University of Georgia

Array programming with NumPy

Abstract Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.

A Three-Dimensional Conceptual Model of Corporate Performance

Offered here is a conceptual model that comprehensively describes essential aspects of corporate social performance. The three aspects of the model address major questions of concern to academics and managers alike: (1) What is included in corporate social responsibility? (2) What are the social issues the organization must address? and (3) What is the organization's philosophy or mode of social responsiveness?

Structural Equation Modeling and Regression: Guidelines for Research Practice

The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research raises the need to compare and contrast the different types of SEM techniques so that research designs can be selected appropriately. After assessing the extent to which these techniques are currently being used in IS research, the article presents a running example which analyzes the same dataset via three very different statistical techniques. It then compares two classes of SEM: covariance-based SEM and partial-least-squares-based SEM. Finally, the article discusses linear regression models and suggests guidelines as to when SEM techniques and when regression techniques should be used. The article concludes with heuristics and rule of thumb thresholds to guide practice, and a discussion of the extent to which practice is in accord with these guidelines.

The DSSAT cropping system model

Functional Biology and Molecular Mechanisms of Host-Pathogen Interactions for Aflatoxin Contamination in Groundnut (Arachis hypogaea L.) and Maize (Zea mays L.)

and closely related species that infect several agricultural commodities including groundnut and maize. The consumption of contaminated commodities adversely affects the health of humans and livestock. Aflatoxin contamination also causes significant economic and financial losses to producers. Research efforts and significant progress have been made in the past three decades to understand the genetic behavior, molecular mechanisms, as well as the detailed biology of host-pathogen interactions. A range of omics approaches have facilitated better understanding of the resistance mechanisms and identified pathways involved during host-pathogen interactions. Most of such studies were however undertaken in groundnut and maize. Current efforts are geared toward harnessing knowledge on host-pathogen interactions and crop resistant factors that control aflatoxin contamination. This study provides a summary of the recent progress made in enhancing the understanding of the functional biology and molecular mechanisms associated with host-pathogen interactions during aflatoxin contamination in groundnut and maize.

Exploring physics teacher identity: Pathways toward equitable instruction among teachers

Physics teacher identity encompasses teachers’ beliefs and views toward being a physics teacher. Physics teacher identity is influenced by teachers’ perspectives on physics teaching and learning. Previous studies on teacher identity in general suggest that the construct is complex and malleable. Studies further suggest that it influences instructional methods, student engagement, and classroom environment. However, within physics education, there has yet to be an examination of how physics teacher identity influences “equitable” teaching approaches. This qualitative study sought to identify various dimensions of physics teacher identity and its implications for physics education research. We interviewed secondary and postsecondary physics teachers ( n = 1 0 ) from different regions of the country with varying years of experience. We searched for consistent themes regarding the influence of physics teacher identity on equitable instruction using a conceptual framework that examines conceptions of self, others, knowledge, and pedagogy. Findings showed that teachers held a variety of conceptions of self and others, while many struggled to describe their conceptions of knowledge and pedagogy. Our findings are significant for physics teachers, educators, and researchers aiming to employ equitable teaching strategies. Ultimately, this study fostered a deeper understanding of the relationship between physics teacher identity and equitable instruction.