A Study of Artificial Intelligence in Education

Artificial Intelligence AI refers to the simulation of human intelligence in machines that are programmed to think and mimic human cognitive processes It includes a wide range of tools that enable machines to carry out operations that otherwise need human intelligence Learning logical thinking problem-solving perception speech recognition and language understanding are some of these tasks One of the key areas where AI will have a profound effect is education Both students and teachers benefit from enhanced educational experiences because of AI It offers a personalized learning experience Although AI in education hasn t received much attention over the years understanding its concepts functions methodology and applications would provide the groundwork for the development of the education secto

ધોરણ અગિયારના આંકડાશાસ્ત્રના વિષયવસ્તુ એકમોની કઠિનતાકક્ષાનું નિર્ધારણ અને કઠિન એકમોના શિક્ષણ માટે આગમન વિચાર પ્રતિમાનની અસરકારક્તા

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Evaluation of chronic gastritis with Helicobacter pylori using updated Sydney system

Background: Helicobacter pylori has been established as a major etiological factor in the pathogenesis of chronic gastritis. The aim of the study was to interpret the histopathological changes in chronic gastritis using updated Sydney system and the association with H. pylori infection.Methods: This was a 3 years study in which 62 gastric endoscopic mucosal biopsies taken from patients presenting with dyspepsia were included. Slides were stained with routine H and E and Giemsa for H. pylori detection in chronic gastritis cases. Grading of the variables were done with reference to Sydney system of classification.Results: Out of 62 gastric biopsy specimens, 55 cases (88.7%) were histopathological diagnosed as chronic gastritis. Among chronic gastritis, 21 (38%) cases showed H. pylori and majority of these being moderately (2+) positive. 27 (49%) cases showed neutrophilic activity with most of them showed mild (1+) activity. Chronic inflammation was seen 52 (94.5%) with majority of these graded as moderate (2+). Intestinal metaplasia was seen in 8 (14.5%) of cases with majority being mild (1+). Atrophy was seen only in 3 (5.4%) of cases with majority being mild (1+). Significant statistical association was found between H. pylori and neutrophilic activity (p<0.001).Conclusions: Histological evaluation of chronic gastritis using updated Sydney system of classification helps in detection of H. pylori infection and prevents further progression of the disease.

A Review of “Music and Movement” Therapies for Children with Autism: Embodied Interventions for Multisystem Development

The rising incidence of Autism Spectrum Disorders (ASDs) has led to a surge in the number of children needing autism interventions. This paper is a call to clinicians to diversify autism interventions and to promote the use of embodied music-based approaches to facilitate multisystem development. Approximately 12% of all autism interventions and 45% of all alternative treatment strategies in schools involve music-based activities. Musical training impacts various forms of development including communication, social-emotional, and motor development in children with ASDs and other developmental disorders as well as typically developing children. In this review, we will highlight the multisystem impairments of ASDs, explain why music and movement therapies are a powerful clinical tool, as well as describe mechanisms and offer evidence in support of music therapies for children with ASDs. We will support our claims by reviewing results from brain imaging studies reporting on music therapy effects in children with autism. We will also discuss the critical elements and the different types of music therapy approaches commonly used in pediatric neurological populations including autism. We provide strong arguments for the use of music and movement interventions as a multisystem treatment tool for children with ASDs. Finally, we also make recommendations for assessment and treatment of children with ASDs, and provide directions for future research

New Exactly Solvable Isospectral Partners for PT Symmetric Potentials

We examine in detail the possibilty of applying Darboux transformation to non Hermitian hamiltonians. In particular we propose a simple method of constructing exactly solvable PT symmetric potentials by applying Darboux transformation to higher states of an exactly solvable PT symmetric potential. It is shown that the resulting hamiltonian and the original one are pseudo supersymmetric partners. We also discuss application of Darboux transformation to hamiltonians with spontaneously broken PT symmetry.Comment: 11 pages, 2 figures, To be published in Journal of Physics A (2004

Methodology in molecular genetics and its applications to diabetology

This article does not have an abstract

Essays on Conditional Heteroscedastic Time Series Models with Asymmetry, Long memory, and Structural Changes

The volatility of asset returns is usually time-varying, necessitating the introduction of models with a conditional heteroskedastic variance structure. In this dissertation, several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroskedastic (GARCH) type models, are further generalized to accommodate more dynamic features of asset returns such as asymmetry, long memory, and structural breaks. First, we introduce a hybrid structure that combines short-memory asymmetric Glosten, Jagannathan, and Runkle (GJR) formulation and the long-memory fractionally integrated GARCH (FIGARCH) process for modeling financial volatility. This formulation not only can model volatility clusters and capture asymmetry but also considers the characteristic of long memory in the volatility. In the second paper, we extend the Hybrid GJR and FIGARCH process to allow for a graduate transition between two regimes by introducing a smooth transition function. Here, the model changes smoothly between the extremes of the asymmetric short and long memory components depending on a transition variable. The third paper proposes a regime-switching asymmetric long memory model, Multiple Regime Hyperbolic GARCH (MR-HYGARCH), where the regime of an asset return is determined by observing asymmetry between positive and negative returns in its past long-term and past short-term periods. Firstly, it introduces a customizable multiple regime switching mechanism, allowing for tailored modeling according to specific problem requirements. Secondly, it proposes a new specification featuring four regimes governed by a dynamic threshold, in contrast to existing threshold GARCH models that rely on a fixed threshold with only two regimes. Finally, a multiplicative component process (MF)2EGARCH that models the conditional variance as the product of a short-term volatility component, modeled as an exponential GARCH (EGARCH) process and a long-term component, is introduced. Overall, the proposed models demonstrate superior performance compared to their respective competing models in both in-sample estimation and out-of-sample forecasting capabilities --Abstract, p. i