Mississippi State University

About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.

Effect of dietary pH on amino acid utilization and the lysine requirement of fingerling channel catfish.

Abstract: The pH of amino acid test diets has been shown to be of major importance in dietary amino acid studies in the channel catfish (Ictalurus punctatus). Maximum growth rate and feed conversion was observed when the test diet was adjusted to pH 7. Growth studies, utilizing a 24% crude protein diet containing an amino acid pattern similar to whole egg protein, indicate that the lysine requirement for fingerling channel catfish is about 1.23% of the diet (dry weight basis) or 5.1% of the dietary protein. The dietary requirement was confirmed by serum free lysine analysis. A marked increase in serum free lysine occurred at a dietary lysine level of approximately 1.2% of the diet.

Dispositional and situational factors: influences on information security policy violations

Abstract: Insiders represent a major threat to the security of an organization’s information resources. Previous research has explored the role of dispositional and situational factors in promoting compliant...

Beam-stack search: integrating backtracking with beam search

Abstract: We describe a method for transforming beam search into a complete search algorithm that is guaranteed to find an optimal solution. Called beam-stack search, the algorithm uses a new data structure, called a beam stack, that makes it possible to integrate systematic backtracking with beam search. The resulting search algorithm is an anytime algorithm that finds a good, sub-optimal solution quickly, like beam search, and then backtracks and continues to find improved solutions until convergence to an optimal solution. We describe a memory-efficient implementation of beam-stack search, called divide-and-conquer beam-stack search, as well as an iterative-deepening version of the algorithm. The approach is applied to domain-independent STRIPS planning, and computational results show its advantages.