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 relationship between marketing orientation and entrepreneurial orientation

Abstract: Increasing environmental uncertainty has focused greater attention on firms’ overall business orientations, particularly on the marketing orientation and the entrepreneurial orientation. The major ...

Plant functional trait change across a warming tundra biome

Abstract: The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature-trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.

The social consequences of self-control: testing the general theory of crime*

Abstract: Gottfredson and Hirschi's recently introduced general theory of crime has received considerable empirical support. Researchers have found that low self-control, the general theory's core concept, is related to lawbreaking and to deviant behaviors considered by Gottfredson and Hirschi to be “analogous” to crime. In this article, we extend this research by assessing the effects of low self-control on crime and analogous behaviors and by using two distinct measures of self-control, an attitudinal measure and the analogous/behavior scale. Thus, following Gottfredson and Hirschi, we use analogous imprudent behaviors as outcomes of low self-control and as indicators of low self-control's effects on crime. We also examine an important but thus far neglected part of the theory: the claim that low self-control has effects not only on crime but also on life chances, life quality, and other social consequences. Consistent with the general theory, we found that both measures of self-control, attitudinal and behavioral, have effects on crime, even when controlling for a range of social factors. Further, the analysis revealed general support for the theory's prediction of negative relationships between low self-control and social consequences other than crime—life outcomes and quality of life.

Diverse Region-Based CNN for Hyperspectral Image Classification

Abstract: Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.