<p>The SPECT-CT machines manufactured by Siemens consists of two heavy detector heads(~1500lbs each) that are moved into various configurations for radionuclide imaging. These detectors are driven by large torque powered by motors in the gantry that enable linear and rotational motion. If the detectors collide with large objects – stools, tables, patient extremities, etc. – they are very likely to damage the objects and get damaged as well. <a>This research work proposes an intelligent real-time object detection system to prevent collisions</a> between detector heads and external objects in the path of the detector’s motion by implementing an end-to-end deep learning object detector. The research extensively documents all the work done in identifying the most suitable object detection framework for this use case, collecting, and processing the image dataset of target objects, training the deep neural net to detect target objects, deploying the trained deep neural net in live demos by implementing a real-time object detection application written in Python, improving the model’s performance, and finally investigating methods to stop detector motion upon detecting external objects in the collision region. We successfully demonstrated that a <i>Caffe</i> version of <i>MobileNet-SSD </i>can be trained and deployed to detect target objects entering the collision region in real-time by following the methodologies outlined in this paper. We then laid out the future work that must be done in order to bring this system into production, such as training the model to detect all possible objects that may be found in the collision region, controlling the activation of the RTOD application, and efficiently stopping the detector motion.</p>

Demos of deploying caffemodel trained for 16000 iterations after the initial training session in the three scenarios outlined in the paper and a minimum confidence score of 30% for detections.

Demos of deploying combo 5 caffemodel trained for 18000 iterations and combo 15 caffemodel trained for 25000 iterations.

Ventilator-associated pneumonia (VAP) is the leading healthcare-acquired infection among ventilated patients in intensive care units (ICU). VAP is a serious patient complication that results in increased hospital length of stay, cost, morbidity, and mortality. The accumulation of subglottic secretions above the endotracheal tube (ETT) cuff increases the risk of VAP, as these secretions may leak around the cuff of the ETT resulting in aspiration and an increased risk for infection. An in depth literature review was done to determine the effectiveness of subglottic secretion aspiration (by means of specialized ETT tubes with intrinsic suction lumens) in decreasing the incidence rate of VAP. Evidenced-based data were gathered from the CINAHL Plus with Full Text, PubMed, and Cochrane Database of Systematic Reviews databases for this review. VAP guidelines recommend subglottic secretion aspiration as a means to prevent its occurrence. However, important variables such as suction pressure, frequency, secretion viscosity, and ETT cuff pressure and volume need to be considered. The interaction among these variables determines the effectiveness of subglottic secretion removal. The goal of this review was to highlight these interactions and provide evidenced-based information for critical care nurses to expand their understanding of the dynamics involved in subglottic secretion aspiration and how to efficiently use this practice to prevent VAP.

Multiple strain path changes during forming lead to complex geometrically necessary dislocation (GND) development in strain gradient fields, inducing internal stresses that contribute to the Bauschinger effect, residual stresses, and springback which alters the final geometrical shape of the part. In order to analyze and design improved processing routes, models must capture the evolution of these internal stresses. However, most models capture the effects of these stresses via phenomenological approaches that require calibration to each new material and strain path. The development of models that capture the underlying physics at the sub-grain level is underway but requires in-depth studies of dislocation behavior (at the relevant meso length scale) in order to guide and validate them. The novel experimental campaign central to this thesis aims to tackle this problem by capturing unprecedented data of dislocation activity for several sheet metals during multiple strain path deformation. The resultant insights provide a new window into multi-path forming of metals, while also aiding the development and validation of two crystal plasticity (CP) models by collaborators at the University of New Hampshire (UNH). The models incorporate internal stresses at the grain and sub-grain levels, respectively. The hardening response due to strain path change during forming of AA6016-T4 was studied at the macro- and micro-level via combined experiments and an elasto-plastic self-consistent (EPSC) model. The experiments demonstrated that possible recombination and/or redirection of dislocations onto different slip systems under strain path change allowed for a gradual elasto-plastic transition, in comparison to a much sharper response upon continued deformation under the same strain path due to buildup and immediate activation of backstresses. The phenomenological backstress law of the EPSC model underpredicted the yield stress response for the strain path change deformations, possibly due to missing sub-grain GND development and an accurate description of associated backstresses. A more detailed experimental study of multi-path deformation for the AA6016-T4 was required in order to guide development of a strain gradient elasto-visco plasticity self-consistent model (SG-EVPSC); the model includes sub-grain strain gradient fields, and related internal stress fields. Total dislocation and GND density were tracked at various points of the deformation, and a complete 3D statistical volume element was characterized, to enable accurate modeling of the microstructure. The tests revealed a relatively lower yield stress response following strain path change, presumably aided by lower latent hardening than self hardening; the tests then showed a rapid accumulation of dislocations on the newly activated slip systems resulting in much higher final dislocation density without affecting the ductility of the pre-strained material. Interestingly, GND development was dominated by the precipitates instead of grain boundaries. These observations are vital for an accurate forming prediction from CPFEA models. Finally, optimized forming conditions of continuous bending under tension produced a ratcheting strain path resulting in a gradual GND development and a more complete retained austenite transformation in quenched-&-partitioned- and TRIP-assisted bainitic ferritic-1180 steels increasing their ductility by at least 360%.

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La SSD (Species Sensitivity Distribution) est une méthode utilisée par les scientifiques et les régulateurs de tous les pays pour fixer la concentration sans danger de divers contaminants sources de stress pour l'environnement. Bien que fort répandue, cette approche souffre de diverses faiblesses sur le plan méthodologique, notamment parce qu'elle repose sur une utilisation partielle des données expérimentales. Cette thèse revisite la SSD actuelle en tentant de pallier ce défaut. Dans une première partie, nous présentons une méthodologie pour la prise en compte des données censurées dans la SSD et un outil web permettant d'appliquer cette méthode simplement. Dans une deuxième partie, nous proposons de modéliser l'ensemble de l'information présente dans les données expérimentales pour décrire la réponse d'une communauté exposée à un contaminant. A cet effet, nous développons une approche hiérarchique dans un paradigme bayésien. A partir d'un jeu de données décrivant l'effet de pesticides sur la croissance de diatomées, nous montrons l'intérêt de la méthode dans le cadre de l'appréciation des risques, de par sa prise en compte de la variabilité et de l'incertitude. Dans une troisième partie, nous proposons d'étendre cette approche hiérarchique pour la prise en compte de la dimension temporelle de la réponse. L'objectif de ce développement est d'affranchir autant que possible l'appréciation des risques de sa dépendance à la date de la dernière observation afin d'arriver à une description fine de son évolution et permettre une extrapolation. Cette approche est mise en œuvre à partir d'un modèle toxico-dynamique pour décrire des données d'effet de la salinité sur la survie d'espèces d'eau douce / Species Sensitivity Distribution (SSD) is a method used by scientists and regulators from all over the world to determine the safe concentration for various contaminants stressing the environment. Although ubiquitous, this approach suffers from numerous methodological flaws, notably because it is based on incomplete use of experimental data. This thesis revisits classical SSD, attempting to overcome this shortcoming. First, we present a methodology to include censored data in SSD with a web-tool to apply it easily. Second, we propose to model all the information present in the experimental data to describe the response of a community exposed to a contaminant. To this aim, we develop a hierarchical model within a Bayesian framework. On a dataset describing the effect of pesticides on diatom growth, we illustrate how this method, accounting for variability as well as uncertainty, provides benefits to risk assessment. Third, we extend this hierarchical approach to include the temporal dimension of the community response. The objective of that development is to remove the dependence of risk assessment on the date of the last experimental observation in order to build a precise description of its time evolution and to extrapolate to longer times. This approach is build on a toxico-dynamic model and illustrated on a dataset describing the salinity tolerance of freshwater species

Growing memory capacities and the increasing number of cores on modern hardware enforces the design of new in-memory indexing structures that reduce the number of memory transfers and minimizes the need for locking to allow massive parallel access. However, most applications depend on hard durability constraints requiring a persistent medium like SSDs, which shorten the latency and throughput gap between main memory and hard disks. In this paper, we present our winning solution of the SIGMOD Programming Contest 2011. It consists of an in-memory indexing structure that provides a balanced read/write performance as well as non-blocking reads and single-lock writes. Complementary to this index, we describe an SSD-optimized logging approach to fit hard durability requirements at a high throughput rate.