Análisis de las influencias extranjeras en la gastronomía peruana con respecto a la decisión de compra del consumidor en los Malls de la Ciudad de Arequipa, 2017

TesisLa presente tesis tiene por título “Análisis de las influencias extranjeras en la gastronomía peruana con respecto a la decisión de compra del consumidor en los malls de la ciudad de Arequipa 2017” la cual fue realizada con un método descriptivo, analizando los ítems y las características influyentes de la gastronomía extranjera con respecto a la decisión de compra del consumidor arequipeño. Es una investigación de tipo descriptiva y no experimental, donde se aplicará una encuesta a los comensales que van, a consumir a los fast food de los malls de la provincia de Arequipa para conocer el motivo de su decisión de compra con respecto a ellos. La determinación de resultados fue realizada a través de datos estadísticos donde se analizará la información obtenida para la obtención de las conclusiones finales de la investigación en la cual se identificará los factores que influyen en la preferencia de los comensales arequipeños con respecto a los servicios brindados en los fast food ubicados en los malls de la ciudad de Arequipa

Classification and multivariate analysis of differences in gross primary production at different elevations using biome-bgc in the páramos, ecuadorian andean region

Gross primary production (GPP) in climate change studies with multi- species and elevation variables are difficult to measure and simulate. Models tend to provide a representation of dynamic process through long-term analysis by using generalized parameterizations. Even, current approaches of modelling do not contemplate easily the variation of GPP at different elevations for different vegetation types in regions like páramos, mainly due to data unavailability. In these models information from cells is commonly averaged, and therefore average elevation, ecophysiology of vegetation, as well as other parameters is generalized. The vegetation model BIOME- BGC was applied to the Ecuadorian Andean region for elevations greater than 4000 masl with the presence of typical vegetation of páramo for 10 years of simulation (period 2000-2009). An estimation of the difference of GPP obtained using a generalized altitude and predominant type of vegetation could lead to a better estimation of the uncertainty in the magnitude of the errors in global climate models. This research explores GPP from 3 different altitudes and 3 vegetation types against 2 main climate drivers (Short Wave Radiation and Vapor Pressure Deficit). Since it is important to measure the possible errors or difference in the use of averaged meteorological and ecophysiological data, here we present a multivariate analysis of the dynamic difference of GPP in time, relative to an altitude and type of vegetation. A copula multivariable model allows us to identify and classify the changes in GPP per type of vegetation and altitude. The Frank copula model of joint distributions was our best fit between GPP and climate drivers and it allowed us to understand better the dependency of the variables. These results can explore extreme situations where averaged simplified approaches could mislead. The change of GPP over time is essential for future climate scenarios of the ecosystem storage and release of carbon to the atmosphere. Our findings suggest that a classification of the difference is highly important to be extended to cells that have similar properties.La producción primaria (GPP) es difícil de medir y simular en estudios de cambio climático con múltiples especies de vegetación y con variabilidad en elevación. Los modelos tienden a proveer una representación de los procesos dinámicos a través de análisis a largo plazo usando parametrizaciones generalizadas. Incluso métodos actualizados de modelación no contemplan fácilmente la variación de GPP a diferentes elevaciones y para diferentes tipos de vegetación en regiones como los páramos, debido principalmente a la inexistencia de datos. En estos modelos, la información de las celdas son comúnmente promediadas y por lo tanto factores como la elevación media,eco-fisiología de la vegetación y otros parámetros son generalizados. El modelo de vegetación BIOME-BGC fue aplicado en un área de estudio dentro de la región andina Ecuatoriana a elevaciones superiores a los 4000 msnm donde existe una presencia típica de vegetación de páramo para 10 años de simulación (periodo 2000-2009). La estimación de la diferencia de la GPP obtenida usando una generalización de altura y tipo de vegetación predominante puede conducir a una mejor estimación de la incertidumbre en la magnitud de los errores en modelos climáticos globales. Este estudio explora la relación entre la GPP de tres tipos de vegetación agrupados de acuerdo a sus formas de crecimiento a tres rangos altitudinales y dos factores climáticos (Radiación de onda corta y deficiencia de presión de vapor). Debido a la importancia de la medición de posibles errores o las diferencias en el uso de valores promedio de datos meteorológicos e ecofisiológicos, aquí presentamos un análisis multivariado de la diferencia dinámica de la GPP en el tiempo con respecto al rango altitu- dinal y al tipo de vegetación. El modelo multivariable Copula nos permite identificar y clasificar los cambios de GPP por tipo de vegetación y por rango altitudinal. El modelo cópula distribuido Frank fue el que mejor se acopló entre la GPP y las variables climáticas y nos permitió entender mejor la dependencia entre estas variables. Los resultados podrían explorar situaciones extremas donde estrategias simplificadas promedio podrían confundir. El cambio de GPP en el tiempo es esencial para futuros escenarios climáticos del almacenamiento y liberación de carbón del ecosistema hacia la atmósfera. Nuestros resultados sugieren que la clasificación de esta diferencia es muy importante que sea extendida a celdas que tienen propiedades similares

Classification and multivariate analysis of differences in gross primary production at different elevations using biome-bgc in the páramos, ecuadorian andean region

Gross primary production (GPP) in climate change studies with multi- species and elevation variables are difficult to measure and simulate. Models tend to provide a representation of dynamic process through long-term analysis by using generalized parameterizations. Even, current approaches of modelling do not contemplate easily the variation of GPP at different elevations for different vegetation types in regions like páramos, mainly due to data unavailability. In these models information from cells is commonly averaged, and therefore average elevation, ecophysiology of vegetation, as well as other parameters is generalized. The vegetation model BIOME- BGC was applied to the Ecuadorian Andean region for elevations greater than 4000 masl with the presence of typical vegetation of páramo for 10 years of simulation (period 2000-2009). An estimation of the difference of GPP obtained using a generalized altitude and predominant type of vegetation could lead to a better estimation of the uncertainty in the magnitude of the errors in global climate models. This research explores GPP from 3 different altitudes and 3 vegetation types against 2 main climate drivers (Short Wave Radiation and Vapor Pressure Deficit). Since it is important to measure the possible errors or difference in the use of averaged meteorological and ecophysiological data, here we present a multivariate analysis of the dynamic difference of GPP in time, relative to an altitude and type of vegetation. A copula multivariable model allows us to identify and classify the changes in GPP per type of vegetation and altitude. The Frank copula model of joint distributions was our best fit between GPP and climate drivers and it allowed us to understand better the dependency of the variables. These results can explore extreme situations where averaged simplified approaches could mislead. The change of GPP over time is essential for future climate scenarios of the ecosystem storage and release of carbon to the atmosphere. Our findings suggest that a classification of the difference is highly important to be extended to cells that have similar properties

AP Students and Predicted Approval Levels of Cheating

Research project analyzing high school students' attitudes towards cheating and how AP approval levels compare with non-AP classes

Altitudinal analysis of carbon stocks in the Antisana páramo, Ecuadorian Andes

Aims The importance of quantifying carbon stocks in terrestrial ecosystems is crucial for determining climate change dynamics. However, the present regional assessments of carbon stocks in tropical grasslands are extrapolated to unsampled areas with a high degree of uncertainty and without considering the carbon and nitrogen composition of vegetation and soil along altitudinal ranges. This study aims to assess carbon and nitrogen concentrations in soil and vegetation, aboveground carbon stocks distribution and soil organic carbon stocks along an altitudinal range in the páramo region in the Ecuadorian Andes. Methods The vegetation inventory was conducted using 15×15 m sampling plots distributed in three altitudinal ranges. Based on the patterns exhibited by the dominant vegetation growth forms, biomass and soil were sampled to quantify the corresponding carbon and nitrogen concentrations. Subsequently, the aboveground live biomass along the páramo altitudinal range was estimated using allometric equations. Finally, soil and vegetation carbon stocks were estimated for the entire basin. Important Findings Altitudinal analysis supported a potential distribution of carbon and nitrogen concentrations in soil, litter and live tissues, where higher concentrations were found in the low altitudinal range mainly for tussocks and acaulescent rosettes. Cellulose in litter showed higher concentrations at low altitudinal ranges for acaulescent rosettes and cushions only. For the same growth forms, lignin patterns in litter were higher in high altitudinal ranges. Soil texture provided complementary information: high percentage of silt was highly correlated to high soil nitrogen and carbon concentration. Tussocks were found to be responsive to altitude with their, highest aboveground carbon stocks occurring at the low altitudinal range, but cushions and acaulescent rosettes responded differently. The established relationships among soil, vegetation and altitude shown in this study must be taken into account to estimate both aboveground and soil organic carbon stocks in páramo regions - such estimates will be considerably inaccurate if these relationships are ignored.Environmental Fluid Mechanic