Carleton University

About: Carleton University is a education organization based out in Ottawa, Ontario, Canada. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 15852 authors who have published 39650 publications receiving 1106610 citations.

Landscape moderation of biodiversity patterns and processes - eight hypotheses

Abstract: Understanding how landscape characteristics affect biodiversity patterns and ecological processes at local and landscape scales is critical for mitigating effects of global environmental change. In this review, we use knowledge gained from human-modified landscapes to suggest eight hypotheses, which we hope will encourage more systematic research on

Application of Social Cognitive Theory to Training for Computer Skills

Abstract: While computer training is widely recognized as an essential contributor to the productive use of computers in organizations, very little research has focused on identifying the processes through which training operates, and the relative effectiveness of different methods for such training. This research examined the training process, and compared a behavior modeling training program, based on Social Cognitive Theory Bandura [Bandura, A. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psych. Rev.842 191--215; Bandura, A. 1978. Reflections on self-efficacy. Adv. Behavioral Res. Therapy1 237--269; Bandura, A. 1982. Self-efficacy mechanism in human agency. Amer. Psychologist372 122--147; Bandura, A. 1986. Social Foundations of Thought and Action. Prentice-Hall, Englewood Cliffs, NJ.], to a more traditional, lecture-based program. According to Social Cognitive Theory, watching others performing a behavior, in this case interacting with a computer system, influences the observers' perceptions of their own ability to perform the behavior, or self-efficacy, and the expected outcomes that they perceive, as well as providing strategies for effective performance. The findings provide only partial support for the research model. Self-efficacy exerted a strong influence on performance in both models. In addition, behavior modeling was found to be more effective than the traditional method for training in Lotus 1-2-3, resulting in higher self-efficacy and higher performance. For WordPerfect, however, modeling did not significantly influence performance. This finding was unexpected, and several possible explanations are explored in the discussion. Of particular surprise were the negative relationships found between outcome expectations and performance. Outcome expectations were expected to positively influence performance, but the results indicated a strong negative effect. Measurement limitations are presented as the most plausible explanation for this result, but further research is necessary to provide conclusive explanations.

Confidence level computation for combining searches with small statistics

Abstract: This article describes an efficient procedure for computing approximate confidence levels for searches for new particles where the expected signal and background levels are small enough to require the use of Poisson statistics. The results of many independent searches for the same particle may be combined easily, regardless of the discriminating variables which may be measured for the candidate events. The effects of systematic uncertainty in the signal and background models are incorporated in the confidence levels. The procedure described allows efficient computation of expected confidence levels.

Predicting the activity of phenolic antioxidants: theoretical method, analysis of substituent effects, and application to major families of antioxidants.

Abstract: A procedure based on density functional theory is used for the calculation of the gas-phase bond dissociation enthalpy (BDE) and ionization potential for molecules belonging to the class of phenolic antioxidants. We show that use of locally dense basis sets (LDBS) vs full basis sets gives very similar results for monosubstituted phenols, and that the LDBS procedure gives good agreement with the change in experimental BDE values for highly substituted phenols in benzene solvent. Procedures for estimating the O--H BDE based on group additivity rules are given and tested. Several interesting classes of phenolic antioxidants are studied with these methods, including commercial antioxidants used as food additives, compounds related to Vitamin E, flavonoids in tea, aminophenols, stilbenes related to resveratrol, and sterically hindered phenols. On the basis of these results we are able to interpret relative rates for the reaction of antioxidants with free radicals, including a comparison of both H-atom-transfer and single-electron-transfer mechanisms, and conclude that in most cases H-atom transfer will be dominant.

Small Area Estimation

Abstract: List of Figures. List of Tables. Foreword. Preface. 1. Introduction. What is a Small Area? Demand for Small Area Statistics. Traditional Indirect Estimators. Small Area Models. Model-Based Estimation. Some Examples. 2. Direct Domain Estimation. Introduction. Design-based Approach. Estimation of Totals. Domain Estimation. Modified Direct Estimators. Design Issues. Proofs. 3. Traditional Demographic Methods. Introduction. Symptomatic Accounting Techniques. Regression Symptomatic Procedures. Dual-system Estimation of Total Population. Derivation of Average MSEs. 4. Indirect Domain Estimation. Introduction. Synthetic Estimation. Composite Estimation. James-Stein Method. Proofs. 5. Small Area Models. Introduction. Basic Area Level (Type A) Mode l. Basic Unit Level (Type B) Model. Extensions: Type A Models. Extensions: Type B Models. Generalized Linear Mixed Models. 6. Empirical Best Linear Unbiased Prediction: Theory. Introduction. General Linear Mixed Model. Block Diagonal Covariance Structure. Proofs. 7. EBLUP: Basic Models. Basic Area Level Model. Basic Unit Level Model. 8. EBLUP: Extensions. Multivariate Fay-Herriot Model. Correlated Sampling Errors. Time Series and Cross-sectional Models. Spatial Models. Multivariate Nested Error Regression Model. Random Error Variances Linear Model. Two-fold Nested Error Regression Model. Two-level Model. 9. Empirical Bayes (EB) Method. Introduction. Basic Area Level Model. Linear Mixed Models. Binary Data. Disease Mapping. Triple-goal Estimation. Empirical Linear Bayes. Constrained LB. Proofs. 10. Hierarchical Bayes (HB) Method. Introduction. MCMC Methods. Basic Area Level Model. Unmatched Sampling and Linking Area Level Models. Basic Unit Level Model. General ANOVA Model. Two-level Models. Time Series and Cross-sectional Models. Multivariate Models. Disease Mapping Models. Binary Data. Exponential Family Models. Constrained HB. Proofs. References. Author Index. Subject Index.