Occupational Therapy Feeding and Eating Interventions for Autism Spectrum Disorders and Pervasive Developmental Disorders: A Systematic Review

Due to the limited evidence and lack of methodological rigor regarding feeding and issues in children with Autism Spectrum Disorders (ASD) and Pervasive Developmental Disorders (PDD), clinicians who treat children with these diagnoses rely on the limited amount of information and many are not aware of evidence-based interventions (Ahearn, Castine, Nault, & Green, 2001; Marshall, Hill, & Dodrill, 2013). The purpose of this scholarly project is to gather, critique, and determine efficacy of occupational therapy feeding and eating interventions for children with ASD and PDD. We systematically reviewed literature for higher-level evidence, as defined by Level III evidence or above, in regards to occupational therapy feeding and eating interventions for children with ASD and PDD in studies that were published between January 2000 and December 2015 and located in PubMed, OT Search, Cumulative Index of Nursing and Allied Health Literature (CINAHL), and the American Journal of Occupational Therapy (AJOT). Our search yielded a total of 7,189 titles and abstracts that were narrowed through the screening process to 27 articles for review. The secondary review resulted in 11 articles, which received a full-text review. A total of 9 articles were found to meet inclusion criteria and be appropriate for critical appraisal. The results of these articles were compiled in an evidence table and a systematic review manuscript was specifically written for the AJOT. Our scholarly project highlights the various discrepancies regarding research for occupational therapy feeding and eating interventions for children with ASD and PDD. Recommendations for future research and implications for occupational therapy practice include the need for higher-level evidence to support the practice of occupational therapy practitioners and the development of a specific protocol to standardize occupational therapy treatment for feeding and eating difficulties among children with ASD and PDD

Dynamic Race Prediction in Linear Time

Writing reliable concurrent software remains a huge challenge for today's programmers. Programmers rarely reason about their code by explicitly considering different possible inter-leavings of its execution. We consider the problem of detecting data races from individual executions in a sound manner. The classical approach to solving this problem has been to use Lamport's happens-before (HB) relation. Until now HB remains the only approach that runs in linear time. Previous efforts in improving over HB such as causally-precedes (CP) and maximal causal models fall short due to the fact that they are not implementable efficiently and hence have to compromise on their race detecting ability by limiting their techniques to bounded sized fragments of the execution. We present a new relation weak-causally-precedes (WCP) that is provably better than CP in terms of being able to detect more races, while still remaining sound. Moreover it admits a linear time algorithm which works on the entire execution without having to fragment it.Comment: 22 pages, 8 figures, 1 algorithm, 1 tabl

Link Prediction with Social Vector Clocks

State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.Comment: 9 pages, 6 figure