The occurrence and potential health risk of microcystins in drinking water of rural areas in China.

Large-scale use of nitrogen and phosphorus fertilizers in agricultural production, environmental pollution and climate warming cause frequent algal blooms and the generation of algal toxins in water bodies in China. Algal pollution is increasing and microcystins (MCs) are detectable in both surface and ground water in China at sub- μg/L and μg/L levels. Toxicological studies show that microcystins have hepatic and  renal toxicity, genotoxicity, tumor-promoting effects, neurotoxicity, reproductive and developmental toxicity. Epidemiological evidence from China further reveals that chronic exposure to MCs through drinking water and liver cancer are positively correlated and demonstrate that MCs in drinking water are a main risk factor in liver cancer. Effectively controlled water pollution, reduced sewage discharge, and enhanced wastewater treatments are pivotal measures to control algal pollution and toxins in the drinking water of rural China

Using data science as a community advocacy tool to promote equity in urban renewal programs: An analysis of Atlanta's Anti-Displacement Tax Fund

Cities across the United States are undergoing great transformation and urban growth. Data and data analysis has become an essential element of urban planning as cities use data to plan land use and development. One great challenge is to use the tools of data science to promote equity along with growth. The city of Atlanta is an example site of large-scale urban renewal that aims to engage in development without displacement. On the Westside of downtown Atlanta, the construction of the new Mercedes-Benz Stadium and the conversion of an underutilized rail-line into a multi-use trail may result in increased property values. In response to community residents' concerns and a commitment to development without displacement, the city and philanthropic partners announced an Anti-Displacement Tax Fund to subsidize future property tax increases of owner occupants for the next twenty years. To achieve greater transparency, accountability, and impact, residents expressed a desire for a tool that would help them determine eligibility and quantify this commitment. In support of this goal, we use machine learning techniques to analyze historical tax assessment and predict future tax assessments. We then apply eligibility estimates to our predictions to estimate the total cost for the first seven years of the program. These forecasts are also incorporated into an interactive tool for community residents to determine their eligibility for the fund and the expected increase in their home value over the next seven years.Comment: Presented at the Data For Good Exchange 201

The Impact of Community Engagement on Undergraduate Social Responsibility Attitudes

The literature on student development cautions that social responsibility attitudes may stagnate or decline as students proceed through college. Given the importance of students’ future professional obligations to society, identifying ways to reverse this trend is crucial. In turn, an important aim of this study, situated at a large public university, is to evaluate the prospects of community engagement as a strategy to foster professional social responsibility development. The study uses longitudinal results from an instrument known as the Generalized Professional Responsibility Assessment (GPRA) to assess personal and professional social responsibility attitudes. The study’s sample includes 128 students who completed a survey both in 2017, when entering college, and in 2019, when near the midpoint of college. Findings indicate that social responsibility attitudes remain stagnant, and that students over that time period attach more importance to salary as compared to helping people when considering job priorities. Yet, results reveal that increased community engagement predicts growth in social responsibility attitudes, even when controlling for students’ pre-college social responsibility attitudes and demographic characteristics. Further, a novel contribution of this study is a focus on two sub-categories of community engagement: discipline-based and peer-based. Discipline-based community engagement appears to foster professional aspects of social responsibility, while community engagement experiences tied to peer interaction appear to exert greater impacts for non-White students. An observation derived from the study is that community engagement, particularly when it connects to a student’s discipline or draws on peer influences, could be an effective strategy to promote social responsibility development

Mobile Internet Quality Estimation using Self-Tuning Kernel Regression

Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture. Spatial data analysis often involves processing datasets of enormous scale. In this work, we focus on large-scale internet-quality open datasets from Ookla. We look into estimating mobile (cellular) internet quality at the scale of a state in the United States. In particular, we aim to conduct estimation based on highly {\it imbalanced} data: Most of the samples are concentrated in limited areas, while very few are available in the rest, posing significant challenges to modeling efforts. We propose a new adaptive kernel regression approach that employs self-tuning kernels to alleviate the adverse effects of data imbalance in this problem. Through comparative experimentation on two distinct mobile network measurement datasets, we demonstrate that the proposed self-tuning kernel regression method produces more accurate predictions, with the potential to be applied in other applications

Graph Annotations in Modeling Complex Network Topologies

The coarsest approximation of the structure of a complex network, such as the Internet, is a simple undirected unweighted graph. This approximation, however, loses too much detail. In reality, objects represented by vertices and edges in such a graph possess some non-trivial internal structure that varies across and differentiates among distinct types of links or nodes. In this work, we abstract such additional information as network annotations. We introduce a network topology modeling framework that treats annotations as an extended correlation profile of a network. Assuming we have this profile measured for a given network, we present an algorithm to rescale it in order to construct networks of varying size that still reproduce the original measured annotation profile. Using this methodology, we accurately capture the network properties essential for realistic simulations of network applications and protocols, or any other simulations involving complex network topologies, including modeling and simulation of network evolution. We apply our approach to the Autonomous System (AS) topology of the Internet annotated with business relationships between ASs. This topology captures the large-scale structure of the Internet. In depth understanding of this structure and tools to model it are cornerstones of research on future Internet architectures and designs. We find that our techniques are able to accurately capture the structure of annotation correlations within this topology, thus reproducing a number of its important properties in synthetically-generated random graphs