Knowledge Diffusion, Trade, and Innovation across Countries and Sectors

This paper provides a unified framework for quantifying the cross-country and cross-sector interactions among trade, innovation, and knowledge diffusion. This framework is used to study the effect of trade liberalization in an endogenous growth model in which comparative advantage and the stock of knowledge are determined by innovation and diffusion. The model is calibrated to match observed cross-country and cross-sector heterogeneity in production, innovation efficiency, and knowledge spillovers. The counterfactual analysis shows that a reduction in trade costs induces a reallocation of R&D and comparative advantage across sectors. Heterogeneous knowledge diffusion amplifies the specialization effects of trade-induced R&D reallocation, becoming an important source of welfare. (JEL F12, F14, O33, O34, O41)

Knowledge capitalization in mechatronic collaborative design

In mechatronic collaborative design, there is a synergic integration of several expert domains, where heterogeneous knowledge needs to be shared. To address this challenge, ontology-based approaches are proposed as a solution to overtake this heterogeneity. However, dynamic exchange between design teams is overlooked. Consequently, parametric-based approaches are developed to use constraints and parameters consistently during collaborative design. The most valuable knowledge that needs to be capitalized, which we call crucial knowledge, is identified with informal solutions. Thus, a formal identification and extraction is required. In this paper, we propose a new methodology to formalize the interconnection between stakeholders and facilitate the extraction and capitalization of crucial knowledge during the collaboration, based on the mathematical theory ‘Category Theory’ (CT). Firstly, we present an overview of most used methods for crucial knowledge identification in the context of collaborative design as well as a brief review of CT basic concepts. Secondly, we propose a methodology to formally extract crucial knowledge based on some fundamental concepts of category theory. Finally, a case study is considered to validate the proposed methodology.

Analysis of Academic Cooperation Network for Returnee Faculty Members to Realize Value from the Perspective of Economics

Drawing on theories of social network and knowledge absorption, this paper examines the direct influence of returnee faculty members (RFMs) over college research performance (CRP) from three aspects, namely, the intensity of cooperative relationship (ICR), research influence (RI), and acquisition capability of heterogeneous knowledge (ACHK). In addition, the authors tested the regulating effect of ICR. The results show that RI of RFMs has a significant positive effect on CRP, ACHK has no significant effect on CRP, and ICR has a significant negative effect and a major regulating effect on CRP.

Knowledge Network Embedding of Transcriptomic Data from Spaceflown Mice Uncovers Signs and Symptoms Associated with Terrestrial Diseases

There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab transcriptomic data from six studies. This integration identified critical symptoms and physiological changes incurred during spaceflight.