Promoting university–industry collaboration in Malaysia: stakeholders’ perspectives on expectations and impediments

University–Industry Collaboration (UIC) has been identified as an essential item on Malaysia’s agenda for transforming itself into knowledge and innovation-based economy. However, despite the efforts, most initiatives have had limited results. This paper reports on an explorative study that sought to understand the contemporary realities of UIC in the Malaysian context. In particular, the study identified the expectations and impediments to UIC, from the perspectives of three groups of stakeholders – the academics, the industry players and policymakers. The findings reveal a number of barriers, including cultural differences, perceived lack of academic expertise and reputation, an inadequacy of institutional policies and regulations, lack of trust, issues of intellectual property rights, and the lack of an appropriate reward system. This paper concludes by proposing policy recommendations and strategies that could be used by the government, university, and the industry to promote further and foster university-industry collaborative opportunities and initiatives.     

Systems biology

Systems biology seeks to study biological systems as a whole, contrary to the reductionist approach that has dominated biology. Such a view of biological systems emanating from strong foundations of molecular level understanding of the individual components in terms of their form, function and interactions is promising to transform the level at which we understand biology. Systems are defined and abstracted at different levels, which are simulated and analysed using different types of mathematical and computational techniques. Insights obtained from systems level studies readily lend to their use in several applications in biotechnology and drug discovery, making it even more important to study systems as a whole.     

A new method for the measurement and analysis of biomechanical energy delivered by kicking

An accurate measurement of force and biomechanical energy that people can impart through kicking is useful for kick-related sports training. Existing methods are indirect measurements or focus on force and not on the total energy of the kick. A kick test rig was designed, constructed and instrumented to measure the force and displacement of a vertical target subjected to kicking. The kick energy was calculated from the recorded force and displacement histories. The methodology for measurement of kick force and energy was validated by an open stance front kick test by 52 volunteers. The results showed that 67% of the participants could achieve an average energy above 100 J. An increasing trend of kick energy with increasing body weight of participant was observed. The participant’s gender had a strong influence on the kick energy, while training in martial arts does not appear to be a significant parameter. A probability analysis showed that one in one hundred adults will be able to kick with energies exceeding 215 J.     

Elementary? Question answering,IBM’s Watson,and the Jeopardy! challenge

In this article, we start with John McCarthy’s definition of Artificial Intelligence and describe select approaches to its sub-areas: natural language processing and question answering. We then outline the challenge that IBM Research undertook to build Watson, a question-answering computer system that would compete at the level of a human champion in real time on the American TV quiz show Jeopardy!. We describe the rules of Jeopardy! and list issues that any contestant faces in competing on Jeopardy!. We describe how IBM’s Watson system addresses these issues with its metrics and DeepQA architecture. We briefly describe how Watson performed in the Jeopardy! Challenge shows, and conclude with some thoughts about Watson and its potential applications.