https://ar.iub.edu.bd/handle/11348/501 2026-04-07T09:48:30Z https://ar.iub.edu.bd/handle/11348/1000 The LHCb Detector and Testing and Performance Evaluation of AdePT in Gaussino Gomes, James Peter To meet the increasing computational demands of LHC experiments, this study explores the potential of GPU acceleration for electromagnetic simulations. The AdePT proto- type is integrated into the Gaussino framework and compared to the standard GEANT4. Initial results show that AdePT, while requiring optimization, demonstrates promising performance gains, especially with larger workloads. The successful simulation of elec- trons using AdePT highlights its potential for accelerating LHCb simulations and contributing to future physics analysis. Furthermore, a brief review of the LHCb detector is provided, encompassing its key subdetectors: the Vertex Locator (VELO), the tracking system (including the Trigger Tracker, Inner Tracker, and Outer Tracker), the Ring Imaging Cherenkov (RICH) detectors, the electromagnetic and hadronic calorimeters (ECAL and HCAL), and the muon system. 2025-02-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/524 Modeling Chaotic Behavior of Dhaka Stock Market Index Values Using the Neuro-fuzzy Model Banik, Shipra; Chanchary, Farah Habib; Habib, Farah; Khan, AFM Khodadad Stock market prediction is an important area of financial forecasting, which attracts great interest of stock investors, stock buyers/sellers, policy makers, applied researchers and many others who are involved in the capital market. This paper aims to develop an efficient model to predict the Dhaka Stock Market Index (DSPI) values using the appropriate forecasting model. It is widely believed that stock data are nonlinear, dynamic and chaotic. In this paper, we propose an adaptive network based fuzzy inference system (ANFIS) to predict DSPI values. We used the daily general DSPI values for the period of March 2003 to October 10, 2006 for the learning and October 11, 2006 to May 31, 2007 for validation. Results obtained by this model are also compared to the back-propagation ANN model and the traditional ARIMA model to show advantages of the proposed ANFIS model. The findings suggest that the ANFIS model can be used as a better predictor for daily general DSPI values as compared to the ANN and the ARIMA models. The review also discussed relevant patents related to our research. 2020-10-24T00:00:00Z https://ar.iub.edu.bd/handle/11348/523 Comparison of Some Parametric and Nonparametric Type One Sample Confidence Intervals for Estimating the Mean of a Positively Skewed Distribution Banik, Shipra; Kibria, B. M. Golam Several researchers considered various interval estimators for estimating the mean of a skewed distribution. Since they considered in different times and under different simulation conditions, their performance are not comparable as a whole. In this article, an attempt has been made to review some existing estimators and compare them under the same simulation condition. In particular, we consider and compare both classical (Student-t, Land-t, Cheb-t, Johnson-t, Chen-t, Hall-t, median-t, Zhou and Dinh, empirical likelihood, etc.) and nonparametric (bootstrap-t, nonparametric bootstrap, empirical likelihood bootstrap, bias corrected acceleration bootstrap, Hall bootstrap-t, empirical Hall bootstrap, etc.) interval estimators for estimating the mean of a positively skewed distribution. A simulation study has been made to compare the performance of the estimators. Both average widths and coverage probabilities are considered as a criterion of the good estimators. Under the large sample sizes, the performances of the estimators are not different. However, they differ significantly when the sample sizes are small and data are from a highly skewed distribution. Some real-life data have been analyzed to illustrate the findings of the article. Based on the simulation study, some possible good interval estimators have been recommended for the practitioners. This article will provide more choices for the practitioners to use best possible interval estimators among many that have been used by several researchers at different times and situations. 2020-11-23T00:00:00Z https://ar.iub.edu.bd/handle/11348/522 Estimating the population coefficient of variation by confidence intervals Banik, Shipra; Kibria, BM Golam Several researchers considered various interval estimators for estimating the population coefficient of variation (CV) of symmetric and skewed distributions. Since they considered at different times and under different simulation conditions, their performances are not comparable as a whole. In this article, an attempt has been made to review some existing estimators along with some proposed methods and compare them under the same simulation condition. In particular, we have considered Hendricks and Robey, Mckay, Miller, Sharma and Krishna, Curto and Pinto, and also some bootstrap proposed interval estimators for estimating the population CV. A simulation study has been conducted to compare the performance of the estimators. Both average widths and coverage probabilities are considered as a criterion of the good estimators. Two real life health related data sets are analyzed to illustrate the findings of the article. Based on the simulation study, some possible good interval estimators have been recommended for the practitioners. 2020-11-22T00:00:00Z