A Spatially Explicit Model of the Wild Hog for Ecological Risk Assessment Activities at the Department of Energy\u27s Savannah River Site

In North America, wild hogs (Sus scrofa) are both sought after as prime game and despised due to their detrimental impacts to the environment from their digging and rooting behavior. They are also a potentially useful indicator species for environmental health for both ecological- and human-based risk assessments. An inductive approach was used to develop probabilistic resource selection models using logistic regression to quantify the likelihood of hogs being in any area of the Department of Energy’s 805 km2 Savannah River Site (SRS) in west-central South Carolina. These models were derived by using available SRS hog hunt data from 1993–2000 and a Geographic Information System database describing the habitat structure of the SRS. The model’s significant parameters indicated that wild hogs preferred hardwoods and avoided pine and shrubby areas. Further, landscape metric analyses revealed that hogs preferred areas with large complex patch areas and low size variation. These resource selection models were then utilized to better estimate exposure of wild hogs to radionuclides and metals in a disturbed riparian ecosystem on the SRS using two different possible diets based on food availability. Contaminant exposure can be better estimated using these resource selection models than has been previously possible, because past practices did not consider home range and habitat utilization probability in heterogeneously contaminated habitats. Had these models not been used, risk calculations would assume that contaminated areas were utilized 100% of the time, thus overestimating exposure by a factor of up to 25

Special Libraries, Fall 1988

Volume 79, Issue 4https://scholarworks.sjsu.edu/sla_sl_1988/1003/thumbnail.jp

Special Libraries, July 1984

Volume 75, Issue 3https://scholarworks.sjsu.edu/sla_sl_1984/1002/thumbnail.jp

Assessing the impact of micro-lending programmes in the informal sector in Cape Town. A case study, The Nations Trust Organization

Magister Artium (Development Studies) - MA(DVS)The promotion of widespread entrepreneurship through the setting up of small and medium sized enterprises (SMMEs ) is crucial for the transformation to a market economy and the democratisation of society in \the new South Africa. SMMEs are recognized as an engine of economic \ \growth and a source of sustainable development. Within this sector micro , 1nd small enterprises are of special importance because they are considered as the cradle of entrepreneurship, particularly in environments facing high ketllPIQYII'1:en!and poverty,. as j!) the case ~~.South Africg

Monitoring and prediction of air polution from traffic in the urban environment

Traffic-related air pollution is now a major concern. The Rio Earth Summit and the Government's commitment to Agenda 21 has led to Local Authorities taking responsibility to manage the growing number of vehicles and to reduce the impact of traffic on the environment. There is an urgent need to effectively monitor urban air quality at reasonable cost and to develop long and short term air pollution prediction models. The aim of the research described was to investigate relationships between traffic characteristics and kerbside air pollution concentrations. Initially, the only pollution monitoring equipment available was basic and required constant supervision. The traffic data was made available from the demand-responsive traffic signal control systems in Leicestershire and Nottinghamshire. However, it was found that the surveys were too short to produce statistically significant results, and no useful conclusions could be drawn. Subsequently, an automatic, remote kerbside monitoring system was developed specifically for this research. The data collected was analysed using multiple regression techniques in an attempt to obtain an empirical relationship which could be used to predict roadside pollution concentrations from traffic and meteorological data. However, the residual series were found to be autocorrelated, which meant that the statistical tests were invalid. It was then found to be possible to fit an accurate model to the data using time series analysis, but that it could not predict levels even in the short-term. Finally, a semi-empirical model was developed by estimating the proportion of vehicles passing a point in each operating mode (cruising, accelerating, decelerating and idling) and using real data to derive the coefficients. Unfortunately, it was again not possible to define a reliable predictive relationship. However, suggestions have been made about how this research could be progressed to achieve its aim

Indicators of community resilience : a study of communities facing impending natural disasters

Disasters are defined as a \u27crisis event in which the demands being placed on a human system, by the event, exceed the systems capacity to respond\u27 (Bolin, 1989, p. 62). In the literature the negative consequences of disasters are focused at the individual level and fail to take into account the context In which Individuals live and where the disaster occurred. Few studies utilize residents within a disaster community to define the factors that are relevant to their disaster experience especially in Australian settings. This present studies view of disasters places the individual within an ecological system to understand their disaster experience and the disaster experience of the community. In viewing the disaster xperience the present study seeks to Identify the stress and growth outcomes as a more holistic account of the disaster experience. The present study utilizes residents from a Western Australian disaster community to identify the salient factors related to the disaster experience. The research questions addressed by the current study are (1) What factors are Important in understanding the experience of community members living with the threat of natural seasonal disasters in Western Australia? (2) What is the relationship between the community, individual and disaster experience variables in different communities in Western Australia? (3) What is the best predictor of posttraumatic stress? (4) What is the best predictor of posttraumatic growth? (5) What variables differentiate high and low stress groups? (6) What variables differentiate high and low growth groups? (7) What are the community and individual factors that mediate the disaster experience in communities in Western Australia? Specifically a) which individual variables impact the posttraumatic stress and posttraumatic growth associated with disasters

Monitoring and prediction of air polution from traffic in the urban environment

Traffic-related air pollution is now a major concern. The Rio Earth Summit and the Government's commitment to Agenda 21 has led to Local Authorities taking responsibility to manage the growing number of vehicles and to reduce the impact of traffic on the environment. There is an urgent need to effectively monitor urban air quality at reasonable cost and to develop long and short term air pollution prediction models. The aim of the research described was to investigate relationships between traffic characteristics and kerbside air pollution concentrations. Initially, the only pollution monitoring equipment available was basic and required constant supervision. The traffic data was made available from the demand-responsive traffic signal control systems in Leicestershire and Nottinghamshire. However, it was found that the surveys were too short to produce statistically significant results, and no useful conclusions could be drawn. Subsequently, an automatic, remote kerbside monitoring system was developed specifically for this research. The data collected was analysed using multiple regression techniques in an attempt to obtain an empirical relationship which could be used to predict roadside pollution concentrations from traffic and meteorological data. However, the residual series were found to be autocorrelated, which meant that the statistical tests were invalid. It was then found to be possible to fit an accurate model to the data using time series analysis, but that it could not predict levels even in the short-term. Finally, a semi-empirical model was developed by estimating the proportion of vehicles passing a point in each operating mode (cruising, accelerating, decelerating and idling) and using real data to derive the coefficients. Unfortunately, it was again not possible to define a reliable predictive relationship. However, suggestions have been made about how this research could be progressed to achieve its aim

AN INVESTIGATION INTO THE DETERMINANTS AND CHARACTERISTICS OF THE ENTREPRENEURIAL UNIVERSITY: EVIDENCE FROM ENTREPRENEURIAL UNIVERSITIES IN THE UK

My first major contribution to knowledge is that practically, I modified the European (EU) framework (2012) by introducing a 3x3 best practice model to advance policy and strategy of entrepreneurship in the higher education sectors. My second major contribution is that theoretically, I used evolutionary resource-based view (RBV) theory to analyse all-encompassing factors influencing how universities co-evolve with their external environment to become more entrepreneurial which has been predominantly utilised as an internal analysis only. An evolutionary view of resource-based theory argues that variation in universities' approaches towards entrepreneurialism is underpinned by their resources and capabilities. Therefore, this research draws on the evolutionary perspective of RBV to explore both internal and external factors. Thereby extending RBV with a taxonomy of factors. My third major contribution is that conceptually, I utilised the strategic corporate entrepreneurship (CE) as a complementary concept to explore how entrepreneurial practices are configured in university settings. This is essential because CE has widely been used to advance the understanding of entrepreneurial activities within established and large private firms only. The strategic view of CE argues that an organisation might not have developed a new business but understand how to explore opportunities in a highly turbulent environment involving multiple actors. In doing so, it provides a comprehensive analysis into the classification of and strategy types behind why some universities are high in entrepreneurial activities than others and how coordination of such activities results in heightening entrepreneurial edge. While branding the activities into classifications, I extend CE with local, national, EU, and international levels of impacts of the entrepreneurial engagement and strategy types. Therefore, the integration of RBV with CE is important to advance our understanding of why and how some pre-1992 (established/old) and post-1992 (new) of the 'self-defined' universities are considered 'entrepreneurial'. Thus, have implications for strategy and management practices. The study develops a 3x3 practical model that can shape strategy, practice, and policy of entrepreneurship in university settings. This is essential because there is a lack of clarity in terms of how the seven components of the entrepreneurial university identified in the EU framework applies to the UK context. Therefore, this qualitative case study research is underpinned by an integrated lens of both RBV theory and CE concept to explore how fifteen (15) UK self-defined entrepreneurial universities are responding to the policy impreative 'becoming more entrepreneurial'. Through the combination of qualitative methods, thirty-two (32) key informant interviews were complemented with document analysis and participant-led visual methods. In contrast to the findings of the EU framework, my analysis generated three taxonomies of factors, three classifications of characteristics, and three typologies of the entrepreneurial university. In doing so, it highlights some policy and practice implications including having a cohesive and coherent strategy and how well-coordinated entrepreneurial activities enhance competitive position in today's higher education marketplace. Consequently, it offers valuable experience for university leaders and managers to deliberate on their strategies and management practices for entrepreneurialism. As such, the primary beneficiaries of the research contributions are universities and the secondary include funding councils, higher education policy planners, small and medium enterprises (SMEs), practitioners, and researchers