Context: Self-learning (i.e., students learning independently) and clinical simulation are essential components in contemporary health professions education (HPE). Self-learning is discussed often, yet the concept is seldom the target of rigorous study. Likewise, simulation modalities are abundant, though educational theory that guides their use in HPE remains elusive. Objectives: This dissertation investigates the effects of directed self-guided learning (DSGL) on novice health professions students’ skill acquisition, retention, and transfer in the context of simulation-based education. The objective is to explore how the combination of external direction and student self-guidance influences: students’ cognitive and metacognitive processes, students’ interactions with the learning environment and available resources, and how students learn in different DSGL contexts. Methods: Three research studies used randomized, controlled experimental designs to address five hypotheses. All studies included a performance assessment one-week after the initial practice session that evaluated skill retention and/or skill transfer. Data analysis employed univariate and multivariate analyses of variance and correlational techniques. Results: Regarding students’ cognitive and metacognitive processes, the data show a relation between DSGL and goal-setting. The results suggest that self-guided students benefit when they are directed to set goals related to performance processes, rather than performance outcomes. Regarding the learning environment, when students are directed to practice on simulators that increase progressively in fidelity (i.e., realism) they self-guide their advancement between those simulators effectively and display successful skill transfer. Finally, self-guided students that controlled their learning progression and learning sequence selected the theoretically most appropriate practice schedule (i.e., progressive learning). Students in this latter group seemed able, surprisingly, to direct their own self-guidance. Conclusions: This dissertation adds support to the hypothesis that self-guided students benefit due to their autonomy in controlling practice conditions to meet their own learning needs. Thus, the question of whether or not DSGL is effective, becomes how best to augment the DSGL experience. The instructional design of elements such as goals lists and task structuring (e.g., progressive increases in simulator fidelity) represent techniques that an educator can use to fulfill the role of director in a student’s SGL.

Aim: This paper is a report on a study that explored the professional development of intensive care unit nurses in journal clubs. Background: Evidence-based practice is important in nursing care (Krom, Batten, & Bautista, 2010). However few nurses feel comfortable using evidence to guide their practice (Pravikoff, Tanner, & Pierce, 2005). Journal clubs are a way to establish science as conversation (Wright, 2004) and foster knowledge translation for evidence-based nursing practice (Goodfellow, 2004). Methods: Monthly journal club meetings were held with the participation of a total of 71 healthcare professionals (65 nurses, 2 physicians, 2 pharmacists, 1 physiotherapist, and 1 respiratory therapist), who worked in two intensive care units of an Ontario hospital. After six months of meetings, 21 individual interviews were conducted with nurses, physicians, pharmacists, and nurse educators. Additional data collection included two focus groups, surveys, a review of staff meeting minutes, and researcher field notes. Findings: Journal clubs provided nurses with incentive to read research articles, improved nurses’ confidence in reading research, created a community of peers who worked collaboratively to improve clinical practice, provided a structure for nurses to reflect-on-practice, and led to reported changes in clinical practice. However, the data suggests that any gains in competence of nurses with the critical appraisal of research articles were probably modest. Barriers to participating in journal clubs and evidence-based practice are also identified. Conclusion: Journal clubs can foster knowledge translation and evidence-based practice through creating a community of practice and by providing nurses with motivation, structure, and confidence to read research articles. However, nurses reported a lack of critical appraisal skills and uncertainty about how to implement evidence into practice. Journal clubs may have a greater impact when implemented alongside other knowledge translation strategies such as working with clinical nurse specialists in order to enhance evidence-based practice.

Electrophilic aromatic substitution (EAS) and directed ortho-metalation (DoM) involve the direct substitution of an arene hydrogen. A major drawback involving EAS is the necessity for harsh forcing conditions for the reaction to proceed. Catalysts such as Lewis acids FeBr3 and AICI3 for the introduction of halogens and acyl groups, respectively, are each highly toxic and corrosive. Textbook preparations of aryl iodides classicaly involved the use of iodine and nitric acid. This approach affords only modest yields and does not provide regiospecific substitution of most substituted aromatics because most contain ortho/para directors which afford mixtures of isomers. The novelty of our procedure for the synthesis of the iodinated aromatics is twofold in that regiospecific para-iodination is observed and hydrocarbon media are utilized. Hydrocarbon media are less hazardous and greener than media used for halogenations reported in literature. This procedure always yields derivatives regiospecifically substituted para to an electron donating substituent. Moreover, this method eliminates the need to use hazardous oxidative catalysts. DoM is a reaction regiospecifically substitute an arene hydrogen at the ortho position. The media used in DoM reactions are less hazardous than those required for a variety of EAS reactions. The only problem for this reaction is use of extremely strong bases, alkyllithium reagents, which are known to be air and water sensitive. However, the DoM reaction does eliminate the need to separate ortho/para isomer mixtures so that only a single product is generated. The metalation yields predominantly products regiospecifically substituted ortho-to the direcing metalating group (DMG). With our deficiency catalysis concept and subsequent purificaion methods, relatively pure ortho-lithiated intermediates have been prepared. The study of catalysts/promoters on the derivatization of these intermediates is anticipated to be extremely insightful. For this study, we have shown that highly selective, efficient ortho-lithiation can be achieved by deficiency catalysis utilizing n-BuLi as the only strong metalating base.

The cycle space of a strongly connected graph has a basis consisting of directed circuits. The concept of relevant circuits is introduced as a generalization of the relevant cycles in undirected graphs. A polynomial time algorithm for the computation of a minimum weight directed circuit basis is outlined. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

A new approach to the process of Directed Evolution is proposed, which utilizes different machine learning algorithms. Directed Evolution is a process of improving a protein for catalytic purposes by introducing random mutations in its sequence to create variants. Through these mutations, Directed Evolution explores the sequence space, which is defined as all the possible sequences for a given number of amino acids. Each variant sequence is divided into one of two classes, positive or negative, according to their activity or stability. By employing machine learning algorithms for feature selection on the sequence of these variants of the protein, attributes or amino acids in its sequence important for the classification into positive or negative, can be identified. Support Vector Machines (SVMs) were utilized to identify the important individual amino acids for any protein, which have to be preserved to maintain its activity. The results for the case of beta-lactamase show that such residues can be identified with high accuracy while using a small number of variant sequences. Another class of machine learning problems, Boolean Learning, was used to extend this approach to identifying interactions between the different amino acids in a proteins sequence using the variant sequences. It was shown through simulations that such interactions can be identified for any protein with a reasonable number of variant sequences. For experimental verification of this approach, two fluorescent proteins, mRFP and DsRed, were used to generate variants, which were screened for fluorescence. Using Boolean Learning, an interacting pair was identified, which was shown to be important for the fluorescence. It was also shown through experiments and simulations that knowing such pairs can increase the fraction active variants in the library. A Boolean Learning algorithm was also developed for this application, which can learn Boolean functions from data in the presence of classification noise.

Parameterized NP-hard problems are NP-hard problems that are associated with special variables called parameters. One example of the problem is to find simple paths of length k in a graph, where the integer k is the parameter. We call this problem the p-path problem. The p-path problem is the parameterized version of the well-known NP-complete problem - the longest simple path problem. There are two main reasons why we study parameterized NP-hard problems. First, many application problems are naturally associated with certain parameters. Hence we need to solve these parameterized NP-hard problems. Second, if parameters take only small values, we can take advantage of these parameters to design very effective algorithms. If a parameterized NP-hard problem can be solved by an algorithm of running time in form of f(k)nO(1), where k is the parameter, f(k) is independent of n, and n is the input size of the problem instance, we say that this parameterized NP-hard problem is fixed parameter tractable (FPT). If a problem is FPT and the parameter takes only small values, the problem can be solved efficiently (it can be solved almost in polynomial time). In this dissertation, first, we introduce several techniques that can be used to design efficient algorithms for parameterized NP-hard problems. These techniques include branch and bound, divide and conquer, color coding and dynamic programming, iterative compression, iterative expansion and kernelization. Then we present our results about how to use these techniques to solve parameterized NP-hard problems, such as the p-path problem and the pd-feedback vertex set problem. Especially, we designed the first algorithm of running time in form of f(k)nO(1) for the pd-feedback vertex set problem. Thus solved an outstanding open problem, i.e. if the pd-feedback vertex set problem is FPT. Finally, we will introduce how to use parameterized algorithm techniques to solve the signaling pathway problem and the motif finding problem from bioinformatics.

The dynamics of the U.S. and Canada natural gas spot markets are evolving through deregulation policies and technological advances. Economic theory suggests that these markets will be integrated. The key question is the extent of integration among the markets. This thesis characterizes the degree of dynamic integration among 11 major natural gas markets, six from the U.S. and five from Canada, and determines each individual markets’ role in price discovery. This is the first study to include numerous Canadian markets in a North American natural gas market study. Causal flows modeling using directed acyclic graphs in conjunction with time series analysis are used to explain the relationships among the markets. Daily gas price data from 1994 to 2009 are used. The 11 natural gas market prices are tied together with nine long-run co-integrating relationships. All markets are included in the co-integration space, providing evidence the markets are integrated. Results show the degree of integration varies by region. Further results indicate no clear price leader exists among the 11 markets. Dawn market is exogenous in contemporaneous time, while Sumas market is an information sink. Henry Hub plays a significant role in the price discovery of markets in the U.S. Midwest and Northeast, but little to markets in the west. The uncertainty of a markets’ price depends primarily on markets located in nearby regions. Policy makers may use information on market integration for important policy matters in efforts of attaining efficiency. Gas traders benefit from knowing the price discovery relationships.

The influences of self-directed readiness and learning satisfaction to the intention of continuing study- The examples of Open Universities in Kaohsiung Area Meng-Fan Chang Abstract Teaching delivery has been inspired by information technology and derived it from traditional face-to-face teaching (or teacher directed learning) to multi-channel learning model, providing a better interactive learning environment within the scope of teaching and learning. This study aims to explore if the intention of continuing study is affected by self-directed learning readiness and learning satisfaction. The relationship between demographic variables and self-directed readiness, learning satisfaction and the intention of continuing study were also discussed. 1055 valid respondents of 1085 students were from the National Open University and the Open University of Kaohsiung. Descriptive statistics, ANOVA one-way, Pearson correlation and multiple regressions were executed to examine the relation among variables. The results of data analysis are shown in the following: 1. Female students of National Open University show less creative learning than males. Nevertheless, they enjoy themselves in learning and have stronger motivation in learning.Aging students show greater self-motivated learning and learning motivation than the young. Students with higher educational background have best performance on self-directed learning readiness. 2.The data demonstrates that enjoy in learning is major factor concerning to teachers¡¦ teaching. 3.Enjoy in learning and self-motivated learning have the highest influence on academic administration service and good incentive respectively. 4.Learning satisfaction plays an important mediating role to self-directed learning readiness and intention of continuing study. Suggestions to educators and directions of this future work are also given in the study. Key words: Self-directed learning readiness, Learning satisfaction, Intention of continuing study, Open University

Abstract Our industrial structure has been changed with the impact of competition recently. The structure of labor has been changed dramatically by the influence of industrial structure. No matter what it is related to industrial upgrade or economic development of knowledge, they all need allocate the human resources. So the corporation can keep its competitive advantage. However, the most important in the corporation is management who can take good care of resources and talent in the firm. They make the entry barrier, so the valuable property in the firm can¡¦t be easily imitated by competitors. The management of talent and its development is positive for the operation of organization. The management has gotten rid of the image of falling behind and bogging down and refusing to change. Now, it turns out to be the strategy driving and interface and continuous innovation after promotion of the self-competition. The research subjects the intention of self-learning of management if it can upgrade the talent of management and show the job performance. Besides, the research subjects perceived organization supporting if it can affect the willing the self-learning. Therefore, the research studies the middle level of management in industries of Taiwan. The methodology uses random sampling. The questionnaires sent out by 403 and effective questionnaires are 200. analyzed by statistics methods of t-test analysis of independence, analysis of variance (ANOVA), Pearson¡¦s product-moment correlation, and multiple regression. The result confirms several important discoveries as follows: 1. As the relation on self-directed learning readiness and management competency and job performance, it can be summarized below: (1).The greater self-directed learning readiness, the greater is the degree of management competency. (2).The greater management competency, the greater is the degree of job performance. (3).The greater self-directed learning readiness, the greater is the degree of job performance. 2. There is a positive correlation in the self-learning and effective learning of self-directed learning readiness and job performance, and it also has high prediction 3. The management competency about professional capability and social communication and leadership is positive correlation to job performance. The most importance is professional capability. 4. Management competency has mediating effect between the self-directed learning readiness and job performance. 5. According to the moderating effect of the perceived organization supporting between the self-directed learning readiness and management competency in the study found that the management should be in favor of learning by effective leading. It can promote the professional capability.

In this study, the novel idea of in-vivo directed evolution was applied in order to achieve variants of the enzyme galactose oxidase with increased activity. This procedure was done under starvation conditions in Escherichia coli BL21 Star (DE3). Previous studies have been carried out in order to improve the activity of this enzyme using directed evolution methods. In this study, the same idea was used in-vivo, during stationary phase adaptive mutations inside the host organism, hence called in-vivo directed evolution. This method gave variants with improved enzyme activity as compared with the wild-type enzyme, and some variants showed activities that were even higher than the variants of previous directed evolution studies, hence making this method a promising approach for the random mutagenesis of genes of interest. The above mentioned mutations are carried out by a special group of polymerases, the error-prone polymerases. Phylogenetic analysis of these error-prone polymerases was also carried out in order to investigate the relationship between the number of error-prone polymerases and the level of complexity of organisms, and both the number of error-prone polymerases and the ratio of error-prone polymerases to total DNA polymerases of six organisms were studied. It was found that as the organism gets more complex, the number of error-prone polymerases and their ratio to the total polymerases increase.