iPad use in fieldwork: formal and informal use to enhance pedagogical practice in a bring your own technology world

We report on use of iPads (and other IOS devices) for student fieldwork use and as electronic field notebooks and to promote active. We have used questionnaires and interviews of tutors and students to elicit their views and technology and iPad use for fieldwork. There is some reluctance for academic staff to relinquish paper notebooks for iPad use, whether in the classroom or on fieldwork, as well as use them for observational and measurement purposes. Students too are largely unaware of the potential of iPads for enhancing fieldwork. Apps can be configured for a wide variety of specific uses that make iPads useful for educational as well as social uses. Such abilities should be used to enhance existing practice as well as make new functionality. For example, for disabled students who find it difficult to use conventional note taking. iPads can be used to develop student self-directed learning and for group contributions. The technology becomes part of the students’ personal learning environments as well as at the heart of their knowledge spaces – academic and social. This blurring of boundaries is due to iPads’ usability to cultivate field use, instruction, assessment and feedback processes. iPads can become field microscopes and entries to citizen science and we see the iPad as the main ‘computing’ device for students in the near future. As part of the Bring Your Own Technology/Device (BYOD) the iPad has much to offer although, both staff and students need to be guided in the most effective use for self-directed education via development of Personal Learning Environments. A more student-oriented pedagogy is suggested to correspond to the increasing use of tablet technologies by students

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

The role of self-teaching in learning orthographic and semantic aspects of new words

This study explores how children learn the meaning (semantics) and spelling patterns (orthography) of novel words encountered in story context. English-speaking children (N = 88) aged 7 to 8 years read 8 stories and each story contained 1 novel word repeated 4 times. Semantic cues were provided by the story context such that children could infer the meaning of the word (specific context) or the category that the word belonged to (general context). Following story reading, posttests indicated that children showed reliable semantic and orthographic learning. Decoding was the strongest predictor of orthographic learning, indicating that self-teaching via phonological recoding was important for this aspect of word learning. In contrast, oral vocabulary emerged as the strongest predictor of semantic learning.

Temperament and self-based correlates of cooperative, competitive and individualistic learning preferences

People vary in the extent to which they prefer cooperative, competitive or individualistic achievement tasks. In the present research, we conducted two studies designed to investigate correlates and possible roots of these social interdependence orientations, namely approach and avoidance temperament, general self-efficacy, implicit theories of intelligence, and contingencies of self-worth based in others’ approval, competition, and academic competence. The results indicated that approach temperament, general self-efficacy, and incremental theory were positively, and entity theory was negatively related to cooperative preferences (|r| range from .11 to .41); approach temperament, general self-efficacy, competition contingencies, and academic competence contingencies were positively related to competitive preferences (|r| range from .16 to .46); and avoidance temperament, entity theory, competitive contingencies, and academic competence contingencies were positively related, and incremental theory was negatively related to individualistic preferences (|r| range from .09 to .15). The findings are discussed with regard to the meaning of each of the three social interdependence orientations, cultural differences among the observed relations, and implications for practicioners.

Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.

Improved SOM learning using simulated annealing

Self-Organizing Map (SOM) algorithm has been extensively used for analysis and classification problems. For this kind of problems, datasets become more and more large and it is necessary to speed up the SOM learning. In this paper we present an application of the Simulated Annealing (SA) procedure to the SOM learning algorithm. The goal of the algorithm is to obtain fast learning and better performance in terms of matching of input data and regularity of the obtained map. An advantage of the proposed technique is that it preserves the simplicity of the basic algorithm. Several tests, carried out on different large datasets, demonstrate the effectiveness of the proposed algorithm in comparison with the original SOM and with some of its modification introduced to speed-up the learning.

The origin, molecular regulation and therapeutic potential of myogenic stem cell populations

Satellite cells, originating in the embryonic dermamyotome, reside beneath the myofibre of mature adult skeletal muscle and constitute the tissue-specific stem cell population. Recent advances following the identification of markers for these cells (including Pax7, Myf5, c-Met and CD34) (CD, cluster of differentiation; c-Met, mesenchymal epithelial transition factor) have led to a greater understanding of the role played by satellite cells in the regeneration of new skeletal muscle during growth and following injury. In response to muscle damage, satellite cells harbour the ability both to form myogenic precursors and to self-renew to repopulate the stem cell niche following myofibre damage. More recently, other stem cell populations including bone marrow stem cells, skeletal muscle side population cells and mesoangioblasts have also been shown to have myogenic potential in culture, and to be able to form skeletal muscle myofibres in vivo and engraft into the satellite cell niche. These cell types, along with satellite cells, have shown potential when used as a therapy for skeletal muscle wasting disorders where the intrinsic stem cell population is genetically unable to repair non-functioning muscle tissue. Accurate understanding of the mechanisms controlling satellite cell lineage progression and self-renewal as well as the recruitment of other stem cell types towards the myogenic lineage is crucial if we are to exploit the power of these cells in combating myopathic conditions. Here we highlight the origin, molecular regulation and therapeutic potential of all the major cell types capable of undergoing myogenic differentiation and discuss their potential therapeutic application.