68 resultados para Academic path


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The problem of a manipulator operating in a noisy workspace and required to move from an initial fixed position P0 to a final position Pf is considered. However, Pf is corrupted by noise, giving rise to Pˆf, which may be obtained by sensors. The use of learning automata is proposed to tackle this problem. An automaton is placed at each joint of the manipulator which moves according to the action chosen by the automaton (forward, backward, stationary) at each instant. The simultaneous reward or penalty of the automata enables avoiding any inverse kinematics computations that would be necessary if the distance of each joint from the final position had to be calculated. Three variable-structure learning algorithms are used, i.e., the discretized linear reward-penalty (DLR-P, the linear reward-penalty (LR-P ) and a nonlinear scheme. Each algorithm is separately tested with two (forward, backward) and three forward, backward, stationary) actions.

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This article summarises recent revisions to the investment development path (IDP) as postulated by Narula and Dunning (2010). The IDP provides a framework to understand the dynamic interaction between foreign direct investment (FDI) and economic development. The revisions take into account some recent changes in the global economic environment. This paper argues that studies based on the IDP should adopt a broader perspective, encompassing the idiosyncratic economic structure of countries as well as the heterogeneous nature of FDI. It is critical to understand the complex forces and interactions that determine the turning points in a country’s IDP, and to more explicitly acknowledge the role of historical, social and political circumstances in hindering or promoting FDI. We discuss some of the implications for Eastern European countries and provide some guidelines for future research.

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In recent issues of this Journal a debate has raged concerning the appropriate nature of academic research in the Asia Pacific region. While we support the desire for both rigor and regional relevance in this research, we wish to demonstrate a strong commonality between the performance of large Asian firms and others from Europe and North America. This prompts us to question the need for a new theory of the MNE based on the experience of Asian firms. Like their counterparts elsewhere, the large Asian firms mostly operate on an intra-regional basis. While in the literature it has been assumed that the path to success for Asian firms is globalization, we show that the data supporting this is confined to a handful of unrepresentative case studies. We also present a bibliometric analysis which shows an overwhelming case study sample selection bias in academic studies towards this small number of unrepresentative cases

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This article explores the nature and impact of path dependence in British rail coal haulage before 1939. It examines the factors which locked Britain's railways into a system of small coal wagons with highly fragmented ownership, the cost penalties of this system, and the reasons that attempts at modernization were unsuccessful. The analysis highlights the importance of decentralized ownership of a highly durable installed base of complementary infrastructure. Technical and institutional interrelatedness blocked incremental modernization, while the political requirement to compensate private wagon owners for the loss of their wagon stock made wholesale rationalization financially unattractive.

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Background. In separate studies and research from different perspectives, five factors are found to be among those related to higher quality outcomes of student learning (academic achievement). Those factors are higher self-efficacy, deeper approaches to learning, higher quality teaching, students’ perceptions that their workload is appropriate, and greater learning motivation. University learning improvement strategies have been built on these research results. Aim. To investigate how students’ evoked prior experience, perceptions of their learning environment, and their approaches to learning collectively contribute to academic achievement. This is the first study to investigate motivation and self-efficacy in the same educational context as conceptions of learning, approaches to learning and perceptions of the learning environment. Sample. Undergraduate students (773) from the full range of disciplines were part of a group of over 2,300 students who volunteered to complete a survey of their learning experience. On completing their degrees 6 and 18 months later, their academic achievement was matched with their learning experience survey data. Method. A 77-item questionnaire was used to gather students’ self-report of their evoked prior experience (self-efficacy, learning motivation, and conceptions of learning), perceptions of learning context (teaching quality and appropriate workload), and approaches to learning (deep and surface). Academic achievement was measured using the English honours degree classification system. Analyses were conducted using correlational and multi-variable (structural equation modelling) methods. Results. The results from the correlation methods confirmed those found in numerous earlier studies. The results from the multi-variable analyses indicated that surface approach to learning was the strongest predictor of academic achievement, with self-efficacy and motivation also found to be directly related. In contrast to the correlation results, a deep approach to learning was not related to academic achievement, and teaching quality and conceptions of learning were only indirectly related to achievement. Conclusions. Research aimed at understanding how students experience their learning environment and how that experience relates to the quality of their learning needs to be conducted using a wider range of variables and more sophisticated analytical methods. In this study of one context, some of the relations found in earlier bivariate studies, and on which learning intervention strategies have been built, are not confirmed when more holistic teaching–learning contexts are analysed using multi-variable methods.