Abstract

AL amyloidosis and multiple myeloma result in extracellular deposition of insoluble fibrillary protein in tissue and organs. Untreated median survival is very poor, and even worse with cardiac involvement. Chemotherapy and peripheral blood stem cell transplantation (PBSCT) have been shown to dramatically improve survival, with hematologic remission documented. Regression of cardiac changes has previously been shown, as assessed by echocardiography (TTE) and cardiac magnetic resonance imaging (CMR). This study is a comparison of TTE and CMR in long-term survivors of cardiac amyloidosis with regression. Four long-term survivors with cardiac amyloidosis and regression of cardiac features on TTE were identified. Mean age was 60 years and average survival was 139 months from the time of diagnosis of cardiac involvement. Statistically significant regression of the cardiac features of cardiac amyloidosis were demonstrated on TTE. In these survivors, post-PBSCT structural assessments were similar between TTE and CMR. Classical strain imaging features of cardiac amyloidosis were only present in 50%. All patients had diffuse, patchy gadolinium enhancement on CMR after PBSCT. Treatment of cardiac amyloidosis with chemotherapy and PBSCT may result in regression of abnormalities on TTE with marked improvement in survival. Post treatment, TTE and CMR structural assessments appear similar. Gadolinium imaging suggests that microscopic residual infiltration persists despite macroscopic regression. Significant cardiac improvements with prolonged survival are seen nonetheless. Multimodality imaging has a vital role in the management of cardiac amyloidosis.

Abstract

Network convergence and new applications running on end-hosts result in increasingly variable and unpredictable traffic patterns. By providing origin-destination pairs with several possible paths, load-balancing has proved itself an excellent tool to face this uncertainty. Formally, load-balancing is defined in terms of a convex link cost function of its load, where the objective is to minimize the total cost. Typically, the link queueing delay is used as this cost since it measures its congestion. Over-simplistic models are used to calculate it, which have been observed to result in suboptimal resource usage and total delay. In this paper we investigate the possibility of learning the delay function from measurements, thus converging to the actual minimum. A novel regression method is used to make the estimation, restricting the assumptions to the minimum (e.g. delay should increase with load). The framework is relatively simple to implement, and we discuss some possible variants. Document type: Part of book or chapter of book

Abstract

This thesis evaluates U.S. Marine Corps Air Traffic Control (MATC) military occupational specialties (MOSs) to determine methods of reducing personnel attrition from the MATC Basic Course and revocation from operational forces. The author analyzes Marine Corps personnel data obtained from the Total Force Data Warehouse and Headquarters Marine Corps. The range of the data analyzed covers a period from fiscal years 1999 through 2008, including 965 MATC Marines. Multivariate regression models are estimated to determine the effects of AFQT score, ASVAB composite scores, demographics, and other measures of performance on the likelihood of personnel attrition and revocation. Results indicate that changing selection criteria for AFQT score or ASVAB composite scores is not warranted. It is recommended that current selection criteria be augmented with improved medical screening at Military Entrance Processing Stations (MEPS) and recruit depots, to include non-cognitive testing, such as Navy Computer Adaptive Personality Scales (NCAPS), during recruitment. It is also recommended that an economic analysis be conducted comparing the cost of personnel losses during training with the cost of losses due to revocation. http://archive.org/details/studyofpersonnel109456830 Major, United States Marine Cor

Abstract

Traffic congestion and its associated environmental effects pose a significant problem for large cities. Consequently, promoting and investing in green travel modes such as cycling is high on the agenda for many transport authorities. In order to target investment in cycling infrastructure and improve the experience of cyclists on the road, it is important to know where they are. Unfortunately, investment in intelligent transportation systems over the years has mainly focussed on monitoring vehicular traffic, and comparatively little is known about where cyclists are on a day to day basis. In London, for example, there are a limited number of automatic cycle counters installed on the network, which provide only part of the picture. These are supplemented by surveys that are carried out infrequently. Activity tracking apps on smart phones and GPS devices such as Strava have become very popular over recent years. Their intended use is to track physical activity and monitor training. However, many people routinely use such apps to record their daily commutes by bicycle. At the aggregate level, these data provide a potentially rich source of information about the movement and behaviour of cyclists. Before such data can be relied upon, however, it is necessary to examine their representativeness and understand their potential biases. In this study, the flows obtained from Strava Metro (SM) are compared with those obtained during the 2013 London Cycle Census (LCC). A set of linear regression models are constructed to predict LCC flows using SM flows along with a number of dummy variables including road type, hour of day, day of week and presence/absence of cycle lane. Cross-validation is used to test the fitted models on unseen LCC sites. SM flows are found to be a statistically significant (p<0.0001) predictor of total flows as measured by the LCC and the models yield R squared statistics of ~0.7 before considering spatio-temporal variation. The initial results indicate that data collected using fitness tracking apps such as Strava are a promising data source for traffic managers. Future work will incorporate the spatio-temporal structure in the data to better account for the spatial and temporal variation in the ratio of SM flows to LCC flows.

Abstract

In most of the metropolitan cities in India, the road use patterns are very different from those of developed countries. In Indian cities, roads are shared by non- motorized vehicles in large numbers. The rapid urbanization in India after independence has resulted in the faster development of 23 metropolitan cities as per the 2001 census. While the alarming increase in road accidents has become a major concern in the country, which takes away more than 90,000 lives every year, a significant share of it is from the major cities. The aim of the present paper is to develop models by analyzing the road accident data at an all India level as well as for major metropolitan cities. The data for the 25 year period from 1977 to 2001 were analyzed to build models to understand the nature and extent of the causes of accidents using the concept of Smeed's formula and Andressen's equations. On the basis of population and motor vehicle growth rates, which were derived from the empirical formulae, the above models were used for estimating road accidents in seven metropolitan cities for the years 2007 and 2010. As the variation between model and observed values is negligible, models seem to perform well for both equations. Document type: Article

Abstract

A novel approach is presented for efficiently training a neural network (NN)-based surrogate model when the training data set is to be generated using a computationally intensive high-fidelity computational model. The approach consists in using a Gaussian Process (GP), and more specifically, its acquisition function, to adaptively sample the parameter space of interest and generate the minimum amount of training data needed to achieve the desired level of approximation accuracy. The overall approach is explained and illustrated with numerical experiments associated with the prediction of the lift-over-drag ratio for a NACA airfoil in a large, two-dimensional parameter space of free-stream Mach number and free-stream angle of attack. The obtained numerical results demonstrate the superior accuracy delivered by the proposed training over standard trainings using uniform and random samplings.

Abstract

Workload and complexity measures are, as of today, often imprecise and subjective. Currently, two commonly used workload and complexity measuring formulas are Monitor Alert Parameter and the “Bars”, both using the same measurement variables; amount of aircraft and time. This study creates formulas for quantifying ATC complexity. The study is done in an approach environment and is developed and tested on Stockholm TMA by the creation of 20 traffic scenarios. Ten air traffic controllers working in Stockholm TMA studied the complexity of the scenarios individually and ranked the scenarios in reference to each other. Five controllers evaluated scenario A1-A10. These scenarios were used as references when creating the formulas. The other half of the scenarios, B1-B10, ranked by another five controllers, was used as validation scenarios. Factors relevant to an approach environment were identified, and the data from the scenarios were extracted according to the identified factors. Moreover, a regression analysis was made with the ambition to reveal appropriate weights for each variable. At the first regression, called formula #1, some parameter values were identical. Also, some parameter weights became negative in the regression analysis. The basic requirements were not met and consequently, additional regressions were done; eventually forming formula #2. Formula #2 showed stable values and plausible parameter weights. When compared to a workload measuring model of today, formula #2 showed better performance. Despite the small amount of data samples, we were able to prove a genuine relation between three, of each other independent, variables and the traffic complexity.