Response analysis of an automobile shipping container

The design and development of automobile shipping containers to reduce enroute damage are discussed. Vibration tests were conducted to determine the system structural integrity. A dynamic analysis was made using NASTRAN and the results of the test and the analysis are compared

Internal agent states : experiments using the swarm leader concept

In recent years, an understanding of the operating principles and stability of natural swarms has proven to be a useful tool for the design and control of artificial robotic agents. Many robotic systems, whose design or control principals are inspired by behavioural aspects of real biological systems such as leader-follower relationship, have been developed. We introduced an algorithm which successfully enhances the navigation performance of a swarm of robots using the swarm leader concept. This paper presents some applications based on that work using the simulations and experimental implementation using a swarming behaviour test-bed at the University of Strathclyde. Experimental and simulation results match closely in a way that confirms the efficiency of the algorithm as well as its applicability

Commodity and product identification for value chain analysis.

This commodity and product identification research was undertaken in the context of the CGIAR Research Program on Aquatic Agricultural Systems (AAS). AAS seeks to reduce poverty and improve food security for the millions of small-scale fishers and farmers who depend on the world’s floodplains, deltas and coasts. The objective of this research is to strengthen the capacity of AAS to undertake value chain studies with high potential impact on smallholders. The capacity-building aspect of this research was focused on the process of commodity and product identification for value chain analysis. Its scope was limited to fish and other aquatic animals and products in the Tonle Sap area identified for AAS intervention. The result of the identification process was the selection of a number of commodities and products that were deemed to involve a high number of smallholders along the value chain and that have high market development potential

Enhancing Small Group Teaching in Plant Sciences: A Research and Development Project in Higher Education

The Department of Plant Sciences at the University of Cambridge uses a range of learning and teaching environments including lectures, practical laboratories and small group tutorials'. Under the auspices of the Cambridge-MIT Institute's Pedagogy Programme, a two-year research and development project concerned with the development of small-group teaching is being undertaken. The research element of this project endeavours to illuminate current practice and identify areas in which evidence-based development might take place. The development element will include professional development activities and the production of curriculum resources including appropriate online material. This is a multi-method study including a series of student questionnaires; focus groups of students; semi-structured interviews with staff members; and the collection of video of small group teaching. In this paper we report selected findings from the 'student data' of the first year of this project.The questionnaire, conducted with two cohorts of students (2nd and 3rd year Undergraduates), used a double-scale questionnaire in which students were asked to report both on the prevalence of a range of teaching and learning practices and on how valuable these were in supporting their learning. This type of questionnaire instrument is particularly appropriate because the data it generates is suggestive of areas for changes in practice. The gaps between 'practices' and 'values' (across both cohorts) suggested that students valued activities which improved their understanding of how elements of the course were interrelated; which related course content to 'authentic' examples; and those in which teachers made explicit the characteristics of 'high quality' student work. Small group teaching, in the view of most students, was best used to extend and explore concepts introduced in lectures rather than simply reinforcing them or assessing student understanding.Data gathered through focus group activities illuminated the questionnaire data, providing detailed accounts of how students managed their own learning, and the roles played in this by lectures, small group teaching and other resources. Students identified the processes of planning and writing essays as key learning activities during which they integrated diverse course content and reflected on problematic knowledge. Questionnaire and focus group data suggested that students had less clear views regarding the value of collaborative learning, peer-assessment or activities such as making presentations to other students. When students talked in positive terms about these activities, they often referred to the learning benefits of preparation for the tasks rather than of the collaborative activities themselves. These views may provide indications of potential barriers to changes in learning and teaching environments, and suggest that any such changes may have to be carefully justified to students in terms of benefits to their own learning. Many of our findings are broadly in accord with other work on teaching and learning in Higher Education settings (such as the 'Oxford Learning Context Project' and the 'Enhancing Teaching-Learning Environments in Undergraduate Courses' Project) in that 'deep learning' and 'authenticity' in learning activities are valued by students, and that the introduction of specific formative practices (such as sharing notions of 'quality') would be welcomed. At the same time, amongst the students in our sample, a view of learning as an individual process of 'learning-as-acquisition' predominates over a view that it is a social process of 'learning-as-participation', and this will inform the planning of the 'development' aspect of the project. We conclude with a discussion of how the approach we have used might be more widely applied both within and beyond the Cambridge-MIT partnership. We also identify potential affordances of, and barriers to, the development of research-informed teaching in Higher Education

Failure to regulate: counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression

Although depressed mood is a normal occurrence in response to adversity in all individuals, what distinguishes those who are vulnerable to major depressive disorder (MDD) is their inability to effectively regulate negative mood when it arises. Investigating the neural underpinnings of adaptive emotion regulation and the extent to which such processes are compromised in MDD may be helpful in understanding the pathophysiology of depression. We report results from a functional magnetic resonance imaging study demonstrating left-lateralized activation in the prefrontal cortex (PFC) when downregulating negative affect in nondepressed individuals, whereas depressed individuals showed bilateral PFC activation. Furthermore, during an effortful affective reappraisal task, nondepressed individuals showed an inverse relationship between activation in left ventrolateral PFC and the amygdala that is mediated by the ventromedial PFC (VMPFC). No such relationship was found for depressed individuals, who instead show a positive association between VMPFC and amygdala. Pupil dilation data suggest that those depressed patients who expend more effort to reappraise negative stimuli are characterized by accentuated activation in the amygdala, insula, and thalamus, whereas nondepressed individuals exhibit the opposite pattern. These findings indicate that a key feature underlying the pathophysiology of major depression is the counterproductive engagement of right prefrontal cortex and the lack of engagement of left lateral-ventromedial prefrontal circuitry important for the downregulation of amygdala responses to negative stimuli

Hierarchical Bayesian inference for ion channel screening dose-response data

Dose-response (or 'concentration-effect') relationships commonly occur in biological and pharmacological systems and are well characterised by Hill curves. These curves are described by an equation with two parameters: the inhibitory concentration 50% (IC50); and the Hill coefficient. Typically just the 'best fit' parameter values are reported in the literature. Here we introduce a Python-based software tool, PyHillFit , and describe the underlying Bayesian inference methods that it uses, to infer probability distributions for these parameters as well as the level of experimental observation noise. The tool also allows for hierarchical fitting, characterising the effect of inter-experiment variability. We demonstrate the use of the tool on a recently published dataset on multiple ion channel inhibition by multiple drug compounds. We compare the maximum likelihood, Bayesian and hierarchical Bayesian approaches. We then show how uncertainty in dose-response inputs can be characterised and propagated into a cardiac action potential simulation to give a probability distribution on model outputs

Adoption of dynamic simulation for an energy performance rating tool for Korean residential buildings : EDEM-SAMSUNG

Currently, there is a high emphasis on reducing the energy consumption and carbon emissions of buildings worldwide. Korea is facing an emerging issue of energy savings in buildings in perspective of new green economic policy. In this context, various policy measures including the energy efficiency ratings for buildings are being implemented for domestic and non-domestic buildings. In practice, design teams tend to prefer easy to use assessment tools to optimise energy performance and carbon ratings while they are concerned about calculation accuracy and the accurate representation of the dynamics involved associated with the characteristics of Korean residential buildings. This paper presents an assessment tool, named ‘EDEM-Samsung’ that aims to address these challenges for Korean residential apartments, which often encounter complex design issues. EDEM-Samsung is a tool that enables users to make rapid decisions identifying the effect of design parameter changes on energy and carbon ratings with an effective user interface and without compromising accuracy. This paper describes the architecture and functionalities of the tool, and the advantages offered to Korean designers