Lehigh University

About: Lehigh University is a education organization based out in Bethlehem, Pennsylvania, United States. It is known for research contribution in the topics: Catalysis & Fracture mechanics. The organization has 12684 authors who have published 26550 publications receiving 770061 citations.

Parent training for Attention Deficit Hyperactivity Disorder: Its impact on parent functioning

Abstract: This study examined changes in parent functioning resulting from parental participation in a behavioral parent training (PT) program specifically designed for school-aged children with attention-deficit hyperactivity disorder (ADHD). Relative to wait list controls, subjects who completed the nine-session PT program showed significant posttreatment gains in both child and parent functioning, which were maintained 2 months after treatment. In particular, there were PT-induced reductions in parenting stress and increases in parenting self-esteem, which accompanied parent-reported improvements in the overall severity of their child's ADHD symptoms. In addition to their statistical importance, these findings are discussed in terms of their clinical significance, utilizing methods developed by Jacobson and Truax (1991).

Polysilicon TFT technology for active matrix OLED displays

Abstract: The integration of active matrix polysilicon TFT technology with organic light emitting diode (OLED) displays has been investigated with the goal of producing displays of uniform brightness. This work identifies and addresses several process integration issues unique to this type of display which are important in achieving bright and uniform displays. Rapid thermal processing has been incorporated to achieve uniform polysilicon microstructure, along with silicides to reduce parasitic source and drain series resistance. Using these processes, TFT drain current nonuniformity has been reduced below 5% for 90% of the devices. This work also introduces transition metals to produce low resistance contacts to ITO and to eliminate hillock formation in the aluminum metallization. These processes, along with spin on glasses for planarization, have been used to produce functional active matrix arrays for OLED displays. The final array pixel performance is also presented.

College Students with ADHD: Current Issues and Future Directions

Abstract: Approximately 2 to 8% of the college population reports clinically significant levels of ADHD symptomatology and at least 25% of college students with disabilities are diagnosed with ADHD. A comprehensive review of the literature was conducted with findings consistently indicating academic deficits associated with ADHD in college students. It is less clear whether this disorder significantly impacts social, psychological, and neuropsychological functioning. Although several self- and parent-report measures have been developed for this population, very few studies of assessment methods have been conducted. Similarly, no controlled studies of psychopharmacological, psychosocial, or educational interventions have been completed in samples of college students with ADHD. Non-prescribed use (i.e., diversion) of stimulant medication is a growing problem with approximately 7% of college students reporting this behavior. Studies, to date, are limited by lack of controlled investigations, use of small samples, and lack of confirmation of diagnostic status. Comprehensive and methodologically sound investigations are needed, especially regarding treatment, to promote the success of students with ADHD in higher education settings.

On the solution of integral equations with strong ly singular kernels

Abstract: In this paper some useful formulas are developed to evaluate integrals having a singularity of the form (t-x) sup-m, m or = 1 Interpreting the integrals with strong singularities in Hadamard sense, the results are used to obtain approximate solutions of singular integral equations A mixed boundary value problem from the theory of elasticity is considered as an example Particularly for integral equations where the kernel contains, in addition to the dominant term (t,x) sup-m, terms which become unbounded at the end points, the present technique appears to be extremely effective to obtain rapidly converging numerical results

SegAN: Adversarial Network with Multi-scale L 1 Loss for Medical Image Segmentation

Abstract: Inspired by classic Generative Adversarial Networks (GANs), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic is trained by maximizing a multi-scale loss function, while the segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.