Globally, intracranial pressure (ICP) monitoring use in severe traumatic brain injury (sTBI) is inconsistent and susceptible to resource limitations and clinical philosophies. For situations without monitoring, there is no published comprehensive management algorithm specific to identifying and treating suspected intracranial hypertension (SICH) outside of the one ad hoc Imaging and Clinical Examination (ICE) protocol in the Benchmark Evidence from South American Trials: Treatment of Intracranial Pressure (BEST:TRIP) trial. As part of an ongoing National Institutes of Health (NIH)-supported project, a consensus conference involving 43 experienced Latin American Intensivists and Neurosurgeons who routinely care for sTBI patients without ICP monitoring, refined, revised, and augmented the original BEST:TRIP algorithm. Based on BEST:TRIP trial data and pre-meeting polling, 11 issues were targeted for development. We used Delphi-based methodology to codify individual statements and the final algorithm, using a group agreement threshold of 80%. The resulting CREVICE (Consensus REVised ICE) algorithm defines SICH and addresses both general management and specific treatment. SICH treatment modalities are organized into tiers to guide treatment escalation and tapering. Treatment schedules were developed to facilitate targeted management of disease severity. A decision-support model, based on the group's combined practices, is provided to guide this process. This algorithm provides the first comprehensive management algorithm for treating sTBI patients when ICP monitoring is not available. It is intended to provide a framework to guide clinical care and direct future research toward sTBI management. Because of the dearth of relevant literature, it is explicitly consensus based, and is provided solely as a resource (a ''consensus-based curbside consult'') to assist in treating sTBI in general intensive care units in resource-limited environments.

Condition assessments and rating systems are frequently used by field engineers to assess inland navigation assets and components. The goal of these assessments is to initiate effective risk-informed budget plans for maintenance and repair/replace. Ideally, a degradation model of every component failure mode in the gate would facilitate maintenance decision-making. However, sometimes there is no clear physical understanding how a damage progresses in time; for example, it isn't clear how the bearing gaps change in time in the quoin blocks of a miter gate. Therefore, this is one motivation for the framework proposed in this paper, which integrates Structural Health Monitoring with a Markov transition matrix built from historical condition assessment.To show the applicability of this framework, two examples are presented of how to find the optimal time to plan for maintenance of components in miter gates i) static maintenance planning based on operational condition assessment (OCA) ratings only and ii) dynamic maintenance planning based on integration of damage diagnostics based on monitoring data and failure prognosis based on OCA ratings. In addition, this paper presents a new Bayesian approach to estimate the ratio of errors in the OCA ratings, which allows for improved accuracy in OCA rating-based prognosis.

Nature has evolved polymers with highly specific functions. In the last decade, a new generation of natural circular proteins has been found in bacteria, plants, and mammals.[1] These large cycles show exceptional stability and a wide range of [*] Dr.

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Many physics-based and surrogate models used in structural health monitoring are affected by different sources of uncertainty such as model approximations and simplified assumptions. Optimal structural health monitoring and prognostics are only possible with uncertainty quantification that leads to an informed course of action. In this article, a Bayesian neural network using variational inference is applied to learn a damage feature from a high-fidelity finite element model. Bayesian neural networks can learn from small and noisy data sets and are more robust to overfitting than artificial neural networks, which make it very suitable for applications such as structural health monitoring. Also, uncertainty estimates obtained from a trained Bayesian neural network model are used to build a cost-informed decision-making process. To demonstrate the applicability of Bayesian neural networks, an example of this approach applied to miter gates is presented. In this example, a degradation model based on real inspection data is used to simulate the damage evolution.

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Forty inmates ranging in age from 63 to 80 were tested and interviewed to determine the degree to which they perceived the prison environment as stressful. They were compared on measures of anxiety, anger, and curiosity with a group of younger inmates and with a standardization group. Their responses on these measures were similar to those of the younger inmates but significantly different from those of the standardization group. The interview, designed to assess the subjects reactions to incarceration, indicated that these older inmates tend to create afacade of adjustment. This normal appearance, which results from a denial and suppression of their feelings, masks their stress and anger. This phenomenon of adjustment tends to inhibit development of programs to cope with this problem.