Relations Among the Structure of Learning Tasks, Achievement, and Changes in Self-Efficacy in Secondary Students.

Although much has been discovered about relations between self-efficacy and academic achievement, questions remain about links between achievement, the structure of learning tasks, and changes in students' self-efficacy as students engage with a single, complex authentic task. Students' self-efficacy for learning (SEL) and for performance (SEP) was tracked as they worked on well- and ill-structured tasks during their regular class. Students reported higher SEL and SEP for a well-structured task. Moderate achievers reported significantly more difficulty with the ill-structured task. SEP was higher and more stable than SEL, especially in early phases of both tasks. After accounting for overall academic achievement, self-efficacy was a negligible predictor of achievement. Students may perceive various features of each task's structure as difficult. Implications concerning relations among self-efficacy, task structure, and achievement are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An axial flux magnetically geared permanent magnet wind generator

This paper presents the design, construction, and experimental performance evaluation of an axial flux magnetically geared permanent magnet (MGPM) machine for wind power application. The optimum electromagnetic design for both magnetically coupled and decoupled configurations is described. Considering the complex structure of the axial flux MGPM machine, special attention is also paid to the mechanical design aspects. The optimized results show that a torque density in excess of 100 kNm/m³ could be achieved for the active gear part. The inherent overload protection of the MGPM machine has also been demonstrated. Furthermore, the design‐related aspects and issues are analyzed and discussed in detail in an attempt to outline problem areas in the design process. Relevant discussions are given and conclusions are drawn. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Classification in the presence of class noise using a probabilistic Kernel Fisher method

In machine learning, class noise occurs frequently and deteriorates the classifier derived from the noisy data set. This paper presents two promising classifiers for this problem based on a probabilistic model proposed by Lawrence and Schölkopf (2001). The proposed algorithms are able to tolerate class noise, and extend the earlier work of Lawrence and Schölkopf in two ways. First, we present a novel incorporation of their probabilistic noise model in the Kernel Fisher discriminant; second, the distribution assumption previously made is relaxed in our work. The methods were investigated on simulated noisy data sets and a real world comparative genomic hybridization (CGH) data set. The results show that the proposed approaches substantially improve standard classifiers in noisy data sets, and achieve larger performance gain in non-Gaussian data sets and small size data sets.