Intelligence-based educational package on fluid mechanics

Series: Lecture notes in computer scienceAuthor name used in this publication: KwokWing Chau2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Internet-based interactive package for diagnostic assessment on learning of fluid mechanics

Author name used in this publication: Kwokwing Chau2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

AI-based teaching package for open channel flow on Internet

Author name used in this publication: Kwokwing Chau2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

STEM@1000mph: developing open educational resources in a live engineering project

Higher education institutions are recognising the clear benefits of open educational resources, and academics are engaging with the development of these resources. This paper presents a case study of OERs being developed using the live, current BloodhoundSSC world land speed record project as a basis. The paper outlines the rationale for the BloodhoundSSC project and its focus on educational engagement across the age spectrum. The work undertaken to develop a web-based repository along with activities to stimulate academic and student engagement are described. The paper explores how academics have engaged with developing OERs based on this openly available content, the issues encountered and ways in which these issues can be mitigated

GAELS Project Final Report: Information environment for engineering

The GAELS project was a collaboration commenced in 1999 between Glasgow University Library and Strathclyde University Library with two main aims:· to develop collaborative information services in support of engineering research at the Universities of Glasgow and Strathclyde· to develop a CAL (computer-aided learning package) package in advanced information skills for engineering research students and staff The project was funded by the Scottish Higher Education Funding Council (SHEFC) from their Strategic Change Initiative funding stream, and funding was awarded initially for one year, with an extension of the grant for a further year. The project ended in June 2001.The funding from SHEFC paid for two research assistants, one based at Glasgow University Library working on collaborative information services and one based at Strathclyde University Library developing courseware. Latterly, after these two research assistants left to take up other posts, there has been a single researcher based at Glasgow University Library.The project was funded to investigate the feasibility of new services to the Engineering Faculties at both Universities, with a view to making recommendations for service provision that can be developed for other subject areas

Competing tasks as an index of intelligence

[Abstract]: Most studies involving competing (or dual) tasks have been concerned with the investigation of models of attention and have stressed the importance of task characteristics in determining competing-task performance. The relatively few studies which have looked at indi¬vidual differences in competing-task performance suggest that measures of this performance could reflect operations which are central to cognitive functioning. This paper examines two key questions which stem from this research: is there a separate ability involved in competing-task performance? Is competing-task performance more indicative of general intellectual functioning? A battery composed of both single and competing tasks was presented to 91 Ss. Two sets of scores, primary and `secondary', were obtained from the competing tasks. The results indicate that `single' and `primary' scores are basically measuring the same thing but that secondary' scores measure what is perhaps a time-sharing factor. There is also some evidence that primary and secondary scores are more indicative of the general factor, as measured by this battery, than their single counterparts

Fire Safety Analysis of a Railway Compartment using Computational Fluid Dynamics

Trains are considered to be the safest on-land transportation means for both passengers and cargo. Train accidents have been mainly disastrous, especially in case of fire, where the consequences are extensive loss of life and goods. The fire would generate smoke and heat which would spread quickly inside the railway compartments. Both heat and smoke are the primary reasons of casualties in a train. This study has been carried out to perform numerical analysis of fire characteristics in a railway compartment using commercial Computational Fluid Dynamics code ANSYS. Non-premixed combustion model has been used to simulate a fire scenario within a railway compartment, while Shear Stress Transport k-ω turbulence model has been used to accurately predict the hot air turbulence parameters within the compartment. The walls of the compartment have been modelled as no-slip stationary adiabatic walls, as is observed in real life conditions. Carbon dioxide concentration (CO2), temperature distribution and air flow velocity within the railway compartment has been monitored. It has been observed that the smoke above the fire source flows to both sides of the compartment. The highest temperature zone is located downstream the fire source, and gradually decreases with the increase in the distance from the fire source. It can be seen that CFD can be used as an effective tool in order to analyse the evolution of fire in railway compartments with reasonable accuracy. The paper also briefly discusses the topical reliability issues

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected