Knowledge Based View of University Tech Transfer—A Systematic Literature Review and Meta-Analysis

Research and technology commercialization at research-intensive universities has helped to develop provincial economies resulting in university startups, the growth of other new companies and associated employment. University technology transfer offices (TTOs) oversee the process of technology transfer into the commercial marketplace and these organizational units can be considered in the context of enabling effective knowledge management. However, what enables productive TTO performance has not been comprehensively researched. Therefore, this research study adopted the knowledge-based view as the theoretical construct to support a comprehensive investigation into this area. This was achieved through employing a systematic literature review (SLR) combined with a robust meta-analysis. The SLR identified an initial total of 10,126 articles in the first step of the review process, with 44 studies included in the quantitative synthesis, and 29 quantitative empirical studies selected for the meta-analysis. The research study identified that the relationship between TTO knowledge management and knowledge deployment as well as startup business performance is where TTOs secure the strongest returns

Diagnostic framework and health check tool for engineering and technology projects

Purpose: Development of a practitioner oriented diagnostic framework and health check tool to support the robust assessment of engineering and technology projects. Design/methodology/approach: The research is based on a literature review that draws together insights on project assessment and critical success factors to establish an integrated systems view of projects. This is extended to allow a comprehensive diagnostic framework to be developed along with a high-level health check tool that can be readily deployed on projects. The utility of the diagnostic framework and health check tool are explored through three illustrative case studies, with two from Canada and one from the United Kingdom. Findings and Originality/value: The performance of engineering and technology projects can be viewed through a systems perspective and being a function of six contributing subsystems that are: process, technology, resources, impact, knowledge and culture. The diagnostic framework that is developed through this research integrates these sub-systems to provide a robust assessment methodology for projects, which is linked to existing best practice for project reviews, performance management and maturity models. The case studies provide managerial insights that are related to the diagnostic framework but crucially also position the approach in the context of industrial applications for construction engineering and technology management.Research limitations/implications: The case study approach includes two case studies from the construction and facilities development sector with the third case study from the research and technology sector. Further work is required to investigate the use of the diagnostic framework and health check tool in other sectors. Practical implications: The health check tool will be of practical benefit to new projects managers that require access to a robust and convenient project review methodology for assessing the status and health of a given portfolio of projects. The tool can also be used periodically and throughout the project lifecycle in order to track the performance of projects. Originality/value: This paper provides a unique view and supporting management framework to help project managers assess the status and health of projects. Value can be associated with an extension to the literature on diagnostic tools for engineering project management as well as the insights provided in the three international case studies, which explore the scope and applicability of the health check tool to be used in support of projects that have encountered difficulties and which require implementation of project recovery strategies.Peer Reviewe

Evaluating the impact of highway construction projects on landscape ecological risks in high altitude plateaus

[EN] In China and other countries, many highway projects are built in extensive and high-altitude flat areas called plateaus. However, research on how the materialisation of these projects produce a series of ecological risks in the landscape is very limited. In this research, a landscape ecological risk analysis model for high-altitude plateaus is proposed. This model is based on the pattern of land uses of the surrounding area. Our study includes buffer analysis, spatial analysis, and geostatistical analysis. We apply the model to the Qumei to Gangba highway, a highway section located in the southeast city of Shigatse at the Chinese Tibet autonomous region. Through global and local spatial autocorrelation analysis, the spatial clustering distribution of ecological risks is also explored. Overall, our study reveals the spatial heterogeneity of ecological risks and how to better mitigate them. According to a comparison of the risk changes in two stages (before and after the highway construction), the impact of highway construction on the ecological environment can be comprehensively quantified. This research will be of interest to construction practitioners seeking to minimize the impact of highway construction projects on the ecological environment. It will also inform future empirical studies in the area of environmental engineering with potential affection to the landscape in high-altitude plateaus.This research is supported by the Branch of China Road and Bridge Corporation (Cambodia) Technology Development Project (No.2020-zlkj-04); National Social Science Fund Projects (No.20BJY010); National Social Science Fund Post-financing Projects (No.19FJYB017); Sichuan-Tibet Railway Major Fundamental Science Problems Special Fund (No.71942006); Qinghai Natural Science Foundation (No. 2020-JY-736); List of Key Science and Technology Projects in China's Transportation Industry in 2018-International Science and Technology Cooperation Project (Nos. 2018-GH-006 and 2019-MS5-100); Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No. E-GKRWJC20202914); Shaanxi Social Science Fund (No. 2017S004); Xi'an Construction Science and Technology Planning Project (Nos. SZJJ201915 and SZJJ201916); Shaanxi Province Higher Education Teaching Reform Project (No. 19BZ016); Fundamental Research for Funds for the Central Universities (Humanities and Social Sciences), Chang'an University (Nos. 300102239616, 300102281669 and 300102231641).Li, C.; Zhang, J.; Philbin, SP.; Yang, X.; Dong, Z.; Hong, J.; Ballesteros-Pérez, P. (2022). Evaluating the impact of highway construction projects on landscape ecological risks in high altitude plateaus. Scientific Reports. 12(1):1-16. https://doi.org/10.1038/s41598-022-08788-811612

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

[EN] With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site.This research is supported by the Branch of China Road and Bridge Corporation (Cambodia) Technology Development Project(No.2020-zlkj-04); National Social Science Fund projects (No.20BJY010); National Social Science Fund Post-financing projects (No.19FJYB017); Sichuan-tibet Railway Major Fundamental Science Problems Special Fund (No.71942006); Qinghai Natural Science Foundation (No.2020-JY-736); List of Key Science and Technology Projects in China's Transportation Industry in 2018-International Science and Technology Cooperation Project (No.2018-GH-006 and No.2019-MS5-100); Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No.E-GKRWJC20202914); Shaanxi Social Science Fund (No.2017S004); Xi'an Construction Science and Technology Planning Project (No.SZJJ201915 and No.SZJJ201916); Shaanxi Province Higher Education Teaching Reform Project (No.19BZ016); Fundamental Research for Funds for the Central Universities (Humanities and Social Sciences), Chang'an University (No.300102239616, No.300102281669 and No.300102231641).Ma, Z.; Zhang, J.; Philbin, SP.; Li, H.; Yang, J.; Feng, Y.; Ballesteros-Pérez, P.... (2021). Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement. Buildings. 11(12):1-18. https://doi.org/10.3390/buildings11120577S118111

Understanding the role of peer pressure on engineering students' learning behavior: A TPB perspective

IntroductionWith the advent of the digital age, the gradually increasing demands of the engineering job market make it inevitable that engineering students face the pressures that arise from academic life with their peers. To address this issue, this study aims to explore the influence of engineering students' peer pressure on learning behavior based on the theory of planned behavior (TPB).MethodsIn addition to attitudes, subjective norms, and perceived behavioral controls inherent in TPB, two new dimensions—gender difference and peer academic ability—were incorporated to construct a framework of the dimensions of peer pressure as affecting engineering students as well as an expanded model of TPB. A questionnaire survey was conducted with 160 college engineering students and a structural equation model (SEM) was used to test the hypotheses.ResultsThe result showed that positive peer pressure can increase engineering students' learning intention and thus promote learning behavior. It was also determined that the TPB model can effectively explain the effect of peer pressure on learning behavior, in addition to expanding and reshaping the relationship between the attitudinal dimension in the TPB model.DiscussionFrom the results, it is clear that positive attitudes toward learning can trigger positive peer pressure. Good group norms can induce peer pressure through rewards and punishments as a way to motivate students' learning intention and learning behaviors. When peer pressure is perceived, students mobilize positive emotions toward learning. Meanwhile, both male and female engineering students are also significantly motivated by high peer achievement, and high-performing female students motivate their male peers, which leads to higher graduation rates

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Critical Analysis and Evaluation of the Technology Pathways for Carbon Capture and Utilization

Carbon capture and utilization (CCU) is the process of capturing unwanted carbon dioxide (CO2) and utilizing for further use. CCU offers significant potential as part of a sustainable circular economy solution to help mitigate the impact of climate change resulting from the burning of hydrocarbons and alongside adoption of other renewable energy technologies. However, implementation of CCU technologies faces a number of challenges, including identifying optimal pathways, technology maturity, economic viability, environmental considerations as well as regulatory and public perception issues. Consequently, this research study provides a critical analysis and evaluation of the technology pathways for CCU in order to explore the potential from a circular economy perspective of this emerging area of clean technology. This includes a bibliographic study on CCU, evaluation of carbon utilization processes, trend estimation of CO2 usage as well as evaluation of methane and methanol production. A value chain analysis is provided to support the development of CCU technologies. The research study aims to inform policy-makers engaged in developing strategies to mitigate climate change through reduced carbon dioxide emission levels and improve our understanding of the circular economy considerations of CCU in regard to production of alternative products. The study will also be of use to researchers concerned with pursuing empirical investigations of this important area of sustainability