The effect of within-row spacing variability on grain yield of corn, Zea mays L.

Call number: LD2668 .T4 1978 S31Master of Scienc

Strategies to Obtain Maximum Usage of Enterprise Resource Planning Systems

Business organizations invest significant resources implementing enterprise resource planning (ERP) systems, yet some organizations utilize less than 75% of the ERP system capabilities. The purpose of this single-site case study was to explore ERP utilization strategies implemented by 4 managers in the information technology (IT) department from 1 organization that uses an ERP system in the Midwest region of the United States. The conceptual framework that grounded this study was the user participation theory. Data were collected through participant interviews and analyzed using traditional text analysis. Member checking was used to strengthen the credibility and trustworthiness of the interpretation of the participants\u27 responses. The emergent themes from the study were user participation, user involvement, user attitude, user system satisfaction, and user preparation. The most prominent utilization strategies identified by the participants related to the user participation theme. The implications for positive social change include the potential optimization of benefits from the ERP system that could allow the organization\u27s leaders to direct their resources to causes that can improve the health and welfare of the geographic population in the operational region

International Space Station EXPRESS Pallet. Ground Demonstration Baseline Design Review

This publication is comprised of the viewgraphs from the presentations of the EXPRESS Pallet Baseline Design Review meeting held July 20, 1995. Individual presentations addressed general requirements and objectives; mechanical, electrical, and data systems; software; operations and KSC (Kennedy Space Center) integration; payload candidates; thermal considerations; ground vs. flight demo; and recommended actions

Exploring the Effect of Financial Literacy Programs on Low-Income Adults

Financial literacy is a necessity of modern adult life. Obtaining control of personal finances is challenging for everyone. The lack of financial literacy in the low income adult grouping has become more problematic as personal finances become more complex. Utilizing a series of interviews the shared experiences of the study participant’s reflected in-depth descriptions of the personal lived experiences relating to financial literacy concepts, educational programs, and future expectations from the participants. This study addresses the perceptions and expectations of low-income adults regarding financial literacy programs and attempts to isolate ways to increase attendance in educational financial literacy programs. Using a series of thematic questions, three significant areas emerged relating to participants’ characteristics, types of services required and access to programs are explored. The results reverse the top down approach of financial program development from what lowincome adults need to learn to participate in mainstream financial sector to what low-income adults want to learn to secure a stable financial future. The conclusions, recommendations and implications reached are generalizable and appropriate for developing best practices delivering financial literacy programs to the low income adult population

Barley

"Barley production in Missouri usually increases following a drought. Livestock producers need feed if corn supplies are low or need pasture if grass is not recovered."--First page.James A. Schaffer (Department of Agronomy), Einar Palm (Department of Plant Pathology), Gene Munson (Department of Entomology, College of Agriculture)New 12/85/5

Sorghum aphid pest management (1985)

Revised 4/85/10M, New 4/92/5M

The non-occurrence of events

No abstract available

A Genetic Programming Approach to Designing Convolutional Neural Network Architectures

The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.Comment: This is the revised version of the GECCO 2017 paper. The code of our method is available at https://github.com/sg-nm/cgp-cn

A Quantitative Investigation into the Design Trade-offs in Decision Support Systems

Users frequently make decisions about which information systems they incorporate into their information analysis and they abandon tools that they perceive as untrustworthy or ineffective. Decision support systems - automated agents that provide complex algorithms - are often effective but simultaneously opaque; meanwhile, simple tools are transparent and predictable but limited in their usefulness. Tool creators have responded by increasing transparency (via explanation) and customizability (via control parameters) of complex algorithms or by improving the effectiveness of simple algorithms (such as adding personalization to keyword search). Unfortunately, requiring user input or attention requires cognitive bandwidth, which could hurt performance in time-sensitive operations. Simultaneously, improving the performance of algorithms typically makes the underlying computations more complex, reducing predictability, increasing potential mistrust, and sometimes resulting in user performance degradation. Ideally, software engineers could create systems that accommodate human cognition, however, not all of the factors that affect decision making in human-agent interaction (HAI) are known. In this work, we conduct a quantitative investigation into the role of human insight, awareness of system operations, cognitive load, and trust in the context of decision support systems. We conduct several experiments with different task parameters that shed light on the relationship between human cognition and the availability of system explanation/control under varying degrees of algorithm error. Human decision making behavior is quantified in terms of which information tools are used, which information is incorporated, and domain decision success. The measurement of intermediate cognitive variables allows for the testing of mediation effects, which facilitates the explanation of effects related to system explanation, control, and error. Key findings are 1) a simple, reliable, domain independent profiling test can predict human decision behavior in the HAI context, 2) correct user beliefs about information systems mediate the effects of system explanations to predict adherence to advice, and 3) explanations from and control over complex algorithms increase trust, satisfaction, interaction, and adherence, but they also cause humans to form incorrect beliefs about data