An assessment of post-disaster psychological stress in hazardous waste operations and emergency response (HAZWOPER) workers

Personnel involved in natural or man-made disaster response and recovery efforts may be exposed to a wide variety of physical and mental stressors that can exhibit long-lasting and detrimental psychopathological outcomes. In a disaster situation, huge numbers of "secondary" responders can be involved in contaminant clean-up and debris removal and can be at risk of developing stress-related mental health outcomes. The Occupational Safety and Health Administration (OSHA) worker training hierarchy typically required for response workers, known as "Hazardous Waste Operations and Emergency Response" (HAZWOPER), does not address the mental health and safety concerns of workers. This study focused on the prevalence of traumatic stress experienced by secondary responders that had received or expressed interest in receiving HAZWOPER training through the National Institute of Environmental Health Sciences Worker Education and Training Program (NIEHS WETP). ^ The study involved the modification of two preexisting and validated survey tools to assess secondary responder awareness of physical, mental, and traumatic stressors on mental health and sought to determine if a need existed to include traumatic stress-related mental health education in the current HAZWOPER training regimen. The study evaluated post-traumatic stress disorder (PTSD), resiliency, mental distress, and negative effects within a secondary responder population of 176 respondents. Elevated PTSD levels were seen in the study population as compared to a general responder population (32.9% positive vs. 8%-22.5% positive). Results indicated that HAZWOPER-trained disaster responders were likely to test positive for PTSD, whereas, untrained responders with no disaster experience and responders who possessed either training or disaster experience only were likely to test PTSD negative. A majority (68.75%) of the population tested below the mean resiliency to cope score (80.4) of the average worker population. Results indicated that those who were trained only or who possessed both training and disaster work experience were more likely to have lower resiliency scores than those with no training or experience. There were direct correlations between being PTSD positive and having worked at a disaster site and experiencing mental distress and negative effects. However, HAZWOPER training status does not significantly correlate with mental distress or negative effect. ^ The survey indicated clear support (91% of respondents) for mental health education. The development of a pre- and post-deployment training module is recommended. Such training could provide responders with the necessary knowledge and skills to recognize the symptomology of PTSD, mental stressors, and physical and traumatic stressors, thus empowering them to employ protective strategies or seek professional help if needed. It is further recommended that pre-deployment mental health education be included in the current HAZWOPER 24- and 40-hour course curriculums, as well as, consideration be given towards integrating a stand-alone post-deployment mental health education training course into the current HAZWOPER hierarchy.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Building the View Graph of a Category by Exploiting Image Realism

We propose a weakly supervised method to arrange images of a given category based on the relative pose between the camera and the object in the scene. Relative poses are points on a sphere centered at the object in a given canonical pose, which we call object viewpoints. Our method builds a graph on this sphere by assigning images with similar viewpoint to the same node and by connecting nodes if they are related by a small rotation. The key idea is to exploit a large unlabeled dataset to validate the likelihood of dominant 3D planes of the object geometry. A number of 3D plane hypotheses are evaluated by applying small 3D rotations to each hypothesis and by measuring how well the deformed images match other images in the dataset. Correct hypotheses will result in deformed images that correspond to plausible views of the object, and thus will likely match well other images in the same category. The identified 3D planes are then used to compute affinities between images related by a change of viewpoint. We then use the affinities to build a view graph via a greedy method and the maximum spanning tree.

Tax differentials and inflow of foreign direct investments : evidence from foreign operations of U.S. multinational companies

This paper concerns the measurement of the impact of tax differentials across countries on inflow of Foreign Direct Investment (FDI) by using comprehensive data on the foreign operations of U.S. multinational corporations that has been collected by the Bureau of Economic Analysis (BEA), the U.S. Department of Commerce. In particular, this research focuses on examining: (1) how responsive FDI locations are to tax differentials across countries, (2) how different the tax effect on FDI inflow is between developed and developing countries, and (3) whether investment location decisions have become more or less sensitive to tax differences between countries over time ranging from the late 1990s to the early 2000s. Estimation results suggest that high rates of corporate income taxation are associated with reduced foreign assets of U.S. multinational firms in all industries by decreasing the return to foreign asset investment. Further, foreign assets of U.S. multinationals in all industries have become more responsive to non-income tax differentials across countries than to income tax differences from 1999 to 2004. Empirical estimates also indicate that foreign investment by American firms is associated with higher tax sensitivity more in developed countries than in those that are developing.

The Effect of Sequence of Operations on Fatigue Life of LSP Treated Open-hole Aluminium Specimens

Fastener holes in aeronautical structures are typical sources of fatigue cracks due to their induced local stress concentration. A very efficient solution to this problem is to establish compressive residual stresses around the fastener holes that retard the fatigue crack nucleation and its subsequent local propagation. Previous work done on the subject of the application of LSP treatment on thin, open-hole specimens [1] has proven that the LSP effect on fatigue life of treated specimens can be detrimental, if the process is not properly optimized. In fact, it was shown that the capability of the LSP to introduce compressive residual stresses around fastener holes in thin-walled structures representative of typical aircraft constructions was not superior to the performance of conventional techniques, such as cold-working.

An investigation of the performance of a new Mechanical thrombectomy device using Bond Graph modelling: application to the extraction of blood clots in the middle cerebral artery

A number of thrombectomy devices using a variety of methods have now been developed to facilitate clot removal. We present research involving one such experimental device recently developed in the UK, called a ‘GP’ Thrombus Aspiration Device (GPTAD). This device has the potential to bring about the extraction of a thrombus. Although the device is at a relatively early stage of development, the results look encouraging. In this work, we present an analysis and modeling of the GPTAD by means of the bond graph technique; it seems to be a highly effective method of simulating the device under a variety of conditions. Such modeling is useful in optimizing the GPTAD and predicting the result of clot extraction. The aim of this simulation model is to obtain the minimum pressure necessary to extract the clot and to verify that both the pressure and the time required to complete the clot extraction are realistic for use in clinical situations, and are consistent with any experimentally obtained data. We therefore consider aspects of rheology and mechanics in our modeling.

Uniformly Reweighted Belief Propagation: A Factor Graph Approach

Tree-reweighted belief propagation is a message passing method that has certain advantages compared to traditional belief propagation (BP). However, it fails to outperform BP in a consistent manner, does not lend itself well to distributed implementation, and has not been applied to distributions with higher-order interactions. We propose a method called uniformly-reweighted belief propagation that mitigates these drawbacks. After having shown in previous works that this method can substantially outperform BP in distributed inference with pairwise interaction models, in this paper we extend it to higher-order interactions and apply it to LDPC decoding, leading performance gains over BP.

A graph-based approach for latency modeling and optimization in multiview video encoding

We present a novel framework for encoding latency analysis of arbitrary multiview video coding prediction structures. This framework avoids the need to consider an specific encoder architecture for encoding latency analysis by assuming an unlimited processing capacity on the multiview encoder. Under this assumption, only the influence of the prediction structure and the processing times have to be considered, and the encoding latency is solved systematically by means of a graph model. The results obtained with this model are valid for a multiview encoder with sufficient processing capacity and serve as a lower bound otherwise. Furthermore, with the objective of low latency encoder design with low penalty on rate-distortion performance, the graph model allows us to identify the prediction relationships that add higher encoding latency to the encoder. Experimental results for JMVM prediction structures illustrate how low latency prediction structures with a low rate-distortion penalty can be derived in a systematic manner using the new model.

Probabilistic Atlas Based Segmentation Using Affine Moment Descriptors and Graph-Cuts

We show a procedure for constructing a probabilistic atlas based on affine moment descriptors. It uses a normalization procedure over the labeled atlas. The proposed linear registration is defined by closed-form expressions involving only geometric moments. This procedure applies both to atlas construction as atlas-based segmentation. We model the likelihood term for each voxel and each label using parametric or nonparametric distributions and the prior term is determined by applying the vote-rule. The probabilistic atlas is built with the variability of our linear registration. We have two segmentation strategy: a) it applies the proposed affine registration to bring the target image into the coordinate frame of the atlas or b) the probabilistic atlas is non-rigidly aligning with the target image, where the probabilistic atlas is previously aligned to the target image with our affine registration. Finally, we adopt a graph cut - Bayesian framework for implementing the atlas-based segmentation.

Graph based study of allergen cross-reactivity of plant lipid transfer proteins (LTPs) using microarray in a multicenter study.

The study of cross-reactivity in allergy is key to both understanding. the allergic response of many patients and providing them with a rational treatment In the present study, protein microarrays and a co-sensitization graph approach were used in conjunction with an allergen microarray immunoassay. This enabled us to include a wide number of proteins and a large number of patients, and to study sensitization profiles among members of the LTP family. Fourteen LTPs from the most frequent plant food-induced allergies in the geographical area studied were printed into a microarray specifically designed for this research. 212 patients with fruit allergy and 117 food-tolerant pollen allergic subjects were recruited from seven regions of Spain with different pollen profiles, and their sera were tested with allergen microarray. This approach has proven itself to be a good tool to study cross-reactivity between members of LTP family, and could become a useful strategy to analyze other families of allergens.

Time series irreversibility: a visibility graph approach

We propose a method to measure real-valued time series irreversibility which combines two different tools: the horizontal visibility algorithm and the Kullback-Leibler divergence. This method maps a time series to a directed network according to a geometric criterion. The degree of irreversibility of the series is then estimated by the Kullback-Leibler divergence (i.e. the distinguishability) between the in and out degree distributions of the associated graph. The method is computationally efficient and does not require any ad hoc symbolization process. We find that the method correctly distinguishes between reversible and irreversible stationary time series, including analytical and numerical studies of its performance for: (i) reversible stochastic processes (uncorrelated and Gaussian linearly correlated), (ii) irreversible stochastic processes (a discrete flashing ratchet in an asymmetric potential), (iii) reversible (conservative) and irreversible (dissipative) chaotic maps, and (iv) dissipative chaotic maps in the presence of noise. Two alternative graph functionals, the degree and the degree-degree distributions, can be used as the Kullback-Leibler divergence argument. The former is simpler and more intuitive and can be used as a benchmark, but in the case of an irreversible process with null net current, the degree-degree distribution has to be considered to identify the irreversible nature of the series

Segmentation of RV in 4D Cardiac MR Volumes using region-merging graph cuts

Non-invasive quantitative assessment of the right ventricular anatomical and functional parameters is a challenging task. We present a semi-automatic approach for right ventricle (RV) segmentation from 4D MR images in two variants, which differ in the amount of user interaction. The method consists of three main phases: First, foreground and background markers are generated from the user input. Next, an over-segmented region image is obtained applying a watershed transform. Finally, these regions are merged using 4D graph-cuts with an intensity based boundary term. For the first variant the user outlines the inside of the RV wall in a few end-diastole slices, for the second two marker pixels serve as starting point for a statistical atlas application. Results were obtained by blind evaluation on 16 testing 4D MR volumes. They prove our method to be robust against markers location and place it favourably in the ranks of existing approaches.

Control of integrated unit operations

As a thermal separation method, distillation is one of the most important technologies in the chemical industry. Given its importance, it is no surprise that increasing efforts have been made in reducing its energy inefficiencies. A great deal of research is focused in the design and optimization of the Divided-Wall Column. Its applications are still reduced due to distrust of its controllability. Previous references studied the decentralized control of DWC but still few papers deal about Model Predictive Control. In this work we present a decentralized control of both a DWC column along with its equivalent MPC schema.