Can Good Come From Bad? An Examination of Adversarial Growth in Division I NCAA Athletes

The purpose of this study was to examine adversarial growth in a sample of Division I NCAA athletes. Male and female athletes (n = 214) from three universities completed the Posttraumatic Growth Inventory from the perspective of an adversity experienced as a college athlete. The athletes reported positive change at low to moderate levels resulting from their most difficult adversity, and indicated the most improvement in personal strength. Female athletes reported greater spiritual growth, as well as more of a change in their ability to relate to others than their male counterparts. Of the three types of adversities analyzed (i.e., time demands, injury, and the mental and physical stress of sport), athletes who reported time demands as their most difficult adversity exhibited more appreciation for life than athletes who cited the mental and physical stress of sport. These findings are consistent with studies of growth in college student nonathletes (e.g., Anderson & Lopez-Baez, 2008; 2011), and support the notion that college is a pivotal time for personal development (Chickering & Reisser, 1993). Practitioners are advised to consider the potential for adversarial growth in the athletes with whom they work so that they may be able to recognize and facilitate the growth process

A preliminary experiment definition for video landmark acquisition and tracking

Six scientific objectives/experiments were derived which consisted of agriculture/forestry/range resources, land use, geology/mineral resources, water resources, marine resources and environmental surveys. Computer calculations were then made of the spectral radiance signature of each of 25 candidate targets as seen by a satellite sensor system. An imaging system capable of recognizing, acquiring and tracking specific generic type surface features was defined. A preliminary experiment definition and design of a video Landmark Acquisition and Tracking system is given. This device will search a 10-mile swath while orbiting the earth, looking for land/water interfaces such as coastlines and rivers

Prevalence of Eating Disorders and Disordered Eating Behaviors Among Male Collegiate Athletes

This article discusses the prevalence of eating disorders and disordered eating behaviors among male collegiate athletes

Sexing white 2D footprints using convolutional neural networks

Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture. We use a machine learning approach and compare this to more traditional methods of discrimination. Two datasets are used, a pilot data set collected from students at Bournemouth University (N = 196) and a larger data set collected by podiatrists at Sheffield NHS Teaching Hospital (N = 2677). Our convolutional neural network can sex a footprint with accuracy of around 90% on a test set of N = 267 footprint images using all image components, which is better than an expert can achieve. However, the quality of the impressions impacts on this success rate, but the results are promising and in time it may be possible to create an automated screening algorithm in which practitioners of whatever sort (medical or forensic) can obtain a first order sexing of a two-dimensional footprint

Sexing Caucasian 2D footprints using convolutional neural networks

Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture. We use a machine learning approach and compare this to more traditional methods of discrimination. Two datasets are used, a pilot data set collected from students at Bournemouth University (N=196) and a larger data set collected by podiatrists at Sheffield NHS Teaching Hospital (N=2677). Our convolutional neural network can sex a footprint with accuracy of around 90% on a test set of N=267 footprint images using all image components, which is better than an expert can achieve. However, the quality of the impressions impacts on this success rate, but the results are promising and in time it may be possible to create an automated screening algorithm in which practitioners of whatever sort (medical or forensic) can obtain a first order sexing of a two-dimensional footprint