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An approach to designing distributed knowledge-based software platform for injection mould industry

WSEAS Transactions on Information Science and Applications 2010年第11期7卷 1368-1382页

作者： Marinov, Milko Magaletti, Nicola Pavlov, Tsvetelin Gaus, Fabian Rotondi, Domenico Vitliemov, Pavel Ivanova, Slavina Dept. of Computer Systems and echnologies University of Ruse Bulgaria Corporate Research Unit TXT e-solutions SpA Italy University Computing and Information Services Centre University of Ruse Bulgaria Laboratory for Machine Tools and Production Engineering of Aachen University Germany Dept. of Manufacturing Engineering University of Ruse Bulgaria IT Project Management Department at Image Group Bulgaria

Nowadays, the globalization of the manufacturing enterprises requires collaboration across frontiers. In order to attain effective collaboration, the information about the product life cycle must be captured and administrated in a way that supports the decision taken during the product development. In this context, the manufacturing process information needs to be shared between manufacturers. The moulding process is quite complex involving many variable process parameters like pressure, temperature and time settings. These process parameters have to be optimally set in order to improve part quality and maximize the production capacity of the injection moulding machine. In this paper we propose an approach to designing distributed knowledge-based software platform for injection mould industry. The user requirements to the software tools and the key features of our design are defined. The basic principles and the architecture of the software platform and decision support tools are considered.

关键词： Knowledge representation

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Abstracts from the 2011 Joint Annual Meeting of the Society for Northwestern Vertebrate Biology and Washington Chapter of the Wildlife Society, Held at the Wesley Inn, Gig Harbor, Washington, March 23–25, 2011

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Northwestern Naturalist 2011年第2期92卷 136-166页

作者： Michael J. Adams Christopher A. Pearl Jessica Y. Adkins Daniel D. Roby Ladd Bayliss Peter J. Loschl Donald E. Lyons Yasuko Suzuki Hannah E. Anderson Sarah T. Hamman Keith B. Aubry Catherine M. Raley John J. Rohrer Eric C. Lofroth Scott H. Fitkin Michael L. Best Hartwell H. Welsh Jamie B. Bettaso Don T. Ashton Hartwell H. Welsh Scott McBain Lindsay M. Biga Andrew R. Blaustein Susan F. Brady R. Steven Wagner Daniel J. Selski James E. Johnson R. Bruce Bury Wynn W. Cudmore Hartwell H. Welsh Nathan D. Chelgren Tara Chestnut Chaucney W. Anderson Julie Kirshtein Megan T. Cook Tiffany S. Garcia Stephen Corn Erin Muths Amanda Kissel Rick Scherer Eric Delvin Jonathan D. Bakker Peter W. Dunwiddie Diana L. Dishman Deborah A. Duffield Harriet R. Huber Dyanna M. Lambourn Karen M. Dvornich Greg Falxa Gary M. Fellers Patrick M. Kleeman David Miller Brandon Fessler Jason T. Irwin Daniel D. Beck R. Steven Wagner Marc P. Hayes John A. Frew Christian E. Grue Chris R. Friesen Robert T. Mason Stevan J. Arnold Suzanne Estes Tiffany S. Garcia Katie M. Dugger Josh Johnson Mike Rochelle A. J. Kroll Stephanie S. Gervasi Caren S. Goldberg Dan Davis Erica Bree Rosenblum Bill Bosworth Lisette P. Waits Purnima Govindarajulu Gregory A. Green Steve Negri Jennifer R. Griffiths Daniel E. Schindler Christian E. Grue Karen M. Dvornich Adam G. Hansen David A. Beauchamp Casey M. Baldwin Marc P. Hayes Gretchen Padgett-Flohr Blake R. Hossack Winsor H. Lowe Paul Stephen Corn Aaron N. Johnston Kris Kendell Dave Prescott Scott Stevens Peter M. Kiffney Carol Volk Kerensa A. King Christian E. Grue James M. Grassley Andrew J. Kroll Matt Hane Josh Johnston Mike Rochelle Stuart Farber Niels Leuthold Michael J. Adams John P. Hayes Bo Li Michael T. Brett Kent B. Livezey Ilon E. Logan Teresa J. Lorenz Kimberly A. Sullivan Amanda V. Bakian Deanna Lynch Nancy Thomas Krysten Schuler Steven Jeffries Joshua Malt Kelly R. McAllister Joanne P. Schuett-Hames Aimee P. McIntyre Jay E. Jones Eric M. Lund Frithiof T. Waterstrat Jack N. Giovanini Stephen D. Duke Marc P. Hayes US Forest Service Pacific Northwest Research Station Olympia WA 98512 kaubry@fs.fed.us US Forest Service Okanogan-Wenatchee National Forest Methow Valley Ranger District Winthrop WA 98862 British Columbia Ministry of Environment Victoria BC V8W9M1 Washington Department of Fish and Wildlife Winthrop WA 98862 Humboldt State University 1 Harpst St. Arcata CA 95521 addsmike14@*** US Forest Service Pacific Southwest Research Station 1700 Bayside Dr. Arcata CA 95521 hwelsh@fs.fed.us US Fish and Wildlife Service Arcata Field Office 1655 Heindon Road Arcata CA 95521 jamie_bettaso@fws.gov Redwood Sciences Laboratory US Forest Service1700 Bayview Drive Arcata CA 99521 dashton@fs.fed.us hwelsh@fs.fed.us McBain &amp Trush 980 7th Street Arcata CA 95521 scott@*** Oregon State University Environmental Sciences Graduate Program Corvallis OR 97331 lindsay.biga@science.oregonstate.edu Oregon State University Department of Zoology Corvallis OR 97331 US Geological Survey Forest and Rangeland Ecosystem Science Center 3200 SW Jefferson Way Corvallis OR 97331 buryb@usgs.gov Life Science Department Chemeketa Community College Salem OR 97309 wynn.cudmore@chemeketa.edu US Forest Service Redwood Sciences Laboratory 1700 Bayview Drive Arcata CA 95521 hwelsh@fs.fed.us USGS Oregon Water Science Center 2130 SW 5th Avenue Portland OR 97207 chestnut@usgs.gov USGS Headquarters 12201 Sunrise Valley Drive Reston VA 20192 US Geological Survey Northern Rocky Mountain Science Center Aldo Leopold Wilderness Research Institute 790 E. Beckwith Avenue Missoula MT 59801 scorn@usgs.gov US Geological Survey Fort Collins Science Center 2150 Centre Avenue Building C Fort Collins CO 80526 Department of Biological Sciences Simon Fraser University Burnaby BC V5A 1S6 Colorado State University Department of Fishery and Wildlife Biology Fort Collins CO 80523 The Nature Conservancy 120 Union Ave SE Olympia WA 98501 edelvin@tnc.org School of Forest Resources University of Washington Seattle WA

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Prediction of novel precursor miRNAs using a context-sensitive Hidden Markov Model (CSHMM)

Prediction of novel precursor miRNAs using a context-sensiti...

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8th Asia-Pacific bioinformatics Conference, APBC 2010

作者： Agarwal, Sumeet Vaz, Candida Bhattacharya, Alok Srinivasan, Ashwin Department of Physics Systems Biology Doctoral Training Centre University of Oxford Parks Road Oxford OX1 3PU United Kingdom Center for Computational Biology and Bioinformatics School of Information Technology Jawaharlal Nehru University New Delhi 110067 India School of Life Sciences Jawaharlal Nehru University New Delhi 110067 India Oxford University Computing Laboratory Wolfson Building Parks Road Oxford OX1 3QD United Kingdom IBM India Research Lab. 4-C Vasant Kunj Institutional Area Phase II Vasant Kunj New Delhi 110070 India School of Computer Science and Engineering Centre for Health Informatics University of New South Wales Sydney Australia

Background: It has been apparent in the last few years that small non coding RNAs (ncRNA) play a very significant role in biological regulation. Among these microRNAs (miRNAs), 22-23 nucleotide small regulatory RNAs, have been a major object of study as these have been found to be involved in some basic biological processes. So far about 706 miRNAs have been identified in humans alone. However, it is expected that there may be many more miRNAs encoded in the human genome. In this report, a "context-sensitive" Hidden Markov Model (CSHMM) to represent miRNA structures has been proposed and tested extensively. We also demonstrate how this model can be used in conjunction with filters as an ab initio method for miRNA identification. Results: The probabilities of the CSHMM model were estimated using known human miRNA sequences. A classifier for miRNAs based on the likelihood score of this "trained" CSHMM was evaluated by: (a) cross-validation estimates using known human sequences, (b) predictions on a dataset of known miRNAs, and (c) prediction on a dataset of non coding RNAs. The CSHMM is compared with two recently developed methods, miPred and CID-miRNA. The results suggest that the CSHMM performs better than these methods. In addition, the CSHMM was used in a pipeline that includes filters that check for the presence of EST matches and the presence of Drosha cutting sites. This pipeline was used to scan and identify potential miRNAs from the human chromosome 19. It was also used to identify novel miRNAs from small RNA sequences of human normal leukocytes obtained by the Deep sequencing (Solexa) methodology. A total of 49 and 308 novel miRNAs were predicted from chromosome 19 and from the small RNA sequences respectively Conclusion: The results suggest that the CSHMM is likely to be a useful tool for miRNA discovery either for analysis of individual sequences or for genome scan. Our pipeline, consisting of a CSHMM and filters to reduce false positives shows promise as

关键词： RNA

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Content-based image retrieval by indexing random subwindows with randomized trees

Content-based image retrieval by indexing random subwindows ...

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作者： Marée, Raphaël Geurts, Pierre Wehenkel, Louis GIGA Bioinformatics Platform University of Liège Belgium Systems and Modeling Unit Department of Electrical Engineering and Computer Science University of Liège Belgium FNRS Belgium

We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted from a sample of images. We also present the possibility of updating the model as new images come in, and the capability of comparing new images using a model previously constructed from a different set of images. The approach is quantitatively evaluated on various types of images and achieves high recognition rates despite its conceptual simplicity and computational efficiency. © 2009 Information Processing Society of Japan.

关键词： Computational efficiency

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Protein Contact Map Prediction Based on an Ensemble Learning Method

Protein Contact Map Prediction Based on an Ensemble Learning...

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International Conference on computer engineering and Technology, ICCET

作者： Narjes Khatoon Habibi Mohammad Hossein Saraee Advanced Database Systems Data Mining and Bioinformatics Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Contact map is the simplified, 2D representation of protein spatial structure. Contact map prediction is an intermediate step to predict protein 3D structure. Ensemble learning-based model is a collection of learners that is more accurate than a single learner. In this paper we propose an ensemble learning method for contact map prediction. Results show that a considerable performance improvement is attainable using this approach instead of using a single model.

关键词： Learning systems Protein engineering Contacts bioinformatics Neural networks Genetic mutations Machine learning Protein sequence Data engineering Database systems

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is-rSNP: a novel technique for in silico regulatory SNP detection

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BMC bioinformatics 2010年第10期11卷 1-1页

作者： Geoff Macintyre James Bailey Adam Kowalczyk Izhak Haviv Department of Computer Science and Software Engineering The University of Melbourne Victoria 3010 Australia NICTA Victoria Research Lab The University of Melbourne Victoria 3010 Australia Department of Biochemistry and Molecular Biology The University of Melbourne Victoria 3010 Australia Ian Potter Centre for Cancer Genomics and Predictive Medicine Peter MacCallum Cancer Centre East Melbourne Victoria Australia Bioinformatics and Systems Integration The Blood and DNA Profiling Facility Baker IDI heart and diabetes Institute 75 Commercial Rd Prahran 3004 Victoria Australia

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Meeting Report from the Second "Minimum Information for Biological and Biomedical Investigations" (MIBBI) workshop

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Standards in genomic sciences 2010年第3期3卷 259-66页

作者： Carsten Kettner Dawn Field Susanna-Assunta Sansone Chris Taylor Jan Aerts Nigel Binns Andrew Blake Cedrik M Britten Ario de Marco Jennifer Fostel Pascale Gaudet Alejandra González-Beltrán Nigel Hardy Jan Hellemans Henning Hermjakob Nick Juty Jim Leebens-Mack Eamonn Maguire Steffen Neumann Sandra Orchard Helen Parkinson William Piel Shoba Ranganathan Philippe Rocca-Serra Annapaola Santarsiero David Shotton Peter Sterk Andreas Untergasser Patricia L Whetzel Beilstein-Institut Frankfurt am Main Germany Centre for Ecology & Hydrology UK Oxford e-Research Centre University of Oxford UK The European Bioinformatics Institute (EBI) UK Faculty of Engineering - ESAT/SCD Leuven University Leuven-Heverlee Belgium Division of Pathway Medicine University of Edinburgh Medical School Edinburgh UK MRC Harwell UK Medical Department University Medical Center Johannes Gutenberg University-Mainz Mainz Germany Consortium for Genomic Technology Milano Italy University of Nova Gorica Nova Gorica Slovenia NIEHS NIH US National Toxicology Program USA Swiss Institute of Bioinformatics Geneva Switzerland Computational and Systems Medicine and Department of Computer Science University College London London UK Department of Computer Science Aberystwyth University Aberystwyth UK Center for Medical Genetics Ghent University Ghent Belgium Department of Plant Biology University of Georgia Athens USA Department of Stress- and Developmental Biology Institute for Plant Biochemistry Halle Germany Peabody Museum of Natural History Yale University New Haven USA Macquarie University Sydney Australia National University of Singapore Singapore Cancer Pharmacology The Mario Negri Institute for Pharmacological Research Milan Italy Image Bioinformatics Research Group Department of Zoology University of Oxford Oxford UK Wellcome Trust Sanger Institute UK Zentrum für Molekulare Biologie der Universität Heidelberg Heidelberg Germany The National Center for Biomedical Ontology / Stanford Center for Biomedical Informatics Research Stanford University Stanford USA

This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and Biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-Institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://***/.

关键词： Minimum Information European bioinformatics Institute Biomedical Ontology Stable Link Breakout Group

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Problem Based Learning in the Software engineering Classroom

Problem Based Learning in the Software Engineering Classroom

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Conference on Software engineering Education & Training (CSEE&T)

作者： Ita Richardson Yvonne Delaney Department of Computer Science & Information Systems and Lero-the Irish Software Engineering Research Centre University of Limerick Ireland Management Development Unit Kemmy Business School University of Limerick Ireland

Software engineering lecturers are faced with the teaching of concepts which sometimes are not easy for inexperienced students to understand. Therefore, it can be useful to consider and use non-traditional teaching methods which can improve studentspsila learning. In this paper, we discuss problem-based learning and how its use can improve studentspsila understanding of concepts. We present factors which should exist in 'pure' problem-based learning. We then describe how one of the authors used problem-based learning in a class who were required to understand information flows through software engineering diagramming techniques, with the ultimate view to being able to analyze and design computerized information systems. This problem-based learning class was observed and analyzed by the second author. The analysis presented focuses on the problem-based learning factors, how they were implemented in class, and the strengths and weaknesses of the use of problem-based learning in this way. In conclusion, the authors discuss how the teaching could be improved through modifying the teaching method for a future class in which problem-based learning will be used. This modification is expected to enhance the studentspsila learning and their experience.

关键词： Software engineering Information systems Problem-solving computer science education systems engineering education computer science Information analysis North America Technological innovation Concrete

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Reports of the AAAI 2009 spring symposia

Reports of the AAAI 2009 spring symposia

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作者： Bao, Jie Bojars, Uldis Choudhury, Tanzeem Ding, Li Greaves, Mark Kapoor, Ashish Louchart, Sandy Mehta, Manish Nebel, Bernhard Nirenburg, Sergei Oates, Tim Roberts, David L. Sanfilippo, Antonio Stojanovic, Nenad Stubbs, Kristen Thomaz, Andrea L. Tsui, Katherine Woelfl, Stefan Tetherless World Constellation Rensselaer Polytechnic Institute United States Social Software Unit of DERI Galway Ireland Computer Science Department Dartmouth College United States Knowledge Systems at Vulcan Inc United States Adaptive Systems and Interaction Group Microsoft Research Redmond United States School for Mathematics and Computer Sciences Heriot-Watt University United Kingdom School ot Interactive Computing Georgia Institute of Technology United States Department of Computer Science University of Freiburg Germany Computer Science and Electrical Engineering Department University of Maryland Baltimore County United States Pacific Northwest National Laboratory Richland WA United States University of Karlsruhe Germany IRobot Corporation United States University of Massachusetts Lowell United States

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's department of computer Science, was pleased to present the 2009 Spring Symposium Series, held Monday through Wednesday, March 23-25, 2009, at Stanford University. The titles of the nine symposia were Agents That Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning systems, Experimental Design for Real- World systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The goal of the Agents That Learn from Human Teachers symposium was to investigate how we can enable software and robotics agents to leam from real-time interaction with an everyday human partner. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long-term research. The Experimental Design symposium discussed the challenges of evaluating AI systems. The Human Behavior Modeling symposium explored reasoning methods for understanding various aspects of human behavior, especially in the context of designing intelligent systems that interact with humans. The Intelligent Event Processing symposium discussed the need for more Al-based approaches in event pro-cessing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies IIAAAI symposium discussed innovations, progress, and novel techniques in the research domain. The Learning by Reading and Learning to Read symposium explored two aspects of making natural language texts semantically accessible to, and processable by, machines. The Social Semantic

关键词： Benchmarking

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Meeting Report: BioSharing at ISMB 2010

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Standards in genomic sciences 2010年第3期3卷 254-8页

作者： Dawn Field Susanna Sansone Edward F Delong Peter Sterk Iddo Friedberg Pascale Gaudet Susanna Lewis Renzo Kottmann Lynette Hirschman George Garrity Guy Cochrane John Wooley Folker Meyer Sarah Hunter Owen White Brian Bramlett Susan Gregurick Hilmar Lapp Sandra Orchard Philippe Rocca-Serra Alan Ruttenberg Nigam Shah Chris Taylor Anne Thessen Centre for Ecology & Hydrology Wallingford UK Oxford e-Research Centre University of Oxford Oxford UK Department of Biological Engineering Massachusetts Institue of Technology Cambridge USA Department of Microbiology Miami University Oxford USA Department of Computer Science and Software Engineering Miami University Oxford USA Swiss Institute of Bioinformatics Geneva 4 Switzerland Lawrence Berkeley National Laboratory Berkeley USA Microbial Genomics Group Max Planck Institute for Marine Microbiology & Jacobs University Bremen Bremen Germany Information Technology Center The MITRE Corporation Bedford USA Department of Microbiology and Molecular Genetics Michigan State University East Lansing USA European Molecular Biology Laboratory (EMBL) Outstation European Bioinformatics Institute (EBI) Cambridge UK University of California San Diego La Jolla USA Argonne National Laboratory Argonne USA Institute for Genome Sciences University of Maryland School of Medicine Baltimore USA Lux Bio Group Portland USA Biological Systems Science Division Department of Energy Washington DC USA National Evolutionary Synthesis Center (NESCent) Durham USA Science Commons c/o Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory Cambridge USA Stanford Center for Biomedical Informatics Research Stanford USA Marine Biological Laboratory USA

This report summarizes the proceedings of the one day BioSharing meeting held at the Intelligent systems for Molecular Biology (ISMB) 2010 conference in Boston, MA, USA This inaugural BioSharing event was hosted by the Genomic Standards Consortium as part of its M3 & BioSharing special interest group (SIG) workshop. The BioSharing event included invited talks from a range of community leaders and a panel discussion at the end of the day. The panel session led to the formal agreement among community leaders to join together to promote cross-community knowledge exchange and collaborations. A key focus of the newly formed Biosharing community will be linking up resources to promote real-world data sharing (virtuous cycle of data) and supporting compliance with data policies through the creation of a one-stop-portal of information. Further information about the newly established BioSharing effort can be found at http://***.

关键词： Community Leader Funding Agency Special Interest Group Panel Discussion Standardization Effort

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