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Development Model for Knowledge Management System (KMS) to Improve University's Performance (Case Studies in Indonesia University of Education)

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International Journal of computer science Issues 2012年第1 1-1期9卷 1-6页

作者： Munir Rohendi, Dedi Study Program of Computer Science Faculty of Mathematics and Sciences Education Indonesia University of Education Bandung Indonesia Mechanical Engineering Education Department Faculty of Technology and Vocational Education Indonesia University of Education Bandung Indonesia

Knowledge Management System (KMS) is one way to organize and documented the knowledge of institution. The purpose of this study is to develop a prototype KMS in organizing and documenting the knowledge in the university and to do a document aggregation based on the total number, subjects and writers (lecturers). The development method of KMS prototype is using sharepoints which are able to collect, store and publish all digital data available at the university which can be accessed online. Aggregation is using the percentage between the number of documents, subjects, and writers (lecturers). The aggregation's result between the number of digital files compared to the number of courses and the number of lecturers are each below 10%. In the future, the university still needs to encourage all lecturers or academic community to increase the collection of digital files that can indirectly improve the quality and educational services. © 2012 International Journal of computer science Issues.

关键词： Knowledge based systems

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Improving parallel scalability for edge plasma transport simulations with neutral gas species

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Computational science and Discovery 2012年第1期5卷

作者： McCourt, M. Rognlien, T.D. McInnes, L.C. Zhang, H. Center for Applied Mathematics 657 Rhodes Hall Cornell University Ithaca NY 14853 United States Fusion Energy Sciences Program Lawrence Livermore National Laboratory 7000 East Avenue Livermore CA 94550 United States Argonne National Laboratory Mathematics and Computer Science Division 9700 South Cass Avenue Argonne IL 60439 United States

Simulating the transport of multi-species plasma and neutral species in the edge region of a tokamak magnetic fusion energy device is computationally intensive and difficult due to coupling among various components, strong nonlinearities and a broad range of temporal scales. In addition to providing boundary conditions for the core plasma, such models aid in the understanding and control of the associated plasma/material-wall interactions, a topic that is essential for the development of a viable fusion power plant. The governing partial differential equations are discretized to form a large nonlinear system that typically must be evolved in time to obtain steady-state solutions. Fully implicit techniques using preconditioned Jacobian-free Newton-Krylov methods with parallel domain-based preconditioners are shown to be robust and efficient for the plasma components. Inclusion of neutral gas components, however, increases the condition number of the system to the point where improved parallel preconditioning is needed. Standard algebraic preconditioners that provide sufficient coupling throughout the global domain to handle the neutrals are not generally scalable. We present a new preconditioner, termed FieldSplit, which exploits the character of the neutral equations to improve the scalability of the combined plasma/neutral system. © 2012 IOP Publishing Ltd.

关键词： Scalability

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Secondary structure predictions for long RNA sequences based on inversion excursions - Preliminary results 12

Secondary structure predictions for long RNA sequences based...

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2012 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2012

作者： Yehdego, Daniel T. Zhang, Boyu Taufer, Michela Kodimala, Vikram Kumar Reddy Vegesna, Rahulsimham Viswakula, Sameera Johnson, Kyle L. Leung, Ming-Ying Computational Science Program University of Texas El Paso United States Department of Computer and Information Sciences University of Delaware United States Bioinformatics Program Border Biomedical Research Center University of Texas El Paso United States Department of Mathematical Sciences University of Texas El Paso United States Department of Biological Sciences University of Texas El Paso United States Department of Mathematics and Statistics Old Dominion University Norfolk VA United States University of Texas M. D. Anderson Cancer Center Houston TX United States

ISBN: (纸本)9781450316705

The tremendous demand on computer memory and computing time for prediction of complex secondary structures limits the applicability of most RNA secondary structure prediction programs available to short RNA sequences. We propose to approach this problem by segmenting a long RNA sequence into shorter non-overlapping chunks, predicting the secondary structures of each chunk individually, and then assembling the prediction results to give the structure of the original sequence. The selection of cutting points is a crucial component of the approach. Noting that stem-loops and pseudoknots always contain an inversion, we developed two cutting methods, the centered and optimized methods, for segmenting long RNA sequences based on inversion excursions. For the majority of the sequences in a dataset of 50 RNAs from the RFAM database, the prediction algorithm PKnotsRG used with these cutting methods produces more accurate secondary structures than those predicted for the whole sequence without segmentation. Both the centered and optimized cutting methods outperform the naïve regular segmentation. These results support our claim that cutting is a promising approach for the prediction of long RNA sequences, and choosing the cutting points intelligently by considering sequence features such as inversion excursions can further enhance prediction accuracy.

关键词： Forecasting

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ESICM LIVES 2016: part two

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Intensive Care Medicine Experimental 2016年第1期4卷 205-404页

作者： S. Sivakumar F. S. Taccone K. A. Desai C. Lazaridis M. Skarzynski M. Sekhon W. Henderson D. Griesdale L. Chapple A. Deane L. Williams R. Strickland K. Lange D. Heyland M. Chapman M. J. Rowland P. Garry J. Westbrook R. Corkill C. A. Antoniades K. T. Pattinson G. Fatania A. J. Strong R. B. Myers C. M. Jermaine C. S. Robertson C. G. Rusin J. Hofmeijer L. Sondag M. C. Tjepkema-Cloostermans A. Beishuizen F. H. Bosch M. J. A. M. van Putten L. Carteron C. Patet D. Solari M. Oddo M. A. Ali C. Dias R. Almeida A. Vaz-Ferreira J. Silva E. Monteiro A. Cerejo A. P. Rocha A. A. Elsayed A. M. Abougabal B. N. Beshey K. M. Alzahaby S. Pozzebon A. Blandino Ortiz S. Cristallini O. Lheureux A. Brasseur J. L. Vincent J. Creteur M. Hravnak K. Yousef Y. Chang E. Crago R. M. Friedlander S. A. Abdelmonem S. A. Tahon T. A. Helmy H. S. Meligy F. Puig I. Dunn-Siegrist J. Pugin S. Gupta D. Govil S. Srinivasan S. J. Patel J. K. N A. Gupta D. S. Tomar M. Shafi R. Harne D. P. Arora N. Talwar S. Mazumdar E. E. Papakrivou D. Makris E. Manoulakas B. Tsolaki B. Karadodas E. Zakynthinos I. Palacios Garcia A. Diaz Martin V. Sanchez Encinares M. Pachón Ibañez J. Garnacho Montero G. Labrador T. Cebrero Cangueiro V. Poulose J. Koh J. W. Kam H. Yeter A. Kara O. Aktepe A. Topeli I. Tsolakoglou G. Intas P. Stergiannis A. A. Kolaros E. Chalari E. Athanasiadou A. Martika G. Fildisis V. Faivre C. Mengelle B. Favier D. Payen A. Poppe M. S. Winkler E. Mudersbach J. Schreiber M. L. Wruck E. Schwedhelm S. Kluge C. Zöllner T. Tavladaki A. M. Spanaki H. Dimitriou E. Kondili C. Choulaki E. Meleti D. Kafetzopoulos D. Georgopoulos G. Briassoulis A. García-de la Torre M. V. de la Torre-Prados T. Tsvetanova-Spasova P. Nuevo-Ortega C. Rueda-Molina A. Fernández-Porcel E. Camara-Sola L. Salido-Díaz A. García-Alcántara D. E. Meleti B. Suberviola J. Riera L. Rellan M. Sanchez J. C. Robles E. Lopez R. Vicente E. Miñambres M. Santibañez M. Le Guen J. Moore N. Mason M. Windpassinger O. Plattner E. Mascha D. I. Sessler Outcomes Research U. Melia J. Fontanet J. P. van den Berg M. Baylor College of Medicine Neurology/Neurocritical Care Houston USA Erasmus Hospital Intensive Care Medicine Brussels Belgium Centre Hospitalier Régional Orléans Réaimation Médicale Orléans France University of British Columbia Vancouver Canada University of Adelaide Discipline of Acute Care Medicine Adelaide Australia Royal Adelaide Hospital Intensive Care Adelaide Australia Griffith University Menzies Health Institute of Queensland Gold Coast Australia University of Adelaide Discipline of Medicine Adelaide Australia Kingston General Hospital Clinical Evaluation Research Unit Kingston Canada University of Oxford Nuffield Department of Clinical Neurosciences Oxford UK Oxford University Hospitals NHS Foundation Trust Neurosciences Intensive Care Unit Oxford UK King’s College Hospital Department of Neurosurgery London UK King’ College London Institute of Psychiatry Psychology and Neuroscience Department of Clinical Neuroscience London UK Rice University Computer Science Houston USA Baylor College of Medicine Neurosurgery Houston USA Baylor College of Medicine Pediatric Cardiology Houston USA University of Twente Clinical Neurophysiology Enschede Netherlands Rijnstate Hospital Neurology Arnhem Netherlands Medical Spectrum Twente Clinical Neurophysiology Enschede Netherlands Medical Spectrum Twente Intensive Care Enschede Netherlands Rijnstate Hospital Intensive Care Arnhem Netherlands CHUV Department of Intensive Care Medicine Neuroscience Critical Care Research Group Lausanne Switzerland Aga Khan University Hospital Anaesthesiology Karachi Pakistan Centro Hospitalar São João Intensive Care Porto Portugal Faculty of Medicine University of Porto Medicine Porto Portugal Faculdade de Ciências Universidade do Porto Departamento de Matemática Porto Portugal Centro de Matemática Universidade do Porto Porto Portugal The Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine Zaragoza Spain Centro Hospitalar

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Erratum to: Methods for evaluating medical tests and biomarkers

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Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7714 LNCS卷 V-VIII页

作者： Vanmechelen, Kurt Altmann, Jörn Rana, Omer F. Hovestadt, Matthias University of Antwerp Department of Mathematics and Computer Science Middelheimlaan 1 2020 Antwerp Belgium Seoul National University College of Engineering Department of Industrial Engineering Technology Management Economics and Policy Program 599 Gwanak-Ro Gwanak-Gu Seoul 151-744 Korea Republic of Cardiff University School of Computer Science Queen's Buildings Newport Road Cardiff CF24 3AA United Kingdom Technical University of Berlin Germany

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Propositional dynamic logic with storing, recovering and parallel composition

Propositional dynamic logic with storing, recovering and par...

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作者： Benevides, Mario R.F. De Freitas, Renata Petrucio, Viana Computer Science Department Systems and Computer Engineering Program Federal University of Rio de Janeiro Brazil Institute of Mathematics Fluminense Federal University Brazil

This work extends Propositional Dynamic Logic (PDL) with parallel composition operator and four atomic programs which formalize the storing and recovering of elements in data structures. A generalization of Kripke semantics is proposed that instead of using set of possible states it uses structured sets of possible states. This new semantics allows for representing data structures and using the five new operator one is capable of reasoning about the manipulation of these data structures. The use of the new language (PRSPDL) is illustrated with some examples. We present sound and complete set of axiom schemata and inference rules to prove all the valid formulas for a restricted fragment called RSPDLo. © 2011 Elsevier B.V. All rights reserved.

关键词： Data structures

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A framework for relating neural activity to freely moving behavior

A framework for relating neural activity to freely moving be...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Justin D. Foster Paul Nuyujukian Oren Freifeld Stephen I. Ryu Michael J. Black Krishna V. Shenoy Department of Electrical Engineering University of Stanford Stanford CA USA Bioengineering and Stanford Medical School University of Stanford Stanford CA USA Division of Applied Mathematics Brown University Providence RI USA Department of Neurosurgery Palo Alto Medical Foundation Palo Alto CA USA Department of Computer Science Brown University Providence RI USA Departments of Electrical Engineering Bioengineering and Neurobiology Neurosciences Program Neurosciences Program University of Stanford Stanford CA USA

Two research communities, motor systems neuroscience and motor prosthetics, examine the relationship between neural activity in the motor cortex and movement. The former community aims to understand how the brain controls and generates movement; the latter community focuses on how to decode neural activity as control signals for a prosthetic cursor or limb. Both have made progress toward understanding the relationship between neural activity in the motor cortex and behavior. However, these findings are tested using animal models in an environment that constrains behavior to simple, limited movements. These experiments show that, in constrained settings, simple reaching motions can be decoded from small populations of spiking neurons. It is unclear whether these findings hold for more complex, full-body behaviors in unconstrained settings. Here we present the results of freely-moving behavioral experiments from a monkey with simultaneous intracortical recording. We investigated neural firing rates while the monkey performed various tasks such as walking on a treadmill, reaching for food, and sitting idly. We show that even in such an unconstrained and varied context, neural firing rates are well tuned to behavior, supporting findings of basic neuroscience. Further, we demonstrate that the various behavioral tasks can be reliably classified with over 95% accuracy, illustrating the viability of decoding techniques despite significant variation and environmental distractions associated with unconstrained behavior. Such encouraging results hint at potential utility of the freely-moving experimental paradigm.

关键词： Legged locomotion Maximum likelihood decoding Kinematics Animals Educational institutions Cameras Neuroscience

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Graph sparsification by effective resistances

Graph sparsification by effective resistances

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作者： Spielman, Daniel A. Srivastava, Nikhil Program in Applied Mathematics Department of Computer Science Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

We present a nearly linear time algorithm that produces high-quality spectral sparsifiers of weighted graphs. Given as input a weighted graph G = (V, E, w) and a parameter ∈ > 0, we produce a weighted subgraph H = (V, E˜, w˜) of G such that |E˜| = O(n logn/∈2) and all x ∈ RV satisfy (1 - ∈) Σuv∈E (x(u) - x(v))2wuv ≤ Σuv∈E˜ (x(u) - x(v))2w˜uv ≤ (1 + ∈) Σuv∈E (x(u) - x(v))2wuv· This improves upon the spectral sparsifiers constructed by Spielman and Teng, which had O(nlogcn) edges for some large constant c, and upon the cut sparsifiers of Benczúr and Karger, which only satisfied these inequalities for x ∈ {0, 1}V. A key ingredient in our algorithm is a subroutine of independent interest: a nearly linear time algorithm that builds a data structure from which we can query the approximate effective resistance between any two vertices in a graph in O(logn) time. © 2011 Society for Industrial and Applied mathematics.

关键词： Clustering algorithms

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Accelerating the Artificial Bee Colony Algorithm by Hardware Parallel Implementations

Accelerating the Artificial Bee Colony Algorithm by Hardware...

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IEEE Latin American Symposium on Circuits and Systems

作者： Daniel M. Mufioz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincon Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Department of Computer Science and Mathematics University of Brasilia

ISBN: (纸本)9781467312073

Artificial bee colony (ABC) is an optimization algorithm inspired on the intelligent behavior of honey bee swarms. It is suitable to be applied when mathematical techniques are impractical or provide suboptimal solutions. As a population-based algorithm, the ABC suffers on large execution times specifically for embedded optimization problems with computational limitations. For that we propose a hardware parallel architecture of the opposition-based ABC algorithm (HPOABC) that facilitates the implementation in Field programmable Gate Arrays (FPGAs). Numerical simulations using four well-known benchmark problems demonstrate that the opposition-based approach allows the algorithm to improve its functionality, preserving the swarm diversity. Additionally, synthesis results point outs that the HPOABC architecture is effectively mapped in hardware and is suitable for embedded applications.

关键词： Global optimization Swarm intelligence Artificial bee colony FPGAs Floating-point arithmetic

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