Built Environment and Risk of Obesity in the United States: A Multilevel Modeling Approach

This dissertation provides a series of exploratory analyses of the relationship between built environment and obesity by using multiple data sets and employing the state-of-art Geographic Information Systems methods. Several built environment factors including street connectivity, walkability and food environment, are for the first time measured across 48 contiguous states of the U.S., built from a fine geographic scale such as the census tract level. Based on the nationwide BRFSS data, the first study used the Geographically Weighted Regression (GWR) model to analyze the obesity rates at the county level. The model results reveal that overall obesity rates are negatively related to walk score and street connectivity, but positively related to poverty rates and metro classification, while the effect of fast-food-to-full-service restaurant ratio is not evident. The strength of each variable’s effect also varies significantly across the country. To mitigate the ecological fallacy, the second study used a multi-level modeling (MLM) approach by accounting for individual attributes such as demographic, socioeconomic and behavior variables. Furthermore, models for areas of different urbanicity levels were tested. The national study found that obesity risk initially increases with the urbanicity level and then drops, resembling an inverted-V shape. The results lend support to the role of built environment in influencing people’s health behavior and outcome, and promote public policies that need to be sensitive to the diversity of demographic groups and geographically adaptable. Defining neighborhoods at the county level may be problematic in the previous MLM study since people’s activity space is seldom countywide. The third study added another level (zip code area) to the MLM analysis of the BRFSS data in Utah. The results showed that at the zip code level, poverty rate and distance to parks are significant and negative covariates of the odds of overweight and obesity; and at the county level, food environment is the sole significant factor with a stronger fast food presence linked to higher odds of overweight and obesity. These findings suggested that obesity risk factors lie in multiple neighborhood levels and built environment need to be defined at a neighborhood size relevant to residents’ activity space

CloudTree: A Library to Extend Cloud Services for Trees

In this work, we propose a library that enables on a cloud the creation and management of tree data structures from a cloud client. As a proof of concept, we implement a new cloud service CloudTree. With CloudTree, users are able to organize big data into tree data structures of their choice that are physically stored in a cloud. We use caching, prefetching, and aggregation techniques in the design and implementation of CloudTree to enhance performance. We have implemented the services of Binary Search Trees (BST) and Prefix Trees as current members in CloudTree and have benchmarked their performance using the Amazon Cloud. The idea and techniques in the design and implementation of a BST and prefix tree is generic and thus can also be used for other types of trees such as B-tree, and other link-based data structures such as linked lists and graphs. Preliminary experimental results show that CloudTree is useful and efficient for various big data applications

A 3E Model on Energy Consumption, Environment Pollution and Economic Growth ---An Empirical Research Based on Panel Data

AbstractThe negative effects of energy consumption and pollution have restrained the Chinese economy from further rapid growth. Therefore to clarify their relationship with economic growth can lay a solid foundation for the decision-making of energy conservation and pollution reduction and ensure the sustainable development of the Chinese economy. Using panel data on the 30 Chinese provinces from 2001 to 2008, this paper builds a 3E model of pollution, energy and production and conducts an empirical study on the interaction between pollutant emission, energy consumption and average GDP

Energy-Efficient Non-Orthogonal Transmission under Reliability and Finite Blocklength Constraints

This paper investigates an energy-efficient non-orthogonal transmission design problem for two downlink receivers that have strict reliability and finite blocklength (latency) constraints. The Shannon capacity formula widely used in traditional designs needs the assumption of infinite blocklength and thus is no longer appropriate. We adopt the newly finite blocklength coding capacity formula for explicitly specifying the trade-off between reliability and code blocklength. However, conventional successive interference cancellation (SIC) may become infeasible due to heterogeneous blocklengths. We thus consider several scenarios with different channel conditions and with/without SIC. By carefully examining the problem structure, we present in closed-form the optimal power and code blocklength for energy-efficient transmissions. Simulation results provide interesting insights into conditions for which non-orthogonal transmission is more energy efficient than the orthogonal transmission such as TDMA.Comment: accepted by IEEE GlobeCom workshop on URLLC, 201

Senior Managers’ Information Behavior in Current Emerging Ubiquitous and Intelligent Computing Environment

Emerging ubiquitous and intelligent information systems, such as the Internet, social computing technologies and artificial intelligence (AI), have facilitated the increasing complexity and dynamism of operational and strategic information in a highly distributed environment. As a result, organizations have been busy seeking approaches and tools to support senior managers in coping with this challenge, from organizational learning to knowledge management, from competitive intelligence to business intelligence, and from management information systems to strategic (executive) information systems. Before embarking on formulating and developing these approaches and tools, senior managers’ informational roles and information behavior should be understood. This paper explores factors influencing and shaping existing senior managers’ information behavior in order to shed light on value-added approaches or technological solutions for supporting and improving informational roles of senior managers. The findings show that information behavior of senior managers is influenced and shaped by a number of factors, mainly the organizational actors and organizational situations, followed by their affective responses and the use of technological tools