In this paper, we present a machine learning approach that generates a system of driver-centered and roadway type-specific deep neural network models for accurate vehicle speed prediction (VSP) in short and long terms in a future horizon. This research focuses on addressing the following issues, proper attributes and window sizes with respect to the lengths of prediction horizons, impacts of roadway types to neural network models, importance of statistical traffic data, and effectiveness of three deep neural network frameworks applied to the short- and long-term VSP problem. Extensive experiments are conducted using the naturalistic driving trips collected from three different drivers on two different routes covering seven different roadway types. Our research results show that the deep learning models structured around roadway types, trained with driver-centered data using optimal window sizes of historical temporal features, including vehicle speed and traffic flow, are effective in both short- and long-term predictions.

Software engineers use a number of different types of software development technical review (SDTR) for the purpose of detecting defects in software products. This paper applies the behavioral theory of group performance to explain the outcomes of software reviews. A program of empirical research is developed, including propositions to both explain review performance and identify ways of improving review performance based on the specific strengths of individuals and groups. Its contributions are to clarify our understanding of what drives defect detection performance in SDTRs and to set an agenda for future research. In identifying individuals' task expertise as the primary driver of review performance, the research program suggests specific points of leverage for substantially improving review performance. It points to the importance of understanding software reading expertise and implies the need for a reconsideration of existing approaches to managing reviews.

OBJECTIVE: To identify impediments to the successful transfer and implementation of packaged information systems through large, divisionalized health services. DESIGN: A case analysis of the failure of an implementation of a critical application in the Public Health System of the State of New South Wales, Australia, was carried out. This application had been proven in the United States environment. MEASUREMENTS: Interviews involving over 60 staff at all levels of the service were undertaken by a team of three. The interviews were recorded and analyzed for key themes, and the results were shared and compared to enable a continuing critical assessment. RESULTS: Two components of the transfer of the system were considered: the transfer from a different environment, and the diffusion throughout a large, divisionalized organization. The analyses were based on the Scott-Morton organizational fit framework. In relation to the first, it was found that there was a lack of fit in the business environments and strategies, organizational structures and strategy-structure pairing as well as the management process-roles pairing. The diffusion process experienced problems because of the lack of fit in the strategy-structure, strategy-structure-management processes, and strategy-structure-role relationships. CONCLUSION: The large-scale developments of integrated health services present great challenges to the efficient and reliable implementation of information technology, especially in large, divisionalized organizations. There is a need to take a more sophisticated approach to understanding the complexities of organizational factors than has traditionally been the case.