas de consultoría de software especializado(Universidad EAFIT, 2022) Wartski Botero, Valeria; Villegas López, Yamile; Orozco Echeverry, César AugustoÍtem Efectividad operacional en una empresa ensambladora de vehículos(Universidad EAFIT, 2004) Gómez Guzmán, Mario Javier; Peláez Gamboa, Luis Fernando; Vélez Rodríguez, AlfonsoÍtem Geometry simplification of open-cell porous materials for elastic deformation FEA(SPRINGER, 2019-01-01) Cortés C.; Osorno M.; Uribe D.; Steeb H.; Ruiz-Salguero O.; Barandiarán I.; Flórez J.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEEstimation of mechanical properties of porous materials is central for their medical and industrial application. However, the massive size of accurate boundary representations (B-Rep) of the foams makes the numerical estimations intractable. Even for small domain sizes, the mesh generation for finite element analysis (FEA) may not terminate. Current efforts for simulating porous materials use statistical predictions of the material structure. The simulated and actual materials present different geometry and topology, with consequences on the simulation results. To overcome these limitations, this manuscript presents a method, which (1) synthesizes an accurate truss abstraction from the raw geometry data, (2) executes efficient FEA simulations, and (3) processes nodal displacements to estimate apparent mechanical moduli of the porous material. The method addresses materials whose ligaments have circular cross-sections. The iso-surface present in the Computer Tomography (CT) scan of the porous material is used to synthesize a truss graph whose edges are truncated cones. Then, optimization and simplification methods are applied to produce a topologically and geometrically correct truss representation for the foam domain. Comparative FEA load simulations are conducted between the full B-Rep and truss representations of the material. The truss model proves to be significantly more efficient for FEA, departing from the Full B-Rep FEA by a maximum of 16% in the estimation of equivalent mechanical moduli. Geometric assessments such as porosity and Hausdorff distance confirm that the truss abstraction is a cost-effective one. Ongoing efforts concentrate on point set geometric algorithms for enforcement of standardized material testing. © 2018 Springer-Verlag London Ltd., part of Springer NatureÍtem Nature-Inspired Protecto-Flexible Impact-Tolerant Materials(Wiley-VCH Verlag, 2020-01-01) Estrada, S.; Ossa, A.; Estrada, S.; Ossa, A.; Universidad EAFIT. Departamento de Ingeniería de Producción; Materiales de IngenieríaThe search for impact-tolerant, light-weight flexible materials has challenged materials scientists and engineers for decades. In this quest, many researchers have focused on studying natural armor as a guide to propose bioinspired materials with enhanced properties. The energy dissipation and flexibility mechanisms activated at different hierarchical structural levels of natural systems are used here as a guide to improve the energy and flexibility of synthetic materials. In particular, fish scales and osteoderms are selected as proper biological models to develop a novel family of cost-effective bioinspired protecto-flexible (Pf) materials. Furthermore, a bullet-proof protecto-flexible prototype is manufactured and tested. The ballistic tests suggest that under real stringent conditions, the system is capable of absorbing high levels of energy while remaining flexible enough to allow movement to the user. Remarkably, the material system developed allows its implementation into realistic high volumes of production with low added costs. Consequently, the proposed strategy for developing bioinspired Pf materials will enable the development of the next generation of high-performance impact-resistant materials. © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, WeinheimÍtem Wind turbine selection method based on the statistical analysis of nominal specifications for estimating the cost of energy(Elsevier Ltd, 2018-10-15) Arias-Rosales, A.; Osorio-Gómez, G.; Universidad EAFIT. Departamento de Ingeniería de Diseño; Ingeniería de Diseño (GRID)Wind turbine selection is a critical engineering problem in the overall cost-effectiveness of a wind project. With the wide spreading and democratization of wind energy technologies, non-expert stakeholders are being faced with the challenge of selecting among very different wind turbines. As a comprehensive indicator, the cost of energy can serve as a guide, but reportedly misleading publicity and commonly unavailable information render its calculation more inaccessible and less reliable. Accordingly, this work proposes a method to compare wind turbines, on the basis of the cost of energy, from only nominal specifications and a standard characterization of the local wind conditions. For this endeavor, it was identified that two key variables are not usually available at a preliminary stage: the total efficiency and a feasible hub height. Through a systematic statistical analysis of the trends in a constructed dataset of 176 turbines, it was possible to establish regression models for the estimation of both variables. These models were tested in a validation set and their estimations were found to correctly characterize the central trend of the data without significant deviations. The uncertainty related to the use of both models was addressed by analyzing the 95% Prediction Intervals and the stochastic rank dominance. The established statistical models were then used as the core of the proposed selection method. When the available information is limited or not trustworthy, the steps of the method can be followed as an approach to estimate the cost of energy of a given horizontal axis wind turbine in a given location. © 2018 Elsevier Ltd