The Challenge of the Market

Identifying markets for new or improved products for minor crops threatened with extinction is one critical component of any strategy intended to enhance prospects for sustainable production and use. Typically, however, activities linked to the sustainability of these commodities avoid addressing questions associated with assessing their commercial potential. After a brief review of this avoidance syndrome, this article presents a sample of practical, low-cost procedures to help evaluate the market prospects and procedures for crops like quinoa, while citing references to other methodological materials covering similar topics.     

A neural network model for the wire bonding process

A crucial step in manufacturing microcircuits is the wire bonding process in which a very thin gold wire must be formed to connect two surfaces in the microcircuit. The quality of the wire bond can be measured by visual inspection and a pull test—both of which are high-reliability, high-cost approaches to statistical process control. Westinghouse wanted to develop a high-reliability, low-cost quality assurance system. In this paper, we report on a year-long study to construct a neural network model that is capable of predicting the quality of wire bonds. The results of our modeling efforts reveal that neural networks are useful tools for statistical process control problems.     

Environment-assisted cracking in austenitic components

Stress corrosion cracking and corrosion fatigue have been responsible for some costly generic material failures in water cooled power reactors. This paper reviews the main features of two of the most widespread problems, namely intergranular stress corrosion cracking of sensitized stainless steel piping in boiling water reactors (BWR) and of nickel alloy steam generator tubes in pressurized water reactors (PWR). Mechanisms, in so far as they are known, are described together with remedial measures and crack growth models applicable to in-plant components.     

The design of experiments when there are errors in all the variables

The Error-in-Variables Model (EVM) applies to a large class of problems in parameter estimation in which there is error in all the variables. This property makes it of more general applicability than the widely used technique of ordinary Least Squares. While there is a voluminous literature on EVM there is nothing on a general method for designing experiments when EVM applies. In this paper we present an extension of the concept of D-optimality to provide such a general method. It is illustrated by applying it to the estimation of copolymer reactivity ratios and to fitting an ellipse.     

Automatic labeling and characterization of objects using artificial neural networks

Existing NASA supported scientific databases are usually developed and managed by a team of database administrators whose main concern is the efficiency of the databases in terms of normalization and data search constructs. The populating of the database is usually done in a manual fashion by row and column as the data become available, and the data dictionary is usually defined by the same team (at times with little input from the end science user). This process is tedious, error prone and self-limiting in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms [i.e., Earth Observing System (EOS)] will be capable of generating data at a rate of over 300 Megabits per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented data-bases that segment, characterize, and catalog, and are manageable in a domain-specific context, and whose contents are available interactively and in near-real-time to the user community. This paper describes work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented database where it can be reviewed and accessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.