FACTORS AFFECTING PARTICIPATION BEHAVIOR OF LIMITED RESOURCE FARMERS IN COST-SHARE PROGRAMS IN ALABAMA

This study examines factors that affect the participation behavior of limited resource farmers in cost-share programs in Alabama. The data was generated from a survey administered to a sample of limited resource farm operators. A binary logit was employed to analyze the data. Results indicate that college education, age, total farm size, as well as membership in conservation association had significant influence on participation.Agricultural and Food Policy,

Design of switch architecture for the geographical cell transport protocol

The Internet is divided into multiple layers to reduce and manage complexity. The International Organization for Standardization (ISO) developed a 7 layer network model and had been revised to a 5 layer TCP/IP based Internet Model. The layers of the Internet can also be divided into top layer TCP/IP protocol suite layers and the underlying transport network layers. SONET/SDH, a dominant transport network, was designed initially for circuit based telephony services. Advancement in the internet world with voice and video services had pushed SONET/SDH to operate with reduced efficiencies and increased costs. Hence, redesign and redeployment of the transport network has been and continues to be a subject of research and development. Several projects are underway to explore new transport network ideas such as G.709 and GMPLS. This dissertation presents the Geographical Cell Transport (GCT) protocol as a candidate for a next generation transport network. The GCT transport protocol and its cell format are described. The benefits provided by the proposed GCT transport protocol as compared to the existing transport networks are investigated. Existing switch architectures are explored and a best architecture to be implemented in VLSI for the proposed transport network input queued virtual output queuing is obtained. The objectives of this switch are high performance, guaranteed fairness among all inputs and outputs, robust behavior under different traffic patterns, and support for Quality of Service (QoS) provisioning. An implementation of this switch architecture is carried out using HDL. A novel pseudo random number generation unit is designed to nullify the bias present in an arbitration unit. The validity of the designed is checked by developing a traffic load model. The speedup factor required in the switch to maintain desired throughput is explored and is presented in detail. Various simulation results are shown to study the behavior of the designed switch under uniform and hotspot traffic. The simulation results show that QoS behavior and the crossing traffic through the switch has not been affected by hotspots

Mixed Reality: The Interface of the Future

The world is slowly moving towards everything being simulated digitally and virtually. Mixed Reality (MR) is the amalgam of the real world with virtual stimuli. It has great prospects in the future in terms of various applications additionally with some challenges. This paper focuses on how Mixed Reality could be used in the future along with the challenges that could arise. Several application areas along with the potential benefits are studied in this research. Three research questions are proposed, analyzed, and concluded through the experiments. While the availability of MR devices could introduce a lot of potential, specific challenges need to be scrutinized by the developers and manufacturers. Overall, MR technology has a chance to enhance personalized, supportive, and interactive experiences for human lives.Comment: 6 pages, 8 figure

Solving Atomic Wave Functions Using Artificial Neural Networks

Carleo and Troyer [3] have recently pointed out the possibility of solving quantum many-body problems by using Artificial Neural Networks (ANN). Their work is based on minimizing a variational wave function to obtain the ground states for various spin-dependent systems. This work is primarily focused on developing efficient method using ANN to solve the ground state wave function for atomic systems. We have developed a theoretical groundwork to represent the wave function of a many-electron atom by using artificial neural network while still preserving its antisymmetric property. By using the Metropolis algorithm, Variational Monte Carlo (VMC), and Stochastic Reconfiguration (SR) methods for minimization, we were able to obtain a highly accurate ground state wave function for the He atom. To verify our optimization algorithm, we reproduced the results for the ground state of a three dimensional Simple Harmonic Oscillator (SHO) given by Teng [18]

Solving Atomic Wave Functions Using Artificial Neural Networks

Carleo and Troyer [3] have recently pointed out the possibility of solving quantum many-body problems by using Artificial Neural Networks (ANN). Their work is based on minimizing a variational wave function to obtain the ground states for various spin-dependent systems. This work is primarily focused on developing efficient method using ANN to solve the ground state wave function for atomic systems. We have developed a theoretical groundwork to represent the wave function of a many-electron atom by using artificial neural network while still preserving its antisymmetric property. By using the Metropolis algorithm, Variational Monte Carlo (VMC), and Stochastic Reconfiguration (SR) methods for minimization, we were able to obtain a highly accurate ground state wave function for the He atom. To verify our optimization algorithm, we reproduced the results for the ground state of a three dimensional Simple Harmonic Oscillator (SHO) given by Teng [18]

Comparative Analysis of CPU and GPU Profiling for Deep Learning Models

Deep Learning(DL) and Machine Learning(ML) applications are rapidly increasing in recent days. Massive amounts of data are being generated over the internet which can derive meaningful results by the use of ML and DL algorithms. Hardware resources and open-source libraries have made it easy to implement these algorithms. Tensorflow and Pytorch are one of the leading frameworks for implementing ML projects. By using those frameworks, we can trace the operations executed on both GPU and CPU to analyze the resource allocations and consumption. This paper presents the time and memory allocation of CPU and GPU while training deep neural networks using Pytorch. This paper analysis shows that GPU has a lower running time as compared to CPU for deep neural networks. For a simpler network, there are not many significant improvements in GPU over the CPU.Comment: 6 pages, 11 figure

A HYDRO-CLIMATIC MODELING FRAMEWORK FOR ADAPTIVE WATER RESOURCES MANAGEMENT IN THE GREAT LAKES BASIN

This dissertation addresses water resources decision making in the Great Lakes Basin by developing a multi-model framework for climate change impact assessment, including integrated climate and hydrologic modeling. Physically based watershed models, using soil moisture accounting and temperature index (degree-day) snowmelt algorithms, are developed, calibrated and validated to simulate baseflow, snowmelt, and surface runoff under historic conditions. Comparison with an existing model of the Great Lakes basin, the NOAA Large Basin Runoff Model (LBRM), showed improvements resulting from the increased spatial resolution and use of a more process-based snow algorithm in the Hydrologic Engineering Center\u27s Hydrologic Modeling System (HECHMS). As an alternative to the physically based hydrologic models, and particularly appealing for ungauged basins or locations where record lengths are short, regional regression models are developed to directly predict selected streamflow quantiles, using physical basin characteristics as well as meteorological variables output by general circulation models (GCMs). Hydrologic responses are evaluated based on different combinations of hydro-climatic modeling approaches, when driven using GCM outputs. The model results, presented in a probabilistic context of multi-model predictions, provide insights to potential model weaknesses, including comparatively low runoff predictions from hydrologic models using temperature proxy potential evapotranspiration (PET) approaches and limited accuracy of regional regression models for small, groundwater-dominated watersheds. Additional insights are gained by replacing the temperature-proxy PET method with an approach that maintains a consistent energy budget between the climate and hydrologic models. Hydrologic projections for the Great Lakes watersheds under future climates are evaluated using the model with a consistent energy budget, and differences in responses are explained by differences in watershed characteristics, aridity index, and the future climate projections. It is proposed that these hydrologic projections inform adaptive water resources decision making through a multi-stage decision model, and applications to water withdrawal permitting and BMP implementation are described. The framework developed herein demonstrates an integrated analysis of climate change impact assessment and will potentially be useful for researchers, water managers, and regulators as an aid to decision making and policy implementation