Uso de los modelos heterocedásticos con Bootstrap en el análisis del Índice General de la Bolsa de Valores de Lima

Universidad Nacional Agraria La Molina. Escuela de Posgrado. Maestría en Estadística AplicadaLa presente investigación es de naturaleza aplicada, y tiene el objetivo de analizar y evaluar la metodología Bootstrap en modelos heterocedásticos aplicados en la predicción del Índice General de la Bolsa de Valores de Lima (IGBVL), periodo 2010 - 2014. Se presenta sucintamente, los conceptos básicos de series temporales, los procesos seriales heterocedásticos, la metodología Bootstrap y sus aplicaciones a la inferencia estadística y a las series temporales, donde se presenta el algoritmo para procesos heterocedásticos GARCH propuesto por Pascual et al. (2006) y generalizados para los modelos EGARCH y TGARCH. Con los procedimientos mostrados fueron obtenidas las predicciones mediante la metodología paramétrica y metodología Bootstrap, que fueron comparados con valores reales y finalmente fueron evaluados los desempeños de ambas metodologías. Del estudio se obtuvo que los modelos que mejor ajustan a la serie son los modelos ARMA(1,1)-GARCH(1,1), ARMA(1,1)-EGARCH(1,1) y ARMA(1,1)-TGARCH(1,1) cada uno de ellos con el supuesto de distribución t de Student con 5 grados de libertad de los residuales, el estudio comparativo mostró que la aplicación de la metodología Bootstrap en la serie de los retornos del Índice General de la Bolsa de Valores de Lima, permite obtener intervalos de predicciones con mayores e iguales amplitudes en algunos horizontes hacia adelante en comparación con la metodología paramétrica, y también permitió construir con un buen desempeño los intervalos de predicción para las volatilidades, así siendo esta una alternativa para la construcción de intervalos de predicción en los modelos GARCH, EGARCH y TGARCH.The present research is from applied nature, and it has the objective of analyzing and evaluating the Bootstrap methodology in heterocedastic models, applied in the prediction of the Indice General de la Bolsa de Valores de Lima (IGBVL), period 2010 - 2014. it presents succinctly, the concepts basic temporal series, heteroskedastic serial processes, the Bootstrap methodology and its applications to statistical inference and time series, in this is show the algorithm for heteroscedastic processes GARCH proposed by Pascual et al. (2006) and generalized for models EGARCH and TGARCH. With the procedures shown, predictions were obtained using parametric methodology and Bootstrap methodology, which were compared with real values and finally the performances of both methodologies were evaluated in terms of their prediction. This study obtained that the models that best fit the series are the ARMA(1,1)-GARCH(1,1), ARMA(1,1)-EGARCH(1,1) and ARMA(1,1)- TGARCH(1,1) models each of with the assumption of t-Student distribution with 5 degrees of freedom of the residuals, the comparative analysis showed that the application of the Bootstrap methodology in the series of the returns of Indice General de la Bolsa de Valores de Lima, allow to obtain prediction intervals with greater and equal amplitudes in some forward horizons compared to the parametric methodology, and also allowed to construct with a good performance the prediction intervals for volatilities, thus being an alternative for the construction of prediction intervals in the GARCH, EGARCH and TGARCH models.Tesi

Proposing empirical correlations and optimization of Nu and Sgen of nanofluids in channels and predicting them using artificial neural network

Getting the best performance from a thermal system requires two fundamental analyses, energy and entropy generation. An ideal mechanism has the highest Nu and the lowest entropy Sgen. As part of this research, a large dataset of fluid flow via tubes has been collected experimentally. As well as the inclusion of nanoparticles, analyses are included as well. By using deep learning algorithms, the Nusselt number and total entropy generation are predicted. In both models, the mean absolute error was lower than 5%. To determine the most accurate model, hyperparameter tuning is performed. That is adjusting all the settings in the neural network to attain the best results. The results of the predictive models are compared against experimental and benchmark results. The study incorporates a massive optimization strategy to fine-tune the predictive capabilities of the models. Furthermore, the model’s predictive abilities are evaluated through the use of the coefficient of determination R2. For water and nanofluids flowing through circular, square, and rectangular cross-sections, the proposed models can predict Nu and Sgen. The results showed remarkable agreement with the experimental results. The models showed an MAE of not higher than 1.33%, which is a great achievement. Also, empirical correlations are proposed for both parameters, and double factorial optimization is implemented. The results showed that to achieve the best results, the Re should be higher than 1600, and the nanoparticle concentration should be 3%. A thorough justification of selected cases is presented as well

Reading Comprehension and Behavior in Children Using E-books vs. Printed Books

The purpose of this research is to investigate the influence that personalized, gamified, and PDF electronic reading practices have on the attitudes which fifth-grade students possess toward e-reading experiences, as well as how these stances affect the students' motivation and reading comprehension while they are learning English as a second/foreign language (EFL). For the purpose of the study, there were a total of 84 fifth-grade kids from public schools in Greece, who participated. These students were split up into three different experimental groups and a control one. Participants in the experimental groups read throughout the treatment period according to a preset schedule using one of three diverse electronic reading formats (PDF, gamified, or customized), whilst participants in the control group read utilizing a paper guided reading plan. The participants' experiences playing video games online were analyzed via a technique called the quasi-experimental approach. According to the findings of the research, the experimental group and the control group did not significantly vary from one another in terms of their levels of reading comprehension. On the other hand, in comparison to the participants in the control group, those who took part in the experiments reported having more favorable sentiments regarding their electronic reading experiences and were more inspired to read. As indicated from the research findings, kids may experience an increase in their desire to read when they use electronic gadgets. This study has implications for educators and policymakers as they consider incorporating digital reading practices into their teaching methods, particularly when it comes to improving students' motivation to read

A Mendelian Randomization Analysis Investigates Causal Associations between Inflammatory Bowel Diseases and Variable Risk Factors

The question of whether variable risk factors and various nutrients are causally related to inflammatory bowel diseases (IBDs) has remained unanswered so far. Thus, this study investigated whether genetically predicted risk factors and nutrients play a function in the occurrence of inflammatory bowel diseases, including ulcerative colitis (UC), non-infective colitis (NIC), and Crohn’s disease (CD), using Mendelian randomization (MR) analysis. Utilizing the data of genome-wide association studies (GWASs) with 37 exposure factors, we ran Mendelian randomization analyses based on up to 458,109 participants. Univariable and multivariable MR analyses were conducted to determine causal risk factors for IBD diseases. Genetic predisposition to smoking and appendectomy as well as vegetable and fruit intake, breastfeeding, n-3 PUFAs, n-6 PUFAs, vitamin D, total cholesterol, whole-body fat mass, and physical activity were related to the risk of UC (p < 0.05). The effect of lifestyle behaviors on UC was attenuated after correcting for appendectomy. Genetically driven smoking, alcohol consumption, appendectomy, tonsillectomy, blood calcium, tea intake, autoimmune diseases, type 2 diabetes, cesarean delivery, vitamin D deficiency, and antibiotic exposure increased the risk of CD (p < 0.05), while vegetable and fruit intake, breastfeeding, physical activity, blood zinc, and n-3 PUFAs decreased the risk of CD (p < 0.05). Appendectomy, antibiotics, physical activity, blood zinc, n-3 PUFAs, and vegetable fruit intake remained significant predictors in multivariable MR (p < 0.05). Besides smoking, breastfeeding, alcoholic drinks, vegetable and fruit intake, vitamin D, appendectomy, and n-3 PUFAs were associated with NIC (p < 0.05). Smoking, alcoholic drinks, vegetable and fruit intake, vitamin D, appendectomy, and n-3 PUFAs remained significant predictors in multivariable MR (p < 0.05). Our results provide new and comprehensive evidence demonstrating that there are approving causal effects of various risk factors on IBDs. These findings also supply some suggestions for the treatment and prevention of these diseases

The role of innovation adoption and circular economy readiness on the environmental sustainability: moderating impact of organizational support

Environmental sustainability is currently the most critical demand on a global scale, and it may be reached through innovation and a circular economy. This component demands the aim of policymakers and researchers. Therefore, this study investigates the effect of innovation adoption and circular economy readiness on Peru's environmental sustainability. The research also examines the moderating effect of organizational support on innovation adoption, preparation for the circular economy, and environmental sustainability in Peru. Using questionnaires, the researchers collected and evaluated the data using smart PLS. The results demonstrated a positive relationship between innovation adoption, circular economy readiness, and environmental sustainability in Peru. The results also demonstrated that organizational support moderates’ innovation uptake, preparation for the circular economy, and environmental sustainability in Peru. The research aids policymakers in formulating environmental sustainability strategies based on innovation uptake and circular economy preparedness.Campus Chimbot

Synthesize of pluronic-based nanovesicular formulation loaded with Pistacia atlantica extract for improved antimicrobial efficiency

One of the current concerns to human health is antibiotic resistance, which promotes the use of antibiotics that are more harmful, expensive, and ineffective. In this condition, researchers are turning to innovative options to combat this alarming situation. Combining herbal medicine with nanotechnology has created a new strategy to increase the effectiveness of phytochemical compounds in overcoming antimicrobial resistance. Pistacia atlantica is one of the promising herbs with medicinal benefits, but its poor solubility in biological fluids is challenging. In this regard, we seek to evaluate the antibacterial efficacy of Pistacia atlantica extract-loaded nanovesicle. Cholesterol, Span 40, and Pluronic F127 modified nanoformulation was developed using an environmentally friendly improved heating technique, and it was evaluated for size distribution, zeta potential, morphology, entrapment efficiency (EE%), release behavior, stability, and antimicrobial performance. By using DLS, spherical nanovesicles were identified with a size distribution of 50–150 nm and a zeta potential of −43 mV. The extract's encapsulation efficiency was 72.03%. The developed loaded nanovesicles demonstrated controlled extract release in the tested 96 h and storage stability of at least 12 months at 25 °C. Also, Comparing the two samples, the encapsulated extract had greater antibacterial activity against Candida albicans, Staphylococcus aureus, and Pseudomonas plecoglossicida with MIC of 1320, 570, and 1100 µg/mL, respectively. Besides reducing the misuse of antibiotics by allowing for the controlled release of drugs made from natural sources, we expect the findings described here to help provide alternative plant-based formulations with greater stability and antibacterial activity