Desarrollo humano

162 Páginas.Esta revisión teórica tiene como objetivo realizar una revisión teórica sobre el Desarrollo Humano en general, que proporcione una visión amplia y profunda sobre todos los procesos que se llevan a cabo durante el crecimiento de una persona. Para esto se consultaron 47 fuentes y se tocaron diferentes autores. Adicionalmente este estudio monográfico permitirá concluir que el tema del Desarrollo humano es vital en la formación del Psicólogo y por tal razón es fundamental profundizar en su estudio. Y por último también permitirá establecer una relación entre la teoría y la realidad actual colombiana, por la que atraviesan los seres humanos, como es el secuestro, la pobreza, et

Avances recientes en la predicción de la demanda de electricidad usando modelos no lineales

La predicción de la demanda es un problema de gran importancia para el sector eléctrico, ya que a partir de sus resultados, los agentes del mercado de energía toman las decisiones más adecuadas para su labor. En este artículo se presenta un análisis de las técnicas y modelos más usados en el pronóstico de la demanda de electricidad y la problemática o dificultades a las que se enfrentan los investigadores al momento de realizar un pronóstico. El análisis muestra que las técnicas más usadas son los modelos ARIMA y las redes neuronales artificiales. Sin embargo, se encontró poca claridad sobre cuál modelo es más adecuado y en qué casos, adicionalmente, los estudios no presentan una recomendación específica para desarrollar modelos de pronóstico de demanda, específicamente en el caso colombiano. Finalmente, se propone realizar un estudio sistemático con el fi n de determinar los modelos más adecuados para predicción de demanda para el caso colombiano

Predicción del consumo de energía en Colombia con modelos no lineales

La predicción de la demanda es un problema de gran importancia para el sector eléctrico, ya que a partir de sus resultados los agentes del mercado de energía toman las decisiones más adecuadas para su labor; por lo cual un buen pronóstico trae consigo grandes beneficios tanto técnicos como financieros. De ahí que se han desarrollado una gran cantidad de modelos tanto lineales como no lineales para el modelaje y pronóstico de la demanda. Sin embargo el proceso de pronóstico de la demanda presenta dificultades relacionadas con bases de datos cortas e insuficientes, las técnicas de predicción no modelan adecuadamente factores relacionados con ciclos y eventos dinámicos como cambios de temperatura. En relación con los modelos, no se tiene una metodología concreta para seleccionar un modelo especifico, no se tiene claridad en que criterios de ajuste usar para seleccionar el mejor modelo dentro de varias alternativas, es difícil incorporar en los modelos variables subjetivas relacionadas con la experiencia y conocimiento de los pronosticadores. A partir de la identificación de las dificultades en el pronóstico, se desarrolla este trabajo con el fin de dar solución a algunas de ellas. Para esto, se realizó un análisis estadístico de la serie de demanda, se ajustaron modelos no lineales como SARIMA, MARS; DAN2, SRT y GSMN; adicionalmente se propuso un nuevo modelo híbrido que combina un modelo SARIMA y un modelo GSMN, finalmente se utilizó un modelo SARIMA ajustado previamente para comparar los resultados, para esto se calcularon los estadísticos de ajuste MAE y RMSE. Para el estudio se utilizó la serie de demanda mensual de Colombia para el periodo 1995:8 – 2010:4, el cual captura la historia desde el inicio del Mercado de Energía Mayorista de Colombia. Los resultados muestran que, de los cinco modelos no lineales ajustados para la serie de demanda mensual de Colombia, el modelo con mejor desempeño es el híbrido, ya que captura de mejor forma la dinámica de la serie y presenta los mejores estadísticos de ajuste (MAE y RMSE). Con el modelo híbrido se obtiene una reducción del 0,45% en error de entrenamiento respecto al modelo SARIMA, sin embargo, en el error de pronóstico se obtiene una reducción máxima del 11,25% respecto al SARIMA, lo cual indica que este modelo aumenta confiabilidad en los datos pronosticados./Abstract: Electricity demand forecasting is a major problem for the electricity sector, because the energy market players use the results of the electricity demand forecasting to make the right decisions for their work, so a good forecasting brings great technical and financial benefits. Hence, there have been a number of models both linear and nonlinear for modeling and forecasting of demand. However, the demand forecasting process has difficulties related with short and insufficient data bases, forecasting techniques do not adequately model factors related with cycles and dynamic events such as temperature changes. In relation with models, do not have a specific methodology to select a specific model, it is unclear what criteria setting can be used to select the best model within several alternatives, it is difficult to incorporate into models subjective variables related with the experience and knowledge of predictors. From the identification of these difficulties in forecasting, we develop this work in order to find solutions to some of them. For this, we performed a statistical analysis of the electricity demand series, nonlinear models as MARS, DAN2, SRT and GSMN were adjusted; additionally we proposed a new hybrid model that combines a SARIMA model and a GSMN model, finally, we used a preset SARIMA model to compare the results, to this we calculated the MAE and RMSE errors. For this project, we used the monthly electrical power demand in Colombia from 1995:8 to 2010:4, this period capture the history from the beginning of the Wholesale Energy Market in Colombia. The results show that the best performing model is the hybrid, because it is the model that better capture the dynamics of the series and has better MAE and RMSE errors than the others non linear models fitted. The hybrid model gives a 0.45% reduction in training error compared with SARIMA results, however, the forecast error gives a maximum reduction of 11.25% compared to SARIMA results, indicating that this model increases reliability of forecast data.Maestrí

Electricity demand forecasting using a sarima-multiplicative single neuron hybrid model

The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artificial neural networks are applied to forecast the remaining nonlinearities in the shocks of the SARIMA model. In this paper, we propose a simple nonlinear time series forecasting model by combining the SARIMA model with a multiplicative single neuron using the same inputs as the SARIMA model. To evaluate the capacity of the new approach, the monthly electricity demand in the Colombian energy market is forecasted and compared with the SARIMA and multiplicative single neuron modelsLa combinación de modelos SARIMA y redes neuronales son una aproximación común para pronosticar series de tiempo no lineales. Mientras la metodología SARIMA es usada para capturar las componentes lineales en la serie de tiempo, las redes neuronales artificiales son aplicadas para pronosticar las no-linealidades remanentes en los residuos del modelo SARIMA. En este artículo, se propone un modelo simple no lineal para el pronóstico de series de tiempo obtenido por la combinación de un modelo SARIMA y una neurona simple multiplicativa que usa las mismas entradas del modelo SARIMA. Para evaluar la capacidad de la nueva aproximación, la demanda mensual de electricidad en el mercado de energía de Colombia es pronosticada y comparada con los modelos SARIMA y la neurona simple multiplicativ

Electricity demand forecasting using a sarimamultiplicative single neuron hybrid model

The combination of SARIMA and neural network models are a common approach for forecasting nonlinear time series. While the SARIMA methodology is used to capture the linear components in the time series, artifi cial neural networks are applied to forecast the remaining nonlinearities in the shocks of the SARIMA model. In this paper, we propose a simple nonlinear time series forecasting model by combining the SARIMA model with a multiplicative single neuron using the same inputs as the SARIMA model. To evaluate the capacity of the new approach, the monthly electricity demand in the Colombian energy market is forecasted and compared with the SARIMA and multiplicative single neuron models

ENGIU: Encuentro Nacional de Grupos de Investigación de UNIMINUTO.

El desarrollo del prototipo para el sistema de detección de Mina Antipersona (MAP), inicia desde el semillero ADSSOF perteneciente al programa de Administración en Seguridad y Salud en el trabajo de la UNIMINUTO, se realiza a partir de un detector de metales que emite una señal audible, que el usuario puede interpretar como aviso de presencia de un objeto metálico, en este caso una MAP. La señal audible se interpreta como un dato, como ese dato no es perceptible a 5 metros de distancia, se implementa el transmisor de Frecuencia Modulada FM por la facilidad de modulación y la escogencia de frecuencia de transmisión de acuerdo con las normas y resolución del Ministerio de Comunicaciones; de manera que esta sea la plataforma base para enviar los datos obtenidos a una frecuencia establecida. La idea es que el ser humano no explore zonas peligrosas y buscar la forma de crear un sistema que permita eliminar ese riesgo, por otro lado, buscar la facilidad de uso de elementos ya disponibles en el mercado

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research