Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems : Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

This paper presents a potential solution to fill a gap in both research and practice that there are few interactions between transnational industry cooperation (TIC) and transnational university cooperation (TUC) in transnational innovation ecosystems. To strengthen the synergies between TIC and TUC for innovation, the first step is to match suitable industrial firms from two countries for collaboration through their common connections to transnational university/academic partnerships. Our proposed matching solution is based on the integration of social science theories and specific artificial intelligence (AI) techniques. While the insights of social sciences, e.g., innovation studies and social network theory, have potential to answer the question of why TIC and TUC should be looked at as synergetic entities with elaborated conceptualization, the method of machine learning, as one specific technic off AI, can help answer the question of how to realize that synergy. On the way towards a transdisciplinary approach to TIC and TUC synergy building, or creating transnational university-industry co-innovation networks, the paper takes an initial step by examining what the supports and gaps of existing studies on the topic are, and using the context of EU-China science, technology and innovation cooperation as a testbed. This is followed by the introduction of our proposed approach and our suggestions for future research.publishedVersionPeer reviewe

Agent-based and contact network modeling applications for Escherichia coli transmission in commercial feedlot settings

Doctor of PhilosophyDepartment of Diagnostic Medicine/PathobiologyMajor Professor Not ListedShiga toxin-producing Escherichia coli (STEC) are recognized as a major food-borne pathogens with outbreaks, human infections, and occasional deaths associated with the consumption of contaminated foods. Cattle are recognized as a primary reservoir for STEC, though the transmission dynamics of STEC in feedlot cattle are not fully understood. Our current understanding of the transmission dynamics is dependent on longitudinal studies of naturally occurring STEC infections in cattle. A certified nonpathogenic strain of E. coli was used for a series of transmission experiments of a known source and concentration to better understand the transmission dynamics of STEC in commercial feedlot settings. The contact networks of feedlot cattle both within and between pens were characterized and an agent-based model was created to examine the influence of direct and indirect transmission routes on the pen-level prevalence of E. coli colonization. Nonpathogenic E. coli strains were used as a surrogate to assess the transmission dynamics for STEC in cattle. Initially, three verified nonpathogenic E. coli strains (O19:H+, O101:H10, and O28:H43) were orally inoculated into weaned Holstein calves. All inoculated strains were able to colonize in the gastrointestinal tract of the calves, shed in their feces, and spread to their pen environment. Pen-level area under the curve (AUC) for fecal shedding concentrations of each nonpathogenic strain were evaluated and the strain with the highest pen average fecal AUC was selected for use in two independent inoculation trials that each occurred in one independent pen of 70 feedlot cattle. E. coli strain (O28:H43) was selected for use in these two inoculation trials. Oral inoculation of five randomly selected steers from each pen occurred for five days at the start of the study period. The inoculated strain was able to colonize, shed in the feces, spread to the pen environment, and transmit between feedlot steers. These data provide baseline data on the shedding and transmission of a nonpathogenic E. coli strain in diary calves, feedlot steers, and its detection in the pen environment. These data can be furthered used as a surrogate to better understand enteric pathogen transmission, such as STEC, in commercial U.S. feedlot systems to explore effective interventions options in a real-world setting. To better inform the construction of network-based disease transmission models for cattle housed within confined-spaced systems, contact network modeling was used to quantify the contacts defined with a spatial threshold (SpTh) of 0.71 m and a minimum contact duration (MCD) of either 10, 30, or 60 seconds within three pens of feedlot cattle across consecutive years. Static, undirected, weighted contact networks were created for the full study duration and at vary timescales (24-h, 6-h, and h) to assess network heterogeneity. The influence on contact networks in feedlot cattle due to the variation in Real-Time Location System (RTLS) average tag read rate observed between the three years though the same system was used was examined. When the networks were down-scaled from higher average tag read rates to match the lower average tag read rates, the overall networks maintained similar network density and clustering, though the average edge weight between pairs of steers decreased. The high-resolution spatial and temporal contact data provided estimates for contact networks within U.S. commercial feedlot pens that can be used to better inform pathogen transmission models. Contact network analysis was used to quantify contacts and compare the resulting static, undirected, weighted contact networks created from two neighboring pens of feedlot cattle and from across the shared fenceline at varying timescales (24-h, 6-h, and h). Contacts within-pen were defined at 0.71 m SpTh with a MCD of 10, 30, or 60 seconds. “Fenceline” contacts were defined with the same SpTh and MCD durations as within-pen contacts but contained a steer from each pen and occurred within the defined fenceline area, any location reading occurring within 1 m of either side of the shared fenceline. On a full study duration, the contact networks created within each feedlot pen were densely connected. On shorter timescales (24-h, 6-h, h) the within-pen contact networks showed greater network heterogeneity in density and clustering metrics. The contact network created across the shared fenceline yielded a total network density of 17%. These findings can be used to better inform the construction of network-based disease transmission models for cattle within confined-spaced systems and additionally accounting for the transmission that occurs over man-made barriers (e.g., fencelines). Finally, an agent-based model was created in Netlogo 6.2 using empirical data to explore the influence of direct steer-to-steer contact and indirect steer-to-pen environmental contact on the pen-level prevalence of E. coli colonization. Agents in the model were defined as individual steers and three pen environment areas (front, middle, rear). Direct contact between pairs of steers in the model was defined as cumulative 10 s temporal sampling windows (TSW) aggregated on a daily (24-h) level. Indirect contact between individual steers and the three pen environment areas were defined by using a proportion of location readings for each steer in each defined pen area for a given study day. Proportions were then averaged for each individual steer by study week in the model. Colony forming units (CFU) of E. coli transmitted by direct contact were modeled as one, five, and ten percent of the average log10CFU/400cm2 of collected hide samples, and CFUs of E. coli per pen area were determined as one, five, and ten percent of the average log10CFU/g of those pen soil surface samples that fell within that pen area for that study week. The colonization period of individual cattle within the model was defined as a weighted average of the time in study days in which all individual steers in the empirical data were positive in the digitally collected fecal samples. Preliminary results are suggestive that direct contact between animals is more important for pen-level prevalence for E. coli colonization than indirect contact with pen environment. Additional work is needed to determine the R-nought of the E. coli modeled to compare it to published values of R-nought of STEC

Social network data analysis and mining applications for the Internet of Data

Social network analysis is an interdisciplinary topic attracting researchers from biology, economics, psychology, and machine learning, with an existing long history based on graph theory. It has since attracted interests from both the research and business communities for a strong potential and variety of applications. In addition, this interest has been fueled by the large success of online social networking sites and the subsequent abundance of social network data produced. An important aspect in this research field is influence maximization in social networks. The goal is to find a set of individuals to be targeted with the aim to drive social contagion and generate a diffusion cascade. We provide here an overview of the models and approaches used to analyze social networks. In this context, we also discuss data preparation and privacy concerns. We further describe different kind of approaches based on centrality measures, which express a sociological interpretation of the data, and stochastic influence and information propagation techniques, which aim at modeling the underlying diffusion processes that govern social interactions