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.

The Fishery Performance Indicators: A Management Tool for Triple Bottom Line Outcomes

Abstract: Pursuit of the triple bottom line of economic, community and ecological sustainability has increased the complexity of fishery management; fisheries assessments require new types of data and analysis to guide science-based policy in addition to traditional biological information and modeling. The authors introduce the Fishery Performance Indicators (FPIs), a broadly applicable and flexible tool for assessing performance in individual fisheries, and for establishing cross-sectional links between enabling conditions, management strategies and triple bottom line outcomes. Conceptually separating measures of performance, the FPIs use 68 individual outcome metrics, coded on a 1 to 5 scale based on expert assessment to facilitate application to data poor fisheries and sectors that can be partitioned into sector based or triple-bottom-line sustainability-based interpretative indicators. Variation among outcomes is explained with 54 similarly structured metrics of inputs, management approaches and enabling conditions. Using 61 initial fishery case studies drawn from industrial and developing countries around the world, the authors demonstrate the inferential importance of tracking economic and community outcomes, in addition to resource status.

The role of nonverbal communication in service encounters

Abstract: Although the verbal components of service encounters have been investigated, the nonverbal aspects of employee‐customer interactions have remained virtually unexplored in the marketing literature. Thus, the purpose of this paper is to explore the importance of service employees’ nonverbal communication during service interactions. Specifically, a conceptual model is presented that links nonverbal communication (kinesics, paralanguage, proxemics, and physical appearance), customer affect, and consumers’ evaluations of service providers (with respect to credibility, friendliness, competence, empathy, courtesy, and trustworthiness). Further, the importance of nonverbal elements is discussed and managerial implications are given.

A Linguistic Analysis of Simplified and Authentic Texts

Abstract: The opinions of second language learning (L2) theorists and researchers are divided over whether to use authentic or simplified reading texts as the means of input for beginning- and intermediate-level L2 learners. Advocates of both approaches cite the use of linguistic features, syntax, and discourse structures as important elements in support of their arguments, but there has been no conclusive study that measures these differences and their implications for L2 learning. The purpose of this article is to provide an exploratory study that fills this gap. Using the computational tool Coh-Metrix, this study investigates the differences between the linguistic structures of sampled simplified texts and those of authentic reading texts in order to provide a better understanding of the linguistic features that comprise these text types. The findings demonstrate that these texts differ significantly, but not always in the manner supposed by the authors of relevant scholarship. This research is meant to enable material developers, publishers, and classroom teachers to judge more accurately the value of both authentic and simplified texts.

Fuzzy data mining and genetic algorithms applied to intrusion detection

Abstract: We are developing a prototype intelligent intrusion detection system (IIDS) to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic and genetic algorithms. This system combines both anomaly based intrusion detection using fuzzy data mining techniques and misuse detection using traditional rule-based expert system techniques. The anomaly-based components are developed using fuzzy data mining techniques. They look for deviations from stored patterns of normal behavior. Genetic algorithms are used to tune the fuzzy membership functions and to select an appropriate set of features. The misuse detection components look for previously described patterns of behavior that are likely to indicate an intrusion. Both network traffic and system audit data are used as inputs for both components.