Economic Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority

This study assesses the initial economic outcomes of the Delta Regional Authority (DRA), which began funding rural development projects in the Mississippi Delta region in 2002. The study focuses on non-metropolitan DRA counties and similar counties elsewhere in the Mississippi Delta region and the southeast, using a quasi-experimental approach that combines matching methods, double and triple difference and switching regression estimation. We find that per capita income and transfer payments grew more rapidly in DRA counties than similar non-DRA counties, and that these impacts are larger in counties in which DRA spending was larger. Each additional dollar of DRA spending per capita is associated with an increase of $15 in personal income per capita between 2002 and 2007, including an increase of$8 in earnings (primarily in the health care and social services sector) and $5 in transfer payments. The increase in transfer payments is mainly due to increased medical transfer payments. We also find that the number of hospital beds per capita increased more in counties where DRA spending per capita was greater. These findings suggest that investments supported by the DRA in improved medical facilities are promoting additional health sector earnings and medical transfer payments.rural economic development programs, economic impacts, Mississippi Delta, Delta Regional Authority, matching estimators, double difference, triple difference estimation, switching regression, Community/Rural/Urban Development, Research Methods/ Statistical Methods, R58, R11, O18, C21,

Visualizing Features From Deep Neural Networks Trained on Alzheimer’s Disease and Few-Shot Learning Models for Alzheimer’s Disease

Alzheimer’s disease is an incurable neural disease, usually affecting the elderly. The afflicted suffer from cognitive impairments that get dramatically worse at each stage. Previous research on Alzheimer’s disease analysis in terms of classification leveraged statistical models such as support vector machines. However, statistical models such as support vector machines train the from numerical data instead of medical images. Today, convolutional neural networks (CNN) are widely considered as the one which can achieve the state-of-the- art image classification performance. However, due to their black box nature, there can be reluctance amongst medical professionals for their use. On the other hand, medical images are not easy to get access to, in contrast to general image datasets, such as CIFAR-100, due to several reasons, including privacy and professional cost, motivating us to train the model with high accuracy based on few samples. This thesis focuses on two perspectives: the first interpreting what the CNN model has learned in each layer and will the learned features vary with different input; and second, how to train a reliable network with high accuracy on few medical imaging samples. To address the questions raised above, two different models are examined. First, we use a conventional residual CNN and experiment with two different training methods. The first uses a standard training schedule where the model’s weights are initialized randomly and the second uses transfer learning where we use the weights of a model trained on a larger dataset of a different task as the initial weights for our model. Our method can yield the accuracies of 98.5% and 99.53%, respectively. Our second model studies metric learning instead of classification. In this method the model learns to group images that are similar. The model is fed with a very small set of samples per class, so-called Few-Shot learning. The goal is to learn how similar an image is from another. The model can learn deep embedded representations of an input such that similar inputs are close together in the embedded space and dissimilar inputs are far apart

A mixed-mode bending apparatus for delamination testing

A mixed-mode delamination test procedure was developed combining double cantilever beam mode I loading and end notch flexure mode II loading on a split unidirectional laminate. By loading the specimen with a lever, a single applied load simultaneously produces mode I and II bending loads on the specimen. This mixed mode bending (MMB) test was analyzed using both finite element procedures and beam theory to calculate the mode I and II components of strain energy release rate, G sub I and G sub II, respectively. The analyses showed that a wide range of G sub I/G sub II ratios could be produced by varying the applied load position on the loading lever. As the delamination extended, the G sub I/G sub II ratios varied by less than 5 percent. The simple beam theory equations were modified to account for the elastic interaction between the two arms of the specimen and to account for shear deformations. The resulting equations agreed closely with the finite element results and provide a basis for selection of G sub I/G sub II test ratios and a basis for computing the mode I and II components of measured delamination toughness. The MMB specimen analysis and test procedures were demonstrated using unidirectional laminates

The Effect of Lift-Drag Ratio and Speed on the Ability to Position a Gliding Aircraft for a Landing on a 5,000-Foot Runway

Flight tests were made to determine the capability of positioning a gliding airplane for a landing on a 5,000-foot runway with special reference to the gliding flight of a satellite vehicle of fixed configuration upon reentry into the earth's atmosphere. The lift-drag ratio and speed of the airplane in the glides were varied through as large a range as possible. The results showed a marked tendency to undershoot the runway when the lift-drag ratios were below certain values, depending upon the speed in the glide. A straight line dividing the successful approaches from the undershoots could be drawn through a lift-drag ratio of about 3 at 100 knots and through a lift-drag ratio of about 7 at 185 knots. Provision of a drag device would be very beneficial, particularly in reducing the tendency toward undershooting at the higher speeds

Modeling U.S. Soy-Based Markets with Directed Acyclic Graphs and Bernanke Structural VAR Methods: The Impacts of High Soy Meal and Soybean Prices

Advanced methods that combine directed acyclic graphs with Bernanke structural vector autoregression models are applied to a monthly system of three U.S. soy-based markets: for soybeans upstream and for the two soybean co-products soy meal and soy oil further downstream. Analyses of the impulse-response function and forecast error variance decompositions provide updated estimates of market-elasticity parameters that drive these markets and updated policy-relevant information on how these monthly markets run and dynamically interact. Results characterize impacts on the three U.S. soy-based markets of increases in U.S. prices of soy meal and soybeans.Industrial Organization,