Related Experiment Videos
University-Industry Interaction Patterns: Past models are analyzed, some recent experiments described, and
Summary
Increasing research and development (R&D) output requires better university-industry coupling. This study argues that serious attempts at R&D collaboration have not been made, and selective university participation is key.
Area of Science:
- Science and Technology Policy
- Higher Education Research
- Innovation Studies
Background:
- National R&D output needs to increase without proportional resource allocation increases.
- Weak coupling between universities and industry/government is a significant inefficiency in the R&D system.
- Past efforts at R&D collaboration have been minimal, suggesting they have not been seriously attempted.
Purpose of the Study:
- To advocate for stronger university-industry and university-government R&D collaboration.
- To address the need for management innovations and structural changes in universities to facilitate R&D coupling.
- To propose a model for effective R&D collaboration by focusing on willing and capable institutions.
Main Methods:
- Historical analysis of R&D collaboration efforts and funding.
- Qualitative assessment of university administrator responses to R&D coupling initiatives.
- Conceptual framework for selecting universities for new R&D collaboration programs.
Main Results:
- Historically, R&D coupling has received negligible funding (less than 0.1% of campus research money).
- University administrator responses to R&D collaboration programs are mixed, with some institutions resistant.
- Effectiveness of new R&D programs depends on selecting universities with appropriate structures and objectives.
Conclusions:
- Serious, large-scale attempts at university-industry/government R&D coupling have not yet been undertaken.
- Universities should diversify their engagement with society; some should focus on detachment, others on interaction.
- Targeted funding for universities committed to and capable of R&D collaboration can establish a new national R&D pattern.
Related Concept Videos
Typical Model Studies
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Protein-protein Interfaces
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...