Building with Employers: An evaluation of Built Environment Courses

This study was carried out to evaluate the delivery of Built Environment (BE) programmes which have long history and credibility from the employers, however, evidence justify the relevancy and effectiveness of the courses was lacking. In line with strategic plan (Southampton Solent University, 2008-13), it was essential to look into the current provisions, students and employer perception of the courses, and gather information to support development of new courses and enhance the existing portfolio to provide high quality learning and teaching in the courses. This study was funded by the Strategic Development Programme with an aim to establish the currency and relevancy of the BE courses. An online survey of employers and alumni was carried out followed by two workshops. A desk study of 25 Universities with similar course provisions was carried out. The finding of the study suggest that employers and alumni are satisfied with the course provisions in terms of the delivery teaching, course content, and what the student learn; there are some opportunities for the broaden the provision but no specific high demand areas were identified. Alumni were happy with the course and have suggested areas of enhancement of the course provision. The current 1 day part time day release model for part time learners was by far the most preferable form of part time delivery and employers were unsure any other alternative form of delivery would be effective for their businesses. However, some employers would consider options if more business specific courses are developed. Employers have expressed their interest for support the course through guest lectures, providing access to construction sites and participating in university events. Solent has the lowest UCAS entry tariff points for BE course by far at 120 compared to the majority of our competitors who range between 220-260. This has highlighted an urgent need to increase entry points to maintain credibility and widen the appeal of the Built Environment courses

James Gregory (1753-1821) and Scottish scientific metaphysics, 1750-1800.

This thesis is a study of some aspects of James Gregory's philosophical and medical thought. Gregory's work is discussed in relation to its local intellectual context of later 18th-century Scottish scientific metaphysics. I show the importance of his writings for understanding how the relationships between epistemology, natural knowledge and religious belief were perceived by some members of the Scottish scientific metaphysics community. This is done empirically by considering Gregory's responses to several other writers. In particular, I show that Gregory's views on causality were put forward to counteract what he perceived as the dangerous influence of Hume's philosophy upon Scottish scientific metaphysicians. This subject is also approached thematically, through what is called the epistemological interiorisation of nature, or the search for the conditions of men's judgements about causes and effects. I identify two principgI strategies for epistemological interiorisation. These are termed 'voluntarist' and 'necessitarian'. I show that while Gregory was a severe critic of what he perceived as the necessitarianism of Hume's philosophy and some other -- forms of scientific metaphysics, Gregory also rejected forms of voluntarism found in the writings of John Stewart, Robert Whytt and Thomas Reid. Finally, Gregory's concern with the nature of cause and effect in physics is related to John Robison's reformation of mechanical philosophy

Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs

In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based techniques, such as A*, with the anytime scalability of sampling-based algorithms, such as Rapidly-exploring Random Trees (RRT). BIT* uses a heuristic to efficiently search a series of increasingly dense implicit RGGs while reusing previous information. It can be viewed as an extension of incremental graph-search techniques, such as Lifelong Planning A* (LPA*), to continuous problem domains as well as a generalization of existing sampling-based optimal planners. It is shown that it is probabilistically complete and asymptotically optimal. We demonstrate the utility of BIT* on simulated random worlds in $\mathbb{R}^2$ and $\mathbb{R}^8$ and manipulation problems on CMU's HERB, a 14-DOF two-armed robot. On these problems, BIT* finds better solutions faster than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster anytime convergence towards the optimum, especially in high dimensions.Comment: 8 Pages. 6 Figures. Video available at http://www.youtube.com/watch?v=TQIoCC48gp

Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Search

Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as respectively used by informed graph-based searches and anytime sampling-based planners. Informed graph-based searches, such as A*, traditionally use heuristics to search a priori graphs in order of potential solution quality. This makes their search efficient but leaves their performance dependent on the chosen approximation. If its resolution is too low then they may not find a (suitable) solution but if it is too high then they may take a prohibitively long time to do so. Anytime sampling-based planners, such as RRT*, traditionally use random sampling to approximate the problem domain incrementally. This allows them to increase resolution until a suitable solution is found but makes their search dependent on the order of approximation. Arbitrary sequences of random samples approximate the problem domain in every direction simultaneously and but may be prohibitively inefficient at containing a solution. This paper unifies and extends these two approaches to develop Batch Informed Trees (BIT*), an informed, anytime sampling-based planner. BIT* solves continuous path planning problems efficiently by using sampling and heuristics to alternately approximate and search the problem domain. Its search is ordered by potential solution quality, as in A*, and its approximation improves indefinitely with additional computational time, as in RRT*. It is shown analytically to be almost-surely asymptotically optimal and experimentally to outperform existing sampling-based planners, especially on high-dimensional planning problems.Comment: International Journal of Robotics Research (IJRR). 32 Pages. 16 Figure

On Recursive Random Prolate Hyperspheroids

This technical note analyzes the properties of a random sequence of prolate hyperspheroids with common foci. Each prolate hyperspheroid in the sequence is defined by a sample drawn randomly from the previous volume such that the sample lies on the new surface (Fig. 1). Section 1 defines the prolate hyperspheroid coordinate system and the resulting differential volume, Section 2 calculates the expected value of the new transverse diameter given a uniform distribution over the existing prolate hyperspheroid, and Section 3 calculates the convergence rate of this sequence. For clarity, the differential volume and some of the identities used in the integration are verified in Appendix A through a calculation of the volume of a general prolate hyperspheroid.Comment: 11 pages, 2 figure