Interview with Dick J. Reavis, political activist, journalist and author. Reavis is a former staff writer for Texas Monthly, professor in English department at North Carolina State University, contributing publications for Soldier of Fortune and The Wall Street Journal, and author of The Ashes of Waco: an Investigation. He discusses childhood memories and early experiences of his father's newspaper publishing career in Oklahoma, Texas, and South Carolina; Experiences in Texas public schools during segregation era; Father's political views and development of his own political views; Experiences as college student at Texas Tech, Panhandle A&M, and University of Texas; Involvement in the civil rights and antiwar movements with Southern Christian Leadership Conference, Student Nonviolent Coordinating Committee, and Students for a Democratic Society, and in various other left causes and organizations; Career in journalism; Personal life, political views and travels.

Evaluating the impact of learning from climate data, in addition to bridge data, on the performance of concrete deck condition rating prediction is critical for identifying the right data needed to enhance bridge maintenance decision making. Few studies have considered such an evaluation and utilized a small size of samples that prevent revealing the knowledge hidden within the big size of data. Although, such evaluation over big data seems quite necessary, class imbalance problem makes it challenging. To alleviate such a problem, five states, including Alabama, Iowa, New York, Pennsylvania, and South Carolina, were selected as the case study. Not only are the states located in three different climatically consistent regions defined by the National Ocean and Atmospheric Administration (NOAA), but also their concrete deck conditions ratings are somewhat balanced. To conduct the evaluation, this research developed the bridge data set pertaining to 56,288 bridges across the afore-mentioned states through employing the GIS technology. The bridge data set contains bridge data derived from National Bridge Inventory (NBI), and climate data derived from Parameter-elevation Relationships on Independent Slopes Model (PRISM) climate maps and NOAA. Then, two machine learning algorithms, including random forest and GBM, were trained - with and without climate …