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A nested case‐control study was conducted to investigate whether an excess of pancreatic cancer, identified in a cohort mortality study with follow‐up from 1946 through 1988. was associated with potential workplace exposures at a New Jersey plastics manufacturing and research and development facility. The study population included 28 male pancreatic cancer cases and 140 randomly selected controls, matched on year of birth and at risk (alive) at the time of the case death. Using plant work history records, department assignments for the two groups were compared according to duration and time since first assignment. Workers assigned to a work area that processed vinyl resins and polyethylene (PE) were shown to be at increased risk. Men assigned more than 16 years to this department had a significantly increased risk ratio of 7.15 (95% confidence intervals [CI]: 1.28–40.1). No excess was seen with shorter duration assignments. Seven of the nine cases began working in this area in the 1940s. Average latency was 32 years, and all but three cases worked 20 years or more in this unit. Over the study period, significant exposure‐related process changes occurred, in addition to the use of numerous chemical additives. Although vinyl and PE processing operations could not be analyzed separately, the pancreatic cancer excess is more likely to be related to vinyl processing. Identification of a causative agent or combination of agents would require investigations with more detailed exposure information. Copyright © 1995 Wiley Periodicals, Inc., A Wiley Company

Accurate identification and tracking of synoptic-scale storm systems in the northern midlatitudes is important for understanding the structure and movement of the midlatitude cloud field which plays a major role in climate change. In this paper, a hybrid neural network/genetic algorithm (NN/GA) approach is presented that analyzes the behavior of storm systems from one time frame to the next. The goal of the hybrid neural network algorithm is to improve classifier output by reducing the number of infeasible solutions using constraint optimization techniques. The input to the hybrid neural network algorithm is the output from a traditional backpropagation neural network. The hybrid NN/GA analyzes the backpropagation neural network output for logical consistencies and makes changes to the classification results based on strength of neural network classifications and satisfaction of logical constraints. The results are compared with classification results obtained using linear discriminant analysis, k-nearest neighbor rule, and backpropagation neural network techniques.

The objective of this study is to compare statistical and unsupervised neural network techniques for determination of correspondences between storm system regions extracted from sequences of satellite images. Analysis was applied to the International Satellite Cloud Climatology Project (ISCCP) low resolution D1 database for selected storm systems during the period April 5 - 9, 1989. Cloud top pressure was used to delineate regions of interest and cloud optical thickness combined with spatial location was used to track regions throughout a given time sequence. The ability of the k-nearest neighbor classifier and of self-organizing maps to determine correspondences between storm regions was assessed. The two techniques generally yielded similar associations between regions of interest throughout the time sequence. Differences in final tracking results between the two techniques occurred primarily as a result of differences in the collections of points from a region in a time step t<SUB>2</SUB> that corresponded to a region in an earlier time step t<SUB>1</SUB>. The tracking results were also compared to the results obtained at the NASA Goddard Institute for Space Studies using sea level pressure data from the National Meteorological Center (NMC). For the storm systems investigated in this study, the storm tracks exhibited the same general tracking behavior with expected variations between cloud system storm centers and low sea level pressure centers.

An evolutionary system was developed for generation of complete tracks of northern midlatitude synoptic-scale storm systems based on optical flow and cloud motion analyses of global satellite-based datasets produced by the International Satellite Cloud Climatology Project (ISCCP). The tracking results were compared with low sea level pressure anomaly (SLPA) tracks obtained from the NASA Goddard Institute for Space Studies (GISS). The SLPA tracks were produced at GISS by analysis of meteorological, ground-based National Center for Environmental Prediction (NCEP) datasets. Results from the evolutionary system were also compared with results from using (a) the k-nearest neighbor rule (k-NN) and (b) self-organizing maps (SOM) to determine correspondences between consecutive locations within a track. The consistency of our evolutionary storm tracking results with the behavior of the low sea level pressure anomaly tracks, the ability of our evolutionary system to generate and evaluate complete tracks, and the close comparison between the results obtained by the evolutionary, k-NN, and SOM analyses of the ISCCP-derived datasets at tracking steps in which proximity or optical flow information sufficed to determine movement, demonstrate the applicability and the potential of evolutionary systems for tracking midlatitude storm systems through low-resolution ISCCP cloud product datasets.

Cloud analyses provide information which is vital to the detection, understanding and prediction of meteorological trends and environmental changes. This paper compares statistical, neural network and genetic algorithm methods for recognition and tracking of midlatitude storm clouds in sequences of low-resolution cloud-top pressure data sets. Regions of interest are identified and tracked from one image frame to the next consecutive frame in an eight-frame sequence. Classification techniques are used to determine the relationships between regions of interest in consecutive time frames. A genetic algorithm procedure is then used to revise classifier outputs to ensure that consistency constraints are not violated. © 1997 Elsevier Science B.V.

The objective of this research is to automate the classification of the temporal behavior of storm cloud systems based on measurements derived from consecutive satellite images. The motivation behind this study is to develop improved descriptions of cloud dynamics which can be used in general circulation models for prediction of global climate change. Analysis was applied to the International Satellite Cloud Climatology Project (ISCCP) low resolution cloud top pressure database for the first six days in April, 1989. A total of 296 midlatitude storm cloud components were tracked between consecutive 3-hour time frames. For each pair of components, temporal correspondence events were classified as either 1.) direct, 2.) merge, 3.) split, or 4.) reject. The reject class, which was used primarily to categorize pairs of unrelated systems, included storm cloud system dissipation and creation as well. Statistical, neural network, and evolutionary techniques were developed for finding solutions to the storm cloud correspondence problem. Evolutionary techniques applied to the problem consisted of 1.) a constraint-handling hybrid evolutionary technique and 2.) a genetic local search algorithm. The results demonstrate the potential of evolutionary techniques to yield meteorologically-feasible solutions, given appropriate constraints, to the two-frame storm tracking problem. © 1998 SPIE. All rights reserved.

In the past fractal dimension has often been computed using a stochastic approach based on a random walk process, which has been found to be very time consuming. More recently, mathematical morphology has been used to compute the fractal dimension in a more timely fashion. This paper describes how the fractal dimension computed using mathematical morphology can be used in the texture analysis of ultrasonic imagery. The discriminatory ability of the fractal dimension as a pattern recognition feature is evaluated and compared to more traditional parameters. This analysis includes comparisons with statistical features in which each parameter is treated as an independent variable and in which interactions between those variables are evaluated. Pattern recognition techniques include Stepwise Discriminant Analysis, Linear Discriminant Analysis, and Nearest Neighbor Analyisis in addition to Backpropagation Neural Network Classifiers. Our results identify the fractal dimension as one of the most important parameters for distinguishing between normal and abnormal livers. In this study, consisting of 186 images, a significant statistical difference was found for both the mean and standard deviation of the fractal dimension between the normal and abnormal groups using parametric and nonparametric statistical techniques. © 1993 SPIE. All rights reserved.

Temporal analysis has been applied to a sequence of cloud top pressure (CTP) images and cloud optical thickness (TAU) images stored in the International Satellite Cloud Climatology Project (ISCCP) D1 database located at the NASA Goddard Institute for Space Studies (GISS). Each pixel in the D1 data set has a resolution of 2.5 degrees or 280 kilometers. These images were collected in consecutive three-hour intervals for the entire month of April 1989. The primary objective of this project was to develop a sequence of storm tracks from the satellite images to follow the formation, progression and dissipation of storm systems over time. Composite images where created by projecting ahead in time and substituting the first available valid pixel for missing data and a variety of CTP and TAU cut-off values were used to identify regions of interest. Region correspondences were determined from one time frame to another yielding the coordinates of storm centers. These tracks were compared to storm tracks computed from sea level pressure data obtain from the National Meteorological Center (NMC) for the same time period. The location of sea level storm center provides insight as to whether storms have occurred anywhere in a region and can be helpful in determining the presence or absence of storms in a general geographic region.

Over the past several years we have been interested in the supervised classification of ultrasonic images of the liver based on quantitative texture features. Our most recent efforts are concerned with the inclusion of features computed from Markov random fields. After adding four such features to our existing model containing 17 features, we employed stepwise discriminant analysis to identify the features that could best discriminate among 184 previously classified normal and abnormal ultrasonic images. Three of the four features derived from Markov random field models were identified by stepwise discriminant analysis as being good discrimination along with 6 existing features. From these results we constructed a backpropagation neural network with an input layer consisting of 9 nodes. We found that this new model yielded slightly better results when compared to earlier models. Our most recent results yielded a sensitivity of 81%, a specificity of 77% and an overall accuracy of 79%.

The objective of this research is to automate the classification of clouds from satellite images providing a method for studying their properties over time. Analysis was applied to the International Satellite Cloud Climatology Project (ISCCP) low resolution (2.5 degrees per pixel) database for January 1987. Our approach differs from earlier studies by taking advantage of cloud top pressure and optical thickness from the ISCCP database, providing more accurate measures of cloud height with less dependency on the sun's angle of illumination. A total of 365 regions of interest (ROI), each classified Storm or Non Storm were used in the analysis. The algorithms used were Backpropagation Artificial Neural Network and Nearest Neighbor Pattern Classification. Each ROI was assigned on identification number between 1 and 365. One third of the ROIs were randomly selected for testing using a random number generator and the remaining ROIs were assigned to be training set. This process was repeated 29 times resulting in a mean classification error of 5.76% for the nearest neighbor algorithm and 3.97% for the backpropagation neural network.