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This article addresses the following research question: does the "entrepreneurial orientation" of a university influence the "entrepreneurial propensity" of its academics? To answer this question, the authors conducted a two-phase study. First, based on a literature review of the entrepreneurial university and Nelles and Vorley's theoretical framework on entrepreneurial architecture, they developed a conceptual model and research instrument to assess academics' perceptions of the characteristics most likely attributed to the entrepreneurial university and their effects on an academic's entrepreneurial propensity. Second, the authors used the research instrument to conduct a pilot study of the effect of a Canadian university's entrepreneurial orientation on its academics' entrepreneurial propensity. The pilot study, using the proposed methodology with detailed accounts of collecting, analyzing and interpreting the data, suggests that the proposed methodology is appropriate for measuring the relationship alluded to in the research question. Furthermore, the methodology can help university leadership in designing possible interventions to correct identified deficiencies in the university's entrepreneurial architecture to stimulate academic entrepreneurship on campus.

University students represent a reservoir of entrepreneurial talent and an inherent source of creativity and innovation. One way to help unleash their talents as an engine of economic growth is by increasing our understanding of elements--internal or external, real or perceived--that lead to and influence the emergence of new ventures led by students. There is evidence in the literature that the university environment and support system (ESS) can affect the entrepreneurial intention of students attending the institution. The university ESS comprises the support mechanisms necessary for entrepreneurial activity and could motivate students to consider entrepreneurship a possible career choice. Kraaijenbrink et al. developed and validated a university ESS scale that helped them identify three motivational factors of the university ESS influencing entrepreneurial intention. Despite its increasing popularity, a detailed study and analysis of the Kraaijenbrink et al. university ESS scale are still lacking in the extant literature. This study fills that gap by conducting an extensive factor analysis of the university ESS scale using a Bayesian approach. To the best of our knowledge, this is the first time this scale has been the subject of such an extensive investigation. We capitalized on the added flexibility of the Bayesian approach to address novel substantive questions through the mathematical model. We used it to enrich our understanding of the university ESS and generate new ideas for possible scale and model modifications. Furthermore, this evaluation provided novel insight into the relationships among the many dimensions of the university ESS. In the future, similar studies conducted by aspiring entrepreneurial universities could help them interpret the efficacy of their efforts to promote entrepreneurial activities among students.

In this study, we aim to understand the influence of the university's environment and support system (ESS) on subjective social norms (SSN) as one of the precursors of the entrepreneurial intention (EI) of university students. For this, we applied a customized mathematical model of EI based on the theory of planned behavior to probe whether the university's ESS can affect SSN and analyze the paths that this influence may follow to form the EI of students. In other words, this study argues that the university plays a critical dual role in shaping the EI of students. First, it can provide support mechanisms to help students translate their ideas into viable business models that may further translate into successful ventures. Second, it can help students gain the support of their families and friends who influence their SSN, thus affecting their EI through the mediating effects of the other two precursors of intention. We collected the data from students in a public university in Atlantic Canada via a structured non-disguised questionnaire to test the hypotheses formulated in this study. We analyzed them through partial least square-structural equation modeling of a second-order mathematical model of EI. Analysis of the data indicates that the mathematical model is appropriate for evaluating the relations among the five constructs of the mathematical model of EI. Results of this study support the hypothesis that the university's ESS may influence students' perceptions of the opinions of important reference people regarding their prospects of becoming entrepreneurs. Furthermore, we determined that the university's ESS influences the EI of students mediated by the more proximal precursors of intention. The effect of the university's ESS is such that it may positively impact the EI of students, but its importance in the mathematical model of EI is still low. These findings can help universities assess their initiatives to promote innovation and entrepreneurship on campus.

Pavement Life Cycle Assessment (LCA) is a comprehensive method to evaluate the environmental impacts of a pavement section. It employs a cradle-to-grave approach assessing critical stages of the pavement's life. Previous LCA case studies used a wide variety of different functional units and factors in order to achieve different goals and scopes. These inconsistent functional units and factors create confusion in understanding the complete picture of environmental impact during the initial construction. Therefore, a set of models of pavement LCA considering every factor of the pavement life cycle phases is needed. Canada is a very large country and the different provinces have different pavement construction practices. Therefore, the goal of this work is to develop a set of useful models for quantifying CO2 emission from pavement construction in Canada. A total of 141 Canadian road sections from the Long Term Pavement Performance (LTPP) database are considered to develop models using machine learning algorithms: multiple linear regression, polynomial regression, decision tree regression and support vector regression. These models determine the significant contributors and quantify the CO2 emission in material production, initial construction, maintenance and use phase. The study also reveals the contribution of Canadian provinces' CO2 emission involved in the life of a pavement. [ABSTRACT FROM AUTHOR]

Livestock manure and organic agriculture wastes are an environmental challenge because they contribute to climate change by emitting greenhouse gases. Converting these organic wastes to biogas and bioenergy is a sustainable solution. Farmers, investors, and governmental departments involved in developing on-farm biogas projects need an informed decision-making process to fund such projects. Thus, estimating the required initial investment for a farm-based biogas plant is crucial. This study aims to develop two methods to estimate the cost of farm-based biogas projects, determine their economic viability, and predict the cost of each part of the plant and its related risks. A database for farm-based biogas projects in Canada and the USA was established and analyzed before developing the models. First, six mathematical models were developed using linear regression to predict the capital cost, engineering and design, operation and maintenance, gross revenue, and net profit using Monte Carlo simulation. Second, the probability of cost of components is calculated. The marginal error of cost prediction in initial modeling is about 7% in total, and the economic viability of a biogas plant for a farm housing less than 300 cows is questionable. [ABSTRACT FROM AUTHOR]

Simple Summary: The Screen Project was initiated in 2017 to offer BRCA1 and BRCA2 genetic screening to all Canadians over the age of 18 who wish to know their mutation status. Study subjects registered online and were sent a saliva sample kit. Of the 1269 tested individuals between March 2017 and January 2019, 30 (2.4%) had a pathogenic mutation in BRCA1 or BRCA2 (20 women and 10 men). Seventy-five percent of the female mutation carriers underwent a bilateral mastectomy and/or salpingo-oophorectomy within a year of receiving a positive result. The study results endorse a model where genetic testing is offered directly to the consumer via a home-delivered kit and a saliva sample, and some clinical guidance for those with positive test results. We believe that testing has substantial benefits, and therefore, we would like to expand testing to as many eligible women as possible. There is limited information of the outcomes of direct-to-consumer testing for BRCA1 and BRCA2 mutations. The Screen Project was initiated in 2017 to offer BRCA1 and BRCA2 genetic screening to all Canadians over the age of 18 who wish to know their mutation status. Patients enrolled in the study from 2017 to 2019 and were followed for one year after the receipt of a genetic test result. Study subjects registered online and were sent a saliva sample kit, which was shipped to the reference laboratory. Pre-test genetic counselling and counselling for mutation-negative subjects was optional and at the individual's discretion. There were 1269 tested individuals between March 2017 and January 2019. A total of 1157 (93%) were women and 87 (7%) were men. Sixty-six percent had a first- or second-degree relative with breast or ovarian cancer. Of the 1269 tested individuals, 30 (2.4%) had a pathogenic mutation in BRCA1 or BRCA2 (20 women and 10 men). Seventy-five percent of the female mutation carriers underwent a bilateral mastectomy and/or salpingo-oophorectomy within a year of receiving a positive result. Genetic counselling was available at no cost to all participants but was requested by only 5% of the non-carriers. The study subjects expressed a high degree of satisfaction with the process. [ABSTRACT FROM AUTHOR]

Pavement vehicle interaction (PVI) is one of the significant components in the use phase of pavement life cycle assessment. This component has attribution with excess fuel consumption, resulting in an increase in the global warming potential (GWP). Several research have been conducted to model and quantify the PVI effect based on pavement roughness and deflection. These models estimate excess fuel consumption considering traffic loading, engineering properties of pavement materials and pavement design specifications. However, a big question remains unanswered: how do various climatic parameters including precipitation, temperature, and freezing index influence the PVI and subsequent GWP? Canada is the second largest country in the world and different climate regions can be found in each of its ten provinces and three territories. Therefore, a new, climate-based clustering approach—rather than geometric boundaries is introduced for climate impact analysis from road system. Twenty-two Long-Term Pavement Performance (LTPP) road sections located in the Canadian highways have been selected for this study. These road sections were clustered based on homogenous climate conditions, using a statistical approach. The research finds that a combination of high precipitation, freezing index and medium temperature are associated with an accelerated rate of International Roughness Index (IRI) and consequent GWP because of a high PVI for poor pavement surface. GWP for roughness is found to be very low where high temperature and a low freezing index exist. Very high traffic loading and low subgrade stiffness, when combined, produced the highest GWP emissions because of deflection based PVI. It was also found that the PVI effect in terms of GWP for both roughness and deflection is always dominant for heavy vehicles over light vehicles traffic. • A new, climate-based clustering approach—rather than geometric boundaries is introduced for climate impact analysis. • HDM IV model and PVI Gen II model was used to estimate the GWP value for pavement roughness and deflection, respectively. • A combination of high precipitation, freezing index and medium temperature results accelerated IRI rate and consequent GWP. • Very high traffic loading and low subgrade stiffness altogether produces the highest GWP because of deflection based PVI. [ABSTRACT FROM AUTHOR]