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The aim of this study was to investigate the predictors of acute stress disorder (ASD) following mild traumatic brain injury (MTBI). Patients who sustained MTBI following a motor vehicle accident (n = 48) were assessed with a structured interview within 18 days of the trauma for the presence of ASD and administered the Beck Depression Inventory (BDI), Coping Style Questionnaire, Dissociative Experiences Scale, and the Eysenck Personality Inventory. ASD was diagnosed in 14.6% of patients and 4.2% were diagnosed with sub-syndromal ASD. BDI scores and avoidant coping were significant predictors of ASD and acute stress severity. This study provides further evidence that traumatic stress reactions occur following MTBI and highlights the possibility of identifying those who may benefit from early intervention.
The fracture and fatigue properties of Si-alloyed LTI pyrolytic carbon and pyrolytic carbon-coated graphite are described as a framework for establishing damage-tolerant analyses for maintaining structural integrity and for predicting the lifetimes of mechanical heart valve prostheses fabricated from these materials.The analyses are based on fracture-mechanics concepts and provide conservative (worst-case) estimates of the time, or number of loading cycles, before the valve will fail, or more precisely for pre-existing defects in valve components to grow subcritically to critical size under elevated physiologic loading and environmental conditions.For structural life in excess of patient life-times, a minimum required detectable defect size is computed which must be detected by quality-control procedures prior to the device entering service; this defect size is typically of the order of tens of microns for such "ceramic" valves, compared to sizes in the hundreds of microns for corresponding metal valves.It is concluded that in light of the brittle nature of pyrolytic carbon and the unacceptable cost of mechanical valve failures, the use of such analyses should be regarded as essential in order to provide maximum assurance of patient safety.
Abstract To produce reaction products with 100% chemical selectivity, and to attain much higher turnover rates and number of turnovers, the development of new methods of catalyst preparation are necessary. Precise control of particle size, the separation between catalyst particles and their superior chemical and thermal stability must be achieved. To this end, five new methods of preparation of high‐technology catalysts are being studied. These are: (A) the method successfully used by W.M.H. Sachtler for metal particle deposition in zeolite cages, (B) the deposition of metal particles by atomic layer epitaxy, (C) controlled nucleation and growth of metal particles by substrate spinning, (D) deposition of metals on oxides by evaporation, (E) preparation of ordered metal arrays by electron‐beam lithography using microelectronic techniques. These different methods are described and discussed, and their present states of development are assessed.
Chapter abstract This chapter spotlights four transversal principles that undergird and animate Bourdieu’s research practice, and can fruitfully guide inquiry on any empirical front: the Bachelardian imperative of epistemological rupture and vigilance; the Weberian command to effect the triple historicization of the agent (habitus), the world (social space, of which field is but a subtype), and the categories of the analyst (epistemic reflexivity); the Leibnizian-Durkheimian invitation to deploy the topological mode of reasoning to track the mutual correspondences between symbolic space, social space, and physical space; and the Cassirer moment urging us to recognize the constitutive efficacy of symbolic structures. The chapter also flags three traps that Bourdieusian explorers of the social world should exercise special care to avoid: the fetishization of concepts, the seductions of “speaking Bourdieuse” while failing to carry out the research operations Bourdieu’s notions stipulate, and the forced imposition of his theoretical framework en bloc.
Abstract For Abstract see ChemInform Abstract in Full Text.
We address the problem of identifying specific instances of a class (cars) from a set of images all belonging to that class. Although we cannot build a model for any particular instance (as we may be provided with only one âtraining â example of it), we can use information extracted from observing other members of the class. We pose this task as a learning problem, in which the learner is given image pairs, labeled as matching or not, and must discover which image features are most consistent for matching instances and discriminative for mismatches. We explore a patch based representation, where we model the distributions of similarity measurements defined on the patches. Finally, we describe an algorithm that selects the most salient patches based on a mutual information criterion. This algorithm performs identification well for our challenging dataset of car images, after matching only a few, well chosen patches.