Performance Measures of Strategic University-Industry Collaborations in Malaysia: Dyadic Multicases Approach

Collaboration without performance measures is likened to a football game without scoreboard. Traditionally, university operated in isolation to the industry and vice versa. University and industry were formed with different agenda and objectives. Fundamentally, university is a non-profit oriented organization while industry is a profit oriented organization. However, industrialization and egalitarian awakening at early 20th century has gradually brought university and industry together. Currently, university and industry are increasingly seeking avenues to collaborate strategically. Nevertheless 50% to 70% of collaborative efforts fail prematurely due to lack of performance measures. In light of that, there is a need to search for a set of holistic performance measures for university-industry collaboration. Therefore, this study is undertaken to determine the performance measures of strategic university-industry collaborations in Malaysia using dyadic multicases approach. The researcher analyzes multiple cases from the perspectives of university and industry within bounded system via qualitative research methodology. Interviews respondents were from university and industry perspectives. From the 68 interviews conducted, university and industry respondents shared their experiences on the needs for performance measures to include trust, commitment, enterprising, communication, complementary, flexibility, commercialization and resources on top of conventional performance measures like agreed objectives, timelines, financial indicators and reporting. With that, a set of holistic performance measures is established from interviews. Hence, the main contributions of the research findings are: (i) contribution to policy-making for the Ministry of Higher Education in Malaysia; and (ii) contribution to the body of knowledge in investigating the performance measures in satisfactory performance of strategic university-industry collaboratio

HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction

Machine learning (ML) applications in weather and climate are gaining momentum as big data and the immense increase in High-performance computing (HPC) power are paving the way. Ensuring FAIR data and reproducible ML practices are significant challenges for Earth system researchers. Even though the FAIR principle is well known to many scientists, research communities are slow to adopt them. Canonical Workflow Framework for Research (CWFR) provides a platform to ensure the FAIRness and reproducibility of these practices without overwhelming researchers. This conceptual paper envisions a holistic CWFR approach towards ML applications in weather and climate, focusing on HPC and big data. Specifically, we discuss Fair Digital Object (FDO) and Research Object (RO) in the DeepRain project to achieve granular reproducibility. DeepRain is a project that aims to improve precipitation forecast in Germany by using ML. Our concept envisages the raster datacube to provide data harmonization and fast and scalable data access. We suggest the Juypter notebook as a single reproducible experiment. In addition, we envision JuypterHub as a scalable and distributed central platform that connects all these elements and the HPC resources to the researchers via an easy-to-use graphical interface

Knowledge Processes and their Role in Innovation - A Comparison of Selected Chinese and Indian Practices

Innovation is today recognized as key to fostering economic development and building technological strengths in firms, industries and countries. While generally described in the common understanding as anything that is new and has an impact on a large scale, it is technology-driven innovation that has assumed prominence in the contemporary environment. Academic research and study of innovation has encompassed a variety of disciplines. From these efforts, innovation has emerged as a complex phenomenon that requires a variety of factors and concepts to describe. As innovation assumes prominence in countries such as India and China, which are aiming to catch up with the more advanced countries, the factors that go to make successful innovations possible are of increasing interest. This thesis examines the different approaches adopted in the field of innovation studies and identifies knowledge processes as key to understanding innovation. The applicability of this has been investigated through detailed research into three industry segments. Based on the research, frameworks of innovation based on knowledge processes have been presented including a comparison of practices in selected Chinese and Indian organizations

Hydrodynamics-Biology Coupling for Algae Culture and Biofuel Production

International audienceBiofuel production from microalgae represents an acute optimization problem for industry. There is a wide range of parameters that must be taken into account in the development of this technology. Here, mathematical modelling has a vital role to play. The potential of microalgae as a source of biofuel and as a technological solution for CO2 fixation is the subject of intense academic and industrial research. Large-scale production of microalgae has potential for biofuel applications owing to the high productivity that can be attained in high-rate raceway ponds. We show, through 3D numerical simulations, that our approach is capable of discriminating between situations where the paddle wheel is rapidly moving water or slowly agitating the process. Moreover, the simulated velocity fields can provide lagrangian trajectories of the algae. The resulting light pattern to which each cell is submitted when travelling from light (surface) to dark (bottom) can then be derived. It will then be reproduced in lab experiments to study photosynthesis under realistic light patterns

National Science Foundation Advisory Committee for Cyberinfrastructure Task Force on Campus Bridging Final Report

The mission of the National Science Foundation (NSF) Advisory Committee on Cyberinfrastructure (ACCI) is to advise the NSF as a whole on matters related to vision and strategy regarding cyberinfrastructure (CI). In early 2009 the ACCI charged six task forces with making recommendations to the NSF in strategic areas of cyberinfrastructure: Campus Bridging; Cyberlearning and Workforce Development; Data and Visualization; Grand Challenges; High Performance Computing (HPC); and Software for Science and Engineering. Each task force was asked to offer advice on the basis of which the NSF would modify existing programs and create new programs. This document is the final, overall report of the Task Force on Campus Bridging.National Science Foundatio

The CIMNE model for generating knowledge on computational engineering and its transfer to society

We present an overview of the model implemented by the International Centre for Numerical Methods in Engineering (CIMNE, www.cimne.com ) for the generation of scientific and technical knowledge on computational engineering, understood in the broad sense, the subsequent generation of product resulting from the research activities and the transfer of these products to society for their exploitation in the industrial sector. We present examples of application of the CIMNE model to a number of academic, scientific and industry activities of CIMNE .Preprin