Metal mines release treated effluents that contain a variety of metals, metalloids, and organics into the aquatic environment. A number of metal mine effluents (MMEs) have been found to contribute to adverse effects in fish and benthic invertebrates, such as decreased diversity and density, however the specific causal factors of toxic responses during chronic exposures to the MMEs are often unknown. Therefore, the overall objective of this dissertation was to explore causative and modifying factors of MME toxicity to a resident fish species, the fathead minnow (Pimephales promelas), during chronic, multi-trophic exposures. The representative MME used in this study was the process water effluent (PWE) of a Canadian metal mine, which is released into Junction Creek in Sudbury, Ontario, Canada. Chronic exposure to the MME has been a source of decreased reproductive output in fathead minnows in several previous studies, however, these same studies were not able to determine the potential causal factors of the reproductive impairment. In order to address the overall objective, several laboratory mesocosm studies were conducted, which consisted of three separate components. The first component included exploring several metals (Cu, Ni, and Se; alone and in mixture) that are consistently present in the MME and are known to cause toxicity at fairly low concentrations as potential causes for decreased egg production in fathead minnows. The second component included evaluating the role of decreased food availability (a possible indirect effect of MME in the receiving environment) as a potential cause of decreased egg production in fathead minnows. The third and final component included examining the role of water chemistry [(increased alkalinity and dissolved organic carbon (DOC)] as potential modifying factors of chronic MME toxicity to fathead minnows. In general, my results suggest that the metals present in the MME likely do not contribute directly to decreased reproductive performance in fathead minnows during chronic exposures, under the conditions examined. Instead, the MME appears to decrease food availability, therefore indirectly influence fathead minnow egg production. Furthermore, water chemistry modifications tested in this thesis were not able to entirely mitigate the reproductive effects in fish induced by the MME, although they did improve egg production relative to unmodified MME. Metal concentrations in fish tissues were not influenced by increases to alkalinity or DOC level in the exposure water, suggesting that bioavailability of metals during chronic exposure to metal-mixtures cannot be fully explained based on our understanding of metal complexation with abiotic ligands (inorganic and organic) during single metal or acute exposures. From a regulatory perspective, water chemistry modifications may somewhat improve fathead minnow reproductive performance during chronic exposure to the MME, however the MME would still not be entirely free of effects relative to the uncontaminated water. Future studies should focus on understanding the factors responsible for decreased food availability in MME-impacted aquatic ecosystems, and further explore potential approaches for ameliorating effluent quality.

High levels of automation in manufacturing industries are leading to data sets of increasing size and dimension. The challenge facing statisticians and field professionals is to develop methodology to help meet this demand. Functional data is one example of high-dimensional data characterized by observations recorded as a function of some continuous measure, such as time. An application considered in this thesis comes from the automotive industry. It involves a production process in which valve seats are force-fitted by a ram into cylinder heads of automobile engines. For each insertion, the force exerted by the ram is automatically recorded every fraction of a second for about two and a half seconds, generating a force profile. We can think of these profiles as individual functions of time summarized into collections of curves. The focus of this thesis is the analysis of functional process data such as the valve seat insertion example. A number of techniques are set forth. In the first part, two ways to model a single curve are considered: a b-spline fit via linear regression, and a nonlinear model based on differential equations. Each of these approaches is incorporated into a mixed effects model for multiple curves, and multivariate process monitoring techniques are applied to the predicted random effects in order to identify anomalous curves. In the second part, a Bayesian hierarchical model is used to cluster low-dimensional summaries of the curves into meaningful groups. The belief is that the clusters correspond to distinct types of processes (e.g. various types of “good” or “faulty” assembly). New observations can be assigned to one of these by calculating the probabilities of belonging to each cluster. Mahalanobis distances are used to identify new observations not belonging to any of the existing clusters. Synthetic and real data are used to validate the results.

High levels of automation in manufacturing industries are leading to data sets of increasing size and dimension. The challenge facing statisticians and field professionals is to develop methodology to help meet this demand. Functional data is one example of high-dimensional data characterized by observations recorded as a function of some continuous measure, such as time. An application considered in this thesis comes from the automotive industry. It involves a production process in which valve seats are force-fitted by a ram into cylinder heads of automobile engines. For each insertion, the force exerted by the ram is automatically recorded every fraction of a second for about two and a half seconds, generating a force profile. We can think of these profiles as individual functions of time summarized into collections of curves. The focus of this thesis is the analysis of functional process data such as the valve seat insertion example. A number of techniques are set forth. In the first part, two ways to model a single curve are considered: a b-spline fit via linear regression, and a nonlinear model based on differential equations. Each of these approaches is incorporated into a mixed effects model for multiple curves, and multivariate process monitoring techniques are applied to the predicted random effects in order to identify anomalous curves. In the second part, a Bayesian hierarchical model is used to cluster low-dimensional summaries of the curves into meaningful groups. The belief is that the clusters correspond to distinct types of processes (e.g. various types of “good” or “faulty” assembly). New observations can be assigned to one of these by calculating the probabilities of belonging to each cluster. Mahalanobis distances are used to identify new observations not belonging to any of the existing clusters. Synthetic and real data are used to validate the results.

Watersheds in Canada are under increasing threats due to the cumulative environmental effects from natural and anthropogenic sources. Cumulative effect assessment (CEA), however, if done at all is typically done on a project-by-project basis. This project-based approach to CEA is not sufficient to address the cumulative effects of multiple stressors in a watershed or a region. As a result, there is now a general consensus that CEA must extend from the project to the more regional scale. The problem, however, is that while the science of how to do watershed CEA (W-CEA) is progressing, the appropriate institutional arrangements to sustain W-CEA have not been addressed. Based on a case study of the South Saskatchewan Watershed (SSW), this research is aimed to identify the institutional requirements necessary to support and sustain W-CEA.<p> The research methods include document reviews and semi-structured interviews with regulators, administrators, watershed coordinators, practitioners, and academics knowledgeable on cumulative effect assessment and project-based environmental assessments (EAs). The findings from this research are presented thematically. First, participants perspectives on cumulative effects, the current state of CEA practice, and general challenges to project-based approaches to CEA are presented. The concept of WCEA is then examined, with a discussion on the need for linking project-based CEA and W-CEA. This is followed by the institutional requirements for W-CEA. The Chapter concludes with foreseeable challenges to implementing W-CEA, as identified by research participants.<p> The key findings include that cumulative effect assessments under project-based EAs are rarely undertaken in the SSW, and the project-based EA approach is faced with considerable challenges. The project-based EA challenges suggested by interview participants are similar to the ones discussed in the literature, and are primarily related to the lack of guidance to proponents regarding boundaries of assessments and thresholds, the lack of data from other project EAs, and the lack of capacity of both proponents and regulators to achieve a good CEA under project EA. These challenges could be addressed by establishing regional objectives at a broader scale, which could provide better context to project-based approaches. Further, interview results revealed several opportunities for the government to take the lead in implementing and sustaining W-CEA, but a multistakeholder approach is essential to W-CEA success. The results also suggest that the establishments of thresholds and data management are necessary components of W-CEA, but that the need for legislation concerning such thresholds and W-CEA initiatives is not agreed upon. At the same time, research results emphasize that the coordination and education among various stakeholders will be difficult to achieve. The lack of financial commitment, political will, and difficulties in establishing cause-effect relationships currently impede the implementation of W-CEA.

The relationship between exchange rate volatility and trade flow has been examined in a number of previous researches. The paper mainly focuses on investigating the impact of exchange rate volatility on export values from Sweden to Germany during 2000:01 and 2011:06. The Auto Regressive Distributed Lag (ARDL) model is employed to obtain the estimates of the long run equilibrium and the short run dynamics, simultaneously. The results indicate that the exchange rate volatility has significant short run effects on export value in majority of estimated industries while its meaningful long run impacts do not appear in any cases. However, applying the “bounds test” approach, the co-integration is also found in more than half cases due to long run impacts of other factors such as foreign income on export earnings.

While prior studies on ERP implementation have largely focused on the importance of best practices, this paper examines the impact of knowledge transfer climate and the competence of the players (i.e., the implementing firm and the consultant team). The model divides factors that influence the result of knowledge transfer during ERP implementation into three categories: those implemented by the firm, those implemented by the consultant, and those related to the impact of the knowledge transfer climate. Competence factors from the first two aspects facilitate the building of a better knowledge transfer climate. Survey results from 101 respondents were subjected to multivariate analysis. The significance of player competence factors is verified, and an understanding of the role that the knowledge transfer climate plays in the knowledge transfer process and the impact on the transfer process is developed. This paper provides a broader, richer model of knowledge transfer networks to promote insight into successful ERP implementation. In practice, the key to effective knowledge transfer is the establishment a positive knowledge transfer climate. To achieve a successful ERP implementation, practitioners should focus on possessing robust competences with ERP implementation partners. Additional research may help ERP implementation project teams more effectively promote knowledge transfer in a wider range of conditions with greater confidence and precision.

In order to meet our needs, consumer always believe that the more wild of selection, the better to choose the best of us. One such assumption is the regularity principle, which asserts that the addition of a new option to the choice set should not increase the probability of choosing any of the original options (Luce 1977). Clearly, both the attraction and compromise effects reflect an increase in the share of the target option after adding a third option. It implies that a new option added to a given set should take shares from existing options in proportion to their original shares. In fact, not all of the consumers react to these "third option" in the same level, because of the different personality traits , everyone have different thoughts even they get the same message. So, we want to know the impact of personality traits on compromise and attraction effects, and the intensity of those effects. We choose ¡§self-confidence¡¨, ¡§need for cognition¡¨, ¡§need for uniqueness¡¨, ¡§locus of control¡¨, and ¡§self-monitoring¡¨ to test the compromise and attraction effects, and we found that people with ¡§low need for uniqueness¡¨ had the strongest compromise effect; and with ¡§high need for cognition¡¨ had the strongest attraction effect. In addition, ¡§high self-confidence¡¨, ¡§high need for cognition¡¨, ¡§high need for uniqueness¡¨, and ¡§low self-monitoring¡¨ groups only exist attraction effect but compromise effect; ¡§low self-confidence¡¨, ¡§low need for cognition¡¨, ¡§low need for uniqueness¡¨ and ¡§high self-monitoring¡¨ groups react not only on attraction effect, but on compromise effect.

The papers of stock-based employee compensation have increased dramatically in recent years, focusing attention on whether stock-based employee compensation can enhance employees¡¦ motivation or impact firm value. A number of recent papers have addressed conflicting evidence as to whether stock-based employee compensation enhance the performance of the firm. Some relatively new studies used use the Ohlson (1995,1999) and Feltham ¡® Ohlson (1999) models to investigate the market¡¦s perception of the economic effect of employee stock options on firm value(Aboody et al.2001; Bell et al., 2002). However, critics have questioned the validity of such studies (For a review of related studies, see Beaver 2002). In fact, stock-based employee compensation can influence firm value through improving performance of firm, and at the same time by diluting the shares of outstanding stocks, thus harms shareholder equity. This study was primarily designed to examine how stock-based employee compensation affects shareholder equity through Incentive and dilute effects. Stock-based employee compensation in this study comprises employee stock bonuses and employee stock options. First, the Incentive and dilute effects are combined in Ohlson model. The hypothesized relationships of constructs, observed variables and operational definitions are defined. The empirical work will be conducted by LISREL method to estimate the coefficients in the model. The estimated results will be dressed the following points. 1.Whether the stock-based employee compensation affects equity valuation. 2.Whether the stock-based employee compensation affects that the intrinsic value through improving abnormal earning? 3.Whether the stock-based employee compensation harms shareholder equity by diluting the shares of outstanding stocks? 4.Discuss employee stock bonuses and employee stock options respectively. In this study, we find the stock-based employee compensation is relevant to the equity value. Employee stock bonuses are relevant to shareholder equity and abnormal earning. In other words, employee stock bonuses have directly incentive effects. Otherwise, employee stock bonuses also have dilute effects. However, the dilute effects are smaller than the incentive effects. On the other hand, employee stock options aren¡¦t relevant to shareholder equity and abnormal earning. Otherwise, employee stock options don¡¦t have direct dilute effects in grant year.

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