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.

Constructivism: The Career and Technical Education Perspective

Abstract: For over three-quarters of a century, the implicit learning theory underlying the curriculum and pedagogy of career and technical education has been behaviorism, but the emerging theory of constructivism may have implications for career and technical education practice in the future. Preparation of workers for entry into and advancement in the workplace of the next decade requires an educational program that provides not only job skills, as career and technical education did throughout the 1900s, but also higher order thinking, problem solving, and collaborative work skills. Classical behaviorist theory does not adequately address the latter kinds of learning, but constructivist theory may. Constructivist principles are examined in light of the fundamental requirements of career and technical education as we move into the new century with a new name for a redesigned profession. Of the three basic types of constructivism discussed, cognitive constructivism is most compatible with career and technical education. The authors recommend a more thorough examination of the relative efficacy of behaviorism and cognitive constructivism to serve as the learning theory on which to base career and technical education in the future. To embrace such a foundational change, leaders in the profession must re-think many of the fundamental assumptions underlying the mission, curriculum, and pedagogy of career and technical education. Perhaps such a rethinking is due.

Carbon fiber reinforced metal matrix composites: Fabrication processes and properties

Abstract: This paper reviews the research and development works conducted over the past few decades on carbon fiber reinforced metal matrix composites (CFR-MMC). The structure and composition of carbon fiber and its bonding to metal matrix have an impact on the properties of the resulting CFR-MMC remarkably. The research efforts on process optimization and utilizing of carbon fibers are discussed in this review. The effect of carbon fiber on structural, physical and mechanical properties of metal matrix composite are studied as well. This review also provide an overview of the research to date on various fabrication methods that is used for production of CFR-MMC.

The Habitat Concept in Ornithology

Abstract: Ornithologists have played a key role in the development of the habitat concept. The conspicuous nature of birds has allowed ornithologists to assemble a vast amount of information relating the distribution and abundance of birds to aspects of the environment (Brown, 1984; Mayr, 1988; Konishi et al., 1989; Morrison et al., 1992). The application of the term “habitat” has been used as a unifying, theoretical concept to explain the diversity of avian life-history patterns (Rotenberry, 1981). However, specific definitions of the term “habitat” are often vague. Definitions have ranged from, for example, how species are associated with broad, landscape-scaled vegetation types, to very detailed descriptions of immediate physical environments used by species (Karr, 1980; Verner et al., 1986; Harris and Kangas, 1988).

Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis

Abstract: Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded, whereas computational burden is significantly relaxed. When the desired object information is known, this task can be achieved by finding the bands that contain the most information about these objects. When the desired object information is unknown, i.e., unsupervised band selection, the objective is to select the most distinctive and informative bands. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose unsupervised band selection algorithms based on band similarity measurement. The experimental result shows that our approach can yield a better result in terms of information conservation and class separability than other widely used techniques.