Facilitating Academic and Mental Health Resilience in Students with a Learning Disability

This qualitative study explored the educational journeys of five postsecondary students with learning disabilities (LD) from the perspective of the students and their families. Using a resilience lens, it examined the challenges that they faced and the capacities and resources that facilitated their resilience and helped them achieve their current level of academic achievement and mental health. A retrospective, multiple case study design was used, and a series of three interviews was conducted with each university student with an LD and their families. The participants identified a number of interactions among the students and their parents, teachers, and peers that helped shape and develop the capacities they needed in order to negotiate for the supports and resources that sustained their well-being. These capacities included an awareness and acceptance of their LD and themselves as learners, the self-advocacy skills they needed in order to seek out and negotiate for the supports and accommodations that would help them succeed, the ability to set lofty yet attainable goals, the perseverance to work toward these goals in spite of setbacks and challenges, and the willingness to use the supports and resources that were available to them

Two-stage reusable launch system utilizing a winged core vehicle and glideback boosters

A near-term technology launch system is described in which Space Shuttle main engines are used on a manned orbiter and also on twin strap-on unmanned boosters. The orbiter has a circular body and clipped delta wings. The twin strap-on boosters have a circular body and deployable oblique wings for a glideback recovery. The dry and gross weights of the system, capable of delivering 70klb of cargo to orbit, are compared with a similar system with hydrocarbon-fueled boosters and with the current Shuttle

Indirect Effects of Energy Development in Grasslands

Grassland landscapes in North America are undergoing rapid industrialization due to energy development facilitated by the growing popularity of fracking and horizontal drilling technology. Each year over 3 million hectares are lost from grassland and shrubland habitats to well infrastructure. Direct footprints from energy infrastructure cause impacts to vegetation cover, available cattle forage, carbon sequestration potential, and usable space for wildlife. However, legacy effects from well construction and noise pollution, light pollution, and altered viewsheds have the potential to impact areas beyond this direct footprint, causing additive and persistent changes to nearby grassland function. While these additional areas may be small on a well pad basis, they may have substantial cumulative impacts over time. To investigate these effects via a diversity of mechanisms, we studied the seasonal habitat selection of northern bobwhite (Colinus virginianus, hereafter bobwhite) in an energy-producing landscape to evaluate space use patterns relative to energy infrastructure. Habitat selection was modeled in the breeding and nonbreeding season using resource Utilization functions (RUFs). We then investigated patterns of vegetation, arthropod, and soil characteristics surrounding well pads to assess small scale environmental gradients extending away from drilling pads via a combination of multivariate and univariate techniques (i.e., Nonmetric dimensional scaling and ANOVA). We found minimal avoidance of energy structures by quail, suggesting a tolerance of moderate development levels. All small-scale effects studied except for soil moisture were impacted at the pad itself (P < 0.01). Off-pad impacts to arthropod abundance and biomass were spatially limited to areas close to pads, while vegetation cover was typically lower than the surrounding habitat beyond 10 m of pads. Soil surface temperature was higher at distances close to well pads, and soil moisture was not different between areas close to and far from well pads. Small-scale gradient results indicate vegetation effects around active drilling pads, potentially increasing erosion and decreasing nesting cover, decreasing carbon sequestration potential, and decreasing forage. Collectively, this research highlights the complexity and importance of impact thresholds in landscape fragmentation.Natural Resources and Ecology Managemen

Reducing Stress and Enhancing the General Well-Being of Teachers Using T’ai Chi Chih® Movements: A Pilot Study

The purpose of this study was to examine the effects of T’ai Chi Chih® (TCC) discipline on the physical symptoms of stress and the general well-being of 35 teachers. Teachers completed 15 hours of instruction and practice in a two-day format with four additional weeks of practice. The 23 teachers who completed pre-posttests showed significant improvement of their general well-being and reduction in perceived stress symptoms. Qualitative data from the teachers supported these results. Within the limitations of the study design it was concluded that this relatively new, easy to learn method of T’ai Chi has potential to enhance well-being and reduce stress symptoms of teachers. Teachers and other educational professionals may want to learn more about T’ai Chi Chih® discipline for its stress reducing benefits and enhancement of general well-being

Unbiased Shape Compactness for Segmentation

We propose to constrain segmentation functionals with a dimensionless, unbiased and position-independent shape compactness prior, which we solve efficiently with an alternating direction method of multipliers (ADMM). Involving a squared sum of pairwise potentials, our prior results in a challenging high-order optimization problem, which involves dense (fully connected) graphs. We split the problem into a sequence of easier sub-problems, each performed efficiently at each iteration: (i) a sparse-matrix inversion based on Woodbury identity, (ii) a closed-form solution of a cubic equation and (iii) a graph-cut update of a sub-modular pairwise sub-problem with a sparse graph. We deploy our prior in an energy minimization, in conjunction with a supervised classifier term based on CNNs and standard regularization constraints. We demonstrate the usefulness of our energy in several medical applications. In particular, we report comprehensive evaluations of our fully automated algorithm over 40 subjects, showing a competitive performance for the challenging task of abdominal aorta segmentation in MRI.Comment: Accepted at MICCAI 201