Changes in Body Fat Phenotype After Four-Month Walking Interventions

A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine.Walking is an excellent health-promoting activity for obese, sedentary individuals. Visceral fat is linked to cardiovascular disease and mortality. We hypothesized that walking (steps/day) would decrease visceral adiposity and improve laboratory markers of cardiometabolic health in a dose-dependent manner. In the primary study, 79 sedentary, overweight subjects (77% female, 65% Caucasian) were enrolled in a 2x2 factorial randomized controlled walking intervention, with steps measured using a wearable Fitbit fitness tracking device. Participants underwent dual x-ray energy absorptiometry and basic cardiometabolic laboratory measurements (glucose, insulin, total cholesterol, HDL, LDL, triglycerides) before and after the intervention. Lean mass increased from 49.9 ± 9.5 to 50.3 ± 9.4 (p=0.05). No significant changes were observed in any of the cardiometabolic outcomes or localization of fat. The change in steps had no correlation with weight, visceral fat, lean mass, and VO2 peak, refuting the original hypothesis. When analyzing common laboratory markers and demographic characteristics, there were no significant predictors for visceral or total fat mass change, with significant heterogeneity of change in the group. Our study supports the likely contribution of genetic and environmental factors to the physical and laboratory changes seen following a walking intervention in sedentary and insufficiently active overweight people.This item is part of the College of Medicine - Phoenix Scholarly Projects 2019 collection. For more information, contact the Phoenix Biomedical Campus Library at [email protected]

A distributed directory scheme for information access in mobile computers

In this paper, we discuss the design aspects of a dynamic distributed directory scheme (DDS) to facilitate efficient and transparent access to information files in mobile environments. The proposed directory interface enables users of mobile computers to view a distributed file system on a network of computers as a globally shared file system. In order to counter some of the limitations of wireless communications, we propose improvised invalidation schemes that avoid false sharing and ensure uninterrupted usage under disconnected and low bandwidth conditions

Digital Library logging using XML

This project explores the various ways in which relevant statistics can be extracted from digital library logs collected in XML. A set of potential statistics that can be used for performing clickstream analysis are listed. Clickstream analysis deals with the path taken by the user when he/she is using the digital library site. This project also involves visualization of the statistics collected. Visualizations are an intuitive way to represent raw data and they can help in gaining more insight into the statistics. The target digital library was CITIDEL and the XML logs collected from this digital library were used in the project. We also designed and developed a prototype for collection of statistics and visualizing them. Implementation of the tools was done using Java and PHP. JpGraph was used for building visualizations in PHP

Deep Hashing Network for Unsupervised Domain Adaptation

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.Comment: CVPR 201

Anomaly detection in hyperspectral signatures using automated derivative spectroscopy methods

The goal of this research was to detect anomalies in remotely sensed Hyperspectral images using automated derivative based methods. A database of Hyperspectral signatures was used that had simulated additive Gaussian anomalies that modeled a weakly concentrated aerosol in several spectral bands. The automated pattern detection system was carried out in four steps. They were: (1) feature extraction, (2) feature reduction through linear discriminant analysis, (3) performance characterization through receiver operating characteristic curves, and (4) signature classification using nearest mean and maximum likelihood classifiers. The Hyperspectral database contained signatures with various anomaly concentrations ranging from weakly present to moderately present and also anomalies in various spectral reflective and absorptive bands. It was found that the automated derivative based detection system gave classification accuracies of 97 percent for a Gaussian anomaly of SNR -45 dB and 70 percent for Gaussian anomaly of SNR -85 dB. This demonstrates the applicability of using derivative analysis methods for pattern detection and classification with remotely sensed Hyperspectral images

A Study on Impact of Social Media Among Students of Adolescentage Group on Individual Performance

Purpose: The objective of the study was to identify how adoptive teens are to social media, their reactions when controlled from using and also the areas of positive and negative outcome in students, especially all these, from their own perception. Academic performance, social intelligence and health are considered and studied from outcome perspective.   Theoretical framework: Recent literature has reported impacts of usage of social media on performance, privacy and health on teens. However, both positive and negative impacts can’t be ruled out.   Design/methodology/approach: The sample population consisted of male and female students in the age group of 14 – 19 from across different schools and colleges in and around Chennai during 2020. Primary data was collected from students across schools and colleges through personal interview with constructive structured questionnaire as well as through online using Google Forms. Judgment sampling method was used   Findings: The direct and indirect impacts of (SM) input such as interactive and entertainment type of apps on the output like academic performance, social intelligence and health, through the mediating processes such as reaction and adoption to SM, are identified, studied and analyzed.   Research, Practical & Social implications: In addition to the overall performance and social intelligence, health (socially) and privacy (management), are the key concerns for the teens that need to be looked at in the long run.   Originality/value: The results indicate that the usage of SM by teens impacts both positively and negatively as well