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Sample records for learning time alt

  1. Academic learning time in physical education (ALT-PE: is it related to fundamental movement skill acquisition and learning?. Tiempo de aprendizaje académico en educación física (ALT-PE: ¿tiene que ver con la adquisición y aprendizaje de habilidades motrices fundamentales?

    Directory of Open Access Journals (Sweden)

    Loza Olave, Edmundo

    2007-01-01

    Full Text Available AbstractIn this study, the relationship between time-variables of the physical education lesson and fundamental movement skill acquisition and learning was examined. One hundred and ten first grade students and their six physical educators participated. Students were pre-, post-, and re-tested both qualitatively and quantitatively on overhand throwing and catching. The Test of Gross Motor Development (Ulrich, 1985 was used for the assessment of qualitative skill performance and the Michigan Educational Assessment Program (MEAP, 1984 for the assessment of quantitative skill performance. Between pre- and post-tests students received an eight lessons unit instruction with emphasis on overhand throwing and catching. A total number of thirty-six students (6 students X 6 schools were selected, andtheir videotaped motor behavior in the lessons was analyzed with the Academic Learning Time ¿ Physical Education (ALT-PE,Parker, 1989. Regression analysis was applied to calculate students´ residual scores of achievement and to enable further data analysis. Correlation analysis between students´ achievement and ALT-PE categories indicated significant relationships between skill acquisition, learning and ALT-PE categories. It can be concluded that time devoted to practice skills, unlike times pent on activities irrelevant to the instructional task oron games, contributes to skill learning.ResumenEn este trabajo se estudia la relación entre variables temporales de las clases de Educación Física y la adquisición y aprendizaje de habilidades motrices fundamentales. En él participaron ciento diez alumnos de primer grado y seis profesores de Educación Física de Grecia. Los alumnos fueron examinados antes, durante y después tanto en términos cuantitativos como cualitativos en cuanto a lanzamientos y recepciones por encima de la cabeza. Se utilizó el Test de Desarrollo Motor Grueso (Ulrich, 1985 para evaluar el rendimiento en habilidades cualitativas y

  2. General Time-Division AltBOC Modulation Technique for GNSS Signals

    Directory of Open Access Journals (Sweden)

    Z. Zhou

    2018-04-01

    Full Text Available In this paper, a general time-division alternate binary offset carrier (GTD-AltBOC modulation method is proposed, which is an extension of TD-AltBOC and time-multiplexed offset-carrier quadrature phase shift keying (TMOC-QPSK with high design flexibility. In this method, binary complex subcarriers and a time-division technique with flexible time slot assignment are used to achieve constant envelope modulation of the signal components with a variable power allocation ratio (PAR. The underlying principle of GTD-AltBOC and the constraints related to the PAR are investigated. For the generation of GTD-AltBOC signals, a lookup table (LUT-based scheme is presented; the minimum required clock rate is half or less of that for existing non-time-division methods. The receiver processing complexities are analyzed for three typical receiving modes, and the power spectral densities (PSDs, cross-correlation functions, multiplexing efficiencies and code-tracking performance are simulated; the results show that GTD-AltBOC enables a significant decrease in receiving complexity compared with existing methods while maintaining high performance in terms of multiplexing efficiency and code tracking.

  3. The LTDP ALTS Project: Contributing to the Continued Understanding and Exploitation of the ATSR Time Series

    Science.gov (United States)

    Clarke, Hannah; Done, Fay; Casadio, Stefano; Mackin, Stephen; Dinelli, Bianca Maria; Castelli, Elisa

    2016-08-01

    The long time-series of observations made by the Along Track Scanning Radiometers (ATSR) missions represents a valuable resource for a wide range of research and EO applications.With the advent of ESA's Long-TermData Preservation (LTDP) programme, thought has turned to the preservation and improved understanding of such long time-series, to support their continued exploitation in both existing and new areas of research, bringing the possibility of improving the existing data set and to inform and contribute towards future missions. For this reason, the 'Long Term Stability of the ATSR Instrument Series: SWIR Calibration, Cloud Masking and SAA' project, commonly known as the ATSR Long Term Stability (or ALTS) project, is designed to explore the key characteristics of the data set and new and innovative ways of enhancing and exploiting it.Work has focussed on: A new approach to the assessment of Short Wave Infra-Red (SWIR) channel calibration.; Developmentof a new method for Total Column Water Vapour (TCWV) retrieval.; Study of the South Atlantic Anomaly (SAA).; Radiative Transfer (RT) modelling for ATSR.; Providing AATSR observations with their location in the original instrument grid.; Strategies for the retrieval and archiving of historical ATSR documentation.; Study of TCWV retrieval over land; Development of new methods for cloud masking This paper provides an overview of these activities and illustrates the importance of preserving and understanding 'old' data for continued use in the future.

  4. Alanine transaminase (ALT) blood test

    Science.gov (United States)

    ... gov/ency/article/003473.htm Alanine transaminase (ALT) blood test To use the sharing features on this page, please enable JavaScript. The alanine transaminase (ALT) blood test measures the level of the enzyme ALT in ...

  5. ALT Strategy, February 2017 - January 2020

    OpenAIRE

    Deepwell, Maren

    2017-01-01

    The Association for Learning Technology (ALT) represents individual and organisational Members from all sectors and parts of the UK. Our Membership includes practitioners, researchers and policy makers with an interest in Learning Technology. Our community grows more diverse as Learning Technology has become recognised as a fundamental part of learning, teaching and assessment. Our charitable objective is "to advance education through increasing, exploring and disseminating knowledge in t...

  6. Identification of critical time-consuming student support activities in e-learning

    NARCIS (Netherlands)

    De Vries, Fred; Kester, Liesbeth; Sloep, Peter; Van Rosmalen, Peter; Pannekeet, Kees; Koper, Rob

    2005-01-01

    Please cite the original publication: De Vries, F., Kester, L., Sloep, P., Van Rosmalen, P., Pannekeet, K., & Koper, R. (2005). Identification of critical time-consuming student support activities in e-learning. Research in Learning Technology (ALT-J), 13(3), 219-229.

  7. Learning During Stressful Times

    Science.gov (United States)

    Shors, Tracey J.

    2012-01-01

    Stressful life events can have profound effects on our cognitive and motor abilities, from those that could be construed as adaptive to those not so. In this review, I discuss the general notion that acute stressful experience necessarily impairs our abilities to learn and remember. The effects of stress on operant conditioning, that is, learned helplessness, as well as those on classical conditioning procedures are discussed in the context of performance and adaptation. Studies indicating sex differences in learning during stressful times are discussed, as are those attributing different responses to the existence of multiple memory systems and nonlinear relationships. The intent of this review is to highlight the apparent plasticity of the stress response, how it might have evolved to affect both performance and learning processes, and the potential problems with interpreting stress effects on learning as either good or bad. An appreciation for its plasticity may provide new avenues for investigating its underlying neuronal mechanisms. PMID:15054128

  8. Post-irradiation time effects on the graft of poly(ethylene-alt-tetrafluoroethylene) (ETFE) films for ion exchange membrane application

    Energy Technology Data Exchange (ETDEWEB)

    Geraldes, Adriana N., E-mail: angeral@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN), Centro de Quimica e Meio Ambiente (CQMA), Av. Professor Lineu Prestes, 2242, 05508-900, Sao Paulo (Brazil); Zen, Heloisa A.; Ribeiro, Geise; Ferreira, Henrique P.; Souza, Camila P.; Parra, Duclerc F.; Santiago, Elisabete I.; Lugao, Ademar B. [Instituto de Pesquisas Energeticas e Nucleares (IPEN), Centro de Quimica e Meio Ambiente (CQMA), Av. Professor Lineu Prestes, 2242, 05508-900, Sao Paulo (Brazil)

    2010-03-15

    Grafting of styrene followed by sulfonation onto poly(ethylene-alt-tetrafluoroethylene) (ETFE) was studied for synthesis of ion exchange membranes. Radiation-induced grafting of styrene onto ETFE films was investigated after simultaneous irradiation (in post-irradiation condition) using a {sup 60}Co source. The ETFE films were irradiated at 20 kGy dose at room temperature and chemical changes were monitored after contact with styrene for grafting. The post-irradiation time was established at 14 days when the films were remained in styrene/toluene 1:1 v/v. After this period the grafting degree was evaluated in the samples. The grafted films were sulfonated using chlorosulfonic acid and 1, 2-dichloroethane 20:80 (v/v) at room temperature for 5 h. The membranes were analyzed by infrared spectroscopy (FTIR), differential scanning calorimeter (DSC), thermogravimetric measurements (TG) and degree of grafting (DOG). The ion exchange capacity (IEC) of membranes was determined by acid-base titration and the values for ETFE membranes were achieved higher than Nafion films. Preliminary single cell performance was made using pure H{sub 2} and O{sub 2} as reactants at a cell temperature of 80 deg. C and atmospheric gas pressure. The fuel cell performance of ETFE films was satisfactory when compared to state-of-art Nafion membranes.

  9. Post-irradiation time effects on the graft of poly(ethylene-alt-tetrafluoroethylene) (ETFE) films for ion exchange membrane application

    Science.gov (United States)

    Geraldes, Adriana N.; Zen, Heloísa A.; Ribeiro, Geise; Ferreira, Henrique P.; Souza, Camila P.; Parra, Duclerc F.; Santiago, Elisabete I.; Lugão, Ademar B.

    2010-03-01

    Grafting of styrene followed by sulfonation onto poly(ethylene-alt-tetrafluoroethylene) (ETFE) was studied for synthesis of ion exchange membranes. Radiation-induced grafting of styrene onto ETFE films was investigated after simultaneous irradiation (in post-irradiation condition) using a 60Co source. The ETFE films were irradiated at 20 kGy dose at room temperature and chemical changes were monitored after contact with styrene for grafting. The post-irradiation time was established at 14 days when the films were remained in styrene/toluene 1:1 v/v. After this period the grafting degree was evaluated in the samples. The grafted films were sulfonated using chlorosulfonic acid and 1, 2-dichloroethane 20:80 (v/v) at room temperature for 5 h. The membranes were analyzed by infrared spectroscopy (FTIR), differential scanning calorimeter (DSC), thermogravimetric measurements (TG) and degree of grafting (DOG). The ion exchange capacity (IEC) of membranes was determined by acid-base titration and the values for ETFE membranes were achieved higher than Nafion ® films. Preliminary single cell performance was made using pure H 2 and O 2 as reactants at a cell temperature of 80 °C and atmospheric gas pressure. The fuel cell performance of ETFE films was satisfactory when compared to state-of-art Nafion ® membranes.

  10. Learning Time and Educational Effectiveness.

    Science.gov (United States)

    Anderson, Lorin W.

    1980-01-01

    To explore the relationship between time and school learning, this paper defines the three kinds of learning time identified by researchers--allocated time, time-on-task, and academic learning time--and relates them to curriculum development. The author cites evidence that time-on-task is related to student achievement and describes two…

  11. Virtueller Medizinischer Campus Graz: eine e-Learning Umgebung wird 5 Jahre alt / Virtual Medical Campus Graz: an e-learning environment has its 5 year-anniversary

    Directory of Open Access Journals (Sweden)

    Reibnegger, Gilbert

    2007-12-01

    Full Text Available Parallel with the initiation of an integrated curriculum at the Medical University of Graz a virtual learning environment was implemented, designated as Virtual Medical Campus (VMC. Several financial support projects made the development of the VMC and its enhancements possible. Learning objects are granular and strictly equipped with a set of metadata conforming the SCORM 2004 2nd edition-standard and are therefore reusable and exchangeable with other study courses or e-Learning-systems. Simple usability allows authors the intuitive creation of content, which may be enriched with interactive and tutorial systems using several built in authoring tools like web-based-training or a Virtual Microscope. In 2005 more than 3300 students applied for human medicine at the Medical University of Graz and it was decided to give a virtual term with a selection process at the end of it.More than 1 million accesses to learning objects and 257,000 web-based-trainings were handled without a single breakdown. This unique interim solution of a virtual term demonstrated the capacity of the VMC-system and the organisational possibility to intercept rushes of application using e-Learning. In the meanwhile the VMC Graz provides 13 study courses at four universities in two different European countries and two international postgraduate programs. The technical development aims at Web 3.0 – “Semantic Web” and the further expansion of co-operations is a present and future strategy.

  12. Editorial ALT-J non-electronica

    Directory of Open Access Journals (Sweden)

    Gabriel Jacobs

    1997-12-01

    Full Text Available The ALT Executive Committee has discussed on a number of occasions the question of making ALT-J wholly available in electronic form on our Web site (at present only the editorial and abstracts of papers appear, and even of turning it into a full-blown electronic journal. I have gently but consistently opposed this move, on occasions to the intense irritation of some of my fellow members, and always at the risk of appearing to be a Luddite. Explicitly ('Who, if not we as learning technologists, should be in the forefront of the soon-to-happen electronic-journal revolution?' and implicitly ('It is estimated that within two years, paper-based journals will be considered the dinosaurs of the academic world', I have increasingly come under pressure to accede to an apparently unstoppable, apparently imminent change in the method and form of scholarly publication. I set out here my reasons for my continued resistance.

  13. [Value of non-invasive models of liver fibrosis in judgment of treatment timing in chronic hepatitis B patients with ALT < 2×upper limit of normal].

    Science.gov (United States)

    Zhou, Q Q; Hu, Y B; Zhou, K; Zhang, W W; Li, M H; Dong, P; Di, J G; Hong, L; Du, Q W; Xie, Y; Sun, Q F

    2016-09-20

    Objective: To investigate the value of non-invasive liver fibrosis models, FIB-4, S index, aspartate aminotransferase to platelet ratio index(APRI), globulin-platelet(GP)model, aspartate aminotransferase/platelet/gamma-glutamyl transpeptidase/alpha-fetoprotein(APGA), and platelet/age/phosphatase/alpha-fetoprotein/aspartate aminotransferase(PAPAS), in the diagnosis of marked liver fibrosis in chronic hepatitis B(CHB)patients with ALT liver biopsy was performed to obtain pathological results, and routine serological tests were performed, including routine blood test, serum biochemical parameters, hepatitis B virus(HBV)markers, and HBV DNA. According to liver pathology, the patients were divided into non-marked liver fibrosis group(S liver fibrosis group(S≥2)with 65 patients. The non-invasive models for predicting liver fibrosis was established with reference to original articles. SPSS 19.0 software was used for statistical analysis, and the receiver operating characteristic(ROC)curve was used to compare the value of different non-invasive models in predicting marked liver fibrosis in this population. Results: All the non-invasive models had a certain diagnostic value for liver fibrosis degree in these patients, and the areas under the ROC curve for APRI, FIB-4, APGA, S index, PAPAS, and GP model were 0.718, 0.691, 0.758, 0.729, 0.673, and 0.691, respectively. APGA had the largest area under the ROC curve(0.758, 95% CI 0.673-0.844), and gamma-glutamyl transpeptidase was significantly positively correlated with liver fibrosis degree. Conclusion: The non-invasive models of liver fibrosis can identify marked liver fibrosis in CHB patients with ALT liver biopsy to the certain degree.

  14. Prime Time for Learning.

    Science.gov (United States)

    Leidy, Vivian; And Others

    1981-01-01

    Five elementary teachers explain how they orient pupils and get learning started on the first day of school--whether or not their supplies or textbooks have arrived--by building learning activities around a common interest like dogs, earthworms, football, or the Statue of Liberty. (Editor/SJL)

  15. Time and Associative Learning.

    Science.gov (United States)

    Balsam, Peter D; Drew, Michael R; Gallistel, C R

    2010-01-01

    In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by "temporal pairing" and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities.

  16. Establishing Time for Professional Learning

    Science.gov (United States)

    Journal of Staff Development, 2013

    2013-01-01

    Time for collaborative learning is an essential resource for educators working to implement college- and career-ready standards. The pages in this article include tools from the workbook "Establishing Time for Professional Learning." The tools support a complete process to help educators effectively find and use time. The following…

  17. Presentation and demonstration at Alt-i-lab 2005

    NARCIS (Netherlands)

    Tattersall, Colin

    2005-01-01

    These documents were used to accompany the Learning Design presentation and demonstration at Alt-i-lab 2005. The slides were shown during a plenary session describing the interoperability demo. The scenario was used to guide the demonstration process, and the UNFOLD handout was used to illustrate

  18. ALT-I pump limiter experiments

    International Nuclear Information System (INIS)

    Goebel, D.M.; Conn, R.W.; Campbell, G.A.

    1987-09-01

    Results from the ALT-I pump limiter experiments in TEXTOR are presented. ALT-I has demonstrated control of the plasma density in a high recycling tokamak by pumping up to 15% of the core efflux. The closed pump limiter designs with restricted entrance geometries to reduce the backflow of neutral gas to the plasma remove over 50% of the ion flux incident on the collection slot. Up to 80% of the entrance ion flux is removed when the edge electron temperature is less than 10 eV and plasma-neutral gas interactions are minimized inside the limiter. Results from a 3-D Monte Carlo neutral gas transport code agree closely with these experimental results. The compound curvature of the head is found to distribute the heat over the surface as predicted in the original designs. Impurity removal experiments demonstrate that significant helium exhaust can be achieved with a pump limiter. During ohmic heating in TEXTOR, the energy and particle confinement times are proportional to the line averaged core density. With ICRH auxiliary heating, tau/sub E/ follow L-mode scaling independent of particle removal by the pump limiter. Pump limiter operation does not directly modify the SOL plasma density and electron temperature, but controls the core plasma density by changing the global recycling at the boundary. The global particle confinement, the particle flux to the limiter, and the edge electron temperature follow the changes in the core density and auxiliary heating power. 25 refs

  19. ALT-II armor tile design for upgraded TEXTOR operation

    International Nuclear Information System (INIS)

    Newberry, B.L.; McGrath, R.T.; Watson, R.D.; Kohlhaas, W.; Finken, K.H.

    1994-01-01

    The upgrade of the TEXTOR tokamak at KFA Juelich was recently completed. This upgrade extended the TEXTOR pulse length from 5 seconds to 10 seconds. The auxiliary heating was increased to a total of 8.0 MW through a combination of neutral beam injection and radio frequency heating. Originally, the inertially cooled armor tiles of the full toroidal belt Advanced Limiter Test -- II (ALT-II) were designed for a 5-second operation with total heating of 6.0 MW. The upgrade of TEXTOR will increase the energy deposited per pulse onto the ALT-II by about 300%. Consequently, the graphite armor tiles for the ALT-II had to be redesigned to avoid excessively high graphite armor surface temperatures that would lead to unacceptable contamination of the plasma. This redesign took the form of two major changes in the ALT-II armor tile geometry. The first design change was an increase of the armor tile thermal mass, primarily by increasing the radial thickness of each tile from 17 mm to 20 mm. This increase in the radial tile dimension reduces the overall pumping efficiency of the ALT-II pump limiter by about 30%. The reduction in exhaust efficiency is unfortunate, but could be avoided only by active cooling of the ALT-II armor tiles. The active cooling option was too complicated and expensive to be considered at this time. The second design change involved redefining the plasma facing surface of each armor tile in order to fully utilize the entire surface area. The incident charged particle heat flux was distributed uniformly over the armor tile surfaces by carefully matching the radial, poloidal and toroidal curvature of each tile to the plasma flow in the TEXTOR boundary layer. This geometry redefinition complicates the manufacturing of the armor tiles, but results in significant thermal performance gains. In addition to these geometry upgrades, several material options were analyzed and evaluated

  20. ALT-II armor tile design for upgraded TEXTOR operation

    International Nuclear Information System (INIS)

    Newberry, B.L.; McGrath, R.T.; Watson, R.D.

    1994-01-01

    The upgrade of the TEXTOR tokamak at KFA Julich will be completed in the spring of 1994. The upgrade will extend the TEXTOR pulse length from 5 seconds to 10 seconds. The auxiliary heating systems are also scheduled to be upgraded so that eventually a total of 8.0 MW auxiliary heating will be available through a combination of neutral beam injection and radio frequency heating. Originally, the inertially cooled armor tiles on the full toroidal belt Advanced Limiter Test - II (ALT-II) were designed for 5-second operation with a total heating power of 6.0 MW. The upgrade of TEXTOR will increase the energy deposited per pulse onto ALT-II by more than 300%. Consequently, the graphite armor tiles for ALT-II had to be redesigned in order to increase their thermal inertia and, thereby, avoid excessively high graphite armor surface temperatures that would lead to unacceptable contamination of the plasma. The armor tile thermal inertia had been increase primarily by expanding the radial thickness of the tiles from 17 mm to 20 mm. This increase in radial tile dimension will reduce the overall pumping efficiency of the ALT-II pump limiter by about 30%. The final armor tile design was a compromise between increasing the power handling capability and reducing the particle exhaust efficiency of ALT-II. The reduction in exhaust efficiency is unfortunate, but could only be avoided by active cooling of the ALT-II armor tiles. The active cooling option was too complicated and expensive to be considered at this time

  1. Timepiece: Extending and Enhancing Learning Time.

    Science.gov (United States)

    Anderson, Lorin W., Ed.; Walberg, Herbert J., Ed.

    This publication offers suggestions for making more productive use of time, a scarce and valued educational resource. The chapter authors, authorities on the use of educational time, write about how to extend and enhance learning time within and outside schools. In "Productive Use of Time," Herbert Walberg describes how learning time can be…

  2. E-Learning, Time and Unconscious Thinking

    Science.gov (United States)

    Mathew, David

    2014-01-01

    This article views the temporal dimensions of e-learning through a psychoanalytic lens, and asks the reader to consider links between online learning and psychoanalysis. It argues that time and its associated philosophical puzzles impinge on both psychoanalytic theory and on e-learning at two specific points. The first is in the distinction…

  3. Communication, timing, and common learning

    Czech Academy of Sciences Publication Activity Database

    Steiner, Jakub; Stewart, C.

    2011-01-01

    Roč. 146, č. 1 (2011), s. 230-247 ISSN 0022-0531 Institutional research plan: CEZ:AV0Z70850503 Keywords : common knowledge * learning * communication Subject RIV: AH - Economics Impact factor: 1.235, year: 2011

  4. Leading Learning in Our Times

    Science.gov (United States)

    Trilling, Bernie

    2010-01-01

    Important tools that schools need to support a 21st century approach to teaching and learning include the usual suspects: the Internet, pen and paper, cell phones, educational games, tests and quizzes, good teachers, caring communities, educational funding, and loving parents. All of these items and more contribute to a 21st century education, but…

  5. Learned Interval Time Facilitates Associate Memory Retrieval

    Science.gov (United States)

    van de Ven, Vincent; Kochs, Sarah; Smulders, Fren; De Weerd, Peter

    2017-01-01

    The extent to which time is represented in memory remains underinvestigated. We designed a time paired associate task (TPAT) in which participants implicitly learned cue-time-target associations between cue-target pairs and specific cue-target intervals. During subsequent memory testing, participants showed increased accuracy of identifying…

  6. Learning about Learning: Action Learning in Times of Organisational Change

    Science.gov (United States)

    Hill, Robyn

    2009-01-01

    This paper explores the conduct and outcomes of an action learning activity during a period of intense organisational change in a medium-sized vocational education and training organisation in Victoria, Australia. This organisation was the subject of significant change due to government-driven and statewide amalgamation, downsizing and sector…

  7. Die Altäre von Selinunt

    OpenAIRE

    Voigts, Clemens

    2014-01-01

    In der Dissertation wurden die Altäre von Selinunt, einer griechischen Kolonie auf Sizilien, mit den Methoden der Bauforschung untersucht. Die neun behandelten Brandopferaltäre sind monumentale Quaderbauten, die bis zu 20 m Länge erreichen. Sie stammen aus der Blütezeit Selinunts, dem 6. und 5. Jh. v. Chr. Die Bauten waren bereits im 19. und 20. Jh. freigelegt worden und wurden nun in Bauaufnahmen dokumentiert, zeichnerisch rekonstruiert und zeitlich eingeordnet. Dabei ließ sich an ihnen eine...

  8. Hard Times for HRD, Lean Times for Learning?: Workplace Participatory Practices as Enablers of Learning

    Science.gov (United States)

    Warhurst, Russell

    2013-01-01

    Purpose: This article aims to show how in times of austerity when formal HRD activity is curtailed and yet the need for learning is greatest, non-formal learning methods such as workplace involvement and participation initiated by line managers can compensate by enabling the required learning and change. Design/methodology/approach: A qualitative…

  9. Students' Pressure, Time Management and Effective Learning

    Science.gov (United States)

    Sun, Hechuan; Yang, Xiaolin

    2009-01-01

    Purpose: This paper aims to survey the status quo of the student pressure and the relationship between their daily time management and their learning outcomes in three different types of higher secondary schools at Shenyang, the capital city of Liaoning Province in mainland China. Design/methodology/approach: An investigation was carried out in 14…

  10. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  11. Editorial: a new direction for ALT-J

    Directory of Open Access Journals (Sweden)

    Gráinne Conole

    2003-12-01

    Full Text Available The Association for Learning Technology celebrated its tenth anniversary this year and it can now be said that learning technology is a recognized research field, with a growing body of researchers and associated conferences and journals. Over the past two years as an editorial team we have undertaken a review of the position of ALT-J in relation to other journals in the area and considered the nature and scope of the papers we publish. We continue to expand our list of referees and have provided them with more detailed review forms to help guide them when considering recommendations on submissions and to encourage detailed, critical (and hopefully! helpful feedback to authors. We would like to take this opportunity to thank all the referees for their involvement and commitment to the journal and for their continued work on refereeing papers. As part of the review process we will be moving to a new publisher, Taylor and Francis, commencing with Volume 12; this is, in part, driven by a desire to increase our international profile and readership, as well as seeking to have a better online presence including electronic access to all papers. We are, after all, a research journal on learning technologies! We are looking forward to taking the journal forward with Taylor and Francis but would also like to take this opportunity to thank our current publisher, University of Wales Press, for all their support and professional work over the years; it has been very good working with them.

  12. Linear time relational prototype based learning.

    Science.gov (United States)

    Gisbrecht, Andrej; Mokbel, Bassam; Schleif, Frank-Michael; Zhu, Xibin; Hammer, Barbara

    2012-10-01

    Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nyström approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain.

  13. ALT Blood Test: MedlinePlus Lab Test Information

    Science.gov (United States)

    ... K. Brunner & Suddarth's Handbook of Laboratory and Diagnostic Tests. 2 nd Ed, Kindle. Philadelphia: Wolters Kluwer Health, Lippincott Williams & Wilkins; c2014. Alanine Aminotransferase (ALT); p. 31. Lab ...

  14. Overcoming Learning Time And Space Constraints Through Technological Tool

    Directory of Open Access Journals (Sweden)

    Nafiseh Zarei

    2015-08-01

    Full Text Available Today the use of technological tools has become an evolution in language learning and language acquisition. Many instructors and lecturers believe that integrating Web-based learning tools into language courses allows pupils to become active learners during learning process. This study investigate how the Learning Management Blog (LMB overcomes the learning time and space constraints that contribute to students’ language learning and language acquisition processes. The participants were 30 ESL students at National University of Malaysia. A qualitative approach comprising an open-ended questionnaire and a semi-structured interview was used to collect data. The results of the study revealed that the students’ language learning and acquisition processes were enhanced. The students did not face any learning time and space limitations while being engaged in the learning process via the LMB. They learned and acquired knowledge using the language learning materials and forum at anytime and anywhere. Keywords: learning time, learning space, learning management blog

  15. An Empirical Investigation of Individual Differences in Time to Learn

    Science.gov (United States)

    Anderson, Lorin W.

    1976-01-01

    Results show that student differences in time-on-task to learn to criterion are alterable and can be minimized over a sequence of learning units given appropriate adaptive learning strategies. (Author/DEP)

  16. Langmuir probe measurements in the TEXTOR tokamak during ALT-I pump limiter experiments

    International Nuclear Information System (INIS)

    Goebel, D.M.; Campbell, G.A.; Conn, R.W.; Leung, W.K.; Dippel, K.H.; Finken, K.H.; Thomas, G.J.; Pontau, A.E.

    1986-04-01

    Langmuir probes have been used to characterize the edge plasma of the TEXTOR tokamak and measure the parameters of the plasma incident on the ALT-I pump limiter during ohmic and ICRH heating. Probes mounted directly on the ALT limiter, and a scanning probe located 90 0 toroidally from the limiter, provide data for the evaluation of pump limiter performance and its effect on the edge plasma. The edge plasma is characterized by density and flux e-folding lengths of about 1.8cm when ALT is the main limiter. These scrape-off lengths do not vary significantly as ALT is moved between the normal 42-46cm minor radii, but increase to over 2.2cm when ALT is inserted to 40cm. The flux to probes at a fixed position in the limiter shadow varies by less than 25% for core density changes of a factor of five. This suggests that the global particle confinement time tau/sub p/, scales as the core density. Estimates from the probes indicate that tau/sub p/ is on the order of the energy confinement time, tau/sub E/. The edge electron temperature, T/sub e/, typically decreases by a factor of two when the core density is raised from 1 to 4 x 10 13 cm -3 . The T/sub e/ profile is essentially flat in the limiter shadow, with values of 10-25 eV depending on the core plasma density and ICRH power. ICRH heating increases the electron temperature and flux in proportion to the coupled power. With ALT as the primary limiter and no direct shadowing, the ion side receives 2 to 3 times the flux of the electron side during both ohmic and ICRH heating. The edge plasma is not directly modified by pump limiter operation, but changes with the core plasma density as particle removal lowers the recycling of neutrals in the boundary

  17. Association of ALT and the metabolic syndrome among Mexican children.

    Science.gov (United States)

    Elizondo-Montemayor, Leticia; Ugalde-Casas, Patricia A; Lam-Franco, Lorena; Bustamante-Careaga, Humberto; Serrano-González, Mónica; Gutiérrez, Norma G; Martínez, Ubaldo

    2014-01-01

    Nonalcoholic fatty liver disease (NAFLD) is emerging as a component of the metabolic syndrome (MetS); Hispanics being particularly predisposed. Alanine aminotransferase (ALT) is considered a marker of NAFLD. The aim of this study was to determine the prevalence and associations between ALT elevations and MetS in normal-weight, overweight and obese Mexican children and adolescents, since data in Mexico is scarce. Body mass index (BMI), waist circumference (WC), percentage body fat, blood pressure, glucose, lipid profiles, ALT and aspartate aminotransferase (AST) were measured in 236, 6-12yo normal-weight, overweight and obese Mexicans from eight public schools. The results showed that elevated ALT (>40 IU/L) was found in 17.7% of the obese and overweight population, with no gender difference. The prevalence of elevated ALT increased linearly across BMI categories (p = 0.001), from 0.0% for the normal-weight group (95%CI 0.0-€“8.0) to 22.4% for the obese one (95%CI 16.2-€“30.2). AST/ALT ratio obese one. The prevalence of MetS was strongly associated with elevated ALT (p = 0.002), 50% in the elevated ALT group (95%CI 34.1-€“65.9) and 24.1% in the normal ALT one (95%CI 18.1-€“31.3). There was also a strong association between MetS and an AST/ALT ratio obese children. © 2014 Asian Oceanian Association for the Study of Obesity . Published by Elsevier Ltd. All rights reserved.

  18. Liver stiffness becomes stable in patients with chronic hepatitis C three months after ALT normalization due to antiviral therapy

    Directory of Open Access Journals (Sweden)

    CHEN Feikai

    2013-10-01

    Full Text Available ObjectiveTo investigate the time for liver stiffness measurement (LSM to become stable in chronic hepatitis C (CHC patients with elevated alanine aminotransferase (ALT levels after ALT normalization due to antiviral therapy. MethodsCHC patients who sought initial treatment at Peking University People′s Hospital were screened for elevated ALT levels from May 2011. Liver stiffness was determined by FibroScan. A total of 29 patients had been included in the study by September 2012, who were followed up regularly after antiviral treatment. ALT tests were repeated every four weeks and LSM every eight weeks until their medians did not change significantly. Comparisons of matched data at two adjacent time points were made with the non-parametric Wilcoxon test, while multiple comparisons of repeated measurements were performed using Bonferroni correction. Correlation between two variables was analyzed with the Spearman rank test. ResultsPatients were followed up until 24 weeks after antiviral treatment, and 24 patients were included in analysis. The median ALT levels were 64, 26, 21, 20, and 22 U/L at baseline and 4, 8, 12, and 24 weeks, respectively (P= 0.000, 0.006, 0.337, and 0.109 for comparisons between two adjacent values. ALT decreased significantly below 1 ULN at 4 weeks after antiviral therapy and stabilized at 8 weeks. The median LSM values were 8.7, 7.8, 6.8, and 6.7 kPa at baseline and 8, 16, and 24 weeks, respectively (P= 0.009, 0.001, and 0188 for comparisons between two adjacent values. LSM decreased significantly within 16 weeks after antiviral therapy and stabilized afterwards. LSM stabilized 12 weeks after ALT normalization. ConclusionLSM becomes stable in CHC patients with elevated ALT levels three months after ALT normalization due to antiviral therapy.

  19. Time factor in e-learning and assessment

    OpenAIRE

    Romero Velasco, Margarida

    2010-01-01

    Peer-reviewed Peer reviewed Time is probably one of the most polysemous words in education. In e-learning, characterization of the time factor is particularly relevant because of the high level of flexibility in the teaching and learning times, and the resulting responsibility of the e-learners in regulating their learning times.

  20. Blended learning pedagogy: the time is now!

    Science.gov (United States)

    Pizzi, Michael A

    2014-07-01

    Pedagogy is rapidly changing. To develop best practice in academia, it is important that we change with the changing needs of students. This article suggests that blended learning is one of the most important pedagogical formats that can enhance student learning, optimize the use of active learning strategies, and potentially improve student learning outcomes.

  1. Learning to stay ahead of time

    DEFF Research Database (Denmark)

    Staunæs, Dorthe; Raffnsøe, Sverre

    2014-01-01

    In the context of an ongoing change, management is required to take the form of a leadership that must be reignited over and over again. The article examines a new art of leadership that may be viewed as an attempt to keep up with these challenges and stay ahead of time. It emerges from...... a pilgrimage leadership learning laboratory on the road to Santiago de la Compostela. This moving lab creates situations of extraordinary intensity that border on hyperreality and force the leader to find him/herself anew on the verge of him/herself. Conceived as pilgrimage, leadership moves ahead of time...... as it reaches into and anticipates a future still unknown. In this setting, anticipatory affects and the virtual take up a predominant role. As it emerges here, leadership distinguishes itself not only from leadership in the traditional sense, but also from management and governmentality....

  2. Precarious Learning and Labour in Financialized Times

    Directory of Open Access Journals (Sweden)

    Jamie Magnusson

    2013-07-01

    Full Text Available Our current globalized economic regimes of financialized capital have systematically altered relations of learning and labour through the dynamics of precarity, debt, and the political economy of new wars. The risks of these regimes are absorbed unevenly across transnational landscapes, creating cartographies of violence and dispossession, particularly among youth, indigenous, working class, and racialized women. Presently there is surprisingly little discussion on the relevance of financialization for adult educators. Transnational resistances organizing against neoliberal restructuring, austerity policies, and debt crises are emerging at the same time that massive investments are being made into homeland security and the carceral state. This paper opens up discussion on the implications of financialized times for educators, and develops an analytic framework for examining how these global realities are best addressed at local sites of adult and higher education.

  3. Kui kapital veab alt, siis turvaliselt / Aleksander Tsapov

    Index Scriptorium Estoniae

    Tsapov, Aleksander

    2006-01-01

    Kuraatorinäitus "Kapital (see veab meid alt)" Tallinna Kunstihoones. Kuraator Simon Sheikh. Fia-Stina Sandlundi, Katya Sanderi, Oliver Ressleri, Ashley Hunti ja Susan Kelly&Stephen Mortoni töödest näitusel

  4. PISCES and ALT-II: Juelich PSI papers

    International Nuclear Information System (INIS)

    Conn, R.W.; Hirooka, Y.; LaBombard, B.

    1988-08-01

    This publication comprises papers from the PISCES and ALT-II Programs at UCLA which were presented at the International Plasma Surface Interactions Meeting held in Juelich, FRG, on May 2-6, 1988. A list of publications from the PISCES and ALT-II contained in this report are: Deuterium pumping and erosion behavior of selected graphite materials under high flux plasma bombardment in PISCES; Erosion and redeposition behavior of selected NET-candidate materials under high-flux hydrogen, deuterium plasma bombardment in PISCES; Presheath profiles in simulated tokamak edge plasmas; Boundary asymmetries and plasma flow to the ALT-II toroidal belt pump limiter; ALT-II toroidal belt pump limiter performance in TEXTOR; and An in-situ spectroscopic erosion yield measurement with applications to sputtering and surface morphology alterations

  5. Correlation between Aminotransferase Ratio (AST/ALT and Other Biochemical Parameters in Chronic Liver Disease of Viral Origin

    Directory of Open Access Journals (Sweden)

    Shah Md Fazlul Karim

    2015-03-01

    Full Text Available Background: In recent years the ratio of aspartate aminotransferase (AST to alanine aminotransferase (ALT in patients of chronic liver disease (CLD of various origins has gained much attention. This variable is readily available, easy to interpret, and inexpensive and the clinical utility of the AST/ALT ratio in the diagnostic workup of patients with CLD is quite promising. Objective: The present study was designed to find out the link between aminotransferase (AST/ALT ratio with commonly measured biochemical parameters of liver function tests in CLD of viral origin. Materials and method: This cross sectional study was carried out in the department of Biochemistry, Sir Salimullah Medical College, Dhaka, Bangladesh. Forty four biopsy proven diagnosed subjects of chronic viral hepatitis without cirrhosis of both sex were selected purposively. With aseptic precaution 5 mL venous blood was collected from each subject and common liver function tests (serum AST, ALT, AST/ALT ratio, alkaline phosphatase, total bilirubin, serum total protein, serum albumin, serum globulin, serum albumin/globulin ratio, prothrombin time and viral serology (HBsAg, Anti HDV antibody, Anti HCV antibody were performed. Data were analyzed by SPSS version 19 for Windows. Pearson’s correlation test was done to determine association between AST/ALT with other biochemical parameters. Results: Mean(±SD age of the study subjects was 32.55±10.55 years (range 20-50 years with 48 (77.7% male and 14 (22.6% female subjects. Pearson’s correlation test was done between AST to ALT ratio with other biochemical parameters and prothrombin time showed significant positive correlation (p <0.01. Conclusion: In our study we found significant positive correlation between AST/ALT with prothrombin time in CLD subjects without cirrhosis.

  6. Army AL&T, July-September 2008

    Science.gov (United States)

    2008-09-01

    Northern Region — Wanda Reed (PCO), Joann Langston (PARC), Debbie Emerson (Alt. to PARC) Southern Region — Julie Silva (PCO), Carol Lowman (Direc- tor/PARC...Charles Jaber (PCO), Roger Engebretson, (Director/PARC), Sharon Oishi (Alt. to PARC) U.S. Army Contracting Command, Southwest Asia — Carol Estes (PCO...Robyn Villafranco, Margarita Ramirez, Diana Fernandez, Donna Reed, Michely Walton, Elisa Mendez , Cynariah Wilkins, Deinor Bolanos, Belinda Kent

  7. In-Time On-Place Learning

    Science.gov (United States)

    Bauters, Merja; Purma, Jukka; Leinonen, Teemu

    2014-01-01

    The aim of this short paper is to look at how mobile video recording devices could support learning related to physical practices or places and situations at work. This paper discusses particular kind of workplace learning, namely learning using short video clips that are related to physical environment and tasks preformed in situ. The paper…

  8. Evidence for alternative lengthening of telomeres in liposarcomas in the absence of ALT-associated PML bodies.

    Science.gov (United States)

    Jeyapalan, Jennie N; Mendez-Bermudez, Aaron; Zaffaroni, Nadia; Dubrova, Yuri E; Royle, Nicola J

    2008-06-01

    Immortalized and cancer cells maintain their telomeres by activation of a telomere maintenance mechanism (TMM). In approximately 85% of cancers telomerase is activated (TA) but in some tumours, in particular sarcomas, an alternative lengthening of telomeres (ALT) pathway is used. Liposarcomas are the most common soft-tissue sarcoma in adults and they activate ALT or telomerase with equal frequency, however no TMM has been identified in approximately 50% of liposarcomas. In our study, we have shown that instability at the minisatellite MS32, usually associated with ALT activation, aids the identification of liposarcomas that have recombination-like activity at telomeres in absence of ALT associated PML-bodies (APBs). Furthermore, using single molecule telomere analysis, we have detected complex telomere mutations directly in ALT positive liposarcomas and interestingly in some liposarcomas with an unknown TMM but high MS32 instability. We have shown by sequence analysis that some of these complex telomere mutations must arise by an inter-molecular recombination-like process rather than by deletion caused by t-loop excision or by unequal telomere-sister-chromatid-exchange (T-SCE), which is known to be elevated in ALT cell lines. Preliminary evidence also suggests that inter-molecular recombination events may be processed differently in liposarcomas with APBs compared to those without. In conclusion, we have shown for the first time, that some telomerase negative liposarcomas without APBs have other features associated with ALT, indicating that the incidence of ALT in these tumours has previously been under-estimated. This has major implications for the use of cancer treatments targeted at TMMs. (c) 2008 Wiley-Liss, Inc.

  9. An algorithm for learning real-time automata

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2007-01-01

    We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe

  10. Quality of E-Learners’ Time and Learning Performance Beyond Quantitative Time-on-Task

    Directory of Open Access Journals (Sweden)

    Margarida Romero

    2011-06-01

    Full Text Available AbstractAlong with the amount of time spent learning (or time-on-task, the quality of learning time has a real influence on learning performance. Quality of time in online learning depends on students’ time availability and their willingness to devote quality cognitive time to learning activities. However, the quantity and quality of the time spent by adult e-learners on learning activities can be reduced by professional, family, and social commitments. Considering that the main time pattern followed by most adult e-learners is a professional one, it may be beneficial for online education programs to offer a certain degree of flexibility in instructional time that might allow adult learners to adjust their learning times to their professional constraints. However, using the time left over once professional and family requirements have been fulfilled could lead to a reduction in quality time for learning. This paper starts by introducing the concept of quality of learning time from an online student-centred perspective. The impact of students’ time-related variables (working hours, time-on-task engagement, time flexibility, time of day, day of week is then analyzed according to individual and collaborative grades achieved during an online master’s degree program. The data show that both students’ time flexibility (r = .98 and especially their availability to learn in the morning are related to better grades in individual (r = .93 and collaborative activities (r = .46.

  11. Working and Learning in Times of Uncertainty

    DEFF Research Database (Denmark)

    This book analyses the challenges of globalisation and uncertainty impacting on working and learning at individual, organisational and societal levels. Each of the contributions addresses two overall questions: How is working and learning affected by uncertainty and globalisation? And, in what ways...... do individuals, organisations, political actors and education systems respond to these challenges? Part 1 focuses on the micro level of working and learning for understanding the learning processes from an individual point of view by reflecting on learners’ needs and situations at work and in school......). Finally, Part 3 addresses the macro level of working and learning by analysing how to govern, structure and organise vocational, professional and adult education at the boundaries of work, education and policy making....

  12. Learning Styles of Medical Students Change in Relation to Time

    Science.gov (United States)

    Gurpinar, Erol; Bati, Hilal; Tetik, Cihat

    2011-01-01

    The aim of the present study was to investigate if any changes exist in the learning styles of medical students over time and in relation to different curriculum models with these learning styles. This prospective cohort study was conducted in three different medical faculties, which implement problem-based learning (PBL), hybrid, and integrated…

  13. ELEVATED ALT AND AST IN AN ASYMPTOMATIC PERSON

    Directory of Open Access Journals (Sweden)

    KEW ST

    2009-01-01

    Full Text Available -Abnormal liver function test with raised alanine aminotransferase (ALT and raised aspartate aminotransferase (AST are commonly seen in primary care setting. -Chronic alcohol consumption, drugs, non-alcoholic steatohepatitis (NASH and chronic viral hepatitis are common causes associated with raised ALT and AST. -In chronic viral hepatitis, the elevation of liver enzyme may not correlate well with the degree of liver damage. -Non-hepatic causes of raised ALT and AST include polymyositis, acute muscles injury, acute myocardial infarction and hypothyroidism. -In the primary care setting, the doctor should obtain a complete history regarding the risk factors for viral hepatitis, substance abuse and request investigations accordingly. -Suspected chronic viral hepatitis and liver cirrhosis are best referred to hepatologist for further management.

  14. Real-time Color Codes for Assessing Learning Process

    OpenAIRE

    Dzelzkalēja, L; Kapenieks, J

    2016-01-01

    Effective assessment is an important way for improving the learning process. There are existing guidelines for assessing the learning process, but they lack holistic digital knowledge society considerations. In this paper the authors propose a method for real-time evaluation of students’ learning process and, consequently, for quality evaluation of teaching materials both in the classroom and in the distance learning environment. The main idea of the proposed Color code method (CCM) is to use...

  15. ALT-II toroidal belt limiter biasing experiments on TEXTOR

    International Nuclear Information System (INIS)

    Doerner, R.; Boedo, J.A.; Gray, D.S.

    1991-01-01

    Edge electric fields have been related to H-mode-like behaviour. The experiments reported here are an attempt to control the SOL profiles by electrostatic biasing of the full toroidal-belt limiter ALT-II. The specific goals are: influencing the edge particle flows, particle removal, power deposition and the global confinement. The ALT-II pump limiter is a full toroidal belt located at 45 o below the outer midplane and consisting of eight graphite covered blades which can be independently biased. Particle scoops located behind the limiter neutralize and direct the incoming plasma into the pumping ducts. (author) 5 refs., 3 figs

  16. Time will tell: The role of mobile learning analytics in self-regulated learning

    NARCIS (Netherlands)

    Tabuenca, Bernardo; Kalz, Marco; Drachsler, Hendrik; Specht, Marcus

    2015-01-01

    This longitudinal study explores the effects of tracking and monitoring time devoted to learn with a mobile tool, on self-regulated learning. Graduate students (n = 36) from three different online courses used their own mobile devices to track how much time they devoted to learn over a period of

  17. Antioxidant and Antifatigue Activities of Polygonatum Alte-lobatum Hayata Rhizomes in Rats

    Directory of Open Access Journals (Sweden)

    Chi-Ting Horng

    2014-11-01

    Full Text Available Polygonatum alte-lobatum Hayata, a rhizomatous perennial herb, belongs to the Liliaceae family and is endemic to Taiwan. We investigated the antioxidant and anti-fatigue activities of P. alte-lobatum in exercised rats. Levels of polyphenols, flavonoids and polysaccharides and 2,2-diphenyl-1-picrylhydrazyl (DPPH free-radical scavenging activity were measured in extracts of P. alte-lobatum (EPA. Sprague-Dawley rats were randomly divided into four groups for 8-week treatment with vehicle (control and low-, medium-, and high-dose EPA (LEPA, MEPA, HEPA; 0, 75, 150, and 375 mg/kg/day, respectively. Exercise performance was evaluated by exhaustive treadmill exercise time and by changes in body composition and biochemical variables at the end of the experiment. EPA contained polyphenols, flavonoids and polysaccharides, with polysaccharide content at least 26 times greater than that of polyphenols and flavonoids. Trend analysis revealed that EPA dose-dependently scavenged DPPH free radicals. EPA treatment dose-dependently increased endurance running time to exhaustion and superoxide dismutase activity and total antioxidant ability of blood. EPA dose-dependently decreased serum urea nitrogen and malondialdehyde levels after exercise. Hepatic glycogen content, an important energy source for exercise, was significantly increased with EPA treatment. EPA could be a potential agent with an anti-fatigue pharmacological function.

  18. The major Alternaria alternata allergen, Alt a 1: A reliable and specific marker of fungal contamination in citrus fruits.

    Science.gov (United States)

    Gabriel, M F; Uriel, N; Teifoori, F; Postigo, I; Suñén, E; Martínez, J

    2017-09-18

    The ubiquitously present spores of Alternaria alternata can spoil a wide variety of foodstuffs, including a variety of fruits belonging to the Citrus genus. The major allergenic protein of A. alternata, Alt a 1, is a species-specific molecular marker that has been strongly associated with allergenicity and phytopathogenicity of this fungal species. This study aimed to evaluate the potential of the detection of Alt a 1 as a reliable indicator of A. alternata contamination in citrus fruits. To accomplish this aim, sixty oranges were artificially infected with a spore suspension of A. alternata. Internal fruit material was collected at different incubation times (one, two and three weeks after the fungal inoculation) and used for both total RNA extraction and protein extraction. Alt a 1 detection was then performed by polymerase chain reaction (PCR) amplification using Alt a 1 specific primers and by enzyme-linked immunosorbent assay (ELISA). The experimental model presented in this work was effective to simulate the typical Alternaria black rot phenotype and its progression. Although both PCR and ELISA techniques have been successfully carried out for detecting Alt a 1 allergen in A. alternata infected oranges, the PCR method was found to be more sensitive than ELISA. Nevertheless, ELISA results were highly valuable to demonstrate that considerable amounts of Alt a 1 are produced during A. alternata fruit infection process, corroborating the recently proposed hypothesis that this protein plays a role in the pathogenicity and virulence of Alternaria species. Such evidence suggests that the detection of Alt a 1 by PCR-based assay may be used as a specific indicator of the presence of pathogenic and allergenic fungal species, A. alternata, in fruits. This knowledge can be employed to control the fungal infection and mitigate agricultural losses as well as human exposure to A. alternata allergens and toxins. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Peek ja Lepik soovisid varade aresti alt vabastamist

    Index Scriptorium Estoniae

    2005-01-01

    LHV endine töötaja Oliver Peek väidab, et tema vastu esitatud süüdistused on alusetud ja tõendamata ning nõuab oma varade aresti alt vabastamist. Kristjan Lepingu kohtuasja arutamine lükati edasi 21. detsembrile. Endiselt käib uurimine, kui palju väidetavalt teabevargusega raha teeniti

  20. Precarious Learning and Labour in Financialized Times

    Science.gov (United States)

    Magnusson, Jamie

    2013-01-01

    Our current globalized economic regimes of financialized capital have systematically altered relations of learning and labour through the dynamics of precarity, debt, and the political economy of new wars. The risks of these regimes are absorbed unevenly across transnational landscapes, creating cartographies of violence and dispossession,…

  1. Learning to trust : network effects through time.

    NARCIS (Netherlands)

    Barrera, D.; Bunt, G. van de

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  2. Learning to trust: network effects through time

    NARCIS (Netherlands)

    Barrera, D.; van de Bunt, G

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  3. Alt a 1 allergen homologs from Alternaria and related taxa: analysis of phylogenetic content and secondary structure.

    Science.gov (United States)

    Hong, Soon Gyu; Cramer, Robert A; Lawrence, Christopher B; Pryor, Barry M

    2005-02-01

    A gene for the Alternaria major allergen, Alt a 1, was amplified from 52 species of Alternaria and related genera, and sequence information was used for phylogenetic study. Alt a 1 gene sequences evolved 3.8 times faster and contained 3.5 times more parsimony-informative sites than glyceraldehyde-3-phosphate dehydrogenase (gpd) sequences. Analyses of Alt a 1 gene and gpd exon sequences strongly supported grouping of Alternaria spp. and related taxa into several species-groups described in previous studies, especially the infectoria, alternata, porri, brassicicola, and radicina species-groups and the Embellisia group. The sonchi species-group was newly suggested in this study. Monophyly of the Nimbya group was moderately supported, and monophyly of the Ulocladium group was weakly supported. Relationships among species-groups and among closely related species of the same species-group were not fully resolved. However, higher resolution could be obtained using Alt a 1 sequences or a combined dataset than using gpd sequences alone. Despite high levels of variation in amino acid sequences, results of in silico prediction of protein secondary structure for Alt a 1 demonstrated a high degree of structural similarity for most of the species suggesting a conservation of function.

  4. Saccharomyces cerevisiae Differential Functionalization of Presumed ScALT1 and ScALT2 Alanine Transaminases Has Been Driven by Diversification of Pyridoxal Phosphate Interactions

    Directory of Open Access Journals (Sweden)

    Erendira Rojas-Ortega

    2018-05-01

    Full Text Available Saccharomyces cerevisiae arose from an interspecies hybridization (allopolyploidiza-tion, followed by Whole Genome Duplication. Diversification analysis of ScAlt1/ScAlt2 indicated that while ScAlt1 is an alanine transaminase, ScAlt2 lost this activity, constituting an example in which one of the members of the gene pair lacks the apparent ancestral physiological role. This paper analyzes structural organization and pyridoxal phosphate (PLP binding properties of ScAlt1 and ScAlt2 indicating functional diversification could have determined loss of ScAlt2 alanine transaminase activity and thus its role in alanine metabolism. It was found that ScAlt1 and ScAlt2 are dimeric enzymes harboring 67% identity and intact conservation of the catalytic residues, with very similar structures. However, tertiary structure analysis indicated that ScAlt2 has a more open conformation than that of ScAlt1 so that under physiological conditions, while PLP interaction with ScAlt1 allows the formation of two tautomeric PLP isomers (enolimine and ketoenamine ScAlt2 preferentially forms the ketoenamine PLP tautomer, indicating a modified polarity of the active sites which affect the interaction of PLP with these proteins, that could result in lack of alanine transaminase activity in ScAlt2. The fact that ScAlt2 forms a catalytically active Schiff base with PLP and its position in an independent clade in “sensu strictu” yeasts suggests this protein has a yet undiscovered physiological function.

  5. Saccharomyces cerevisiae Differential Functionalization of Presumed ScALT1 and ScALT2 Alanine Transaminases Has Been Driven by Diversification of Pyridoxal Phosphate Interactions

    Science.gov (United States)

    Rojas-Ortega, Erendira; Aguirre-López, Beatriz; Reyes-Vivas, Horacio; González-Andrade, Martín; Campero-Basaldúa, Jose C.; Pardo, Juan P.; González, Alicia

    2018-01-01

    Saccharomyces cerevisiae arose from an interspecies hybridization (allopolyploidiza-tion), followed by Whole Genome Duplication. Diversification analysis of ScAlt1/ScAlt2 indicated that while ScAlt1 is an alanine transaminase, ScAlt2 lost this activity, constituting an example in which one of the members of the gene pair lacks the apparent ancestral physiological role. This paper analyzes structural organization and pyridoxal phosphate (PLP) binding properties of ScAlt1 and ScAlt2 indicating functional diversification could have determined loss of ScAlt2 alanine transaminase activity and thus its role in alanine metabolism. It was found that ScAlt1 and ScAlt2 are dimeric enzymes harboring 67% identity and intact conservation of the catalytic residues, with very similar structures. However, tertiary structure analysis indicated that ScAlt2 has a more open conformation than that of ScAlt1 so that under physiological conditions, while PLP interaction with ScAlt1 allows the formation of two tautomeric PLP isomers (enolimine and ketoenamine) ScAlt2 preferentially forms the ketoenamine PLP tautomer, indicating a modified polarity of the active sites which affect the interaction of PLP with these proteins, that could result in lack of alanine transaminase activity in ScAlt2. The fact that ScAlt2 forms a catalytically active Schiff base with PLP and its position in an independent clade in “sensu strictu” yeasts suggests this protein has a yet undiscovered physiological function. PMID:29867852

  6. Active learning reduces annotation time for clinical concept extraction.

    Science.gov (United States)

    Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

    2017-10-01

    To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Time to rethink the neural mechanisms of learning and memory.

    Science.gov (United States)

    Gallistel, Charles R; Balsam, Peter D

    2014-02-01

    Most studies in the neurobiology of learning assume that the underlying learning process is a pairing - dependent change in synaptic strength that requires repeated experience of events presented in close temporal contiguity. However, much learning is rapid and does not depend on temporal contiguity, which has never been precisely defined. These points are well illustrated by studies showing that the temporal relations between events are rapidly learned- even over long delays- and that this knowledge governs the form and timing of behavior. The speed with which anticipatory responses emerge in conditioning paradigms is determined by the information that cues provide about the timing of rewards. The challenge for understanding the neurobiology of learning is to understand the mechanisms in the nervous system that encode information from even a single experience, the nature of the memory mechanisms that can encode quantities such as time, and how the brain can flexibly perform computations based on this information. Copyright © 2013 Elsevier Inc. All rights reserved.

  8. Professional Learning in Part-time University Study

    DEFF Research Database (Denmark)

    Rasmussen, Palle

    2007-01-01

    The theme of this article is adult students' learning in part-time studies at university level in Denmark. One issue discussed is the interplay of research and teaching in this kind of study programme. Examples are presented from the Master of Learning Processes study programme at Aalborg...

  9. Radiologists' preferences for just-in-time learning.

    Science.gov (United States)

    Kahn, Charles E; Ehlers, Kevin C; Wood, Beverly P

    2006-09-01

    Effective learning can occur at the point of care, when opportunities arise to acquire information and apply it to a clinical problem. To assess interest in point-of-care learning, we conducted a survey to explore radiologists' attitudes and preferences regarding the use of just-in-time learning (JITL) in radiology. Following Institutional Review Board approval, we invited 104 current radiology residents and 86 radiologists in practice to participate in a 12-item Internet-based survey to assess their attitudes toward just-in-time learning. Voluntary participation in the survey was solicited by e-mail; respondents completed the survey on a web-based form. Seventy-nine physicians completed the questionnaire, including 47 radiology residents and 32 radiologists in practice; the overall response rate was 42%. Respondents generally expressed a strong interest for JITL: 96% indicated a willingness to try such a system, and 38% indicated that they definitely would use a JITL system. They expressed a preference for learning interventions of 5-10 min in length. Current and recent radiology trainees have expressed a strong interest in just-in-time learning. The information from this survey should be useful in pursuing the design of learning interventions and systems for delivering just-in-time learning to radiologists.

  10. ALT-I pump limiter results on TEXTOR

    International Nuclear Information System (INIS)

    Dippel, K.H.; Finken, K.H.; Guthrie, S.E.; Malinowski, M.E.; Pontau, A.E.; Campbell, G.A.; Goebel, D.M.; Conn, R.W.

    1985-01-01

    The ALT-I pump limiter is used to control hydrogen fluxes from the TEXTOR tokamak. The performance of two different modules, the open fixed geometry (FG) and the closed variable geometry (VG) is discussed. In unpumped scoop limiter operation, the pressure in the ALT-I chamber increases to 3x10 -4 torr(FG) and 2x10 -3 torr(VG). With pumping, the fraction of particles incident on the neutralizer plate that is removed is 25-50%(FG) and 50-80%(VG). These removed particles are estimated to be 2-4(8)%(FG) and 6-13%(VG) of the total plasma outflux (Nsub(e)/tausub(p)). The collection of helium from the plasma using the FG module is approximately half as effective as hydrogen collection. The higher particle removal efficiency for the VG module is attributed to lower neutral backstreaming. (author)

  11. Increasing instruction time in school does increase learning

    DEFF Research Database (Denmark)

    Andersen, Simon Calmar; Humlum, Maria; Nandrup, Anne Brink

    2016-01-01

    Increasing instruction time in school is a central element in the attempts of many governments to improve student learning, but prior research—mainly based on observational data—disputes the effect of this approach and points out the potential negative effects on student behavior. Based on a large......-scale, cluster-randomized trial, we find that increasing instruction time increases student learning and that a general increase in instruction time is at least as efficient as an expert-developed, detailed teaching program that increases instruction with the same amount of time. These findings support the value...... of increased instruction time....

  12. Online Quiz Time Limits and Learning Outcomes in Economics

    Science.gov (United States)

    Evans, Brent; Culp, Robert

    2015-01-01

    In an effort to better understand the impact of timing limits, the authors compare the learning outcomes of students who completed timed quizzes with students who took untimed quizzes in economics principles courses. Students were assigned two online quizzes--one timed and one untimed--and re-tested on the material the following class day. Our…

  13. Time for Learning: An Exploratory Analysis of NAEP Data

    Science.gov (United States)

    Ginsburg, Alan; Chudowsky, Naomi

    2012-01-01

    This report uses NAEP background data to track time and learning since the mid-1990s in three areas: student absenteeism; classroom instructional time in mathematics, reading, music and the visual arts; and homework time expected by teachers. Key report findings are: (1) Students with higher rates of "monthly absenteeism" score…

  14. A Model for Learning Over Time: The Big Picture

    Science.gov (United States)

    Amato, Herbert K.; Konin, Jeff G.; Brader, Holly

    2002-01-01

    Objective: To present a method of describing the concept of “learning over time” with respect to its implementation into an athletic training education program curriculum. Background: The formal process of learning over time has recently been introduced as a required way for athletic training educational competencies and clinical proficiencies to be delivered and mastered. Learning over time incorporates the documented cognitive, psychomotor, and affective skills associated with the acquisition, progression, and reflection of information. This method of academic preparation represents a move away from a quantitative-based learning module toward a proficiency-based mastery of learning. Little research or documentation can be found demonstrating either the specificity of this concept or suggestions for its application. Description: We present a model for learning over time that encompasses multiple indicators for assessment in a successive format. Based on a continuum approach, cognitive, psychomotor, and affective characteristics are assessed at different levels in classroom and clinical environments. Clinical proficiencies are a common set of entry-level skills that need to be integrated into the athletic training educational domains. Objective documentation is presented, including the skill breakdown of a task and a matrix to identify a timeline of competency and proficiency delivery. Clinical Advantages: The advantages of learning over time pertain to the integration of cognitive knowledge into clinical skill acquisition. Given the fact that learning over time has been implemented as a required concept for athletic training education programs, this model may serve to assist those program faculty who have not yet developed, or are in the process of developing, a method of administering this approach to learning. PMID:12937551

  15. Timing of quizzes during learning: Effects on motivation and retention.

    Science.gov (United States)

    Healy, Alice F; Jones, Matt; Lalchandani, Lakshmi A; Tack, Lindsay Anderson

    2017-06-01

    This article investigates how the timing of quizzes given during learning impacts retention of studied material. We investigated the hypothesis that interspersing quizzes among study blocks increases student engagement, thus improving learning. Participants learned 8 artificial facts about each of 8 plant categories, with the categories blocked during learning. Quizzes about 4 of the 8 facts from each category occurred either immediately after studying the facts for that category (standard) or after studying the facts from all 8 categories (postponed). In Experiment 1, participants were given tests shortly after learning and several days later, including both the initially quizzed and unquizzed facts. Test performance was better in the standard than in the postponed condition, especially for categories learned later in the sequence. This result held even for the facts not quizzed during learning, suggesting that the advantage cannot be due to any direct testing effects. Instead the results support the hypothesis that interrupting learning with quiz questions is beneficial because it can enhance learner engagement. Experiment 2 provided further support for this hypothesis, based on participants' retrospective ratings of their task engagement during the learning phase. These findings have practical implications for when to introduce quizzes in the classroom. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  16. Alternative Liquid Fuels Simulation Model (AltSim).

    Energy Technology Data Exchange (ETDEWEB)

    Williams, Ryan; Baker, Arnold Barry; Drennen, Thomas E.

    2009-12-01

    The Alternative Liquid Fuels Simulation Model (AltSim) is a high-level dynamic simulation model which calculates and compares the production and end use costs, greenhouse gas emissions, and energy balances of several alternative liquid transportation fuels. These fuels include: corn ethanol, cellulosic ethanol from various feedstocks (switchgrass, corn stover, forest residue, and farmed trees), biodiesel, and diesels derived from natural gas (gas to liquid, or GTL), coal (coal to liquid, or CTL), and coal with biomass (CBTL). AltSim allows for comprehensive sensitivity analyses on capital costs, operation and maintenance costs, renewable and fossil fuel feedstock costs, feedstock conversion ratio, financial assumptions, tax credits, CO{sub 2} taxes, and plant capacity factor. This paper summarizes the structure and methodology of AltSim, presents results, and provides a detailed sensitivity analysis. The Energy Independence and Security Act (EISA) of 2007 sets a goal for the increased use of biofuels in the U.S., ultimately reaching 36 billion gallons by 2022. AltSim's base case assumes EPA projected feedstock costs in 2022 (EPA, 2009). For the base case assumptions, AltSim estimates per gallon production costs for the five ethanol feedstocks (corn, switchgrass, corn stover, forest residue, and farmed trees) of $1.86, $2.32, $2.45, $1.52, and $1.91, respectively. The projected production cost of biodiesel is $1.81/gallon. The estimates for CTL without biomass range from $1.36 to $2.22. With biomass, the estimated costs increase, ranging from $2.19 per gallon for the CTL option with 8% biomass to $2.79 per gallon for the CTL option with 30% biomass and carbon capture and sequestration. AltSim compares the greenhouse gas emissions (GHG) associated with both the production and consumption of the various fuels. EISA allows fuels emitting 20% less greenhouse gases (GHG) than conventional gasoline and diesels to qualify as renewable fuels. This allows several of the

  17. Human learning: Power laws or multiple characteristic time scales?

    Directory of Open Access Journals (Sweden)

    Gottfried Mayer-Kress

    2006-09-01

    Full Text Available The central proposal of A. Newell and Rosenbloom (1981 was that the power law is the ubiquitous law of learning. This proposition is discussed in the context of the key factors that led to the acceptance of the power law as the function of learning. We then outline the principles of an epigenetic landscape framework for considering the role of the characteristic time scales of learning and an approach to system identification of the processes of performance dynamics. In this view, the change of performance over time is the product of a superposition of characteristic exponential time scales that reflect the influence of different processes. This theoretical approach can reproduce the traditional power law of practice – within the experimental resolution of performance data sets - but we hypothesize that this function may prove to be a special and perhaps idealized case of learning.

  18. Rescue excavations on Alt-Laari settlement site, Tartumaa / Anti Lillak, Heiki Valk

    Index Scriptorium Estoniae

    Lillak, Anti

    2009-01-01

    Alt-Laari linnusasula tekkis nähtavasti rooma rauaajal. Kui Alt-Laari linnus oli kasutused arvatavasti I aastatuhandest II aastatuhandeni pKr. siis asula kestis edasi keskajal ning jäeti maha hiljemalt 14. sajandil

  19. Time representation in reinforcement learning models of the basal ganglia

    Directory of Open Access Journals (Sweden)

    Samuel Joseph Gershman

    2014-01-01

    Full Text Available Reinforcement learning models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between reinforcement learning models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both reinforcement learning and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.

  20. A gene expression signature classifying telomerase and ALT immortalization reveals an hTERT regulatory network and suggests a mesenchymal stem cell origin for ALT

    DEFF Research Database (Denmark)

    Lafferty-Whyte, K; Cairney, C J; Will, M B

    2009-01-01

    Telomere length is maintained by two known mechanisms, the activation of telomerase or alternative lengthening of telomeres (ALT). The molecular mechanisms regulating the ALT phenotype are poorly understood and it is unknown how the decision of which pathway to activate is made at the cellular le......TERT in different tumour types and normal tissues. We also show evidence to suggest a novel mesenchymal stem cell origin for ALT immortalization in cell lines and mesenchymal tissues....

  1. E-learning for Part-Time Medical Studies

    Directory of Open Access Journals (Sweden)

    Półjanowicz Wiesław

    2016-12-01

    Full Text Available Distance education undoubtedly has many advantages, such as individualization of the learning process, unified transmission of teaching materials, the opportunity to study at any place and any time, reduction of financial costs for commuting to classes or accommodation of participants, etc. Adequate working conditions on the e-learning portal must also be present, eg. well-prepared, substantive courses and good communication between the participants. Therefore, an important element in the process of conducting e-learning courses is to measure the increase of knowledge and satisfaction of participants with distance learning. It allows for fine-tuning the content of the course and for classes to be properly organized. This paper presents the results of teaching and assessment of satisfaction with e-learning courses in “Problems of multiculturalism in medicine”, “Selected issues of visual rehabilitation” and “Ophthalmology and Ophthalmic Nursing”, which were carried out experimentally at the Faculty of Health Sciences at the Medical University of Bialystok for nursing students for the 2010/2011 academic year. The study group consisted of 72 part-time students who learnt in e-learning mode and the control group of 87 students who learnt in the traditional way. The students’ opinions about the teaching process and final exam scores were analyzed based on a specially prepared survey questionnaire. Organization of e-learning classes was rated positively by 90% of students. The average result on the final exams for all distance learning subjects was at the level of 82%, while for classes taught in the traditional form it was 81%. Based on these results, we conclude that distance learning is as effective as learning according to the traditional form in medical education studies.

  2. 21 CFR 862.1030 - Alanine amino transferase (ALT/SGPT) test system.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Alanine amino transferase (ALT/SGPT) test system... Test Systems § 862.1030 Alanine amino transferase (ALT/SGPT) test system. (a) Identification. An alanine amino transferase (ALT/SGPT) test system is a device intended to measure the activity of the...

  3. A novel time series link prediction method: Learning automata approach

    Science.gov (United States)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2017-09-01

    Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

  4. Enrichment in Massachusetts Expanded Learning Time (ELT) Schools. Issue Brief

    Science.gov (United States)

    Caven, Meghan; Checkoway, Amy; Gamse, Beth; Luck, Rachel; Wu, Sally

    2012-01-01

    This brief highlights key information about enrichment activities, which represent one of the main components of the Massachusetts Expanded Learning Time (ELT) initiative. Over time, the ELT initiative has supported over two dozen schools across the Commonwealth. A comprehensive evaluation of the ELT initiative found that implementation of the…

  5. Learning and Teaching Problems in Part-Time Higher Education.

    Science.gov (United States)

    Trotman-Dickenson, D. I.

    1988-01-01

    Results of a British survey of the administrations of six universities and six public colleges, employers, and employees who were part-time students are reported and discussed. The survey assessed the perceptions of those groups concerning problems in the instruction and learning of part-time students. (MSE)

  6. DSLR Double Star Astrometry Using an Alt-Az Telescope

    Science.gov (United States)

    Frey, Thomas; Haworth, David

    2014-07-01

    The goal of this project was to determine if the double star's angular separation and position angle measurements could be successfully measured with a motor driven, alt-azimuth Dobsonian-mounted Newtonian telescope (without a field rotator), and a digital single-lens reflex (DSLR) camera. Additionally, the project was constrained by using as much existing equipment as much as possible, including an Apple MacBook Pro laptop and a Canon T2i camera. This project was additionally challenging because the first author had no experience with astrophotography.

  7. Active controllers and the time duration to learn a task

    Science.gov (United States)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  8. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  9. Polysynchronous: Dialogic Construction of Time in Online Learning

    Science.gov (United States)

    Oztok, Murat; Wilton, Lesley; Zingaro, Daniel; Mackinnon, Kim; Makos, Alexandra; Phirangee, Krystle; Brett, Clare; Hewitt, Jim

    2014-01-01

    Online learning has been conceptualized for decades as being delivered in one of two modes: synchronous or asynchronous. Technological determinism falls short in describing the role that the individuals' psychological, social and pedagogical factors play in their perception, experience and understanding of time online. This article explores…

  10. Probability Learning: Changes in Behavior across Time and Development

    Science.gov (United States)

    Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

    2018-01-01

    Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience--within a learning session and over development--influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults…

  11. Instructional Advice, Time Advice and Learning Questions in Computer Simulations

    Science.gov (United States)

    Rey, Gunter Daniel

    2010-01-01

    Undergraduate students (N = 97) used an introductory text and a computer simulation to learn fundamental concepts about statistical analyses (e.g., analysis of variance, regression analysis and General Linear Model). Each learner was randomly assigned to one cell of a 2 (with or without instructional advice) x 2 (with or without time advice) x 2…

  12. Adaptation and learning: characteristic time scales of performance dynamics.

    Science.gov (United States)

    Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh

    2009-12-01

    A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.

  13. Online gaming for learning optimal team strategies in real time

    Science.gov (United States)

    Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

    2010-04-01

    This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

  14. Time-sensitive Customer Churn Prediction based on PU Learning

    OpenAIRE

    Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

    2018-01-01

    With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

  15. The human CTC1/STN1/TEN1 complex regulates telomere maintenance in ALT cancer cells

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Chenhui; Jia, Pingping; Chastain, Megan; Shiva, Olga; Chai, Weihang, E-mail: wchai@wsu.edu

    2017-06-15

    Maintaining functional telomeres is important for long-term proliferation of cells. About 15% of cancer cells are telomerase-negative and activate the alternative-lengthening of telomeres (ALT) pathway to maintain their telomeres. Recent studies have shown that the human CTC1/STN1/TEN1 complex (CST) plays a multi-faceted role in telomere maintenance in telomerase-expressing cancer cells. However, the role of CST in telomere maintenance in ALT cells is unclear. Here, we report that human CST forms a functional complex localizing in the ALT-associated PML bodies (APBs) in ALT cells throughout the cell cycle. Suppression of CST induces telomere instabilities including telomere fragility and elevates telomeric DNA recombination, leading to telomere dysfunction. In addition, CST deficiency significantly diminishes the abundance of extrachromosomal circular telomere DNA known as C-circles and t-circles. Suppression of CST also results in multinucleation in ALT cells and impairs cell proliferation. Our findings imply that the CST complex plays an important role in regulating telomere maintenance in ALT cells. - Highlights: • CST localizes at telomeres and ALT-associated PML bodies in ALT cells throughout the cell cycle. • CST is important for promoting telomeric DNA replication in ALT cells. • CST deficiency decreases ECTR formation and increases T-SCE. • CST deficiency impairs ALT cell proliferation and results in multinucleation.

  16. Activation of the ALT pathway for telomere maintenance can affect other sequences in the human genome.

    Science.gov (United States)

    Jeyapalan, Jennie N; Varley, Helen; Foxon, Jenny L; Pollock, Raphael E; Jeffreys, Alec J; Henson, Jeremy D; Reddel, Roger R; Royle, Nicola J

    2005-07-01

    Immortal human cells maintain telomere length by the expression of telomerase or through the alternative lengthening of telomeres (ALT). The ALT mechanism involves a recombination-like process that allows the rapid elongation of shortened telomeres. However, it is not known whether activation of the ALT pathway affects other sequences in the genome. To address this we have investigated, in ALT-expressing cell lines and tumours, the stability of tandem repeat sequences known to mutate via homologous recombination in the human germline. We have shown extraordinary somatic instability in the human minisatellite MS32 (D1S8) in ALT-expressing (ALT+) but not in normal or telomerase-expressing cell lines. The MS32 mutation frequency varied across 15 ALT+ cell lines and was on average 55-fold greater than in ALT- cell lines. The MS32 minisatellite was also highly unstable in three of eight ALT+ soft tissue sarcomas, indicating that somatic destabilization occurs in vivo. The MS32 mutation rates estimated for two ALT+ cell lines were similar to that seen in the germline. However, the internal structures of ALT and germline mutant alleles are very different, indicating differences in the underlying mutation mechanisms. Five other hypervariable minisatellites did not show elevated instability in ALT-expressing cell lines, indicating that minisatellite destabilization is not universal. The elevation of MS32 instability upon activation of the ALT pathway and telomere length maintenance suggests there is overlap between the underlying processes that may be tractable through analysis of the D1S8 locus.

  17. Spaced learning and innovative teaching: school time, pedagogy of attention and learning awareness

    Directory of Open Access Journals (Sweden)

    Garzia Maeca

    2016-06-01

    Full Text Available Currently, the ‘time’ variable has taken on the function of instructional and pedagogical innovation catalyst, after representing-over the years-a symbol of democratisation, learning opportunity and instruction quality, able to incorporate themes such as school dropout, personalisation and vocation into learning. Spaced Learning is a teaching methodology useful to quickly seize information in long-term memory based on a particular arrangement of the lesson time that comprises three input sessions and two intervals. Herein we refer to a teachers’ training initiative on Spaced Learning within the programme ‘DocentiInFormAzione’ in the EDOC@WORK3.0 Project in Apulia region in 2015. The training experience aimed at increasing teachers’ competencies in the Spaced Learning method implemented in a context of collaborative reflection and reciprocal enrichment. The intent of the article is to show how a process of rooting of the same culture of innovation, which opens to the discovery (or rediscovery of effective teaching practices sustained by scientific evidences, can be successfully implemented and to understand how or whether this innovation- based on the particular organisation of instructional time-links learning awareness to learning outcomes.

  18. A Parametric Learning and Identification Based Robust Iterative Learning Control for Time Varying Delay Systems

    Directory of Open Access Journals (Sweden)

    Lun Zhai

    2014-01-01

    Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.

  19. Learning of time series through neuron-to-neuron instruction

    Energy Technology Data Exchange (ETDEWEB)

    Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

    2003-02-07

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

  20. Learning of time series through neuron-to-neuron instruction

    International Nuclear Information System (INIS)

    Miyazaki, Y; Kinzel, W; Shinomoto, S

    2003-01-01

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

  1. Finite time convergent learning law for continuous neural networks.

    Science.gov (United States)

    Chairez, Isaac

    2014-02-01

    This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. Influence of learning strategy on response time during complex value-based learning and choice.

    Directory of Open Access Journals (Sweden)

    Shiva Farashahi

    Full Text Available Measurements of response time (RT have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning. Alternatively, they could learn reward values of options' features (e.g. color, shape and combine these values to estimate reward values for individual options (feature-based learning. We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

  3. Real-time individualized training vectors for experiential learning.

    Energy Technology Data Exchange (ETDEWEB)

    Willis, Matt; Tucker, Eilish Marie; Raybourn, Elaine Marie; Glickman, Matthew R.; Fabian, Nathan

    2011-01-01

    Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.

  4. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

  5. Representation Learning from Time Labelled Heterogeneous Data for Mobile Crowdsensing

    Directory of Open Access Journals (Sweden)

    Chunmei Ma

    2016-01-01

    Full Text Available Mobile crowdsensing is a new paradigm that can utilize pervasive smartphones to collect and analyze data to benefit users. However, sensory data gathered by smartphone usually involves different data types because of different granularity and multiple sensor sources. Besides, the data are also time labelled. The heterogeneous and time sequential data raise new challenges for data analyzing. Some existing solutions try to learn each type of data one by one and analyze them separately without considering time information. In addition, the traditional methods also have to determine phone orientation because some sensors equipped in smartphone are orientation related. In this paper, we think that a combination of multiple sensors can represent an invariant feature for a crowdsensing context. Therefore, we propose a new representation learning method of heterogeneous data with time labels to extract typical features using deep learning. We evaluate that our proposed method can adapt data generated by different orientations effectively. Furthermore, we test the performance of the proposed method by recognizing two group mobile activities, walking/cycling and driving/bus with smartphone sensors. It achieves precisions of 98.6% and 93.7% in distinguishing cycling from walking and bus from driving, respectively.

  6. Effect of chronotype and student learning time on mathematical ability based on self-regulated learning

    Science.gov (United States)

    Ratnaningsih, N.; El Akbar, R. R.; Hidayat, E.

    2018-05-01

    One of ways to improve students' learning ability is conduct a research, with purpose to obtain a method to improve students' ability. Research often carried out on the modification of teaching methods, uses of teaching media, motivation, interests and talents of students. Research related to the internal condition of students becomes very interesting to studied, including research on circadian rhythms. Every person in circadian rhythms has its own Chronotype, which divided into two types namely early type and night late type. Chronotype affects the comfort in activity, for example a person with Chronotype category of early type tends to be more comfort in daytime activities. The purpose of this study is to examine the conditions of students, related Chronotype suitable or appropriate for student learning time. This suitability then studied in relation to the ability of learning mathematics with self- regulated learning approach. This study consists of three stages; (i) student Chronotype measurement, (ii) data retrieval, and (iii) analysis of research results. The results show the relationship between the students' learning ability in mathematics to learning time corresponding to Chronotype.

  7. Fixation and escape times in stochastic game learning

    International Nuclear Information System (INIS)

    Realpe-Gomez, John; Szczesny, Bartosz; Galla, Tobias; Dall’Asta, Luca

    2012-01-01

    Evolutionary dynamics in finite populations is known to fixate eventually in the absence of mutation. We here show that a similar phenomenon can be found in stochastic game dynamical batch learning, and investigate fixation in learning processes in a simple 2×2 game, for two-player games with cyclic interaction, and in the context of the best-shot network game. The analogues of finite populations in evolution are here finite batches of observations between strategy updates. We study when and how such fixation can occur, and present results on the average time-to-fixation from numerical simulations. Simple cases are also amenable to analytical approaches and we provide estimates of the behaviour of so-called escape times as a function of the batch size. The differences and similarities with escape and fixation in evolutionary dynamics are discussed. (paper)

  8. The 3 R's of Learning Time: Rethink, Reshape, Reclaim

    Science.gov (United States)

    Sackey, Shera Carter

    2012-01-01

    The Learning School Alliance is a network of schools collaborating about professional practice. The network embodies Learning Forward's purpose to advance effective job-embedded professional learning that leads to student outcomes. A key component of Learning Forward's Standards for Professional Learning is a focus on collaborative learning,…

  9. The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

    Science.gov (United States)

    Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

    2011-06-01

    The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

  10. Oscillations, Timing, Plasticity, and Learning in the Cerebellum.

    Science.gov (United States)

    Cheron, G; Márquez-Ruiz, J; Dan, B

    2016-04-01

    The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.

  11. A Distance Learning Review--The Communicational Module "Learning on Demand--Anywhere at Any Time"

    Science.gov (United States)

    Tatkovic, Nevenka; Ruzic, Maja

    2004-01-01

    The society of knowledge refers to the society marked with the principle which requires that knowledge, information and life-time learning hold a key to success in the world of IT technology. Internet, World Wide Web, Web Based Education and ever so growing speed of IT and communicational technologies have enabled the application of new modes,…

  12. Overlay improvements using a real time machine learning algorithm

    Science.gov (United States)

    Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

    2014-04-01

    While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

  13. Preliminary design analysis of the ALT-II limiter for TEXTOR

    International Nuclear Information System (INIS)

    Koski, J.A.; Boyd, R.D.; Kempka, S.M.; Romig, A.D. Jr.; Smith, M.F.; Watson, R.D.; Whitley, J.B.; Conn, R.W.; Grotz, S.P.

    1983-01-01

    Installation of a large toroidal belt pump limiter, Advanced Limiter Test II (ALT-II), on the TEXTOR tokamak at Juelich, FRG is anticipated for early 1986. This paper discusses the preliminary mechanical design and materials considerations undertaken as part of the feasibility study phase for ALT-II

  14. Production of the Allergenic Protein Alt a 1 by Alternaria Isolates from Working Environments

    Directory of Open Access Journals (Sweden)

    Justyna Skóra

    2015-02-01

    Full Text Available The aim of the study was to evaluate the ability of Alternaria isolates from workplaces to produce Alt a 1 allergenic protein, and to analyze whether technical materials (cellulose, compost, leather present within the working environment stimulate or inhibit Alt a 1 production (ELISA test. Studies included identification of the isolated molds by nucleotide sequences analyzing of the ITS1/ITS2 regions, actin, calmodulin and Alt a 1 genes. It has been shown that Alternaria molds are significant part of microbiocenosis in the archive, museum, library, composting plant and tannery (14%–16% frequency in the air. The presence of the gene encoding the Alt a 1 protein has been detected for the strains: Alternaria alternata, A. lini, A. limoniasperae A. nobilis and A. tenuissima. Environmental strains produced Alt a 1 at higher concentrations (1.103–6.528 ng/mL than a ATCC strain (0.551–0.975 ng/mL. It has been shown that the homogenization of the mycelium and the use of ultrafiltration allow a considerable increase of Alt a 1 concentration. Variations in the production of Alt a 1 protein, depend on the strain and extraction methods. These studies revealed no impact of the technical material from the workplaces on the production of Alt a 1 protein.

  15. ELEVATED ALANINE AMINOTRANSFERASE (ALT IN BLOOD DONORS: AN ASSESSMENT OF THE MAIN ASSOCIATED CONDITIONS AND ITS RELATIONSHIP TO THE DEVELOPMENT OF HEPATITIS C

    Directory of Open Access Journals (Sweden)

    Fernando Lopes GONÇALES Jr.

    1998-07-01

    Full Text Available The determination of aminotranferases levels is very useful in the diagnosis of hepatopathies. In recent years, an elevated serum ALT level in blood donors has been associated with an increased risk of post-transfusion hepatitis (PTH. The purpose of the study was to research the factors associated with elevated ALT levels in a cohort of voluntary blood donors and to evaluate the relationship between increased ALT levels and the development of hepatitis C (HCV infection. 166 volunteer blood donors with elevated ALT at the time of their first donation were studied. All of the donors were questioned about previous hepatopathies, exposure to hepatitis, exposure to chemicals, use of medication or drugs, sexual behaviour, contact with blood or secretions and their intake of alcohol. Every three months, the serum levels of AST, ALT, alkaline phosphatase, gamma glutamyl transpeptidase, cholesterol, triglyceride and glycemia are assessed over a two year follow-up. The serum thyroid hormone levels as well as the presence of auto-antibodies were also measured. Abdominal ultrasound was performed in all patients with persistently elevated ALT or AST levels. A needle biopsy of liver was performed in 9 donors without definite diagnostic after medical investigation. The presence of anti-HCV antibodies in 116 donors were assayed again the first clinical evaluation. At the end of follow-up period (2 years later 71 donors were tested again for the presence of anti-HCV antibodies. None of donors resulted positive for hepatitis B or hepatitis C markers during the follow-up. Of the 116 donors, 101 (87% had persistently elevated ALT serum levels during the follow-up. Obesity and alcoholism were the principal conditions related to elevated ALT serum levels in 91/101 (90.1% donors. Hypertriglyceridemia, hypercholesterolemia, hypothyroidism and diabetes mellitus also were associated with increased ALT levels. Only 1/101 (0.9% had mild chronic active non A-G viral

  16. The Negative Impact of Community Stressors on Learning Time: Examining Inequalities between California High Schools

    Science.gov (United States)

    Mirra, Nicole; Rogers, John

    2015-01-01

    Allocated classroom time is not the same as time available for learning--a host of economic and social stressors undermine learning time in schools serving low-income students. When time is limited, it is hard to meet rigorous learning standards. The challenge is compounded in high-poverty schools where community stressors place additional demands…

  17. What time is it? Deep learning approaches for circadian rhythms.

    Science.gov (United States)

    Agostinelli, Forest; Ceglia, Nicholas; Shahbaba, Babak; Sassone-Corsi, Paolo; Baldi, Pierre

    2016-06-15

    Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken. We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO_CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO_CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO_CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO_CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO_CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO_CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp. All data and software are publicly available on the CircadiOmics web portal: circadiomics.igb.uci.edu/ fagostin@uci.edu or pfbaldi@uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  18. Preparation of a Sepia Melanin and Poly(ethylene-alt-maleic Anhydride Hybrid Material as an Adsorbent for Water Purification

    Directory of Open Access Journals (Sweden)

    Guido Panzarasa

    2018-01-01

    Full Text Available Meeting the increasing demand of clean water requires the development of novel efficient adsorbent materials for the removal of organic pollutants. In this context the use of natural, renewable sources is of special relevance and sepia melanin, thanks to its ability to bind a variety of organic and inorganic species, has already attracted interest for water purification. Here we describe the synthesis of a material obtained by the combination of sepia melanin and poly(ethylene-alt-maleic anhydride (P(E-alt-MA. Compared to sepia melanin, the resulting hybrid displays a high and fast adsorption efficiency towards methylene blue (a common industrial dye for a wide pH range (from pH 2 to 12 and under high ionic strength conditions. It is easily recovered after use and can be reused up to three times. Given the wide availability of sepia melanin and P(E-alt-MA, the synthesis of our hybrid is simple and affordable, making it suitable for industrial water purification purposes.

  19. Forecasting air quality time series using deep learning.

    Science.gov (United States)

    Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

    2018-04-13

    This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution

  20. Ensemble Deep Learning for Biomedical Time Series Classification

    Directory of Open Access Journals (Sweden)

    Lin-peng Jin

    2016-01-01

    Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

  1. Evidence for an alternation strategy in time-place learning.

    Science.gov (United States)

    Pizzo, Matthew J; Crystal, Jonathon D

    2004-11-30

    Many different conclusions concerning what type of mechanism rats use to solve a daily time-place task have emerged in the literature. The purpose of this study was to test three competing explanations of time-place discrimination. Rats (n = 10) were tested twice daily in a T-maze, separated by approximately 7 h. Food was available at one location in the morning and another location in the afternoon. After the rats learned to visit each location at the appropriate time, tests were omitted to evaluate whether the rats were utilizing time-of-day (i.e., a circadian oscillator) or an alternation strategy (i.e., visiting a correct location is a cue to visit the next location). Performance on this test was significantly lower than chance, ruling out the use of time-of-day. A phase advance of the light cycle was conducted to test the alternation strategy and timing with respect to the light cycle (i.e., an interval timer). There was no difference between probe and baseline performance. These results suggest that the rats used an alternation strategy to meet the temporal and spatial contingencies in the time-place task.

  2. The Impact of Students' Temporal Perspectives on Time-on-Task and Learning Performance in Game Based Learning

    Science.gov (United States)

    Romero, Margarida; Usart, Mireia

    2013-01-01

    The use of games for educational purposes has been considered as a learning methodology that attracts the students' attention and may allow focusing individuals on the learning activity through the [serious games] SG game dynamic. Based on the hypothesis that students' Temporal Perspective has an impact on learning performance and time-on-task,…

  3. Learning the language of time: Children's acquisition of duration words.

    Science.gov (United States)

    Tillman, Katharine A; Barner, David

    2015-05-01

    Children use time words like minute and hour early in development, but take years to acquire their precise meanings. Here we investigate whether children assign meaning to these early usages, and if so, how. To do this, we test their interpretation of seven time words: second, minute, hour, day, week, month, and year. We find that preschoolers infer the orderings of time words (e.g., hour>minute), but have little to no knowledge of the absolute durations they encode. Knowledge of absolute duration is learned much later in development - many years after children first start using time words in speech - and in many children does not emerge until they have acquired formal definitions for the words. We conclude that associating words with the perception of duration does not come naturally to children, and that early intuitive meanings of time words are instead rooted in relative orderings, which children may infer from their use in speech. Copyright © 2015 Elsevier Inc. All rights reserved.

  4. PPARα regulates the hepatotoxic biomarker alanine aminotransferase (ALT1) gene expression in human hepatocytes

    International Nuclear Information System (INIS)

    Thulin, Petra; Rafter, Ingalill; Stockling, Kenneth; Tomkiewicz, Celine; Norjavaara, Ensio; Aggerbeck, Martine; Hellmold, Heike; Ehrenborg, Ewa; Andersson, Ulf; Cotgreave, Ian; Glinghammar, Bjoern

    2008-01-01

    In this work, we investigated a potential mechanism behind the observation of increased aminotransferase levels in a phase I clinical trial using a lipid-lowering drug, the peroxisome proliferator-activated receptor (PPAR) α agonist, AZD4619. In healthy volunteers treated with AZD4619, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities were elevated without an increase in other markers for liver injury. These increases in serum aminotransferases have previously been reported in some patients receiving another PPARα agonist, fenofibrate. In subsequent in vitro studies, we observed increased expression of ALT1 protein and mRNA in human hepatocytes after treatment with fenofibric acid. The PPAR effect on ALT1 expression was shown to act through a direct transcriptional mechanism involving at least one PPAR response element (PPRE) in the proximal ALT1 promoter, while no effect of fenofibrate and AZD4619 was observed on the ALT2 promoter. Binding of PPARs to the PPRE located at - 574 bp from the transcriptional start site was confirmed on both synthetic oligonucleotides and DNA in hepatocytes. These data show that intracellular ALT expression is regulated by PPAR agonists and that this mechanism might contribute to increased ALT activity in serum

  5. Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning

    Directory of Open Access Journals (Sweden)

    David Afolabi

    2017-11-01

    Full Text Available The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting

  6. Pattern of AST and ALT changes in Relation to Hemolysis in sickle cell Disease

    Directory of Open Access Journals (Sweden)

    K. Nsiah

    2011-01-01

    Full Text Available Background Elevated aminotransferase levels are commonly associated with compromised hepatic integrity from various insults. In sickle cell disease, aspartate transaminase (AST is also released via intravascular hemolysis. This study was done to determine the pattern of changes in AST and alanine transaminase (ALT, in particular the AST:ALT ratio, and to relate these to the hemolytic state, which we consider to be more important than hepatic and cardiac dysfunction in some individuals with sickle cell disease. Methods Serum aminotransferase levels were measured in 330 subjects with sickle cell disease, as well as hemoglobin, reticulocytes, and lactate dehydrogenase. The AST:ALT ratio was designated as a hemolytic marker, and simple and multivariate regression analyses were carried out between this ratio and other hemolytic markers. Results Mean AST and ALT levels were 48.24 % 27.78 and 26.48 % 22.73 U/L, respectively. However, for 49 subjects without sickle cell disease, mean AST and ALT levels were the same, ie, 23.0 U/L. In the subjects with sickle cell disease, the increases in AST levels were far higher than for ALT, supporting its release via intravascular hemolysis. In 95.8% of the subjects with sickle cell disease, the AST:ALT ratio was > 1, but our results did not suggest overt malfunctioning of the liver and heart in the majority of subjects. Conclusion Regression analyses support the use of the AST:ALT ratio as a hemolytic marker, because it has an inverse association with the hemoglobin level. Whether in steady state or in crisis, provided hepatic and cardiac integrity has not been compromised, subjects with sickle cell disease would have higher AST levels due to the hemolytic nature of the condition. This is the first report highlighting the AST:ALT ratio in sickle cell disease.

  7. Using Online Lectures to Make Time for Active Learning

    Science.gov (United States)

    Prunuske, Amy J.; Batzli, Janet; Howell, Evelyn; Miller, Sarah

    2012-01-01

    To make time in class for group activities devoted to critical thinking, we integrated a series of short online lectures into the homework assignments of a large, introductory biology course at a research university. The majority of students viewed the online lectures before coming to class and reported that the online lectures helped them to complete the in-class activity and did not increase the amount of time they devoted to the course. In addition, students who viewed the online lecture performed better on clicker questions designed to test lower-order cognitive skills. The in-class activities then gave the students practice analyzing the information in groups and provided the instructor with feedback about the students’ understanding of the material. On the basis of the results of this study, we support creating hybrid course models that allow students to learn the fundamental information outside of class time, thereby creating time during the class period to be dedicated toward the conceptual understanding of the material. PMID:22714412

  8. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  9. EDUCATIONAL LEAPFROGGING IN THE mLEARNING TIME

    Directory of Open Access Journals (Sweden)

    Abdel Rahman IBRAHIM SULEIMAN

    2014-07-01

    Full Text Available In this theoretical study, researcher tries to shed light on the modern strategy of education, Mobile learning is this strategy, which has become a reality exists in the educational institutions and aims researcher of this study. Trying to figure out the reality of Mobil Determining if the mobile learning part of the E-Learning. Trying for identify future of mobile learning. And the researcher collect the information and the data from previous research in addition to what has been published on websites and blogs and has reached the researcher to achieve the successes of Mobile learning at the level of the educational process now , and that this strategy of mobile learning is not part of the e-learning, and generation of generations , but a new way for the development of the educational process educational , researcher is expected to evolve Mobile learning expands even at the all levels of educational.

  10. Car-following Behavior Model Learning Using Timed Automata

    NARCIS (Netherlands)

    Zhang, Yihuan; Lin, Q.; Wang, Jun; Verwer, S.E.; Dochain, D.; Henrion, D.; Peaucelle, D.

    Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common

  11. Mental Time Travel, Memory and the Social Learning Strategies Tournament

    Science.gov (United States)

    Fogarty, L.; Rendell, L.; Laland, K. N.

    2012-01-01

    The social learning strategies tournament was an open computer-based tournament investigating the best way to learn in a changing environment. Here we present an analysis of the impact of memory on the ability of strategies entered into the social learning strategies tournament (Rendell, Boyd, et al., 2010) to modify their own behavior to suit a…

  12. Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

    Science.gov (United States)

    Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.

    2014-01-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…

  13. Alte Harfe. Vollständige Sammlung alter estnischer Volkslieder : Vorrede / Jakob Hurt

    Index Scriptorium Estoniae

    Hurt, Jakob, 1839-1907

    2005-01-01

    Eessõna rmt. : Hurt, Jakob. Vana kannel. 1. kogu : täieline kogu vanu eesti rahvalaulusid = Alte Harfe : vollständige Sammlung alter estnischen Volkslieder. Tartu, 1875-1886. - (Eesti Kirjameeste Seltsi toimetised ; 3)

  14. The Role of ATRX in the Alternative Lengthening of Telomeres (ALT Phenotype

    Directory of Open Access Journals (Sweden)

    João P. Amorim

    2016-09-01

    Full Text Available Telomeres are responsible for protecting chromosome ends in order to prevent the loss of coding DNA. Their maintenance is required for achieving immortality by neoplastic cells and can occur by upregulation of the telomerase enzyme or through a homologous recombination-associated process, the alternative lengthening of telomeres (ALT. The precise mechanisms that govern the activation of ALT or telomerase in tumor cells are not fully understood, although cellular origin may favor one of the other mechanisms that have been found thus far in mutual exclusivity. Specific mutational events influence ALT activation and maintenance: a unifying frequent feature of tumors that acquire this phenotype are the recurrent mutations of the Alpha Thalassemia/Mental Retardation Syndrome X-Linked (ATRX or Death-Domain Associated Protein (DAXX genes. This review summarizes the established criteria about this phenotype: its prevalence, theoretical molecular mechanisms and relation with ATRX, DAXX and other proteins (directly or indirectly interacting and resulting in the ALT phenotype.

  15. A Lecture Supporting System Based on Real-Time Learning Analytics

    Science.gov (United States)

    Shimada, Atsushi; Konomi, Shin'ichi

    2017-01-01

    A new lecture supporting system based on real-time learning analytics is proposed. Our target is on-site classrooms where teachers give their lectures, and a lot of students listen to teachers' explanation, conduct exercises etc. We utilize not only an e-Learning system, but also an e-Book system to collect real-time learning activities during the…

  16. The Advanced Limiter Test-I (ALT-I) variable-geometry pump limiter module

    International Nuclear Information System (INIS)

    Pontau, A.E.; Malinowski, M.E.; Ver Berkmoes, A.A.; Guthrie, S.E.; Watson, R.D.; Goebel, D.M.; Campbell, G.A.

    1984-01-01

    The ALT-I variable geometry module has been designed to address many of the issues not previously settled by earlier experiments. The goal is to study the basic processes involved in pump limiter operation as well as demonstrate its utility and effect on the plasma. The flexibility and extensive instrumentation of ALT-I will offer a unique opportunity to parameterize operation and facilitate the engineering design of future pump limiters. (orig.)

  17. The 'donations for decreased ALT (D4D)' prosocial behavior incentive scheme for NAFLD patients.

    Science.gov (United States)

    Sumida, Yoshio; Yoshikawa, Toshikazu; Tanaka, Saiyu; Taketani, Hiroyoshi; Kanemasa, Kazuyuki; Nishimura, Tekeshi; Yamaguchi, Kanji; Mitsuyoshi, Hironori; Yasui, Kohichiroh; Minami, Masahito; Naito, Yuji; Itoh, Yoshito

    2014-12-01

    Physicians often experience difficulties in motivating patients with non-alcoholic fatty liver disease (NAFLD) to undergo lifestyle changes. The aim of this study is to examine whether 'Donations for Decreased alanine aminotransferase (ALT)' (D4D) prosocial behavior incentive can serve as an effective intrinsic motivational factor in comparison with conventional dietary and exercise intervention alone for NAFLD patients. Twenty-five NAFLD patients with elevated ALT were randomly assigned to a control group that received conventional dietary and exercise intervention alone, or a donation group whereby, as an incentive, we would make a monetary donation to the United Nations World Food Programme (WFP) based on the decrease in their ALT levels achieved over 12 weeks, in addition to receiving control intervention. In a donation group, we would donate US$1 to the WFP for every 1 IU/l of decrease in their ALT levels. There were no differences of pre-treatment clinical characteristics between the two groups. Significant reductions of ALT levels were achieved only in a donation group, although post-treatment ALT levels were not different between the two groups. These patients raised a total of $316 for the WFP. Promoting patients' intrinsic motivation by incorporating 'D4D' prosocial behavior incentive into conventional dietary and exercise intervention may provide a means to improve NAFLD. © The Author 2013. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  18. Machine learning in heart failure: ready for prime time.

    Science.gov (United States)

    Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

    2018-03-01

    The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

  19. In real time: exploring nursing students' learning during an international experience.

    Science.gov (United States)

    Afriyie Asenso, Barbara; Reimer-Kirkham, Sheryl; Astle, Barbara

    2013-10-11

    Abstract Nursing education has increasingly turned to international learning experiences to educate students who are globally minded and aware of social injustices in local and global communities. To date, research with international learning experiences has focused on the benefits for the students participating, after they have completed the international experience. The purpose of this qualitative study was to explore how nursing students learn during the international experience. The sample consisted of eight nursing students who enrolled in an international learning experience, and data were collected in "real time" in Zambia. The students were observed during learning activities and were interviewed three times. Three major themes emerged from the thematic analysis: expectations shaped students' learning, engagement facilitated learning, and critical reflection enhanced learning. Implications are discussed, related to disrupting media representations of Africa that shape students' expectations, and educational strategies for transformative learning and global citizenship.

  20. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

    Science.gov (United States)

    Pecevski, Dejan; Maass, Wolfgang

    2016-01-01

    Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

  1. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

    Science.gov (United States)

    Pecevski, Dejan

    2016-01-01

    Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

  2. Museums as spaces and times for learning and social participation.

    Directory of Open Access Journals (Sweden)

    César M.

    2014-12-01

    Full Text Available A museum is valued according to its collections, communication and knowledge exchange with visitors (Primo, 1999. Museums should be in dialogue with the public, contributing to their development (Skramstad, 2004 and collective memory (Wertsch, 2004. Social interactions and working in participants’ zone of proximal development (Vygotsky, 1934/1962 play an important role in non-formal learning opportunities that take place at museums. The National Museum of Natural History and Science (Lisbon University offers weekly holiday programmes for children and teenagers, aiming at developing scientific literacy in intercultural and inclusive spaces and times, facilitating knowledge appropriation and social participation. We studied these programmes, assuming an interpretive approach (Denzin, 2002 and developing an intrinsic case study (Stake, 1995. The main participants were these children and teenagers, their parents, and museum educational agents. Data collecting instruments included observation, interviews, questionnaires, children and teenagers’ protocols and tasks inspired in projective techniques. Data treatment and analysis was based on a narrative content analysis (Clandinin & Connelly, 1998 from which inductive categories emerged (Hamido & César, 2009. Some examples illuminate participants’ expectancies, their engagement in activities, and the contributions of social interactions and non-formal education to the development of scientific literacy.

  3. Alternagin-C (ALT-C), a disintegrin-like protein from Rhinocerophis alternatus snake venom promotes positive inotropism and chronotropism in fish heart.

    Science.gov (United States)

    Monteiro, D A; Kalinin, A L; Selistre-de-Araujo, H S; Vasconcelos, E S; Rantin, F T

    2016-02-01

    Alternagin-C (ALT-C) is a disintegrin-like protein purified from the venom of the snake, Rhinocerophis alternatus. Recent studies showed that ALT-C is able to induce vascular endothelial growth factor (VEGF) expression, endothelial cell proliferation and migration, angiogenesis and to increase myoblast viability. This peptide, therefore, can play a crucial role in tissue regeneration mechanisms. The aim of this study was to evaluate the effects of a single dose of alternagin-C (0.5 mg kg(-1), via intra-arterial) on in vitro cardiac function of the freshwater fish traíra, Hoplias malabaricus, after 7 days. ALT-C treatment increased the cardiac performance promoting: 1) significant increases in the contraction force and in the rates of contraction and relaxation with concomitant decreases in the values of time to the peak tension and time to half- and 90% relaxation; 2) improvement in the cardiac pumping capacity and maximal electrical stimulation frequency, shifting the optimum frequency curve upward and to the right; 3) increases in myocardial VEGF levels and expression of key Ca(2+)-cycling proteins such as SERCA (sarcoplasmic reticulum Ca(2+)-ATPase), PLB (phospholamban), and NCX (Na(+)/Ca(2+) exchanger); 4) abolishment of the typical negative force-frequency relationship of fish myocardium. In conclusion, this study indicates that ALT-C improves cardiac function, by increasing Ca(2+) handling efficiency leading to a positive inotropism and chronotropism. The results suggest that ALT-C may lead to better cardiac output regulation indicating its potential application in therapies for cardiac contractile dysfunction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Project Management in Real Time: A Service-Learning Project

    Science.gov (United States)

    Larson, Erik; Drexler, John A., Jr.

    2010-01-01

    This article describes a service-learning assignment for a project management course. It is designed to facilitate hands-on student learning of both the technical and the interpersonal aspects of project management, and it involves student engagement with real customers and real stakeholders in the creation of real events with real outcomes. As…

  5. Age and time effects on implicit and explicit learning

    NARCIS (Netherlands)

    Verneau, M.; Kamp, J. van der; Savelsbergh, G.J.P.; Looze, M.P. de

    2014-01-01

    Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

  6. Age and Time Effects on Implicit and Explicit Learning

    NARCIS (Netherlands)

    Verneau, M.M.N.; van der Kamp, J.; Savelsbergh, G.J.P.; de Looze, M.P.

    2014-01-01

    Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

  7. Language Learning Attitudes: Ingrained Or Shaped In Time?

    Directory of Open Access Journals (Sweden)

    Gökçe DİŞLEN DAĞGÖL

    2017-09-01

    Full Text Available Language learning has become an essential need in today’s world. From academic to social settings, humans need to communicate in a different language to survive in their community. However, despite this increasing importance of language, it is difficult to say we have attained successful language learning on a large scale since there are a lot of factors in language learning process. Language attitudes, one of these factors, influence this process both positively and negatively, depending on how we view learning a foreign language. Therefore, this study deals with the issue of language attitudes to uncover learners’ language conceptions and probable effects on their learning. Moreover, this study aims to reveal the potential role of past learning experiences on the development of language beliefs positively or negatively. Thus, 35 university students in their 1st, 2nd, 3rd and 4th years constitute the participants of the study. Based on mixed research design, the study is comprised of both quantitative and qualitative data. Quantitative data were gathered through Attitude Scale towards English Course, and the analyses were performed with Statistical Packages for Social Sciences (SPSS 17.0 version for Windows. The qualitative data were collected from students’ reports of their own autobiographies regarding their previous language learning experiences in elementary, secondary, high school and university years, and were subjected to the content analysis. The study showed language attitudes from behavioural, cognitive and affective perspectives and found out different factors in shaping their learning conceptions.

  8. Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities.

    Science.gov (United States)

    MaBouDi, HaDi; Shimazaki, Hideaki; Giurfa, Martin; Chittka, Lars

    2017-06-01

    The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn. We show that inhibitory spike-timing dependent plasticity (modelling non-associative plasticity by exposure to different stimuli) in the synapses from local neurons to projection neurons decorrelates the projection neurons' outputs. The strength of the decorrelations is regulated by global inhibitory feedback within antennal lobes to the projection neurons. By additionally modelling octopaminergic modification of synaptic plasticity among local neurons in the antennal lobes and projection neurons to LHN connections, the model can discriminate and generalize olfactory stimuli. Although positive patterning can be accounted for by the l-ALT model, negative patterning requires further processing and mushroom body circuits. Thus, our model explains several-but not all-types of associative olfactory learning and generalization by a few neural layers of odour processing in the l-ALT. As an outcome of the combination between non-associative and associative learning, the modelling approach allows us to link changes in structural organization of honeybees' antennal lobes with their behavioural performances over the course of their life.

  9. The Relationship between Motivation, Learning Approaches, Academic Performance and Time Spent

    Science.gov (United States)

    Everaert, Patricia; Opdecam, Evelien; Maussen, Sophie

    2017-01-01

    Previous literature calls for further investigation in terms of precedents and consequences of learning approaches (deep learning and surface learning). Motivation as precedent and time spent and academic performance as consequences are addressed in this paper. The study is administered in a first-year undergraduate course. Results show that the…

  10. Incremental Impact of Time on Students' Use of E-Learning via Facebook

    Science.gov (United States)

    Moghavvemi, Sedigheh; Salarzadeh Janatabadi, Hashem

    2018-01-01

    The majority of studies utilised the cross-sectional method to measure students' intention to learn and investigate their corresponding learning behaviours. Only a few studies have measured the process of change in students' learning behaviour in the context of time. The main purpose of this study is to determine the effects of using a Facebook…

  11. Crumpled Molecules and Edible Plastic: Science Learning Activation in Out-of-School Time

    Science.gov (United States)

    Dorph, Rena; Schunn, Christian D.; Crowley, Kevin

    2017-01-01

    The Coalition for Science After School highlights the dual nature of outcomes for science learning during out-of- school time (OST): Learning experiences should not only be positive in the moment, but also position youth for future success. Several frameworks speak to the first set of immediate outcomes--what youth learn, think, and feel as the…

  12. EFFECTS OF COOPERATIVE LEARNING MODEL TYPE STAD JUST-IN TIME BASED ON THE RESULTS OF LEARNING TEACHING PHYSICS COURSE IN PHYSICS SCHOOL IN PHYSICS PROGRAM FACULTY UNIMED

    Directory of Open Access Journals (Sweden)

    Teguh Febri Sudarma

    2013-06-01

    Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students

  13. Single machine total completion time minimization scheduling with a time-dependent learning effect and deteriorating jobs

    Science.gov (United States)

    Wang, Ji-Bo; Wang, Ming-Zheng; Ji, Ping

    2012-05-01

    In this article, we consider a single machine scheduling problem with a time-dependent learning effect and deteriorating jobs. By the effects of time-dependent learning and deterioration, we mean that the job processing time is defined by a function of its starting time and total normal processing time of jobs in front of it in the sequence. The objective is to determine an optimal schedule so as to minimize the total completion time. This problem remains open for the case of -1 < a < 0, where a denotes the learning index; we show that an optimal schedule of the problem is V-shaped with respect to job normal processing times. Three heuristic algorithms utilising the V-shaped property are proposed, and computational experiments show that the last heuristic algorithm performs effectively and efficiently in obtaining near-optimal solutions.

  14. The helicase domain of Polθ counteracts RPA to promote alt-NHEJ.

    Science.gov (United States)

    Mateos-Gomez, Pedro A; Kent, Tatiana; Deng, Sarah K; McDevitt, Shane; Kashkina, Ekaterina; Hoang, Trung M; Pomerantz, Richard T; Sfeir, Agnel

    2017-12-01

    Mammalian polymerase theta (Polθ) is a multifunctional enzyme that promotes error-prone DNA repair by alternative nonhomologous end joining (alt-NHEJ). Here we present structure-function analyses that reveal that, in addition to the polymerase domain, Polθ-helicase activity plays a central role during double-strand break (DSB) repair. Our results show that the helicase domain promotes chromosomal translocations by alt-NHEJ in mouse embryonic stem cells and also suppresses CRISPR-Cas9- mediated gene targeting by homologous recombination (HR). In vitro assays demonstrate that Polθ-helicase activity facilitates the removal of RPA from resected DSBs to allow their annealing and subsequent joining by alt-NHEJ. Consistent with an antagonistic role for RPA during alt-NHEJ, inhibition of RPA1 enhances end joining and suppresses recombination. Taken together, our results reveal that the balance between HR and alt-NHEJ is controlled by opposing activities of Polθ and RPA, providing further insight into the regulation of repair-pathway choice in mammalian cells.

  15. Machine learning application in the life time of materials

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Materials design and development typically takes several decades from the initial discovery to commercialization with the traditional trial and error development approach. With the accumulation of data from both experimental and computational results, data based machine learning becomes an emerging field in materials discovery, design and property prediction. This manuscript reviews the history of materials science as a disciplinary the most common machine learning method used in materials sc...

  16. THE CHIMERIC ALT-VASTUS LATERALIS FREE FLAP IN RECONSTRUCTION OF ADVANCED BRONJ OF THE MAXILLA

    Directory of Open Access Journals (Sweden)

    Francesca Toia

    2015-04-01

    Full Text Available ntroduction Bisphosphonate-related osteonecrosis of the jaw (BRONJ is a dangerous complication of bisphosphonates, a class of pharmaceutical agents used in numerous bone disorders. No gold standard therapy exists, but recent literature suggests that, in advanced stages, the best results are achieved with aggressive debridement. In this paper, we report our experience of treatment of stage 3 BRONJ of the maxilla with extensive surgical debridement and reconstruction with a chimeric ALT-Vastus lateralis flap. Methods Five selected patients with stage 3 BRONJ underwent partial maxillectomy with disease-free margins followed by immediate reconstruction with a chimeric ALT-Vastus lateralis free flap. Results Only two patients experienced minor complications. All other patients healed uneventfully within two weeks and donor site morbidity was minimal. Conclusions Our data suggest that aggressive debridement and reconstruction with a chimeric ALT -Vastus lateralis flap is an effective option for the treatment of stage III BRONJ of the maxilla.

  17. Environmental Stress Induces Trinucleotide Repeat Mutagenesis in Human Cells by Alt-Nonhomologous End Joining Repair.

    Science.gov (United States)

    Chatterjee, Nimrat; Lin, Yunfu; Yotnda, Patricia; Wilson, John H

    2016-07-31

    Multiple pathways modulate the dynamic mutability of trinucleotide repeats (TNRs), which are implicated in neurodegenerative disease and evolution. Recently, we reported that environmental stresses induce TNR mutagenesis via stress responses and rereplication, with more than 50% of mutants carrying deletions or insertions-molecular signatures of DNA double-strand break repair. We now show that knockdown of alt-nonhomologous end joining (alt-NHEJ) components-XRCC1, LIG3, and PARP1-suppresses stress-induced TNR mutagenesis, in contrast to the components of homologous recombination and NHEJ, which have no effect. Thus, alt-NHEJ, which contributes to genetic mutability in cancer cells, also plays a novel role in environmental stress-induced TNR mutagenesis. Published by Elsevier Ltd.

  18. #AltPlanets: Exploring the Exoplanet Catalogue with Neural Networks

    Science.gov (United States)

    Laneuville, M.; Tasker, E. J.; Guttenberg, N.

    2017-12-01

    The launch of Kepler in 2009 brought the number of known exoplanets into the thousands, in a growth explosion that shows no sign of abating. While the data available for individual planets is presently typically restricted to orbital and bulk properties, the quantity of data points allows the potential for meaningful statistical analysis. It is not clear how planet mass, radius, orbital path, stellar properties and neighbouring planets influence one another, therefore it seems inevitable that patterns will be missed simply due to the difficulty of including so many dimensions. Even simple trends may be overlooked if they fall outside our expectation of planet formation; a strong risk in a field where new discoveries have destroyed theories from the first observations of hot Jupiters. A possible way forward is to take advantage of the capabilities of neural network autoencoders. The idea of such algorithms is to learn a representation (encoding) of the data in a lower dimension space, without a priori knowledge about links between the elements. This encoding space can then be used to discover the strongest correlations in the original dataset.The key point is that trends identified by a neural network are independent of any previous analysis and pre-conceived ideas about physical processes. Results can reveal new relationships between planet properties and verify existing trends. We applied this concept to study data from the NASA Exoplanet Archive and while we have begun to explore the potential use of neural networks for exoplanet data, there are many possible extensions. For example, the network can produce a large number of 'alternative planets' whose statistics should match the current distribution. This larger dataset could highlight gaps in the parameter space or indicate observations are missing particular regimes. This could guide instrument proposals towards objects liable to yield the most information.

  19. A matter of timing: harm reduction in learned helplessness.

    Science.gov (United States)

    Richter, Sophie Helene; Sartorius, Alexander; Gass, Peter; Vollmayr, Barbara

    2014-11-03

    Learned helplessness has excellent validity as an animal model for depression, but problems in reproducibility limit its use and the high degree of stress involved in the paradigm raises ethical concerns. We therefore aimed to identify which and how many trials of the learned helplessness paradigm are necessary to distinguish between helpless and non-helpless rats. A trial-by-trial reanalysis of tests from 163 rats with congenital learned helplessness or congenital non-learned helplessness and comparison of 82 rats exposed to inescapable shock with 38 shock-controls revealed that neither the first test trials, when rats showed unspecific hyperlocomotion, nor trials of the last third of the test, when almost all animals responded quickly to the stressor, contributed to sensitivity and specificity of the test. Considering only trials 3-10 improved the classification of helpless and non-helpless rats. The refined analysis allows abbreviation of the test for learned helplessness from 15 trials to 10 trials thereby reducing pain and stress of the experimental animals without losing statistical power.

  20. DeepRT: deep learning for peptide retention time prediction in proteomics

    OpenAIRE

    Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

    2017-01-01

    Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

  1. Towards Real-Time Speech Emotion Recognition for Affective E-Learning

    Science.gov (United States)

    Bahreini, Kiavash; Nadolski, Rob; Westera, Wim

    2016-01-01

    This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order…

  2. Food Gardening and Intergenerational Learning in Times of ...

    African Journals Online (AJOL)

    The focus of discussion is the intergenerational interactions and learning ... pastoralism and, to a lesser degree, cultivation (Mayer, 1971; Mostert, 1992). ... discouraged about the hard physical work and rather limited economic ... in the Amanzi for Food project, a middle-aged female participant, Mrs Peters, has involved a.

  3. Real-Time Barcode Detection and Classification Using Deep Learning

    DEFF Research Database (Denmark)

    Hansen, Daniel Kold; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund

    2017-01-01

    Barcodes, in their different forms, can be found on almost any packages available in the market. Detecting and then decoding of barcodes have therefore great applications. We describe how to adapt the state-of-the- art deep learning-based detector of You Only Look Once (YOLO) for the purpose...

  4. Food Gardening and Intergenerational Learning in Times of ...

    African Journals Online (AJOL)

    Uncertainty is a universal phenomenon, a lived experience, an unease about acting ... uncertainty through mediations of knowledge, the formation of new social relations and ... Environmental Affairs and Tourism, 53% of young people in the country are ... Bubomi learning network connected to the Amanzi for Food project.

  5. Performance of an Optimized Paper-Based Test for Rapid Visual Measurement of Alanine Aminotransferase (ALT in Fingerstick and Venipuncture Samples.

    Directory of Open Access Journals (Sweden)

    Sidhartha Jain

    Full Text Available A paper-based, multiplexed, microfluidic assay has been developed to visually measure alanine aminotransferase (ALT in a fingerstick sample, generating rapid, semi-quantitative results. Prior studies indicated a need for improved accuracy; the device was subsequently optimized using an FDA-approved automated platform (Abaxis Piccolo Xpress as a comparator. Here, we evaluated the performance of the optimized paper test for measurement of ALT in fingerstick blood and serum, as compared to Abaxis and Roche/Hitachi platforms. To evaluate feasibility of remote results interpretation, we also compared reading cell phone camera images of completed tests to reading the device in real time.96 ambulatory patients with varied baseline ALT concentration underwent fingerstick testing using the paper device; cell phone images of completed devices were taken and texted to a blinded off-site reader. Venipuncture serum was obtained from 93/96 participants for routine clinical testing (Roche/Hitachi; subsequently, 88/93 serum samples were captured and applied to paper and Abaxis platforms. Paper test and reference standard results were compared by Bland-Altman analysis.For serum, there was excellent agreement between paper test and Abaxis results, with negligible bias (+4.5 U/L. Abaxis results were systematically 8.6% lower than Roche/Hitachi results. ALT values in fingerstick samples tested on paper were systematically lower than values in paired serum tested on paper (bias -23.6 U/L or Abaxis (bias -18.4 U/L; a correction factor was developed for the paper device to match fingerstick blood to serum. Visual reads of cell phone images closely matched reads made in real time (bias +5.5 U/L.The paper ALT test is highly accurate for serum testing, matching the reference method against which it was optimized better than the reference methods matched each other. A systematic difference exists between ALT values in fingerstick and paired serum samples, and can be

  6. A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing

    Directory of Open Access Journals (Sweden)

    Vivian V. Valentin

    2014-07-01

    Full Text Available The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB category learning and procedural memory dominates information-integration (II category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning – results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500ms compared to delays of 0 and 1000ms, and highly impaired with delays of 2.5 seconds or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 seconds. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

  7. Exploring Complex Engineering Learning Over Time with Epistemic Network Analysis

    OpenAIRE

    Svarovsky, Gina Navoa

    2011-01-01

    Recently, K-12 engineering education has received increased attention as a pathway to building stronger foundations in math andscience and introducing young people to the profession. However, the National Academy of Engineering found that many K-12engineering programs focus heavily on engineering design and science and math learning while minimizing the development ofengineering habits of mind. This narrowly-focused engineering activity can leave young people – and in particular, girls – with...

  8. Measurement and purification of Alanine aminotransferase (ALT enzyme activity in patients with celiac disease

    Directory of Open Access Journals (Sweden)

    Taghreed U. Mohammed

    2017-09-01

    Full Text Available Celiac disease (CD is the most common genetically - based disease in correlation with food intolerance. The aim of this study is to measure the activity of ALT enzyme and purify enzyme from sera women with celiac disease. Alanine aminotransferase (ALT activity has been assayed in (30 women serum samples with celiac disease, age range between (20-40 year and (30 serum of healthy women as control group, age range between (22-38 year. In the present study, the mean value of ALT activity was significantly higher in patients with celiac disease than healthy group (p<0.01. The ALT enzyme was partial purified from sera women with celiac disease by dialysis, gel filtration using Sephadex G- 50 and ion exchange chromatography using DEAE- cellulose A-50 . The results showed a single peak by using gel filtration and the activity reached 31-15 U/L .Two isoenzymes were obtained by using ion exchange chromatography and the purity degree of isoenzymse (I, II were (5.7 and (5.53 fold respectively

  9. Effect of Ramadan fasting on alanine transferase (ALT in nonalcoholic fatty liver disease (NAFLD

    Directory of Open Access Journals (Sweden)

    Hojjatolah Rahimi

    2017-09-01

    Full Text Available Background: The effects of Ramadan fasting on NAFLD are unknown and there are very limited studies have done in this area. Some nutritional and behavioral changes of fasting people in Ramadan can affect NAFLD. These include nutrition with high fat and calories, altering in weight and sleep and low physical activity. We decided to evaluate the effects of these changes on one of the important indicators of deterioration of NAFLD, ALT.Methods: Sixty patients with fatty liver disease performed two consecutive ALT exams before and after Ramadan month of whom finally 34 were fasting and 26 nonfasting. After collecting data they were divided in two groups of fasting and nonfasting and compared using SPSS software.Results: Mean ALT change from before to after Ramadan was higher and positive in fasting (+7.38±8.47 IU/L compared to nonfastng patients that was negative (-0.12±10.15 IU/L (P=0.002 and this change was mainly in patients who had fasted 21-30 days.Conclusion: Ramadan fasting may increase ALT. It is needed to perform more studies in patients with NAFLD during Ramadan fasting with larger sample size and in various conditions especially weight loss with patients’ education for observing dietary regimen.

  10. Once upon a time.... Storytelling to enhance teaching and learning.

    Science.gov (United States)

    Lordly, Daphne

    2007-01-01

    The impact of storytelling in the classroom was examined, as was what motivates individuals to engage in storytelling. A storytelling methodology was introduced in an undergraduate nutrition course as an opportunity to enhance the teaching and learning environment. A 28-item, multi-part, self-administered survey was then distributed to the class (n=17). Survey responses (n=15, 88% response) indicate that educators' and students' storytelling can positively influence the learning environment. This occurs through the creation of a greater focus on personalized information, glimpses of real-life experience, a connection with a topic as participants recognize similarities in their own personal experience and knowledge, and connections between different topics and through the emphasis on key concepts. Stories initiate useful conversations about unexplored struggles within practice, such as the emotional dimension(s) of an issue or what it means to be professional. Students are motivated to participate in storytelling through an external focus on others (i.e., helping others to learn) and an internal focus on self (i.e., seeking a connection with others to promote social dialogue). Several challenges related to the use of storytelling in the classroom emerged. Storytelling develops ways of knowing and dialoguing about issues, which has the potential to influence how students will approach their professional practice.

  11. Understanding the effects of time on collaborative learning processes in problem based learning: a mixed methods study.

    Science.gov (United States)

    Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

    2014-10-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.

  12. Time Spent, Workload, and Student and Faculty Perceptions in a Blended Learning Environment

    Science.gov (United States)

    Schumacher, Christie; Arif, Sally

    2016-01-01

    Objective. To evaluate student perception and time spent on asynchronous online lectures in a blended learning environment (BLE) and to assess faculty workload and perception. Methods. Students (n=427) time spent viewing online lectures was measured in three courses. Students and faculty members completed a survey to assess perceptions of a BLE. Faculty members recorded time spent creating BLEs. Results. Total time spent in the BLE was less than the allocated time for two of the three courses by 3-15%. Students preferred online lectures for their flexibility, students’ ability to apply information learned, and congruence with their learning styles. Faculty members reported the BLE facilitated higher levels of learning during class sessions but noted an increase in workload. Conclusion. A BLE increased faculty workload but was well received by students. Time spent viewing online lectures was less than what was allocated in two of the three courses. PMID:27667839

  13. Influence of gender, BMI, and ethnicity on serum ALT levels of healthy students of a medical school

    International Nuclear Information System (INIS)

    Bilal, M.; Tariq, A.; Khan, S.; Quratulain, A.; Tariq, A.; Shahid, M.F.; Khan, M.W.; Naveed, A.K.

    2011-01-01

    Background: Alanine Aminotransferase (ALT) is an enzyme found in Liver and indicates injury to Hepatocytes. It is influenced by various factors. The objectives of this study were to identify the correlates of ALT activity among healthy medical students of Army Medical College, National University of Sciences and Technology, aged 18-22 years. This was to establish the mean ALT levels of the students and compare them with those in various parts of the world and observe various correlations that exist and factors that may influence ALT levels. Methods: This population included 143 volunteer students (93 men and 50 women) selected on the basis of negative answers to a detailed medical questionnaire including past medical history, drug and alcohol consumption, on the absence of clinical signs of liver disease, on the negativity of serological testing for Hepatitis B and C virus. Results: The mean ALT level of the entire population was 28.7 IU/L. A major sex-difference in ALT value was observed, the mean ALT value being higher in men than in women (32.1+- 21.7 vs. 22.6 +- 9.7 IU/L, p<0.004). According to WHO criteria for Asians, normal BMI was taken from 18.5-23.0 Kg/m/sup 2/. There was a positive significant correlation between serum ALT level and BMI (p<0.002). ALT level strongly correlates with body mass index and gender. There was no significant variation in ALT levels among Punjabis and Sindhis, Balochis, Pathans, and Kashmiris. Conclusion: We suggest the need of taking into account these parameters in a clinical interpretation of ALT level. (author)

  14. The right time to learn: mechanisms and optimization of spaced learning

    Science.gov (United States)

    Smolen, Paul; Zhang, Yili; Byrne, John H.

    2016-01-01

    For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning. PMID:26806627

  15. Generalization bounds of ERM-based learning processes for continuous-time Markov chains.

    Science.gov (United States)

    Zhang, Chao; Tao, Dacheng

    2012-12-01

    Many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed (i.i.d.). However, the assumption of i.i.d. samples is not suitable for practical application to problems in which samples are time dependent. In this paper, we are mainly concerned with the empirical risk minimization (ERM) based learning process for time-dependent samples drawn from a continuous-time Markov chain. This learning process covers many kinds of practical applications, e.g., the prediction for a time series and the estimation of channel state information. Thus, it is significant to study its theoretical properties including the generalization bound, the asymptotic convergence, and the rate of convergence. It is noteworthy that, since samples are time dependent in this learning process, the concerns of this paper cannot (at least straightforwardly) be addressed by existing methods developed under the sample i.i.d. assumption. We first develop a deviation inequality for a sequence of time-dependent samples drawn from a continuous-time Markov chain and present a symmetrization inequality for such a sequence. By using the resultant deviation inequality and symmetrization inequality, we then obtain the generalization bounds of the ERM-based learning process for time-dependent samples drawn from a continuous-time Markov chain. Finally, based on the resultant generalization bounds, we analyze the asymptotic convergence and the rate of convergence of the learning process.

  16. Online Learning Solutions for Freeway Travel Time Prediction

    NARCIS (Netherlands)

    Van Lint, J.W.C.

    2008-01-01

    Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time

  17. Business Faculty Time Management: Lessons Learned from the Trenches

    Science.gov (United States)

    Cummings, Richard G.; Holmes, Linda E.

    2009-01-01

    Teaching, research, and service expectations of the academic profession may sometimes seem overwhelming. Although much has been written about time management in general, there has not been much written about time management in the academic professions and even less written about time management for academics in the business disciplines. This paper…

  18. Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiming Gui

    2014-01-01

    Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

  19. Learning of pitch and time structures in an artificial grammar setting.

    Science.gov (United States)

    Prince, Jon B; Stevens, Catherine J; Jones, Mari Riess; Tillmann, Barbara

    2018-04-12

    Despite the empirical evidence for the power of the cognitive capacity of implicit learning of structures and regularities in several modalities and materials, it remains controversial whether implicit learning extends to the learning of temporal structures and regularities. We investigated whether (a) an artificial grammar can be learned equally well when expressed in duration sequences as when expressed in pitch sequences, (b) learning of the artificial grammar in either duration or pitch (as the primary dimension) sequences can be influenced by the properties of the secondary dimension (invariant vs. randomized), and (c) learning can be boosted when the artificial grammar is expressed in both pitch and duration. After an exposure phase with grammatical sequences, learning in a subsequent test phase was assessed in a grammaticality judgment task. Participants in both the pitch and duration conditions showed incidental (not fully implicit) learning of the artificial grammar when the secondary dimension was invariant, but randomizing the pitch sequence prevented learning of the artificial grammar in duration sequences. Expressing the artificial grammar in both pitch and duration resulted in disproportionately better performance, suggesting an interaction between the learning of pitch and temporal structure. The findings are relevant to research investigating the learning of temporal structures and the learning of structures presented simultaneously in 2 dimensions (e.g., space and time, space and objects). By investigating learning, the findings provide further insight into the potential specificity of pitch and time processing, and their integrated versus independent processing, as previously debated in music cognition research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  20. Patrika - alt

    Indian Academy of Sciences (India)

    brilliant printers

    the meeting from around the country a greater interaction with Fellows. There were sixteen 30-minutes ... development in plants; (e) experimental evolutionary biology; (f) discrete groups of ..... were provided by “deep ecology”, a doctrine of questionable intellectual .... pollination, dispersal, mutualisms etc. The advantage of.

  1. The Role of Age and Occupational Future Time Perspective in Workers' Motivation to Learn

    Science.gov (United States)

    Kochoian, Nané; Raemdonck, Isabel; Frenay, Mariane; Zacher, Hannes

    2017-01-01

    The purpose of this paper is to better understand the relationship between employees' chronological age and their motivation to learn, by adopting a lifespan perspective. Based on socioemotional selectivity theory, we suggest that occupational future time perspective mediates the relationship between age and motivation to learn. In accordance with…

  2. Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

    NARCIS (Netherlands)

    Noroozi, O.; Busstra, M.C.; Mulder, M.; Biemans, H.J.A.; Tobi, H.; Geelen, A.; Veer, van 't P.; Chizari, M.

    2012-01-01

    This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs.

  3. Effects of Business School Student's Study Time on the Learning Process

    Science.gov (United States)

    Tetteh, Godson Ayertei

    2016-01-01

    Purpose: This paper aims to clarify the relationship between the student's study time and the learning process in the higher education system by adapting the total quality management (TQM) principles-process approach. Contrary to Deming's (1982) constancy of purpose to improve the learning process, some students in higher education postpone their…

  4. Flexible Learning and Teaching: Looking Beyond the Binary of Full-time/Part-time Provision in South African Higher Education

    Directory of Open Access Journals (Sweden)

    Barbara M Jones

    2015-06-01

    Full Text Available This paper engages with literature on flexible learning and teaching in order to explore whether it may be possible, within the South African context, to have flexible learning and teaching provide a third way which goes beyond the current practice of full-time/part-time provision. This binary classification of students is a proxy for day-time/after-hours delivery.  The argument is made that effective, flexible learning and teaching requires a fundamental shift in thinking about learning and teaching in higher education that moves us beyond such binaries. The paper proposes that in order to ensure access and success for students, ‘common knowledge’ (Edwards, 2010 will need to be co-constructed which understands flexible learning and teaching in ways which will meet needs of a diversity of students, including working students. It will require ‘resourceful leadership’ (Edwards, 2014 within the university that recognises, enhances and gives purpose to the capability of colleagues at every level of the systems they lead. Also, it will require the building of ‘common knowledge’ between certain sectors of universities and particular workplaces.

  5. Mutational analysis of the activator of late transcription, Alt , in the lactococcal bacteriophage TP901-1

    DEFF Research Database (Denmark)

    Pedersen, Margit; Hammer, Karin

    2007-01-01

    An activator protein, Alt, synthesized during the early state of lytic infection is required for transcription of the late operon in the lactococcal phage TP901-1. In order to identify amino acid residues in the Alt protein required for activation of the TP901-1 late promoter, Plate, hydroxylamine...

  6. Association between ALT level and the rate of cardio/cerebrovascular events in HIV-positive individuals

    DEFF Research Database (Denmark)

    Sabin, Caroline A; Ryom, Lene; Kovari, Helen

    2013-01-01

    An inverse association between serum alanine aminotransferase (ALT) levels and the risk of myocardial infarction (MI) has been reported in the general population. We investigated associations between ALT levels and the risk of various cardiovascular and cerebrovascular outcomes in a large cohort ...

  7. Theory in learning technology

    Directory of Open Access Journals (Sweden)

    Laura Czerniewicz

    2011-12-01

    Full Text Available This special issue is being published at a significant point in time in relation tosimultaneous changes in higher education, in technology and in the field of learningtechnology itself. As the 2011 ALT C conference themes clearly state, learningtechnology needs to learn to thrive in a colder and more challenging climate. In thisdifficult political and economic environment technological trends continue todevelop in terms of mobility, cloud computing, ubiquity and the emergence of whathas been called big data. E-learning has become mainstream and the field of learningtechnology itself is beginning to stabilise as a profession. Profession here isunderstood as a knowledge-based occupation and a form of cultural work where thetasks addressed are human problems amenable to expert advice and distinguishablefrom other kinds of work by the fact that it is underpinned by abstract knowledge(Macdonald, 1995.

  8. Benefits for Voice Learning Caused by Concurrent Faces Develop over Time.

    Science.gov (United States)

    Zäske, Romi; Mühl, Constanze; Schweinberger, Stefan R

    2015-01-01

    Recognition of personally familiar voices benefits from the concurrent presentation of the corresponding speakers' faces. This effect of audiovisual integration is most pronounced for voices combined with dynamic articulating faces. However, it is unclear if learning unfamiliar voices also benefits from audiovisual face-voice integration or, alternatively, is hampered by attentional capture of faces, i.e., "face-overshadowing". In six study-test cycles we compared the recognition of newly-learned voices following unimodal voice learning vs. bimodal face-voice learning with either static (Exp. 1) or dynamic articulating faces (Exp. 2). Voice recognition accuracies significantly increased for bimodal learning across study-test cycles while remaining stable for unimodal learning, as reflected in numerical costs of bimodal relative to unimodal voice learning in the first two study-test cycles and benefits in the last two cycles. This was independent of whether faces were static images (Exp. 1) or dynamic videos (Exp. 2). In both experiments, slower reaction times to voices previously studied with faces compared to voices only may result from visual search for faces during memory retrieval. A general decrease of reaction times across study-test cycles suggests facilitated recognition with more speaker repetitions. Overall, our data suggest two simultaneous and opposing mechanisms during bimodal face-voice learning: while attentional capture of faces may initially impede voice learning, audiovisual integration may facilitate it thereafter.

  9. Geological Time, Biological Events and the Learning Transfer Problem

    Science.gov (United States)

    Johnson, Claudia C.; Middendorf, Joan; Rehrey, George; Dalkilic, Mehmet M.; Cassidy, Keely

    2014-01-01

    Comprehension of geologic time does not come easily, especially for students who are studying the earth sciences for the first time. This project investigated the potential success of two teaching interventions that were designed to help non-science majors enrolled in an introductory geology class gain a richer conceptual understanding of the…

  10. Learning for sustainability in times of accelerating change

    NARCIS (Netherlands)

    Wals, A.E.J.; Corcoran, P.B.

    2012-01-01

    We live in turbulent times, our world is changing at accelerating speed. Information is everywhere, but wisdom appears in short supply when trying to address key inter-related challenges of our time such as; runaway climate change, the loss of biodiversity, the depletion of natural resources, the

  11. Time series forecasting based on deep extreme learning machine

    NARCIS (Netherlands)

    Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

    2017-01-01

    Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

  12. Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students.

    Science.gov (United States)

    Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng

    2014-01-01

    The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Mre11 and Blm-Dependent Formation of ALT-Like Telomeres in Ku-Deficient Ustilago maydis.

    Directory of Open Access Journals (Sweden)

    Eun Young Yu

    2015-10-01

    Full Text Available A subset of human cancer cells uses a specialized, aberrant recombination pathway known as ALT to maintain telomeres, which in these cells are characterized by complex aberrations including length heterogeneity, high levels of unpaired C-strand, and accumulation of extra-chromosomal telomere repeats (ECTR. These phenotypes have not been recapitulated in any standard budding or fission yeast mutant. We found that eliminating Ku70 or Ku80 in the yeast-like fungus Ustilago maydis results initially in all the characteristic telomere aberrations of ALT cancer cells, including C-circles, a highly specific marker of ALT. Subsequently the ku mutants experience permanent G2 cell cycle arrest, accompanied by loss of telomere repeats from chromosome ends and even more drastic accumulation of very short ECTRs (vsECTRs. The deletion of atr1 or chk1 rescued the lethality of the ku mutant, and "trapped" the telomere aberrations in the early ALT-like stage. Telomere abnormalities are telomerase-independent, but dramatically suppressed by deletion of mre11 or blm, suggesting major roles for these factors in the induction of the ALT pathway. In contrast, removal of other DNA damage response and repair factors such as Rad51 has disparate effects on the ALT phenotypes, suggesting that these factors process ALT intermediates or products. Notably, the antagonism of Ku and Mre11 in the induction of ALT is reminiscent of their roles in DSB resection, in which Blm is also known to play a key role. We suggest that an aberrant resection reaction may constitute an early trigger for ALT telomeres, and that the outcomes of ALT are distinct from DSB because of the unique telomere nucleoprotein structure.

  14. Distributing learning over time: the spacing effect in children's acquisition and generalization of science concepts.

    Science.gov (United States)

    Vlach, Haley A; Sandhofer, Catherine M

    2012-01-01

    The spacing effect describes the robust finding that long-term learning is promoted when learning events are spaced out in time rather than presented in immediate succession. Studies of the spacing effect have focused on memory processes rather than for other types of learning, such as the acquisition and generalization of new concepts. In this study, early elementary school children (5- to 7-year-olds; N = 36) were presented with science lessons on 1 of 3 schedules: massed, clumped, and spaced. The results revealed that spacing lessons out in time resulted in higher generalization performance for both simple and complex concepts. Spaced learning schedules promote several types of learning, strengthening the implications of the spacing effect for educational practices and curriculum. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

  15. Giving English Language Learners the Time They Need to Succeed: Profiles of Three Expanded Learning Time Schools

    Science.gov (United States)

    Farbman, David A.

    2015-01-01

    With the number of students who are English language learners (ELLs) likely to double in coming years, it is more important than ever for schools across the U.S. to design and implement educational practices and strategies that best meet ELLs' learning needs, says the report, "Giving English Language Learners the Time They Need to…

  16. Dissociable effects of practice variability on learning motor and timing skills.

    Science.gov (United States)

    Caramiaux, Baptiste; Bevilacqua, Frédéric; Wanderley, Marcelo M; Palmer, Caroline

    2018-01-01

    Motor skill acquisition inherently depends on the way one practices the motor task. The amount of motor task variability during practice has been shown to foster transfer of the learned skill to other similar motor tasks. In addition, variability in a learning schedule, in which a task and its variations are interweaved during practice, has been shown to help the transfer of learning in motor skill acquisition. However, there is little evidence on how motor task variations and variability schedules during practice act on the acquisition of complex motor skills such as music performance, in which a performer learns both the right movements (motor skill) and the right time to perform them (timing skill). This study investigated the impact of rate (tempo) variability and the schedule of tempo change during practice on timing and motor skill acquisition. Complete novices, with no musical training, practiced a simple musical sequence on a piano keyboard at different rates. Each novice was assigned to one of four learning conditions designed to manipulate the amount of tempo variability across trials (large or small tempo set) and the schedule of tempo change (randomized or non-randomized order) during practice. At test, the novices performed the same musical sequence at a familiar tempo and at novel tempi (testing tempo transfer), as well as two novel (but related) sequences at a familiar tempo (testing spatial transfer). We found that practice conditions had little effect on learning and transfer performance of timing skill. Interestingly, practice conditions influenced motor skill learning (reduction of movement variability): lower temporal variability during practice facilitated transfer to new tempi and new sequences; non-randomized learning schedule improved transfer to new tempi and new sequences. Tempo (rate) and the sequence difficulty (spatial manipulation) affected performance variability in both timing and movement. These findings suggest that there is a

  17. Making time for learning-oriented leadership in multidisciplinary hospital management groups.

    Science.gov (United States)

    Singer, Sara J; Hayes, Jennifer E; Gray, Garry C; Kiang, Mathew V

    2015-01-01

    Although the clinical requirements of health care delivery imply the need for interdisciplinary management teams to work together to promote frontline learning, such interdisciplinary, learning-oriented leadership is atypical. We designed this study to identify behaviors enabling groups of diverse managers to perform as learning-oriented leadership teams on behalf of quality and safety. We randomly selected 12 of 24 intact groups of hospital managers from one hospital to participate in a Safety Leadership Team Training program. We collected primary data from March 2008 to February 2010 including pre- and post-staff surveys, multiple interviews, observations, and archival data from management groups. We examined the level and trend in frontline perceptions of managers' learning-oriented leadership following the intervention and ability of management groups to achieve objectives on targeted improvement projects. Among the 12 intervention groups, we identified higher- and lower-performing intervention groups and behaviors that enabled higher performers to work together more successfully. Management groups that achieved more of their performance goals and whose staff perceived more and greater improvement in their learning-oriented leadership after participation in Safety Leadership Team Training invested in structures that created learning capacity and conscientiously practiced prescribed learning-oriented management and problem-solving behaviors. They made the time to do these things because they envisioned the benefits of learning, valued the opportunity to learn, and maintained an environment of mutual respect and psychological safety within their group. Learning in management groups requires vision of what learning can accomplish; will to explore, practice, and build learning capacity; and mutual respect that sustains a learning environment.

  18. Alternative Mixed Assessment Project (ALT.A.: The Mexican E-Learning Experience

    Directory of Open Access Journals (Sweden)

    Mónica A. López-Campos

    2010-11-01

    Full Text Available The aim of this paper is to present an experiment of mixed evaluation (summative/formative of questions formulated by students in a distance-education environment carried out in the Total Quality Management course in the B.S. degree in Industrial Engineering offered by Mexican public universities. Questions generated by students were evaluated using a specially-designed quantitative tool: Matrix Observation of four criteria with binary scoring. The experiment showed: (1 how is it possible to enrich the evaluation process, and formalize students' skills hardly recognizable with traditional forms of assessment; and (2 how the teacher-student interaction can be increased significantly by the technique mixed evaluation of questions in reverse, i.e. by the students.

  19. Using Response Times to Assess Learning Progress: A Joint Model for Responses and Response Times

    Science.gov (United States)

    Wang, Shiyu; Zhang, Susu; Douglas, Jeff; Culpepper, Steven

    2018-01-01

    Analyzing students' growth remains an important topic in educational research. Most recently, Diagnostic Classification Models (DCMs) have been used to track skill acquisition in a longitudinal fashion, with the purpose to provide an estimate of students' learning trajectories in terms of the change of fine-grained skills overtime. Response time…

  20. Genel Anestezi Altında Çocukta Diş Tedavisi

    OpenAIRE

    Gülhan, A.; Sandallı, N.

    2013-01-01

    ÖZETBu makalede çocuklarda genel anestezi altında diş tedavisinin koşulları anlatıldı ve normal tedavi koşulları sağlanamadığı takdirde, genel anesteziye başvurmanın yararlarına ve sakıncalarına değinildi. Genel anestezi indikasyonları sıralanarak, bunlar arasında en sık rastladığımız «Güç çocuklarım" genci anesteziye başvurmadan Önce hangi yollara başvurularak normal tedavinin deneneceğinden bahsedildi.Bir hastane ortamında genel anestezi altında çalışmaya karar verildikten sonra, bir a...

  1. Biomaterial properties evaluation of poly(vinyl acetate- alt-maleic anhydride)/chitosan nanocapsules

    Science.gov (United States)

    Raţă, Delia Mihaela; Popa, Marcel; Chailan, Jean-François; Zamfir, Carmen Lăcrămioara; Peptu, Cătălina Anişoara

    2014-08-01

    Nanocapsules with diameter around 100 nm based on a natural polymer (chitosan) and a synthetic polymer poly(vinyl acetate- alt-maleic anhydride) [poly(MAVA)] by interfacial condensation method were prepared. The present study proposes a new type of biocompatible nanocapsules based on poly(vinyl acetate- alt-maleic anhydride-chitosan) (MCS) able to become a reliable support for inclusion and release of drugs. The spherical shape of the nanocapsules was evidenced by scanning electron microscopy. Nanocapsules presented a good Norfloxacin loading and release capacity. Haemocompatibility tests have demonstrated that the nanocapsules present a low toxicity and a good compatibility with sanguine medium. The biocompatibility properties of the nanocapsules after their intraperitoneal administration in rats were evidenced by histopathological examination of different organs (brain, liver, kidney, and lung). The results are encouraging and the nanocapsules can be used as controlled drug delivery systems.

  2. Preliminary design analysis of the ALT-II limiter for TEXTOR

    International Nuclear Information System (INIS)

    Koski, J.A.; Boyd, R.D.; Kempka, S.M.; Romig, A.D. Jr.; Smith, M.F.; Watson, R.D.; Whitley, J.B.; Conn, R.W.; Grotz, S.P.

    1984-01-01

    Installation of a large toroidal belt pump limiter, Advanced Limiter Test II (ALT-II), on the TEXTOR tokamak at Juelich, FRG is anticipated for early 1986. This paper discusses the preliminary mechanical design and materials considerations undertaken as part of the feasibility study phase for ALT-II. Since the actively cooled limiter blade is the component in direct contact with the plasma edge, and thus subject to the severe plasma environment, most preliminary design efforts have concentrated on analysis of the blade. The screening process which led to the recommended preliminary design consisting of a dispersion strenghthened copper or OFHC copper cover plate over an austenitic stainless steel base plate is discussed. A 1 to 3 mm thick low atomic number coating consisting of a graded plasma-sprayed Silicon Carbide-Aluminium composite is recommended subject to further experiment and evaluation. Thermal-hydraulic and stress analyses of the limiter blade are also discussed. (orig.)

  3. A Time to Define: Making the Specific Learning Disability Definition Prescribe Specific Learning Disability

    Science.gov (United States)

    Kavale, Kenneth A.; Spaulding, Lucinda S.; Beam, Andrea P.

    2009-01-01

    Unlike other special education categories defined in U.S. law (Individuals with Disabilities Education Act), the definition of specific learning disability (SLD) has not changed since first proposed in 1968. Thus, although the operational definition of SLD has responded to new knowledge and understanding about the construct, the formal definition…

  4. A presentation system for just-in-time learning in radiology.

    Science.gov (United States)

    Kahn, Charles E; Santos, Amadeu; Thao, Cheng; Rock, Jayson J; Nagy, Paul G; Ehlers, Kevin C

    2007-03-01

    There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system-called TEMPO-was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system's design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.

  5. [A technological device for optimizing the time taken for blind people to learn Braille].

    Science.gov (United States)

    Hernández, Cesar; Pedraza, Luis F; López, Danilo

    2011-10-01

    This project was aimed at designing and putting an electronic prototype into practice for improving the initial time taken by visually handicapped people for learning Braille, especially children. This project was mainly based on a prototype digital electronic device which identifies and translates material written by a user in Braille by a voice synthesis system, producing artificial words to determine whether a handicapped person's writing in Braille has been correct. A global system for mobile communications (GSM) module was also incorporated into the device which allowed it to send text messages, thereby involving innovation in the field of articles for aiding visually handicapped people. This project's main result was an easily accessed and understandable prototype device which improved visually handicapped people's initial learning of Braille. The time taken for visually handicapped people to learn Braille became significantly reduced whilst their interest increased, as did their concentration time regarding such learning.

  6. Peer-assisted learning: time for nomenclature clarification

    Directory of Open Access Journals (Sweden)

    Alexander Olaussen

    2016-07-01

    Full Text Available Background: Peer-assisted learning (PAL is used throughout all levels of healthcare education. Lack of formalised agreement on different PAL programmes may confuse the literature. Given the increasing interest in PAL as an education philosophy, the terms need clarification. The aim of this review is to 1 describe different PAL programmes, 2 clarify the terminology surrounding PAL, and 3 propose a simple pragmatic way of defining PAL programmes based on their design. Methods: A review of current PAL programmes within the healthcare setting was conducted. Each programme was scrutinised based on two aspects: the relationship between student and teacher, and the student to teacher ratio. The studies were then shown to fit exclusively into the novel proposed classification. Results: The 34 programmes found, demonstrate a wide variety in terms used. We established six terms, which exclusively applied to the programmes. The relationship between student and teacher was categorised as peer-to-peer or near-peer. The student to teacher ratio suited three groupings, named intuitively ‘Mentoring’ (1:1 or 1:2, ‘Tutoring’ (1:3–10, and ‘Didactic’ (1:>10. From this, six novel terms – all under the heading of PAL – are suggested: ‘Peer Mentoring’, ‘Peer Tutoring’, ‘Peer Didactic’, ‘Near-Peer Mentoring’, ‘Near-Peer Tutoring’, and ‘Near-Peer Didactic’. Conclusions: We suggest herein a simple pragmatic terminology to overcome ambiguous terminology. Academically, clear terms will allow effective and efficient research, ensuring furthering of the educational philosophy.

  7. Peer-assisted learning: time for nomenclature clarification

    Science.gov (United States)

    Olaussen, Alexander; Reddy, Priya; Irvine, Susan; Williams, Brett

    2016-01-01

    Background Peer-assisted learning (PAL) is used throughout all levels of healthcare education. Lack of formalised agreement on different PAL programmes may confuse the literature. Given the increasing interest in PAL as an education philosophy, the terms need clarification. The aim of this review is to 1) describe different PAL programmes, 2) clarify the terminology surrounding PAL, and 3) propose a simple pragmatic way of defining PAL programmes based on their design. Methods A review of current PAL programmes within the healthcare setting was conducted. Each programme was scrutinised based on two aspects: the relationship between student and teacher, and the student to teacher ratio. The studies were then shown to fit exclusively into the novel proposed classification. Results The 34 programmes found, demonstrate a wide variety in terms used. We established six terms, which exclusively applied to the programmes. The relationship between student and teacher was categorised as peer-to-peer or near-peer. The student to teacher ratio suited three groupings, named intuitively ‘Mentoring’ (1:1 or 1:2), ‘Tutoring’ (1:3–10), and ‘Didactic’ (1:>10). From this, six novel terms – all under the heading of PAL – are suggested: ‘Peer Mentoring’, ‘Peer Tutoring’, ‘Peer Didactic’, ‘Near-Peer Mentoring’, ‘Near-Peer Tutoring’, and ‘Near-Peer Didactic’. Conclusions We suggest herein a simple pragmatic terminology to overcome ambiguous terminology. Academically, clear terms will allow effective and efficient research, ensuring furthering of the educational philosophy. PMID:27415590

  8. An algorithm for learning real-time automata (extended abstract)

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2007-01-01

    A common model for discrete event systems is a deterministic finite automaton (DFA). An advantage of this model is that it can be interpreted by domain experts. When observing a real-world system, however, there often is more information than just the sequence of discrete events: the time at which

  9. No Time to Think: Policy, Pedagogy and Professional Learning

    Science.gov (United States)

    Leonard, Simon N.; Roberts, Philip

    2016-01-01

    In this study, we seek to illuminate the effects of the global policy convergence in education through a close study of its enactment within an Australian Teacher Education course. Building on an examination of the changing priorities of a cohort of pre-service teachers over a short space of time, we argue that the enactment of New Public…

  10. Time and Practice: Learning to Become a Geographer

    Science.gov (United States)

    Downs, Roger M.

    2014-01-01

    A goal of geography education is fostering geographic literacy for all and building significant expertise for some. How much time and practice do students need to become literate or expert in geography? There is not an answer to this question. Using two concepts from cognitive psychology--the ideas of ten thousand hours and deliberate…

  11. Problem based learning: the effect of real time data on the website to student independence

    Science.gov (United States)

    Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.

    2018-05-01

    Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.

  12. The Effect of Inquiry Training Learning Model Based on Just in Time Teaching for Problem Solving Skill

    Science.gov (United States)

    Turnip, Betty; Wahyuni, Ida; Tanjung, Yul Ifda

    2016-01-01

    One of the factors that can support successful learning activity is the use of learning models according to the objectives to be achieved. This study aimed to analyze the differences in problem-solving ability Physics student learning model Inquiry Training based on Just In Time Teaching [JITT] and conventional learning taught by cooperative model…

  13. Rhythm and timing in autism: Learning to dance

    Directory of Open Access Journals (Sweden)

    Pat eAmos

    2013-04-01

    Full Text Available In recent years, a significant body of research has focused on challenges to neural connectivity as a key to understanding autism. In contrast to attempts to identify a single static, primarily brain-based deficit, children and adults diagnosed with autism are increasingly perceived as out of sync with their internal and external environments in dynamic ways that must also involve operations of the peripheral nervous systems. The noisiness that seems to occur in both directions of neural flow may help explain challenges to movement and sensing, and ultimately to entrainment with circadian rhythms and social interactions. across the autism spectrum. Profound differences in the rhythm and timing of movement have been tracked to infancy. Difficulties with self-synchrony inhibit praxis, and can disrupt the dance of relationships through which caregiver and child build meaning. Different sensory aspects of a situation may fail to match up; ultimately, intentions and actions themselves may be uncoupled. This uncoupling may help explain the expressions of alienation from the actions of one’s body which recur in the autobiographical autism literature. Multi-modal/cross-modal coordination of different types of sensory information into coherent events may be difficult to achieve because amodal properties (e.g. rhythm and tempo that help unite perceptions are unreliable. One question posed to the connectivity research concerns the role of rhythm and timing in this operation, and whether these can be mobilized to reduce overload and enhance performance. A case is made for developmental research addressing how people with autism actively explore and make sense of their environments. The parent/author recommends investigating approaches such as scaffolding interactions via rhythm, following the person’s lead, slowing the pace, discriminating between intentional communication and stray motor patterns, and organizing information through one sensory mode at

  14. The time course of ethanol tolerance: associative learning

    Directory of Open Access Journals (Sweden)

    J.L.O. Bueno

    2007-11-01

    Full Text Available The effect of different contextual stimuli on different ethanol-induced internal states was investigated during the time course of both the hypothermic effect of the drug and of drug tolerance. Minimitters were surgically implanted in 16 Wistar rats to assess changes in their body temperature under the effect of ethanol. Rat groups were submitted to ethanol or saline trials every other day. The animals were divided into two groups, one receiving a constant dose (CD of ethanol injected intraperitoneally, and the other receiving increasing doses (ID during the 10 training sessions. During the ethanol training sessions, conditioned stimuli A (tone and B (buzzer were presented at "state +" (35 min after drug injection and "state -" (170 min after drug injection, respectively. Conditioned stimuli C (bip and D (white noise were presented at moments equivalent to stimuli A and B, respectively, but during the saline training sessions. All stimuli lasted 15 min. The CD group, but not the ID group, developed tolerance to the hypothermic effect of ethanol. Stimulus A (associated with drug "state +" induced hyperthermia with saline injection in the ID group. Stimulus B (associated with drug "state -" reduced ethanol tolerance in the CD group and modulated the hypothermic effect of the drug in the ID group. These results indicate that contextual stimuli acquire modulatory conditioned properties that are associated with the time course of both the action of the drug and the development of drug tolerance.

  15. TEXPLORE temporal difference reinforcement learning for robots and time-constrained domains

    CERN Document Server

    Hester, Todd

    2013-01-01

    This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuou...

  16. The time course of location-avoidance learning in fear of spiders.

    Science.gov (United States)

    Rinck, Mike; Koene, Marieke; Telli, Sibel; Moerman-van den Brink, Wiltine; Verhoeven, Barbara; Becker, Eni S

    2016-01-01

    Two experiments were designed to study the time course of avoidance learning in spider fearfuls (SFs) under controlled experimental conditions. To achieve this, we employed an immersive virtual environment (IVE): While walking freely through a virtual art museum to search for specific paintings, the participants were exposed to virtual spiders. Unbeknown to the participants, only two of four museum rooms contained spiders, allowing for avoidance learning. Indeed, the more SF the participants were, the faster they learned to avoid the rooms that contained spiders (Experiment. 1), and within the first six trials, high fearfuls already developed a preference for starting their search task in rooms without spiders (Experiment 2). These results illustrate the time course of avoidance learning in SFs, and they speak to the usefulness of IVEs in fundamental anxiety research.

  17. Time course influences transfer of visual perceptual learning across spatial location.

    Science.gov (United States)

    Larcombe, S J; Kennard, C; Bridge, H

    2017-06-01

    Visual perceptual learning describes the improvement of visual perception with repeated practice. Previous research has established that the learning effects of perceptual training may be transferable to untrained stimulus attributes such as spatial location under certain circumstances. However, the mechanisms involved in transfer have not yet been fully elucidated. Here, we investigated the effect of altering training time course on the transferability of learning effects. Participants were trained on a motion direction discrimination task or a sinusoidal grating orientation discrimination task in a single visual hemifield. The 4000 training trials were either condensed into one day, or spread evenly across five training days. When participants were trained over a five-day period, there was transfer of learning to both the untrained visual hemifield and the untrained task. In contrast, when the same amount of training was condensed into a single day, participants did not show any transfer of learning. Thus, learning time course may influence the transferability of perceptual learning effects. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

    OpenAIRE

    Richard Chiou; Yongjin (james) Kwon; Tzu-Liang (bill) Tseng; Robin Kizirian; Yueh-Ting Yang

    2010-01-01

    This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote c...

  19. A real-time articulatory visual feedback approach with target presentation for second language pronunciation learning.

    Science.gov (United States)

    Suemitsu, Atsuo; Dang, Jianwu; Ito, Takayuki; Tiede, Mark

    2015-10-01

    Articulatory information can support learning or remediating pronunciation of a second language (L2). This paper describes an electromagnetic articulometer-based visual-feedback approach using an articulatory target presented in real-time to facilitate L2 pronunciation learning. This approach trains learners to adjust articulatory positions to match targets for a L2 vowel estimated from productions of vowels that overlap in both L1 and L2. Training of Japanese learners for the American English vowel /æ/ that included visual training improved its pronunciation regardless of whether audio training was also included. Articulatory visual feedback is shown to be an effective method for facilitating L2 pronunciation learning.

  20. Learning characteristics of a space-time neural network as a tether skiprope observer

    Science.gov (United States)

    Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

    1993-01-01

    The Software Technology Laboratory at the Johnson Space Center is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

  1. Learning Constructive Primitives for Real-time Dynamic Difficulty Adjustment in Super Mario Bros

    OpenAIRE

    Shi, Peizhi; Chen, Ke

    2017-01-01

    Among the main challenges in procedural content generation (PCG), content quality assurance and dynamic difficulty adjustment (DDA) of game content in real time are two major issues concerned in adaptive content generation. Motivated by the recent learning-based PCG framework, we propose a novel approach to seamlessly address two issues in Super Mario Bros (SMB). To address the quality assurance issue, we exploit the synergy between rule-based and learning-based methods to produce quality gam...

  2. Why Hong Kong students favour more face-to-face classroom time in blended learning

    OpenAIRE

    Henri,James; Lee,Sandra

    2007-01-01

    A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students in...

  3. A Study of Time Spent Working at Learning Centers. Technical Report #17.

    Science.gov (United States)

    Omori, Sharon; And Others

    This study examined the proportion of time children in the Kamehameha Early Education Program schools spend at actual school work in learning centers. Systematic time-sampled observations using multiple observers were conducted in December-January and again in March-April. The subjects, 12 children (6 kindergarteners and 6 first graders) were…

  4. An Integrated Theory of Prospective Time Interval Estimation: The Role of Cognition, Attention, and Learning

    Science.gov (United States)

    Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

    2007-01-01

    A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary…

  5. Learning Management System Calendar Reminders and Effects on Time Management and Academic Performance

    Science.gov (United States)

    Mei, Jianyang

    2016-01-01

    This research project uses a large research university in the Midwest as a research site to explore the time management skills of international students and analyzes how using the Course Hack, an online Learning Management System (LMS) calendar tool, improves participants' time management skills and positively impacts their academic performance,…

  6. Evaluation of Online Log Variables That Estimate Learners' Time Management in a Korean Online Learning Context

    Science.gov (United States)

    Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

    2016-01-01

    The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…

  7. Cerebellar motor learning versus cerebellar motor timing: the climbing fibre story

    Science.gov (United States)

    Llinás, Rodolfo R

    2011-01-01

    Abstract Theories concerning the role of the climbing fibre system in motor learning, as opposed to those addressing the olivocerebellar system in the organization of motor timing, are briefly contrasted. The electrophysiological basis for the motor timing hypothesis in relation to the olivocerebellar system is treated in detail. PMID:21486816

  8. Time-place learning over a lifetime : Absence of memory loss in trained old mice

    NARCIS (Netherlands)

    Mulder, Cornelis K; Reckman, Gerlof A R; Gerkema, Menno P; van der Zee, Eddy A

    Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an

  9. A longitudinal study on time perspectives: relations with academic delay of gratification and learning environment

    NARCIS (Netherlands)

    Peetsma, T.; Schuitema, J.; van der Veen, I.

    2012-01-01

    After they start secondary school (at age 12 in the Netherlands), students' time perspectives on school and professional career and self-regulated learning decrease, while their perspectives on leisure increase. We aimed to investigate relations in the developments in time perspectives and delay of

  10. An integrated theory of prospective time interval estimation : The role of cognition, attention, and learning

    NARCIS (Netherlands)

    Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

    A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval

  11. Time-Place Learning over a Lifetime: Absence of Memory Loss in Trained Old Mice

    Science.gov (United States)

    Mulder, Cornelis K.; Reckman, Gerlof A. R.; Gerkema, Menno P.; Van der Zee, Eddy A.

    2015-01-01

    Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an experience-based daily schedule. Mice were tested for…

  12. Real-time yield estimation based on deep learning

    Science.gov (United States)

    Rahnemoonfar, Maryam; Sheppard, Clay

    2017-05-01

    Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.

  13. Examining the Effect of Time Constraint on the Online Mastery Learning Approach towards Improving Postgraduate Students' Achievement

    Science.gov (United States)

    Ee, Mong Shan; Yeoh, William; Boo, Yee Ling; Boulter, Terry

    2018-01-01

    Time control plays a critical role within the online mastery learning (OML) approach. This paper examines the two commonly implemented mastery learning strategies--personalised system of instructions and learning for mastery (LFM)--by focusing on what occurs when there is an instructional time constraint. Using a large data set from a postgraduate…

  14. Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

    Directory of Open Access Journals (Sweden)

    Nicolas Frémaux

    2013-04-01

    Full Text Available Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD learning of Doya (2000 to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

  15. On learning science and pseudoscience from prime-time television programming

    Science.gov (United States)

    Whittle, Christopher Henry

    The purpose of the present dissertation is to determine whether the viewing of two particular prime-time television programs, ER and The X-Files, increases viewer knowledge of science and to identify factors that may influence learning from entertainment television programming. Viewer knowledge of scientific dialogue from two science-based prime-time television programs, ER, a serial drama in a hospital emergency room and The X-Files, a drama about two Federal Bureau of Investigation agents who pursue alleged extraterrestrial life and paranormal activity, is studied. Level of viewing, education level, science education level, experiential factors, level of parasocial interaction, and demographic characteristics are assessed as independent variables affecting learning from entertainment television viewing. The present research involved a nine-month long content analysis of target television program dialogue and data collection from an Internet-based survey questionnaire posted to target program-specific on-line "chat" groups. The present study demonstrated that entertainment television program viewers incidentally learn science from entertainment television program dialogue. The more they watch, the more they learn. Viewing a pseudoscientific fictional television program does necessarily influence viewer beliefs in pseudoscience. Higher levels of formal science study are reflected in more science learning and less learning of pseudoscience from entertainment television program viewing. Pseudoscience learning from entertainment television programming is significantly related to experience with paranormal phenomena, higher levels of viewer parasocial interaction, and specifically, higher levels of cognitive parasocial interaction. In summary, the greater a viewer's understanding of science the more they learn when they watch their favorite science-based prime-time television programs. Viewers of pseudoscience-based prime-time television programming with higher levels

  16. Why Hong Kong students favour more face-to-face classroom time in blended learning

    Directory of Open Access Journals (Sweden)

    James Henri

    Full Text Available A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students indicated a preference for a combination of online and face-to-face teaching, with more face-to-face class time in that mix. These findings would also be informative for other part-time programs using blended teaching and learning models.

  17. Estimating the implicit component of visuomotor rotation learning by constraining movement preparation time.

    Science.gov (United States)

    Leow, Li-Ann; Gunn, Reece; Marinovic, Welber; Carroll, Timothy J

    2017-08-01

    When sensory feedback is perturbed, accurate movement is restored by a combination of implicit processes and deliberate reaiming to strategically compensate for errors. Here, we directly compare two methods used previously to dissociate implicit from explicit learning on a trial-by-trial basis: 1 ) asking participants to report the direction that they aim their movements, and contrasting this with the directions of the target and the movement that they actually produce, and 2 ) manipulating movement preparation time. By instructing participants to reaim without a sensory perturbation, we show that reaiming is possible even with the shortest possible preparation times, particularly when targets are narrowly distributed. Nonetheless, reaiming is effortful and comes at the cost of increased variability, so we tested whether constraining preparation time is sufficient to suppress strategic reaiming during adaptation to visuomotor rotation with a broad target distribution. The rate and extent of error reduction under preparation time constraints were similar to estimates of implicit learning obtained from self-report without time pressure, suggesting that participants chose not to apply a reaiming strategy to correct visual errors under time pressure. Surprisingly, participants who reported aiming directions showed less implicit learning according to an alternative measure, obtained during trials performed without visual feedback. This suggests that the process of reporting can affect the extent or persistence of implicit learning. The data extend existing evidence that restricting preparation time can suppress explicit reaiming and provide an estimate of implicit visuomotor rotation learning that does not require participants to report their aiming directions. NEW & NOTEWORTHY During sensorimotor adaptation, implicit error-driven learning can be isolated from explicit strategy-driven reaiming by subtracting self-reported aiming directions from movement directions, or

  18. Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

    Science.gov (United States)

    Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

    2015-12-01

    We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

  19. The Education and Training of Learning Technologists: A Competences Approach (Report to IEEE Technical Committee on Learning Technologies)

    Science.gov (United States)

    Hartley, Roger; Kinshuk; Koper, Rob; Okamoto, Toshio; Spector, J. Michael

    2010-01-01

    The educational and training requirements of Advanced Learning Technology (ALT) need to engage with curricula that reflect the varied requirements of the workplace and of society. Students have a range of interests and ambitions in ALT which the instructional process has to accommodate and support. With these considerations in mind the IEEE…

  20. Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

    Science.gov (United States)

    Wu, Juan; Bai, Min

    2018-05-01

    We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing the least-squares inversion in seismic imaging. The IDL is proposed to overcome the drawback of traditional dictionary learning algorithm in losing partial texture information. Firstly, the noisy image is divided into overlapped image patches, and some random patches are extracted for dictionary learning. Then, we apply the IDL technology to minimize the coherency between atoms during dictionary learning. Finally, the sparse representation problem is solved by a sparse coding algorithm, and image is restored by those sparse coefficients. By reducing the correlation among atoms, it is possible to preserve most of the small-scale features in the image while removing much of the long-wavelength noise. The application of the IDL method to regularization of seismic images from least-squares reverse time migration shows successful performance.

  1. Serum ALT levels as a surrogate marker for serum HBV DNA levels in HBeAg-negative pregnant women.

    Science.gov (United States)

    Sangfelt, Per; Von Sydow, Madeleine; Uhnoo, Ingrid; Weiland, Ola; Lindh, Gudrun; Fischler, Björn; Lindgren, Susanne; Reichard, Olle

    2004-01-01

    In Stockholm, Sweden, the majority of pregnant women positive for hepatitis B surface antigen (HBsAg) are hepatitis Be antigen (HBeAg) negative. Newborns to HBeAg positive mothers receive vaccination and hepatitis B immunoglobulin (HBIg). Newborns to HBeAg negative mothers receive vaccine and HBIg only if the mothers have elevated ALT levels. The aim of this study was to retrospectively evaluate ALT levels as a surrogate marker for HBV DNA levels in HBeAg negative carrier mothers. Altogether 8947 pregnant women were screened for HBV markers from 1999 to 2001 at the Virology Department, Karolinska Hospital. Among mothers screened 192 tested positive for HBsAg (2.2%). 13 of these samples could not be retrieved. Of the remaining 179 sera, 8 (4%) tested positive for HBeAg and 171 (95.5%) were HBeAg negative. Among the HBeAg negative mothers, 9 had HBV DNA levels > 10(5) copies/ml, and of these 7 had normal ALT levels indicating low sensitivity of an elevated ALT level as a surrogate marker for high HBV DNA level. Furthermore, no correlation was found between ALT and HBV DNA levels. Hence, it is concluded that the use of ALT as a surrogate marker for high viral replication in HBeAg negative mothers could be questioned.

  2. Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples

    Directory of Open Access Journals (Sweden)

    Mingchen Yao

    2015-01-01

    Full Text Available Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.. However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM principle for sequences of time-dependent samples (TDS. In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.

  3. Optimal critic learning for robot control in time-varying environments.

    Science.gov (United States)

    Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

    2015-10-01

    In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

  4. Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

    Science.gov (United States)

    Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

    2013-12-01

    How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

  5. Do learning collaboratives strengthen communication? A comparison of organizational team communication networks over time.

    Science.gov (United States)

    Bunger, Alicia C; Lengnick-Hall, Rebecca

    Collaborative learning models were designed to support quality improvements, such as innovation implementation by promoting communication within organizational teams. Yet the effect of collaborative learning approaches on organizational team communication during implementation is untested. The aim of this study was to explore change in communication patterns within teams from children's mental health organizations during a year-long learning collaborative focused on implementing a new treatment. We adopt a social network perspective to examine intraorganizational communication within each team and assess change in (a) the frequency of communication among team members, (b) communication across organizational hierarchies, and (c) the overall structure of team communication networks. A pretest-posttest design compared communication among 135 participants from 21 organizational teams at the start and end of a learning collaborative. At both time points, participants were asked to list the members of their team and rate the frequency of communication with each along a 7-point Likert scale. Several individual, pair-wise, and team level communication network metrics were calculated and compared over time. At the individual level, participants reported communicating with more team members by the end of the learning collaborative. Cross-hierarchical communication did not change. At the team level, these changes manifested differently depending on team size. In large teams, communication frequency increased, and networks grew denser and slightly less centralized. In small teams, communication frequency declined, growing more sparse and centralized. Results suggest that team communication patterns change minimally but evolve differently depending on size. Learning collaboratives may be more helpful for enhancing communication among larger teams; thus, managers might consider selecting and sending larger staff teams to learning collaboratives. This study highlights key future

  6. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    International Nuclear Information System (INIS)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J; Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M

    2016-01-01

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  7. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J [McGill University Health Centre, Montreal, Quebec (Canada); Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M [McGill University, Montreal, Quebec (Canada)

    2016-06-15

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  8. Time to learn: the outlook for renewal of patient-centred education in the digital age.

    Science.gov (United States)

    Glick, T H; Moore, G T

    2001-05-01

    Major forces in society and within health systems are fragmenting patient care and clinical learning. The distancing of physician and trainee from the patient undermines learning about the patient-doctor relationship. The disconnection of care and learning from one successive venue to another impedes the ability of trainees to learn about illness longitudinally. As a conceptual piece, our methods have been those of witnessing the experiences of patients, practitioners, and students over time and observing the impact of fragmented systems and changing expectations on care and learning. We have reflected on the opportunities created by digital information systems and interactive telemedicine to help renew essential relationships. Although there is, as yet, little in the literature on educational or health outcomes of this kind of technological enablement, we anticipate opportunities for a renewed focus on the patient in that patient's own space and time. Multimedia applications can achieve not only real-time connections, but can help construct a "virtual patient" as a platform for supervision and assessment, permitting preceptors to evaluate trainee-patient interactions, utilization of Web-based data and human resources, and on-line professionalism. Just as diverse elements in society are capitalizing upon digital technology to create advantageous relationships, all of the elements in the complex systems of health care and medical training can be better connected, so as to put the patient back in the centre of care and the trainee's ongoing relationship to the patient back in the centre of education.

  9. Pointillist, Cyclical, and Overlapping: Multidimensional Facets of Time in Online Learning

    Directory of Open Access Journals (Sweden)

    Pekka Ihanainen

    2011-11-01

    Full Text Available A linear, sequential time conception based on in-person meetings and pedagogical activities is not enough for those who practice and hope to enhance contemporary education, particularly where online interactions are concerned. In this article, we propose a new model for understanding time in pedagogical contexts. Conceptual parts of the model will be employed as a “cultural technology” to help us relate to evolving phenomena, both physical and virtual. We label these constructs as pointillist, cyclical, and overlapping times.Pointillist time and learning takes place in “dots” of actions that consist of small, discrete moments (e.g., tweeting. Producing, receiving, and sharing ideas in this context are separate points in each actor’s timeline. Cyclical time and learning emerges from intensive periods, which are highly visible in online forums. This construct reveals itself through interactions that often exist in multiple online environments. Overlapping time and learning involves various configurations of linear, pointillist, and cyclical layers, which are mainly evident through the simultaneous uses of social communication technologies.Pointillist, cyclical, and overlapping time constructs enable new orientations for conceptualizing time in pedagogy. In this article we also introduce de-, re-, and en- modes of these pedagogies that connect with approaches to meet the needs of learners for individualization, personalization, and cyborgization.

  10. Neuromodulated Spike-Timing-Dependent Plasticity and Theory of Three-Factor Learning Rules

    Directory of Open Access Journals (Sweden)

    Wulfram eGerstner

    2016-01-01

    Full Text Available Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulatorson synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide 'when' to create new memories in response to a flow of sensory stimuli.In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discusssome experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity.We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

  11. Tensorial dynamic time warping with articulation index representation for efficient audio-template learning.

    Science.gov (United States)

    Le, Long N; Jones, Douglas L

    2018-03-01

    Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples.

  12. Learning of temporal motor patterns: An analysis of continuous vs. reset timing

    Directory of Open Access Journals (Sweden)

    Rodrigo eLaje

    2011-10-01

    Full Text Available Our ability to generate well-timed sequences of movements is critical to an array of behaviors, including the ability to play a musical instrument or a video game. Here we address two questions relating to timing with the goal of better understanding the neural mechanisms underlying temporal processing. First, how does accuracy and variance change over the course of learning of complex spatiotemporal patterns? Second, is the timing of sequential responses most consistent with starting and stopping an internal timer at each interval or with continuous timing?To address these questions we used a psychophysical task in which subjects learned to reproduce a sequence of finger taps in the correct order and at the correct times—much like playing a melody at the piano. This task allowed us to calculate the variance of the responses at different time points using data from the same trials. Our results show that while standard Weber’s law is clearly violated, variance does increase as a function of time squared, as expected according to the generalized form of Weber’s law—which separates the source of variance into time-dependent and time-independent components. Over the course of learning, both the time-independent variance and the coefficient of the time-dependent term decrease. Our analyses also suggest that timing of sequential events does not rely on the resetting of an internal timer at each event.We describe and interpret our results in the context of computer simulations that capture some of our psychophysical findings. Specifically, we show that continuous timing, as opposed to reset timing, is expected from population clock models in which timing emerges from the internal dynamics of recurrent neural networks.

  13. An assessment of racial differences in the upper limits of normal ALT levels in children and the effect of obesity on elevated values.

    Science.gov (United States)

    Kliethermes, S; Ma, M; Purtell, C; Balasubramanian, N; Gonzalez, B; Layden, T J; Cotler, S J

    2017-10-01

    Childhood obesity is a risk factor for non-alcoholic fatty liver disease and poses important public health issues for children. Racial differences in alanine aminotransferase (ALT) levels among children have not been described. This study aimed to identify racial differences in upper limit normal (ULN) ALT levels and evaluate the effect of obesity on elevated levels in children without other metabolic risk factors. National Health and Nutrition Examination Surveys and clinical data from the Loyola University Health System were used to determine ULN ALT by race and gender. Quantile regression was used to evaluate the impact of obesity on elevated ALT and to identify potential risk factors for ALT above the ULN. Upper limit normal (ULN) ALT was approximately 28.0 and 21.0-24.0 U/L for boys and girls, respectively. No significant difference in ULN ALT across race was observed. Obesity was significantly associated with elevated ALT; obese children with elevated ALT had values 10 U/L higher than normal-weight children. Racial differences in ALT levels among adults are not evident in children. Obesity, in the absence of metabolic risk factors and other causes of liver disease, is associated with elevated ALT, providing evidence against the concept of healthy obesity in children. © 2016 World Obesity Federation.

  14. "Almha La Vengadora": Protagonista del indigenismo de neovanguardia alteño

    Directory of Open Access Journals (Sweden)

    Maria Irina Soto-Mejia

    2016-03-01

    Este artículo discute la obra de Crispín Portugal (1976-2007, un escritor alteño, en el contexto de la lucha contra la pobreza y el racismo que enfrentan a las ciudades de El Alto y La Paz, y del movimiento en contra del mercado  que comenzó en Argentina produciendo libros con tapas de cartón compradas a los cartoneros. Portugal y otros escritores indígenas iniciaron un proyecto editorial llamado Yerba Mala Cartonera, que cuenta con su propio blog, para diseminar la literatura boliviana que no cuenta con otros medios de difusión. A través de la revisión de un cuento de Portugal (Almha La Vengadora que describe a una chola luchadora de El Alto cuyo nombre en el cuadrilátero es “la vengadora”, este trabajo plantea la configuración de un indigenismo alteño de neovanguardia equiparable al vanguardismo de Gamaliel Churata.  Con este propósito, presta especial atención a El Alto –la ciudad indígena más grande de Latinoamérica- situándolo como escenario clave para el desarrollo de una neovanguardia que se alimenta del neoliberalismo fallido para configurar una propuesta de modernidad que recupera el pasado indígena aymara para inventar otras formas de futuro.

  15. A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

    Science.gov (United States)

    Halloran, John T; Rocke, David M

    2018-05-04

    Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l 2 -SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l 2 -SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l 2 -SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade .

  16. A Social Practice Theory of Learning and Becoming across Contexts and Time

    Science.gov (United States)

    Penuel, William R.; DiGiacomo, Daniela K.; Van Horne, Katie; Kirshner, Ben

    2016-01-01

    This paper presents a social practice theory of learning and becoming across contexts and time. Our perspective is rooted in the Danish tradition of critical psychology (Dreier, 1997; Mørck & Huniche, 2006; Nissen, 2005), and we use social practice theory to interpret the pathway of one adolescent whom we followed as part of a longitudinal…

  17. Active Learning and Just-in-Time Teaching in a Material and Energy Balances Course

    Science.gov (United States)

    Liberatore, Matthew W.

    2013-01-01

    The delivery of a material and energy balances course is enhanced through a series of in-class and out-of-class exercises. An active learning classroom is achieved, even at class sizes over 150 students, using multiple instructors in a single classroom, problem solving in teams, problems based on YouTube videos, and just-in-time teaching. To avoid…

  18. Assessment of Stand-Alone Displays for Time Management in a Creativity-Driven Learning Environment

    DEFF Research Database (Denmark)

    Frimodt-Møller, Søren

    2017-01-01

    This paper considers the pros and cons of stand-alone displays, analog (e.g. billboards, blackboards, whiteboards, large pieces of paper etc.) as well as digital (e.g. large shared screens, digital whiteboards or similar), as tools for time management processes in a creativity-driven learning...

  19. Part-Time Community College Instructors Teaching in Learning Communities: An Exploratory Multiple Case Study

    Science.gov (United States)

    Paterson, John W.

    2017-01-01

    Community colleges have a greater portion of students at-risk for college completion than four-year schools and faculty at these institutions are overwhelmingly and increasingly part-time. Learning communities have been identified as a high-impact practice with numerous benefits documented for community college instructors and students: a primary…

  20. Nuclear power plant monitoring using real-time learning neural network

    International Nuclear Information System (INIS)

    Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.

    1994-01-01

    In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)

  1. Focusing on Student Learning to Guide the Use of Staff Time

    Science.gov (United States)

    Bates, Imelda; Baume, David; Assinder, Susan

    2010-01-01

    The paper develops and illustrates a model for designing courses. The model gives explicit attention to educational considerations, principally to the importance of active, goal-directed student learning. It also explores economic considerations, principally how to make the best possible use of the time of the teacher in planning and running the…

  2. Pre-Service Post Graduate Teachers' First Time Experience with Constructivist Learning Environment (CLE) Using MOODLE

    Science.gov (United States)

    Boopathiraj, C.; Chellamani, K.

    2015-01-01

    The aim of this study is to enlighten and discuss Post Graduate student teachers' first time experiences and their level of satisfaction with the use of Moodle Learning Management System (LMS) during their "Research Methods in Education" course offered online. This study investigated 30 pre-service Post Graduate student teachers' to…

  3. Perceived Learning and Timely Graduation for Business Undergraduates Taking an Online or Hybrid Course

    Science.gov (United States)

    Blau, Gary; Drennan, Rob B.; Hochner, Arthur; Kapanjie, Darin

    2016-01-01

    An online survey tested the impact of background, technological, and course-related variables on perceived learning and timely graduation for a complete data sample of 263 business undergraduates taking at least one online or hybrid course in the fall of 2015. Hierarchical regression results showed that course-related variables (instructor…

  4. Examining the Relations of Time Management and Procrastination within a Model of Self-Regulated Learning

    Science.gov (United States)

    Wolters, Christopher A.; Won, Sungjun; Hussain, Maryam

    2017-01-01

    The primary goal of this study was to investigate whether college students' academic time management could be used to understand their engagement in traditional and active forms of procrastination within a model of self-regulated learning. College students (N = 446) completed a self-report survey that assessed motivational and strategic aspects of…

  5. Cerebral activation related to implicit sequence learning in a Double Serial Reaction Time task

    NARCIS (Netherlands)

    van der Graaf, FHCE; Maguire, RP; Leenders, KL; de Jong, BM

    2006-01-01

    Using functional magnetic resonance imaging (fMRI), we examined the distribution of cerebral activations related to implicitly learning a series of fixed stimulus-response combinations. In a novel - bimanual - variant of the Serial Reaction Time task (SRT), simultaneous finger movements of the two

  6. Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics.

    Science.gov (United States)

    Geilleit, R; Hen, Z Q; Chong, C Y; Loh, A P; Pang, N L; Peterson, G M; Ng, K C; Huis, A; de Korne, D F

    2018-04-09

    Various technologies have been developed to improve hand hygiene (HH) compliance in inpatient settings; however, little is known about the feasibility of machine learning technology for this purpose in outpatient clinics. To assess the effectiveness, user experiences, and costs of implementing a real-time HH notification machine learning system in outpatient clinics. In our mixed methods study, a multi-disciplinary team co-created an infrared guided sensor system to automatically notify clinicians to perform HH just before first patient contact. Notification technology effects were measured by comparing HH compliance at baseline (without notifications) with real-time auditory notifications that continued till HH was performed (intervention I) or notifications lasting 15 s (intervention II). User experiences were collected during daily briefings and semi-structured interviews. Costs of implementation of the system were calculated and compared to the current observational auditing programme. Average baseline HH performance before first patient contact was 53.8%. With real-time auditory notifications that continued till HH was performed, overall HH performance increased to 100% (P machine learning system were estimated to be 46% lower than the observational auditing programme. Machine learning technology that enables real-time HH notification provides a promising cost-effective approach to both improving and monitoring HH, and deserves further development in outpatient settings. Copyright © 2018 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

  7. Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum

    Directory of Open Access Journals (Sweden)

    Tjeerd V. olde Scheper

    2018-01-01

    Full Text Available Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized

  8. Autonomous learning by simple dynamical systems with a discrete-time formulation

    Science.gov (United States)

    Bilen, Agustín M.; Kaluza, Pablo

    2017-05-01

    We present a discrete-time formulation for the autonomous learning conjecture. The main feature of this formulation is the possibility to apply the autonomous learning scheme to systems in which the errors with respect to target functions are not well-defined for all times. This restriction for the evaluation of functionality is a typical feature in systems that need a finite time interval to process a unit piece of information. We illustrate its application on an artificial neural network with feed-forward architecture for classification and a phase oscillator system with synchronization properties. The main characteristics of the discrete-time formulation are shown by constructing these systems with predefined functions.

  9. Incremental Predictive Value of Serum AST-to-ALT Ratio for Incident Metabolic Syndrome: The ARIRANG Study

    Science.gov (United States)

    Ahn, Song Vogue; Baik, Soon Koo; Cho, Youn zoo; Koh, Sang Baek; Huh, Ji Hye; Chang, Yoosoo; Sung, Ki-Chul; Kim, Jang Young

    2016-01-01

    Aims The ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) is of great interest as a possible novel marker of metabolic syndrome. However, longitudinal studies emphasizing the incremental predictive value of the AST-to-ALT ratio in diagnosing individuals at higher risk of developing metabolic syndrome are very scarce. Therefore, our study aimed to evaluate the AST-to-ALT ratio as an incremental predictor of new onset metabolic syndrome in a population-based cohort study. Material and Methods The population-based cohort study included 2276 adults (903 men and 1373 women) aged 40–70 years, who participated from 2005–2008 (baseline) without metabolic syndrome and were followed up from 2008–2011. Metabolic syndrome was defined according to the harmonized definition of metabolic syndrome. Serum concentrations of AST and ALT were determined by enzymatic methods. Results During an average follow-up period of 2.6-years, 395 individuals (17.4%) developed metabolic syndrome. In a multivariable adjusted model, the odds ratio (95% confidence interval) for new onset of metabolic syndrome, comparing the fourth quartile to the first quartile of the AST-to-ALT ratio, was 0.598 (0.422–0.853). The AST-to-ALT ratio also improved the area under the receiver operating characteristic curve (AUC) for predicting new cases of metabolic syndrome (0.715 vs. 0.732, P = 0.004). The net reclassification improvement of prediction models including the AST-to-ALT ratio was 0.23 (95% CI: 0.124–0.337, Pmetabolic syndrome and had incremental predictive value for incident metabolic syndrome. PMID:27560931

  10. CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

    Science.gov (United States)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-04-01

    CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

  11. Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

    Science.gov (United States)

    Vaccaro, James M.; Yaworsky, Paul S.

    1999-06-01

    Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

  12. Children benefit differently from night- and day-time sleep in motor learning.

    Science.gov (United States)

    Yan, Jin H

    2017-08-01

    Motor skill acquisition occurs while practicing (on-line) and when asleep or awake (off-line). However, developmental questions still remain about whether children of various ages benefit similarly or differentially from night- and day-time sleeping. The likely circadian effects (time-of-day) and the possible between-test-interference (order effects) associated with children's off-line motor learning are currently unknown. Therefore, this study examines the contributions of over-night sleeping and mid-day napping to procedural skill learning. One hundred and eight children were instructed to practice a finger sequence task using computer keyboards. After an equivalent 11-h interval in one of the three states (sleep, nap, wakefulness), children performed the same sequence in retention tests and a novel sequence in transfer tests. Changes in the movement time and sequence accuracy were evaluated between ages (6-7, 8-9, 10-11years) during practice, and from skill training to retrievals across three states. Results suggest that night-time sleeping and day-time napping improved the tapping speed, especially for the 6-year-olds. The circadian factor did not affect off-line motor learning in children. The interference between the two counter-balanced retrieval tests was not found for the off-line motor learning. This research offers possible evidence about the age-related motor learning characteristics in children and a potential means for enhancing developmental motor skills. The dynamics between age, experience, memory formation, and the theoretical implications of motor skill acquisition are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks.

    Science.gov (United States)

    Muhei-aldin, Othman; VanSwearingen, Jessie; Karim, Helmet; Huppert, Theodore; Sparto, Patrick J; Erickson, Kirk I; Sejdić, Ervin

    2014-04-30

    Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks. In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity. Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition. Most of the current literature does not examine stationarity prior to processing. The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Autism: Too eager to learn? Event related potential findings of increased dependency on intentional learning in a serial reaction time task.

    Science.gov (United States)

    Zwart, Fenny S; Vissers, Constance Th W M; van der Meij, Roemer; Kessels, Roy P C; Maes, Joseph H R

    2017-09-01

    It has been suggested that people with autism spectrum disorder (ASD) have an increased tendency to use explicit (or intentional) learning strategies. This altered learning may play a role in the development of the social communication difficulties characterizing ASD. In the current study, we investigated incidental and intentional sequence learning using a Serial Reaction Time (SRT) task in an adult ASD population. Response times and event related potentials (ERP) components (N2b and P3) were assessed as indicators of learning and knowledge. Findings showed that behaviorally, sequence learning and ensuing explicit knowledge were similar in ASD and typically developing (TD) controls. However, ERP findings showed that learning in the TD group was characterized by an enhanced N2b, while learning in the ASD group was characterized by an enhanced P3. These findings suggest that learning in the TD group might be more incidental in nature, whereas learning in the ASD group is more intentional or effortful. Increased intentional learning might serve as a strategy for individuals with ASD to control an overwhelming environment. Although this led to similar behavioral performances on the SRT task, it is very plausible that this intentional learning has adverse effects in more complex social situations, and hence contributes to the social impairments found in ASD. Autism Res 2017, 10: 1533-1543. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

  15. Experiments with Online Reinforcement Learning in Real-Time Strategy Games

    DEFF Research Database (Denmark)

    Toftgaard Andersen, Kresten; Zeng, Yifeng; Dahl Christensen, Dennis

    2009-01-01

    Real-time strategy (RTS) games provide a challenging platform to implement online reinforcement learning (RL) techniques in a real application. Computer, as one game player, monitors opponents' (human or other computers) strategies and then updates its own policy using RL methods. In this article......, we first examine the suitability of applying the online RL in various computer games. Reinforcement learning application depends on both RL complexity and the game features. We then propose a multi-layer framework for implementing online RL in an RTS game. The framework significantly reduces RL...... the effectiveness of our proposed framework and shed light on relevant issues in using online RL in RTS games....

  16. Event timing in associative learning: from biochemical reaction dynamics to behavioural observations.

    Directory of Open Access Journals (Sweden)

    Ayse Yarali

    Full Text Available Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning; if, on the other hand the odour follows the shock during training, it is approached later on (relief learning. During training, an odour-induced Ca(++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca(++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca(++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems.

  17. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Christian Albers

    Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  18. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  19. Thermal load distribution on the ALT-II limiter of TEXTOR-94 during RI mode operation and during disruptions

    International Nuclear Information System (INIS)

    Finken, K.H.; Denner, T.; Mank, G.

    2000-01-01

    Thermographic measurements using an IR scanner have been performed at the pump limiter ALT-II of TEXTOR-94 during RI mode discharges and during disruptions. The measurements on the RI mode discharges were done to complete the TEXTOR database which had shown a structured decay pattern of the deposited power. It was found that the underlying radial heat flux can be described by two exponential decay functions. This structure, which generates an unexpected heat component close to the tangent line, has been observed in all discharge conditions including the RI mode. During disruptions, the heat is released in short pulses with a typical duration of 0.01-0.1 ms. The radial decay length of these pulses has a similar shape to the heat flux during normal discharges: it consists again of a strong component close to the tangent line with a radial decay length of 2-5 mm and probably one with a decay length of the order of 1 cm. The heat is released at the time when the edge electron temperature of the plasma drops, when intense hydrogen and carbon fluxes occur near the walls, and when electrical currents in the limiter blades are excited. In a tentative interpretation, the temporal and spatial structure of the heat pulse is attributed to the presence and growth of a laminar zone at the plasma edge, which is connected with the ergodization of the plasma edge during a disruption. (author)

  20. Solvent effect on post-irradiation grafting of styrene onto poly(ethylene-alt-tetrafluoroethylene) (ETFE) films

    Science.gov (United States)

    Napoleão Geraldes, Adriana; Augusto Zen, Heloísa; Ribeiro, Geise; Fernandes Parra, Duclerc; Benévolo Lugão, Ademar

    2013-03-01

    Radiation-induced grafting of styrene onto ETFE films in different solvent was investigated after simultaneous irradiation (in post-irradiation condition) using a 60Co source. Grafting of styrene followed by sulfonation onto poly(ethylene-alt-tetrafluoroethylene) (ETFE) are currently studied for synthesis of ion exchange membranes. The ETFE films were immersed in styrene/toluene, styrene/methanol and styrene/isopropyl alcohol and irradiated at 20 and 100 kGy doses at room temperature. The post-irradiation time was established at 14 day and the grafting degree was evaluated. The grafted films were sulfonated using chlorosulfonic acid and 1,2-dichloroethane 20:80 (v/v) at room temperature for 5 h. The degree of grafting (DOG) was determined gravimetrically and physical or chemical changes were evaluated by differential scanning calorimeter analysis (DSC), thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). The ion exchange capacity (IEC) values showed the best performance of sulfonation for ETFE membranes grafted in toluene solvent. Surface images of the grafted films by SEM technique have presented a strong effect of the solvents on the films morphology.

  1. Developing Mentors: Adult participation, practices, and learning in an out-of-school time STEM program

    Science.gov (United States)

    Scipio, Deana Aeolani

    This dissertation examines learning within an out-of-school time (OST) Science, Technology, Engineering, and Mathematics (STEM) broadening participation program. The dissertation includes an introduction, three empirical chapters (written as individual articles), and a conclusion. The dissertation context is a chemical oceanography OST program for middle school students called Project COOL---Chemical Oceanography Outside the Lab. The program was a collaboration between middle school OST programming, a learning sciences research laboratory, and a chemical oceanography laboratory. Both labs were located at a research-based university in the Pacific Northwest of the United States. Participants include 34 youth, 12 undergraduates, and five professional scientists. The dissertation data corpus includes six years of ethnographic field notes across three field sites, 400 hours of video and audio recordings, 40 hours of semi-structured interviews, and more than 100 participant generated artifacts. Analysis methods include comparative case analysis, cognitive mapping, semiotic cluster analysis, video interaction analysis, and discourse analysis. The first empirical article focuses on synthesizing productive programmatic features from four years of design-based research.. The second article is a comparative case study of three STEM mentors from non-dominant communities in the 2011 COOL OST Program. The third article is a comparative case study of undergraduates learning to be mentors in the 2014 COOL OST Program. Findings introduce Deep Hanging as a theory of learning in practice. Deep Hanging entails authentic tasks in rich contexts, providing access, capitalizing on opportunity, and building interpersonal relationships. Taken together, these three chapters illuminate the process of designing a rich OST learning environment and the kinds of learning in practice that occurred for adult learners learning to be mentors through their participation in the COOL OST program. In

  2. Real time eye tracking using Kalman extended spatio-temporal context learning

    Science.gov (United States)

    Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

    2017-06-01

    Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

  3. Ephedra alte (joint pine): an invasive, problematic weedy species in forestry and fruit tree orchards in Jordan.

    Science.gov (United States)

    Qasem, Jamal R

    2012-01-01

    A field survey was carried out to record plant species climbed by Ephedra alte in certain parts of Jordan during 2008-2010. Forty species of shrubs, ornamental, fruit, and forest trees belonging to 24 plant families suffered from the climbing habit of E. alte. Growth of host plants was adversely affected by E. alte growth that extended over their vegetation. In addition to its possible competition for water and nutrients, the extensive growth it forms over host species prevents photosynthesis, smothers growth and makes plants die underneath the extensive cover. However, E. alte did not climb all plant species, indicating a host preference range. Damaged fruit trees included Amygdalus communis, Citrus aurantifolia, Ficus carica, Olea europaea, Opuntia ficus-indica, and Punica granatum. Forestry species that were adversely affected included Acacia cyanophylla, Ceratonia siliqua, Crataegus azarolus, Cupressus sempervirens, Pinus halepensis, Pistacia atlantica, Pistacia palaestina, Quercus coccifera, Quercus infectoria, Retama raetam, Rhamnus palaestina, Rhus tripartita, and Zizyphus spina-christi. Woody ornamentals attacked were Ailanthus altissima, Hedera helix, Jasminum fruticans, Jasminum grandiflorum, Nerium oleander, and Pyracantha coccinea. Results indicated that E. alte is a strong competitive for light and can completely smother plants supporting its growth. A. communis, F. carica, R. palaestina, and C. azarolus were most frequently attacked.

  4. Ephedra alte (Joint Pine: An Invasive, Problematic Weedy Species in Forestry and Fruit Tree Orchards in Jordan

    Directory of Open Access Journals (Sweden)

    Jamal R. Qasem

    2012-01-01

    Full Text Available A field survey was carried out to record plant species climbed by Ephedra alte in certain parts of Jordan during 2008–2010. Forty species of shrubs, ornamental, fruit, and forest trees belonging to 24 plant families suffered from the climbing habit of E. alte. Growth of host plants was adversely affected by E. alte growth that extended over their vegetation. In addition to its possible competition for water and nutrients, the extensive growth it forms over host species prevents photosynthesis, smothers growth and makes plants die underneath the extensive cover. However, E. alte did not climb all plant species, indicating a host preference range. Damaged fruit trees included Amygdalus communis, Citrus aurantifolia, Ficus carica, Olea europaea, Opuntia ficus-indica, and Punica granatum. Forestry species that were adversely affected included Acacia cyanophylla, Ceratonia siliqua, Crataegus azarolus, Cupressus sempervirens, Pinus halepensis, Pistacia atlantica, Pistacia palaestina, Quercus coccifera, Quercus infectoria, Retama raetam, Rhamnus palaestina, Rhus tripartita, and Zizyphus spina-christi. Woody ornamentals attacked were Ailanthus altissima, Hedera helix, Jasminum fruticans, Jasminum grandiflorum, Nerium oleander, and Pyracantha coccinea. Results indicated that E. alte is a strong competitive for light and can completely smother plants supporting its growth. A. communis, F. carica, R. palaestina, and C. azarolus were most frequently attacked.

  5. Ephedra alte (Joint Pine): An Invasive, Problematic Weedy Species in Forestry and Fruit Tree Orchards in Jordan

    Science.gov (United States)

    Qasem, Jamal R.

    2012-01-01

    A field survey was carried out to record plant species climbed by Ephedra alte in certain parts of Jordan during 2008–2010. Forty species of shrubs, ornamental, fruit, and forest trees belonging to 24 plant families suffered from the climbing habit of E. alte. Growth of host plants was adversely affected by E. alte growth that extended over their vegetation. In addition to its possible competition for water and nutrients, the extensive growth it forms over host species prevents photosynthesis, smothers growth and makes plants die underneath the extensive cover. However, E. alte did not climb all plant species, indicating a host preference range. Damaged fruit trees included Amygdalus communis, Citrus aurantifolia, Ficus carica, Olea europaea, Opuntia ficus-indica, and Punica granatum. Forestry species that were adversely affected included Acacia cyanophylla, Ceratonia siliqua, Crataegus azarolus, Cupressus sempervirens, Pinus halepensis, Pistacia atlantica, Pistacia palaestina, Quercus coccifera, Quercus infectoria, Retama raetam, Rhamnus palaestina, Rhus tripartita, and Zizyphus spina-christi. Woody ornamentals attacked were Ailanthus altissima, Hedera helix, Jasminum fruticans, Jasminum grandiflorum, Nerium oleander, and Pyracantha coccinea. Results indicated that E. alte is a strong competitive for light and can completely smother plants supporting its growth. A. communis, F. carica, R. palaestina, and C. azarolus were most frequently attacked. PMID:22645486

  6. [Impact analysis of shuxuetong injection on abnormal changes of ALT based on generalized boosted models propensity score weighting].

    Science.gov (United States)

    Yang, Wei; Yi, Dan-Hui; Xie, Yan-Ming; Yang, Wei; Dai, Yi; Zhi, Ying-Jie; Zhuang, Yan; Yang, Hu

    2013-09-01

    To estimate treatment effects of Shuxuetong injection on abnormal changes on ALT index, that is, to explore whether the Shuxuetong injection harms liver function in clinical settings and to provide clinical guidance for its safe application. Clinical information of traditional Chinese medicine (TCM) injections is gathered from hospital information system (HIS) of eighteen general hospitals. This is a retrospective cohort study, using abnormal changes in ALT index as an outcome. A large number of confounding biases are taken into account through the generalized boosted models (GBM) and multiple logistic regression model (MLRM) to estimate the treatment effects of Shuxuetong injections on abnormal changes in ALT index and to explore possible influencing factors. The advantages and process of application of GBM has been demonstrated with examples which eliminate the biases from most confounding variables between groups. This serves to modify the estimation of treatment effects of Shuxuetong injection on ALT index making the results more reliable. Based on large scale clinical observational data from HIS database, significant effects of Shuxuetong injection on abnormal changes in ALT have not been found.

  7. The medical laboratory issues about recommendation on uniform cutoff values of “normal” ALT in the ACG guidelines

    Directory of Open Access Journals (Sweden)

    YU Qian

    2018-01-01

    Full Text Available In the recent American clinical guidelines dealing with laboratory tests for evaluation of liver disease, the American College of Gastroenterology (ACG recommends ALT upper reference limits of 33 U/L for males and 25 U/L for females respectively, and that individuals with ALT above these “normal” cutoffs should be further investigated. Considering the differences between laboratory assays measuring ALT in our country, the standardization of methods and the consistency of results can not be completely ensured. The uniform “normal” range of ALT recommended by the ACG guidelines is largely based on findings from foreign studies and may not be suitable to Chinese population. On the other hand, reference upper/lower limits should not simply be equated with clinical decision thresholds. However, due to improper application of the related concepts of the above medical laboratory issues, simply recommending the uniform reference range of the ALT may lead to overdiagnosis and unnecessary follow-up examinations.

  8. Predicting the time of conversion to MCI in the elderly: role of verbal expression and learning.

    Science.gov (United States)

    Oulhaj, Abderrahim; Wilcock, Gordon K; Smith, A David; de Jager, Celeste A

    2009-11-03

    Increasing awareness that minimal or mild cognitive impairment (MCI) in the elderly may be a precursor of dementia has led to an increase in the number of people attending memory clinics. We aimed to develop a way of predicting the period of time before cognitive impairment occurs in community-dwelling elderly. The method is illustrated by the use of simple tests of different cognitive domains. A cohort of 241 normal elderly volunteers was followed for up to 20 years with regular assessments of cognitive abilities using the Cambridge Cognitive Examination (CAMCOG); 91 participants developed MCI. We used interval-censored survival analysis statistical methods to model which baseline cognitive tests best predicted the time to convert to MCI. Out of several baseline variables, only age and CAMCOG subscores for expression and learning/memory were predictors of the time to conversion. The time to conversion was 14% shorter for each 5 years of age, 17% shorter for each point lower in the expression score, and 15% shorter for each point lower in the learning score. We present in tabular form the probability of converting to MCI over intervals between 2 and 10 years for different combinations of expression and learning scores. In apparently normal elderly people, subtle measurable cognitive deficits that occur within the normal range on standard testing protocols reliably predict the time to clinically relevant cognitive impairment long before clinical symptoms are reported.

  9. Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

    Science.gov (United States)

    Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

    2018-02-01

    Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

  10. Adjustment to subtle time constraints and power law learning in rapid serial visual presentation

    Directory of Open Access Journals (Sweden)

    Jacqueline Chakyung Shin

    2015-11-01

    Full Text Available We investigated whether attention could be modulated through the implicit learning of temporal information in a rapid serial visual presentation (RSVP task. Participants identified two target letters among numeral distractors. The stimulus-onset asynchrony immediately following the first target (SOA1 varied at three levels (70, 98, and 126 ms randomly between trials or fixed within blocks of trials. Practice over three consecutive days resulted in a continuous improvement in the identification rate for both targets and attenuation of the attentional blink (AB, a decrement in target (T2 identification when presented 200-400 ms after another target (T1. Blocked SOA1s led to a faster rate of improvement in RSVP performance and more target order reversals relative to random SOA1s, suggesting that the implicit learning of SOA1 positively affected performance. The results also reveal power law learning curves for individual target identification as well as the reduction in the AB decrement. These learning curves reflect the spontaneous emergence of skill through subtle attentional modulations rather than general attentional distribution. Together, the results indicate that implicit temporal learning could improve high level and rapid cognitive processing and highlights the sensitivity and adaptability of the attentional system to subtle constraints in stimulus timing.

  11. A Human Long Non-Coding RNA ALT1 Controls the Cell Cycle of Vascular Endothelial Cells Via ACE2 and Cyclin D1 Pathway

    Directory of Open Access Journals (Sweden)

    Wen Li

    2017-10-01

    Full Text Available Background/Aims: ALT1 is a novel long non-coding RNA derived from the alternatively spliced transcript of the deleted in lymphocytic leukemia 2 (DLEU2. To date, ALT1 biological roles in human vascular endothelial cells have not been reported. Methods: ALT1 was knocked down by siRNAs. Cell proliferation was analyzed by cck-8. The existence and sequence of human ALT1 were identified by 3’ rapid amplification of cDNA ends. The interaction between lncRNA and proteins was analyzed by RNA-Protein pull down assay, RNA immunoprecipitation, and mass spectrometry analysis. Results: ALT1 was expressed in human umbilical vein endothelial cells (HUVECs. The expression of ALT1 was significantly downregulated in contact-inhibited HUVECs and in hypoxia-induced, growth-arrested HUVECs. Knocking down of ALT1 inhibited the proliferation of HUVECs by G0/G1 cell cycle arrest. We observed that angiotensin converting enzyme Ⅱ(ACE2 was a direct target gene of ALT1. Knocking-down of ALT1 or its target gene ACE2 could efficiently decrease the expression of cyclin D1 via the enhanced ubiquitination and degradation, in which HIF-1α and protein von Hippel-Lindau (pVHL might be involved. Conclusion: The results suggested the human long non-coding RNA ALT1 is a novel regulator for cell cycle of HUVECs via ACE2 and cyclin D1 pathway.

  12. Twist-3 effect from the longitudinally polarized proton for ALT in hadron production from pp collisions

    Directory of Open Access Journals (Sweden)

    Yuji Koike

    2016-08-01

    Full Text Available We compute the contribution from the longitudinally polarized proton to the twist-3 double-spin asymmetry ALT in inclusive (light hadron production from proton–proton collisions, i.e., p↑p→→hX. We show that using the relevant QCD equation-of-motion relation and Lorentz invariance relation allows one to eliminate the twist-3 quark-gluon correlator (associated with the longitudinally polarized proton in favor of one-variable twist-3 quark distributions and the (twist-2 transversity parton density. Including this result with the twist-3 pieces associated with the transversely polarized proton and unpolarized final-state hadron (which have already been calculated in the literature, we now have the complete leading-order cross section for this process.

  13. Incontri di Fisica delle Alte Energie Italian Meeting on High Energy Physics Napoli

    CERN Document Server

    Carlino, Gianpaolo; Merola, Leonardo; Paolucci, Pierluigi; Ricciardi, Giulia; IFAE 2007

    2008-01-01

    This book collects the Proceedings of the Workshop "Incontri di Fisica delle Alte Energie (IFAE) 2007, Napoli, 11-13 April 2007". This is the sixth edition of a series of meetings on fundamental research in particle physics and was attended by about 160 researchers. Presentations, both theoretical and experimental, addressed the status of Physics of the Standard Model and beyond, Flavour phyisc, Neutrino and Astroparticle physics, new technology in high energy physics. Special emphasis was given to the expectations of the forthcoming Large Hadron Collider, due in operation at the end of 2007. The venue of plenary sessions interleaved with parallel ones allowed for a rich exchange of ideas, presented in these Proceedings, that form a coherent picture of the findings and of the open questions in this extremely challenging cultural field. The venue of plenary sessions interleaved with parallel ones allowed for a rich exchange of ideas, presented in these Proceedings, that form a coherent picture of the findings ...

  14. Comparing the characteristics of highly cited titles and highly alted titles

    Energy Technology Data Exchange (ETDEWEB)

    Didegah, F.; Bowman, T.D.; Bowman, S.; Hartley, J.

    2016-07-01

    This study examines differences in the types of titles for articles that show high altmetric activity (highly alted articles) versus highly cited articles. This work expands on previous research on document titles in combination with a grounded theory approach to develop a codebook in which articles were manually coded based on 11 characteristics. The results show that there are differences and similarities in titles across many of the examined characteristics; highly cited titles and highly mentioned titles on Wikipedia have some similar characteristics such as they have the the highest percentage of substantive words; in addition, there are no or very few titles referencing outside or with humor/lightness on both platforms. Twitter and Facebook also showed some similarities having the highest percentage of humorous/light titles and lowest percentage of substantive words in their titles. (Author)

  15. 2017-04-28_W88 ALT 370 Program Overview(OUO).

    Energy Technology Data Exchange (ETDEWEB)

    Daniels, Vonceil

    2017-04-01

    All major program milestones have been met and the program is executing within budget. The ALT 370 program achieved Phase 6.4 authorization in February of this year. Five component Final Design Reviews (FDRs) have been completed, indicating progress in finalizing the design and development phase of the program. A series of ground-based qualification activities have demonstrated that designs are meeting functional requirements. The first fully functional flight test, FCET-53, demonstrated end-to-end performance in normal flight environments in February. Similarly, groundbased nuclear safety and hostile environments testing indicates that the design meets requirements in these stringent environments. The first in a series of hostile blast tests was successfully conducted in April.

  16. Diorganosilacetylene-alt-diorganosilvinylene polymers and a process densifying porous silicon-carbide bodies

    Science.gov (United States)

    Barton, Thomas J.; Ijadi-Maghsoodi, Sina; Pang, Yi

    1994-05-17

    The present invention provides linear organosilicon polymers including acetylene and vinylene moieties, and a process for their preparation. These diorganosilacetylene-alt-diorganosilvinylene linear polymers can be represented by the formula: --[--(R.sup.1)(R.sup.2)Si--C.tbd.C--(R.sup.3)(R.sup.4)Si--CH=CH--].sub.n-- , wherein n.gtoreq.2; and each R.sup.1, R.sup.2, R.sup.3, and R.sup.4 is independently selected from the group consisting of hydrogen, halogen, alkyl, alkenyl, aryl, and aralkyl radicals. The polymers are soluble in organic solvents, air stable, and can be pulled into fibers or cast into films. They can be thermally converted into silicon carbide ceramic materials.

  17. Touchscreen Facilitates Young Children’s Transfer of Learning to Tell Time

    Directory of Open Access Journals (Sweden)

    Fuxing Wang

    2016-11-01

    Full Text Available Young children are devoting increasing time to playing on handheld touchscreen devices (e.g., iPads. Though thousands of touchscreen apps are claimed to be educational, there is a lack of sufficient evidence examining the impact of touchscreens on children’s learning outcomes. In the present study, the two questions we focused on were (a whether using a touchscreen was helpful in teaching children to tell time, and (b to what extent young children could transfer what they had learned on the touchscreen to other media. A pre- and posttest design was adopted. After learning to read the time on the iPad touchscreen for 10 minutes, three groups of 5- to 6-year-old children (N = 65 were respectively tested with an iPad touchscreen, a toy clock or a drawing of a clock on paper. The results revealed that posttest scores in the iPad touchscreen test group were significantly higher than those at pretest, indicating that the touchscreen itself could provide support for young children’s learning. Similarly, regardless of being tested with a toy clock or paper drawing, children’s posttest performance was also better than pretest, suggesting that children could transfer what they had learned on an iPad touchscreen to other media. However, comparison among groups showed that children tested with the paper drawing underperformed those tested with the other two media. The theoretical and practical implications of the results, as well as limitations of the present study, are discussed.

  18. Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

    Directory of Open Access Journals (Sweden)

    Alexandru D. Iordan

    2018-01-01

    Full Text Available Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA and 20 older adults (OA were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of

  19. A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

    Science.gov (United States)

    Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

    2016-12-01

    Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

  20. EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

    Directory of Open Access Journals (Sweden)

    Ting Wang

    2014-01-01

    Full Text Available Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

  1. Influencing Work-Related Learning: The Role of Job Characteristics and Self-Directed Learning Orientation in Part-Time Vocational Education

    Science.gov (United States)

    Gijbels, David; Raemdonck, Isabel; Vervecken, Dries

    2010-01-01

    Based on the Demand-Control-Support (DCS) model, the present paper aims to investigate the influence of job characteristics such as job demands, job control, social support at work and self-directed learning orientation on the work-related learning behaviour of workers. The present study was conducted in a centre for part-time vocational education…

  2. Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.

    Science.gov (United States)

    Vaughan, Adam; Bohac, Stanislav V

    2015-10-01

    Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion timing predictions must contend with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis, especially during transients. In previous work, an abstract cycle-to-cycle mapping function coupled with ϵ-Support Vector Regression was shown to predict experimentally observed cycle-to-cycle combustion timing over a wide range of engine conditions, despite some of the aforementioned difficulties. The main limitation of the previous approach was that a partially acasual randomly sampled training dataset was used to train proof of concept offline predictions. The objective of this paper is to address this limitation by proposing a new online adaptive Extreme Learning Machine (ELM) extension named Weighted Ring-ELM. This extension enables fully causal combustion timing predictions at randomly chosen engine set points, and is shown to achieve results that are as good as or better than the previous offline method. The broader objective of this approach is to enable a new class of real-time model predictive control strategies for high variability HCCI and, ultimately, to bring HCCI's low engine-out NOx and reduced CO2 emissions to production engines. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

    2016-01-01

    Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

  4. Sensitivity analysis of machine-learning models of hydrologic time series

    Science.gov (United States)

    O'Reilly, A. M.

    2017-12-01

    Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

  5. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  6. Kinesthetic Astronomy: Significant Upgrades to the Sky Time Lesson that Support Student Learning

    Science.gov (United States)

    Morrow, C. A.; Zawaski, M.

    2004-12-01

    This paper will report on a significant upgrade to the first in a series of innovative, experiential lessons we call Kinesthetic Astronomy. The Sky Time lesson reconnects students with the astronomical meaning of the day, year, and seasons. Like all Kinesthetic Astronomy lessons, it teaches basic astronomical concepts through choreographed bodily movements and positions that provide educational sensory experiences. They are intended for sixth graders up through adult learners in both formal and informal educational settings. They emphasize astronomical concepts and phenomenon that people can readily encounter in their "everyday" lives such as time, seasons, and sky motions of the Sun, Moon, stars, and planets. Kinesthetic Astronomy lesson plans are fully aligned with national science education standards, both in content and instructional practice. Our lessons offer a complete learning cycle with written assessment opportunities now embedded throughout the lesson. We have substantially strengthened the written assessment options for the Sky Time lesson to help students translate their kinesthetic and visual learning into the verbal-linguistic and mathematical-logical realms of expression. Field testing with non-science undergraduates, middle school science teachers and students, Junior Girl Scouts, museum education staff, and outdoor educators has been providing evidence that Kinesthetic Astronomy techniques allow learners to achieve a good grasp of concepts that are much more difficult to learn in more conventional ways such as via textbooks or even computer animation. Field testing of the Sky Time lesson has also led us to significant changes from the previous version to support student learning. We will report on the nature of these changes.

  7. Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

    Science.gov (United States)

    Wehmeyer, Christoph; Noé, Frank

    2018-06-01

    Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

  8. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  9. KOMPARASI KEMAMPUAN KOMUNIKASI MATEMATIS SISWA DENGAN MODEL LEARNING CYCLE DAN TIME TOKEN

    Directory of Open Access Journals (Sweden)

    Arin Ayundhita

    2014-11-01

    Full Text Available Tujuan penelitian ini untuk mengetahui apakah model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token pada siswa kelas VIII materi keliling dan luas lingkaran dapat mencapai ketuntasan belajar dan untuk mengetahui manakah yang lebih baik antara model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token. Populasi dalam penelitian ini adalah siswa kelas VIII SMP Negeri 1 Sine Kabupaten Ngawi tahun pelajaran 2013/2014. Dengan menggunakan teknik cluster random sampling, terpilih sampel yaitu siswa kelas VIII A sebagai kelas eksperimen 1 dan kelas VIII E sebagai kelas eksperimen 2. Pengumpulan data dilakukan dengan metode dokumentasi, tes, dan observasi. Analisis data menggunakan uji proporsi dan uji perbedaan dua rata-rata. Dari hasil uji ketuntasan belajar diperoleh siswa kelas eksperimen 1 mencapai ketuntasan belajar klasikal sementara kelas eksperimen 2 belum mencapai ketuntasan belajar klasikal. Dari hasil uji perbedaan rata-rata satu pihak diperoleh rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 1 lebih baik daripada rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 2. Simpulan yang diperoleh adalah model pembelajaran Learning Cycle 5E lebih baik dari pembelajaran dengan model Time Token.

  10. Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

    Science.gov (United States)

    Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

    2017-07-01

    Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

  11. Incorporating Real-time Earthquake Information into Large Enrollment Natural Disaster Course Learning

    Science.gov (United States)

    Furlong, K. P.; Benz, H.; Hayes, G. P.; Villasenor, A.

    2010-12-01

    Although most would agree that the occurrence of natural disaster events such as earthquakes, volcanic eruptions, and floods can provide effective learning opportunities for natural hazards-based courses, implementing compelling materials into the large-enrollment classroom environment can be difficult. These natural hazard events derive much of their learning potential from their real-time nature, and in the modern 24/7 news-cycle where all but the most devastating events are quickly out of the public eye, the shelf life for an event is quite limited. To maximize the learning potential of these events requires that both authoritative information be available and course materials be generated as the event unfolds. Although many events such as hurricanes, flooding, and volcanic eruptions provide some precursory warnings, and thus one can prepare background materials to place the main event into context, earthquakes present a particularly confounding situation of providing no warning, but where context is critical to student learning. Attempting to implement real-time materials into large enrollment classes faces the additional hindrance of limited internet access (for students) in most lecture classrooms. In Earth 101 Natural Disasters: Hollywood vs Reality, taught as a large enrollment (150+ students) general education course at Penn State, we are collaborating with the USGS’s National Earthquake Information Center (NEIC) to develop efficient means to incorporate their real-time products into learning activities in the lecture hall environment. Over time (and numerous events) we have developed a template for presenting USGS-produced real-time information in lecture mode. The event-specific materials can be quickly incorporated and updated, along with key contextual materials, to provide students with up-to-the-minute current information. In addition, we have also developed in-class activities, such as student determination of population exposure to severe ground

  12. Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

    Science.gov (United States)

    Li, Jinna; Kiumarsi, Bahare; Chai, Tianyou; Lewis, Frank L; Fan, Jialu

    2017-12-01

    Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.

  13. Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

    Science.gov (United States)

    Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

    2015-05-01

    This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

  14. Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

    Directory of Open Access Journals (Sweden)

    Richard Chiou

    2010-06-01

    Full Text Available This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote controlling of the robots. The uniqueness of the project lies in making this process Internet-based, and remote robot operated and visualized in 3D. This 3D system approach provides the students with a more realistic feel of the 3D robotic laboratory even though they are working remotely. As a result, the 3D visualization technology has been tested as part of a laboratory in the MET 205 Robotics and Mechatronics class and has received positive feedback by most of the students. This type of research has introduced a new level of realism and visual communications to online laboratory learning in a remote classroom.

  15. Learning motion concepts using real-time microcomputer-based laboratory tools

    Science.gov (United States)

    Thornton, Ronald K.; Sokoloff, David R.

    1990-09-01

    Microcomputer-based laboratory (MBL) tools have been developed which interface to Apple II and Macintosh computers. Students use these tools to collect physical data that are graphed in real time and then can be manipulated and analyzed. The MBL tools have made possible discovery-based laboratory curricula that embody results from educational research. These curricula allow students to take an active role in their learning and encourage them to construct physical knowledge from observation of the physical world. The curricula encourage collaborative learning by taking advantage of the fact that MBL tools present data in an immediately understandable graphical form. This article describes one of the tools—the motion detector (hardware and software)—and the kinematics curriculum. The effectiveness of this curriculum compared to traditional college and university methods for helping students learn basic kinematics concepts has been evaluated by pre- and post-testing and by observation. There is strong evidence for significantly improved learning and retention by students who used the MBL materials, compared to those taught in lecture.

  16. Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.

    Directory of Open Access Journals (Sweden)

    Pavel Sanda

    2017-09-01

    Full Text Available Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.

  17. Investigating the Relationship Between Self-Directed Learning Readiness and Time Management Skills in Turkish Undergraduate Nursing Students.

    Science.gov (United States)

    Ertuğ, Nurcan; Faydali, Saide

    The aims of this study were to determine self-directed learning and time management skills of undergraduate nursing students and to investigate the relationship between the concepts. The use of self-directed learning has increased as an educational strategy in recent years. This descriptive and correlational study was conducted with 383 undergraduate nursing students in Turkey. Data were collected using a sociodemographic questionnaire, the Self-Directed Learning Readiness Scale, and Time Management Questionnaire. Mean scores were as follows: self-directed learning readiness, 159.12 (SD = 20.8); time management, 87.75 (SD = 12.1). A moderate positive correlation was found between self-directed learning readiness and time management values. Time management scores were 78.42 when self-directed learning readiness was ≤149 and 90.82 when self-directed learning readiness was ≥ 150, with a statistically significant difference (p = .000). Level of self-directed learning and academic achievement were higher in students who managed their time well.

  18. Defibrillator charging before rhythm analysis significantly reduces hands-off time during resuscitation

    DEFF Research Database (Denmark)

    Hansen, L. K.; Folkestad, L.; Brabrand, M.

    2013-01-01

    BACKGROUND: Our objective was to reduce hands-off time during cardiopulmonary resuscitation as increased hands-off time leads to higher mortality. METHODS: The European Resuscitation Council (ERC) 2005 and ERC 2010 guidelines were compared with an alternative sequence (ALT). Pulseless ventricular...... physicians were included. All had prior experience in advanced life support. Chest compressions were shorter interrupted using ALT (mean, 6.7 vs 13.0 seconds). Analyzing data for ventricular tachycardia scenarios only, hands-off time was shorter using ALT (mean, 7.1 vs 18.2 seconds). In ERC 2010 vs ALT, 12...... physicians were included. Two physicians had not prior experience in advanced life support. Hands-off time was reduced using ALT (mean, 3.9 vs 5.6 seconds). Looking solely at ventricular tachycardia scenarios, hands-off time was shortened using ALT (mean, 4.5 vs 7.6 seconds). No significant reduction...

  19. Time to Engage? Texting to Support and Enhance First Year Undergraduate Learning

    Directory of Open Access Journals (Sweden)

    Geraldine Jones

    2009-04-01

    Full Text Available In this paper we discuss a case study investigating how the academic and personal development of first year students on an undergraduate sports education degree can be supported and enhanced with mobile SMS communication. SMS-based technologies were introduced in response to students’ particular needs (in transition to Higher Education and characteristics (‘digital natives’. Despite being unaccustomed to using their mobile phones for academic study, students willingly participated in SMS communication with their tutor via a texting management service. Drawing on evidence from two student surveys, focus groups and a tutor’s journal, we illustrate the potential that mobile SMS communication has to link and establish continuity between face to face teaching sessions and online learning activities in the Virtual Learning Environment (VLE. Many students perceived the SMS communication to have had a positive impact on their management of study time. We link our findings with the existing literature and argue that mobile text based communication has the potential to support the development of time management skills, an important component of self regulatory learning, a skill which has been shown to be key in making a successful transition.

  20. Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

    International Nuclear Information System (INIS)

    Voyant, Cyril; Notton, Gilles; Darras, Christophe; Fouilloy, Alexis; Motte, Fabrice

    2017-01-01

    As global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique. We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the model outputs. The described method has been successfully applied to four meteorological stations in Mediterranean area. - Highlights: • Solar irradiation predictions require confidence bands. • There are a lot of kinds of uncertainties to take into account in order to propose prediction bands. • the ranking of different kinds of uncertainties is essential to propose an operational tool for the grid managers.

  1. Large-scale machine learning and evaluation platform for real-time traffic surveillance

    Science.gov (United States)

    Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

    2016-09-01

    In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

  2. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

    Science.gov (United States)

    Grossberg, Stephen

    2015-09-24

    This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory

  3. CORRELATION OF INTEREST TO LEARN AND USE TIME LEARNING WITH LEARNING ACHIEVEMENT AUTOMOTIVE ELECTRICAL IN CLASS XII LIGHT VEHICLE ENGINEERING SMK PIRI I YOGYAKARTA ACADEMIC YEAR 2013/2014

    Directory of Open Access Journals (Sweden)

    Ari Pujiatmoko

    2014-06-01

    Full Text Available The purpose of this study were: 1 to determine whether there is a correlation between students' interest in learning and the learning achievement of automotive electrical, 2 to determine whether there is a correlation between the use of time studying the learning achievement of automotive electrical, 3 to determine whether there is a correlation between student interest and use the time to learn and the learning achievement of students of class XII automotive electrical TKR SMK PIRI 1 Yogyakarta academic year 2013/2014.  This research was conducted in class XII TKR SMK PIRI 1 Yogyakarta academic year 2013/2014. This study is an ex-post facto. This study used two independent variables and the interest in learning the use of learning time, while the dependent variable is the electrical automotive learning achievement. This study is a population study by the respondent amounted to 100 students. Techniques of data collection using questionnaire techniques and engineering documentation. Research instrument in this study is a questionnaire interest in learning, inquiry learning time management and documentation of student achievement. Trials using the instrument validity and reliability test. The analysis technique used is the prerequisite test for normality, linearity, and multicollinearity. Then test hypotheses using partial correlation analysis techniques and correlation.  The results showed that: 1 students' interest to have a strong positive correlation with school performance automotive electrical ρ value of 0.737; 2 the use of learning time have a low positive correlation with school performance automotive electrical ρ value of 0.275; 3 interest student learning and the use of study time has a very strong positive correlation with learning achievement of students of class XII automotive electrical TKR SMK PIRI I Yogyakarta academic year 2013/2014 as evidenced by the value of R = 0.811.

  4. Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification

    DEFF Research Database (Denmark)

    Sarkar, Achintya Kumar; Tan, Zheng-Hua

    2017-01-01

    In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN) feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval......, and the TCL method aims to exploit this temporal structure. More specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting speech signals, in contrast to existing DNN based BN feature extraction methods that train DNNs using labeled data...... to discriminate speakers or pass-phrases or phones or a combination of them. In the context of speaker verification, speech data of fixed pass-phrases are used for TCL-BN training, while the pass-phrases used for TCL-BN training are excluded from being used for SV, so that the learned features can be considered...

  5. Robust Monotonically Convergent Iterative Learning Control for Discrete-Time Systems via Generalized KYP Lemma

    Directory of Open Access Journals (Sweden)

    Jian Ding

    2014-01-01

    Full Text Available This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.

  6. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM synapses

    Directory of Open Access Journals (Sweden)

    Stefano eAmbrogio

    2016-03-01

    Full Text Available We present a novel one-transistor/one-resistor (1T1R synapse for neuromorphic networks, based on phase change memory (PCM technology. The synapse is capable of spike-timing dependent plasticity (STDP, where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

  7. Naised käsu korras firmade juhatusse! Jah või ei? / Kairi Alt, Aveli Kippari, Karl Koort... [jt.

    Index Scriptorium Estoniae

    2011-01-01

    Küsimusele vastavad Columbus IT Partner Eesti AS personalijuht Kairi Alt, Stele ja Riveli omanik Aveli Kippari, AS Panaviatic'i turundusjuht Karl Koort, OÜ Tarkvara Tehnoloogia Arenduskeskuse tegevjuht Indrek Vainu, AS PricewaterhouseCoopers auditiosakonna direktor Eva Jansen, Kalev Chocolate Factory tootmisjuht Hardo Reinike

  8. Kas erivajadustega lapsed saavad õigel ajal abi? / Ene Mägi, Urve Raudsepp-Alt, Ale Sprenk, Peeter Aas

    Index Scriptorium Estoniae

    2009-01-01

    Küsimusele vastavad: Tallinna Ülikooli Kasvatusteaduste Instituudi eri- ja sotsiaalpedagoogika osakonna juhataja Ene Mägi, Tallinna Haridusameti üldhariduse osakonna peaspetsialist Urve Raudsepp-Alt, Krabi põhikooli direktor Ale Sprenk, Põlva Maavalitsuse haridus-, kultuuri- ja sotsiaalosakonna juhataja Peeter Aas

  9. The Transition between Telomerase and ALT Mechanisms in Hodgkin Lymphoma and Its Predictive Value in Clinical Outcomes

    Directory of Open Access Journals (Sweden)

    Radhia M’kacher

    2018-05-01

    Full Text Available Background: We analyzed telomere maintenance mechanisms (TMMs in lymph node samples from HL patients treated with standard therapy. The TMMs correlated with clinical outcomes of patients. Materials and Methods: Lymph node biopsies obtained from 38 HL patients and 24 patients with lymphadenitis were included in this study. Seven HL cell lines were used as in vitro models. Telomerase activity (TA was assessed by TRAP assay and verified through hTERT immunofluorescence expression; alternative telomere lengthening (ALT was also assessed, along with EBV status. Results: Both TA and ALT mechanisms were present in HL lymph nodes. Our findings were reproduced in HL cell lines. The highest levels of TA were expressed in CD30−/CD15− cells. Small cells were identified with ALT and TA. Hodgkin and Reed Sternberg cells contained high levels of PML bodies, but had very low hTERT expression. There was a significant correlation between overall survival (p < 10−3, event-free survival (p < 10−4, and freedom from progression (p < 10−3 and the presence of an ALT profile in lymph nodes of EBV+ patients. Conclusion: The presence of both types of TMMs in HL lymph nodes and in HL cell lines has not previously been reported. TMMs correlate with the treatment outcome of EBV+ HL patients.

  10. Information extraction from dynamic PS-InSAR time series using machine learning

    Science.gov (United States)

    van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.

    2017-12-01

    Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account

  11. Statistical Learning and Adaptive Decision-Making Underlie Human Response Time Variability in Inhibitory Control

    Directory of Open Access Journals (Sweden)

    Ning eMa

    2015-08-01

    Full Text Available Response time (RT is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop, and stop-signal onset time, SSD (stop-signal delay, with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop and SSD. The human behavioral data (n=20 bear out this prediction, showing P(stop and SSD both to be significant, independent predictors of RT, with P(stop being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  12. Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

    Science.gov (United States)

    Ma, Ning; Yu, Angela J

    2015-01-01

    Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  13. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    International Nuclear Information System (INIS)

    Veronesi, F; Grassi, S

    2016-01-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners. (paper)

  14. Time series classification using k-Nearest neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

    Directory of Open Access Journals (Sweden)

    Jiří Fejfar

    2012-01-01

    Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.

  15. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    Science.gov (United States)

    Veronesi, F.; Grassi, S.

    2016-09-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

  16. Morphological control of calcium oxalate particles in the presence of poly-(styrene-alt-maleic acid)

    International Nuclear Information System (INIS)

    Yu Jiaguo; Tang Hua; Cheng Bei; Zhao Xiujian

    2004-01-01

    Calcium oxalate (CaOx) particles exhibiting different shapes and phase structures were fabricated by a simple precipitation reaction of sodium oxalate with calcium chloride in the absence and presence of poly-(styrene-alt-maleic acid) (PSMA) as a crystal modifier at room temperature. The as-obtained products were characterized with scanning electron microscopy (SEM) and X-ray diffraction (XRD). The effects of reaction conditions including pH, [Ca 2+ ]/[C 2 O 4 2- ] ratio and concentration of PSMA and CaC 2 O 4 on the crystal forms and morphologies of the as-obtained calcium oxalate were investigated. The results show that various crystal morphologies of calcium oxalate, such as parallelograms, plates, spheres, bipyramids etc. can be obtained depending on the experimental conditions. Higher polymer concentration favors formation of the metastable calcium oxalate dihydrate (COD) crystals. Lower pH is beneficial to the formation of plate-like CaOx crystals. Especially, the monodispersed parallelogram-like CaOx crystals can be produced by PSMA as an additive at pH 2. PSMA may act as a good inhibitor for urolithiasis since it induces the formation of COD and reduces the particle size of CaOx. This research may provide new insight into the morphological control of CaOx particles and the prevention of urolithiasis

  17. Plasma influence on throat conductance of the TEXTOR pump limiter ALT-I

    International Nuclear Information System (INIS)

    Hardtke, A.; Finken, K.H.; Reiter, D.; Dippel, K.H.; Goebel, D.M.; McGrath, R.T.; Sagara, A.

    1989-01-01

    On the TEXTOR pump limiter ALT-I conductance measurements for the backstreaming of gas from the pump limiter vessel through the pump limiter entrance have been performed. In these experiments neutral gas has been injected into the pump limiter plenum during a short pulse. The influence of the instreaming plasma results in a reduction of the conductance of the outstreaming gas. For helium the conductance is reduced to about 40% of the molecular conductance when a plasma flux of 0.8 A/cm 2 (T e =T i =11 eV) is streaming into the pump limiter throat. The reduction of the conductance for backstreaming hydrogen and deuterium under the same plasma conditions is smaller; about 70% of the molecular conductance is obtained. This reduction can be explained by an increased recycling of ions which have been produced in the throat back to the neutralizer plate. The experimental results can be reproduced by Monte Carlo neutral transport code calculations if the recycling coefficient is about 0.85 for hydrogen and deuterium and about 0.95 for helium ions. Processes causing these high recycling coefficients are discussed and their influence is estimated. (orig.)

  18. Why are they late? Timing abilities and executive control among students with learning disabilities.

    Science.gov (United States)

    Grinblat, Nufar; Rosenblum, Sara

    2016-12-01

    While a deficient ability to perform daily tasks on time has been reported among students with learning disabilities (LD), the underlying mechanism behind their 'being late' is still unclear. This study aimed to evaluate the organization in time, time estimation abilities, actual performance time pertaining to specific daily activities, as well as the executive functions of students with LD in comparison to those of controls, and to assess the relationships between these domains among each group. The participants were 27 students with LD, aged 20-30, and 32 gender and age-matched controls who completed the Time Organization and Participation Scale (TOPS) and the Behavioral Rating Inventory of Executive Function-Adult version (BRIEF-A). In addition, their ability to estimate the time needed to complete the task of preparing a cup of coffee as well as their actual performance time were evaluated. The results indicated that in comparison to controls, students with LD showed significantly inferior organization in time (TOPS) and executive function abilities (BRIEF-A). Furthermore, their time estimation abilities were significantly inferior and they required significantly more time to prepare a cup of coffee. Regression analysis identified the variables that predicted organization in time and task performance time among each group. The significance of the results for both theoretical and clinical implications are discussed. What this paper adds? This study examines the underlying mechanism of the phenomena of being late among students with LD. Following a recent call for using ecologically valid assessments, the functional daily ability of students with LD to prepare a cup of coffee and to organize time were investigated. Furthermore, their time estimation and executive control abilities were examined as a possible underlying mechanism for their lateness. Although previous studies have indicated executive control deficits among students with LD, to our knowledge, this

  19. Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation

    Science.gov (United States)

    Hulshof, Casper D.; de Jong, Ton

    2006-01-01

    Students encounter many obstacles during scientific discovery learning with computer-based simulations. It is hypothesized that an effective type of support, that does not interfere with the scientific discovery learning process, should be delivered on a "just-in-time" base. This study explores the effect of facilitating access to…

  20. A Virtual Learning Environment for Part-Time MASW Students: An Evaluation of the WebCT

    Science.gov (United States)

    Chan, Charles C.; Tsui, Ming-sum; Chan, Mandy Y. C.; Hong, Joe H.

    2008-01-01

    This study aims to evaluate the perception of a cohort of social workers studying for a part-time master's program in social work in using the popular Web-based learning platform--World Wide Web Course Tools (WebCT) as a complimentary method of teaching and learning. It was noted that social work profession began incorporating computer technology…

  1. Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning

    Directory of Open Access Journals (Sweden)

    Yingfeng Cai

    2016-01-01

    Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.

  2. Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

    Science.gov (United States)

    Wei, Qinglai; Li, Benkai; Song, Ruizhuo

    2018-04-01

    In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

  3. Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data.

    Science.gov (United States)

    Janik, M; Bossew, P; Kurihara, O

    2018-07-15

    Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or predictors in a complicated way. In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon ( 222 Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms "learn" from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques. By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resampled on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values. Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical

  4. Automated business process management – in times of digital transformation using machine learning or artificial intelligence

    Directory of Open Access Journals (Sweden)

    Paschek Daniel

    2017-01-01

    Full Text Available The continuous optimization of business processes is still a challenge for companies. In times of digital transformation, faster changing internal and external framework conditions and new customer expectations for fastest delivery and best quality of goods and many more, companies should set up their internal process at the best way. But what to do if framework conditions changed unexpectedly? The purpose of the paper is to analyse how the digital transformation will impact the Business Process Management (BPM while using methods like machine learning or artificial intelligence. Therefore, the core components will be explained, compared and set up in relation. To identify application areas interviews and analysis will be held up with digital companies. The finding of the paper will be recommendation for action in the field of BPM and process optimization through machine learning and artificial intelligence. The Approach of optimizing and management processes via machine learning and artificial intelligence will support companies to decide which tool will be the best for automated BPM.

  5. Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

    Science.gov (United States)

    Yu, Tao; Cai, Weiwei; Liu, Yingzheng

    2018-04-01

    Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

  6. Optimizing Earth Data Search Ranking using Deep Learning and Real-time User Behaviour

    Science.gov (United States)

    Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; McGibbney, L. J.; Greguska, F. R., III

    2017-12-01

    Finding Earth science data has been a challenging problem given both the quantity of data available and the heterogeneity of the data across a wide variety of domains. Current search engines in most geospatial data portals tend to induce end users to focus on one single data characteristic dimension (e.g., term frequency-inverse document frequency (TF-IDF) score, popularity, release date, etc.). This approach largely fails to take account of users' multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset out of a vast range of available datasets. With users interacting with search engines, sufficient information is already hidden in the log files. Compared with explicit feedback data, information that can be derived/extracted from log files is virtually free and substantially more timely. In this dissertation, I propose an online deep learning framework that can quickly update the learning function based on real-time user clickstream data. The contributions of this framework include 1) a log processor that can ingest, process and create training data from web logs in a real-time manner; 2) a query understanding module to better interpret users' search intent using web log processing results and metadata; 3) a feature extractor that identifies ranking features representing users' multidimensional interests of geospatial data; and 4) a deep learning based ranking algorithm that can be trained incrementally using user behavior data. The search ranking results will be evaluated using precision at K and normalized discounted cumulative gain (NDCG).

  7. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    Directory of Open Access Journals (Sweden)

    Juan Pardo

    2015-04-01

    Full Text Available Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  8. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    Science.gov (United States)

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-01-01

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698

  9. Changes in recognition memory over time: an ERP investigation into vocabulary learning.

    Directory of Open Access Journals (Sweden)

    Shekeila D Palmer

    Full Text Available Although it seems intuitive to assume that recognition memory fades over time when information is not reinforced, some aspects of word learning may benefit from a period of consolidation. In the present study, event-related potentials (ERP were used to examine changes in recognition memory responses to familiar and newly learned (novel words over time. Native English speakers were taught novel words associated with English translations, and subsequently performed a Recognition Memory task in which they made old/new decisions in response to both words (trained word vs. untrained word, and novel words (trained novel word vs. untrained novel word. The Recognition task was performed 45 minutes after training (Day 1 and then repeated the following day (Day 2 with no additional training session in between. For familiar words, the late parietal old/new effect distinguished old from new items on both Day 1 and Day 2, although response to trained items was significantly weaker on Day 2. For novel words, the LPC again distinguished old from new items on both days, but the effect became significantly larger on Day 2. These data suggest that while recognition memory for familiar items may fade over time, recognition of novel items, conscious recollection in particular may benefit from a period of consolidation.

  10. Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

    Science.gov (United States)

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-04-21

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  11. Using machine learning to identify structural breaks in single-group interrupted time series designs.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

  12. On the best learning algorithm for web services response time prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin

    2013-01-01

    In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....

  13. Retroperitoneal laparoscopic nephrectomy: the effect of the learning curve, and concentrating expertise, on operating times.

    Science.gov (United States)

    Skinner, Adrian; Maoate, Kiki; Beasley, Spencer

    2010-05-01

    Laparoscopic nephrectomy is an accepted alternative to open nephrectomy. We analyzed our first 80 procedures of laparoscopic nephrectomy to evaluate the effect of experience and configuration of service on operative times. A retrospective review of 80 consecutive children who underwent retroperitoneal laparoscopic nephrectomy or heminephrectomy during an 11-year period from 1997 at Christchurch Hospital (Christchurch, New Zealand) was conducted. Operative times, in relation to the experience of the surgeon for this procedure, were analyzed. Four surgeons, assisted by an annually rotating trainee registrar, performed the procedure in 26 girls and 54 boys (range, 8 months to 15 years). Operating times ranged from 38 to 225 minutes (mean, 104). The average operative time fell from 105 to 90 minutes. One surgeon performed 40% of the procedures and assisted with a further 55%. The operative times for all surgeons showed a tendency to reduce, but this was not marked. Most procedures were performed by two surgeons working together, although one surgeon was involved in the majority of cases. The lead surgeon is often assisted by a fellow consultant colleague. Operative times were influenced by experience, but not markedly so. The shorter operative times and minimal "learning curve," compared with other reported series, may, in part, be due to the involvement of two surgeons experienced in laparoscopy for the majority of cases.

  14. Scheduling with Learning Effects and/or Time-Dependent Processing Times to Minimize the Weighted Number of Tardy Jobs on a Single Machine

    Directory of Open Access Journals (Sweden)

    Jianbo Qian

    2013-01-01

    Full Text Available We consider single machine scheduling problems with learning/deterioration effects and time-dependent processing times, with due date assignment consideration, and our objective is to minimize the weighted number of tardy jobs. By reducing all versions of the problem to an assignment problem, we solve them in O(n4 time. For some important special cases, the time complexity can be improved to be O(n2 using dynamic programming techniques.

  15. Mirror symmetric bimanual movement priming can increase corticomotor excitability and enhance motor learning.

    Directory of Open Access Journals (Sweden)

    Winston D Byblow

    Full Text Available Repetitive mirror symmetric bilateral upper limb may be a suitable priming technique for upper limb rehabilitation after stroke. Here we demonstrate neurophysiological and behavioural after-effects in healthy participants after priming with 20 minutes of repetitive active-passive bimanual wrist flexion and extension in a mirror symmetric pattern with respect to the body midline (MIR compared to an control priming condition with alternating flexion-extension (ALT. Transcranial magnetic stimulation (TMS indicated that corticomotor excitability (CME of the passive hemisphere remained elevated compared to baseline for at least 30 minutes after MIR but not ALT, evidenced by an increase in the size of motor evoked potentials in ECR and FCR. Short and long-latency intracortical inhibition (SICI, LICI, short afferent inhibition (SAI and interhemispheric inhibition (IHI were also examined using pairs of stimuli. LICI differed between patterns, with less LICI after MIR compared with ALT, and an effect of pattern on IHI, with reduced IHI in passive FCR 15 minutes after MIR compared with ALT and baseline. There was no effect of pattern on SAI or FCR H-reflex. Similarly, SICI remained unchanged after 20 minutes of MIR. We then had participants complete a timed manual dexterity motor learning task with the passive hand during, immediately after, and 24 hours after MIR or control priming. The rate of task completion was faster with MIR priming compared to control conditions. Finally, ECR and FCR MEPs were examined within a pre-movement facilitation paradigm of wrist extension before and after MIR. ECR, but not FCR, MEPs were consistently facilitated before and after MIR, demonstrating no degradation of selective muscle activation. In summary, mirror symmetric active-passive bimanual movement increases CME and can enhance motor learning without degradation of muscle selectivity. These findings rationalise the use of mirror symmetric bimanual movement as a

  16. Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

    Science.gov (United States)

    Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

    2015-01-01

    Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

  17. Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning.

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai

    2014-07-01

    In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

  18. A Call to Action for Research in Digital Learning: Learning without Limits of Time, Place, Path, Pace…or Evidence

    Science.gov (United States)

    Cavanaugh, Cathy; Sessums, Christopher; Drexler, Wendy

    2015-01-01

    This essay is a call for rethinking our approach to research in digital learning. It plots a path founded in social trends and advances in education. A brief review of these trends and advances is followed by discussion of what flattened research might look like at scale. Scaling research in digital learning is crucial to advancing understanding…

  19. Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

    Directory of Open Access Journals (Sweden)

    Peter U. Diehl

    2015-08-01

    Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

  20. Prevalence of epilepsy and seizure disorders as causes of apparent life- threatening event (ALTE) in children admitted to a tertiary hospital.

    Science.gov (United States)

    Anjos, Alessandra Marques dos; Nunes, Magda Lahorgue

    2009-09-01

    To determine the prevalence and describe clinical characteristics of seizure disorders and epilepsy as causes of apparent life- threatening event (ALTE) in children admitted at the emergency and followed in a tertiary hospital. Cross-sectional study with prospective data collection using specific guidelines to determine the etiology of ALTE. During the study, 30 (4.2%) children admitted to the hospital had a diagnosis of ALTE. There was a predominance of males (73%) and term infants (70%). Neonatal neurological disorders and neuropsychomotor development delay were found respectively in 13.4% and 10% of the cases. Etiological investigation revealed that 50% of the cases were idiopathic, and 13.4% were caused by epilepsy or seizure disorders. Although all patients had recurrent ALTE events, epilepsy had not been previously suspected. Epilepsy should be included in the differential diagnosis of ALTE, particularly when events are recurrent.

  1. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Science.gov (United States)

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

  2. Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

    Science.gov (United States)

    Ak, Ronay; Fink, Olga; Zio, Enrico

    2016-08-01

    The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

  3. Selective visual scaling of time-scale processes facilitates broadband learning of isometric force frequency tracking.

    Science.gov (United States)

    King, Adam C; Newell, Karl M

    2015-10-01

    The experiment investigated the effect of selectively augmenting faster time scales of visual feedback information on the learning and transfer of continuous isometric force tracking tasks to test the generality of the self-organization of 1/f properties of force output. Three experimental groups tracked an irregular target pattern either under a standard fixed gain condition or with selectively enhancement in the visual feedback display of intermediate (4-8 Hz) or high (8-12 Hz) frequency components of the force output. All groups reduced tracking error over practice, with the error lowest in the intermediate scaling condition followed by the high scaling and fixed gain conditions, respectively. Selective visual scaling induced persistent changes across the frequency spectrum, with the strongest effect in the intermediate scaling condition and positive transfer to novel feedback displays. The findings reveal an interdependence of the timescales in the learning and transfer of isometric force output frequency structures consistent with 1/f process models of the time scales of motor output variability.

  4. A real-time standard parts inspection based on deep learning

    Science.gov (United States)

    Xu, Kuan; Li, XuDong; Jiang, Hongzhi; Zhao, Huijie

    2017-10-01

    Since standard parts are necessary components in mechanical structure like bogie and connector. These mechanical structures will be shattered or loosen if standard parts are lost. So real-time standard parts inspection systems are essential to guarantee their safety. Researchers would like to take inspection systems based on deep learning because it works well in image with complex backgrounds which is common in standard parts inspection situation. A typical inspection detection system contains two basic components: feature extractors and object classifiers. For the object classifier, Region Proposal Network (RPN) is one of the most essential architectures in most state-of-art object detection systems. However, in the basic RPN architecture, the proposals of Region of Interest (ROI) have fixed sizes (9 anchors for each pixel), they are effective but they waste much computing resources and time. In standard parts detection situations, standard parts have given size, thus we can manually choose sizes of anchors based on the ground-truths through machine learning. The experiments prove that we could use 2 anchors to achieve almost the same accuracy and recall rate. Basically, our standard parts detection system could reach 15fps on NVIDIA GTX1080 (GPU), while achieving detection accuracy 90.01% mAP.

  5. Hybrid and Blended Learning: Modifying Pedagogy across Path, Pace, Time, and Place

    Science.gov (United States)

    O'Byrne, W. Ian; Pytash, Kristine E.

    2015-01-01

    Hybrid or blended learning is defined as a pedagogical approach that includes a combination of face-to-face instruction with computer-mediated instruction. The terms "blended learning", "hybrid learning", and "mixed-mode learning" are used interchangeably in current research; however, in the United States,…

  6. Learning Over Time: Using Rapid Prototyping Generative Analysis Experts and Reduction of Scope to Operationalize Design

    Science.gov (United States)

    2010-05-04

    during the Vietnam Conflict. 67 David A. Kolb , Experiential Learning : Experience as the Source of Learning and Development. (Upper Saddle River, NJ...Essentials for Military Applications. Newport Paper #10. Newport: Newport War College Press. 1996. Kolb , David A. Experiential Learning : Experience... learning over analysis. A broad review of design theory suggests that four techniques - rapid prototyping, generative analysis, use of experts, and

  7. Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

    Science.gov (United States)

    Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

  8. Technical Note: Deep learning based MRAC using rapid ultra-short echo time imaging.

    Science.gov (United States)

    Jang, Hyungseok; Liu, Fang; Zhao, Gengyan; Bradshaw, Tyler; McMillan, Alan B

    2018-05-15

    In this study, we explore the feasibility of a novel framework for MR-based attenuation correction for PET/MR imaging based on deep learning via convolutional neural networks, which enables fully automated and robust estimation of a pseudo CT image based on ultrashort echo time (UTE), fat, and water images obtained by a rapid MR acquisition. MR images for MRAC are acquired using dual echo ramped hybrid encoding (dRHE), where both UTE and out-of-phase echo images are obtained within a short single acquisition (35 sec). Tissue labeling of air, soft tissue, and bone in the UTE image is accomplished via a deep learning network that was pre-trained with T1-weighted MR images. UTE images are used as input to the network, which was trained using labels derived from co-registered CT images. The tissue labels estimated by deep learning are refined by a conditional random field based correction. The soft tissue labels are further separated into fat and water components using the two-point Dixon method. The estimated bone, air, fat, and water images are then assigned appropriate Hounsfield units, resulting in a pseudo CT image for PET attenuation correction. To evaluate the proposed MRAC method, PET/MR imaging of the head was performed on 8 human subjects, where Dice similarity coefficients of the estimated tissue labels and relative PET errors were evaluated through comparison to a registered CT image. Dice coefficients for air (within the head), soft tissue, and bone labels were 0.76±0.03, 0.96±0.006, and 0.88±0.01. In PET quantification, the proposed MRAC method produced relative PET errors less than 1% within most brain regions. The proposed MRAC method utilizing deep learning with transfer learning and an efficient dRHE acquisition enables reliable PET quantification with accurate and rapid pseudo CT generation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  9. Interactive Learning Modules: Enabling Near Real-Time Oceanographic Data Use In Undergraduate Education

    Science.gov (United States)

    Kilb, D. L.; Fundis, A. T.; Risien, C. M.

    2012-12-01

    The focus of the Education and Public Engagement (EPE) component of the NSF's Ocean Observatories Initiative (OOI) is to provide a new layer of cyber-interactivity for undergraduate educators to bring near real-time data from the global ocean into learning environments. To accomplish this, we are designing six online services including: 1) visualization tools, 2) a lesson builder, 3) a concept map builder, 4) educational web services (middleware), 5) collaboration tools and 6) an educational resource database. Here, we report on our Fall 2012 release that includes the first four of these services: 1) Interactive visualization tools allow users to interactively select data of interest, display the data in various views (e.g., maps, time-series and scatter plots) and obtain statistical measures such as mean, standard deviation and a regression line fit to select data. Specific visualization tools include a tool to compare different months of data, a time series explorer tool to investigate the temporal evolution of select data parameters (e.g., sea water temperature or salinity), a glider profile tool that displays ocean glider tracks and associated transects, and a data comparison tool that allows users to view the data either in scatter plot view comparing one parameter with another, or in time series view. 2) Our interactive lesson builder tool allows users to develop a library of online lesson units, which are collaboratively editable and sharable and provides starter templates designed from learning theory knowledge. 3) Our interactive concept map tool allows the user to build and use concept maps, a graphical interface to map the connection between concepts and ideas. This tool also provides semantic-based recommendations, and allows for embedding of associated resources such as movies, images and blogs. 4) Education web services (middleware) will provide an educational resource database API.

  10. Lessons Learned from Real-Time, Event-Based Internet Science Communications

    Science.gov (United States)

    Phillips, T.; Myszka, E.; Gallagher, D. L.; Adams, M. L.; Koczor, R. J.; Whitaker, Ann F. (Technical Monitor)

    2001-01-01

    For the last several years the Science Directorate at Marshall Space Flight Center has carried out a diverse program of Internet-based science communication. The Directorate's Science Roundtable includes active researchers, NASA public relations, educators, and administrators. The Science@NASA award-winning family of Web sites features science, mathematics, and space news. The program includes extended stories about NASA science, a curriculum resource for teachers tied to national education standards, on-line activities for students, and webcasts of real-time events. The focus of sharing science activities in real-time has been to involve and excite students and the public about science. Events have involved meteor showers, solar eclipses, natural very low frequency radio emissions, and amateur balloon flights. In some cases, broadcasts accommodate active feedback and questions from Internet participants. Through these projects a pattern has emerged in the level of interest or popularity with the public. The pattern differentiates projects that include science from those that do not, All real-time, event-based Internet activities have captured public interest at a level not achieved through science stories or educator resource material exclusively. The worst event-based activity attracted more interest than the best written science story. One truly rewarding lesson learned through these projects is that the public recognizes the importance and excitement of being part of scientific discovery. Flying a camera to 100,000 feet altitude isn't as interesting to the public as searching for viable life-forms at these oxygen-poor altitudes. The details of these real-time, event-based projects and lessons learned will be discussed.

  11. Calculation of upper esophageal sphincter restitution time from high resolution manometry data using machine learning.

    Science.gov (United States)

    Jungheim, Michael; Busche, Andre; Miller, Simone; Schilling, Nicolas; Schmidt-Thieme, Lars; Ptok, Martin

    2016-10-15

    After swallowing, the upper esophageal sphincter (UES) needs a certain amount of time to return from maximum pressure to the resting condition. Disturbances of sphincter function not only during the swallowing process but also in this phase of pressure restitution may lead to globus sensation or dysphagia. Since UES pressures do not decrease in a linear or asymptotic manner, it is difficult to determine the exact time when the resting pressure is reached, even when using high resolution manometry (HRM). To overcome this problem a Machine Learning model was established to objectively determine the UES restitution time (RT) and moreover to collect physiological data on sphincter function after swallowing. HRM-data of 15 healthy participants performing 10 swallows each were included. After manual annotation of the RT interval by two swallowing experts, data were transferred to the Machine Learning model, which applied a sequence labeling modeling approach based on logistic regression to learn and objectivize the characteristics of all swallows. Individually computed RT values were then compared with the annotated values. Estimates of the RT were generated by the Machine Learning model for all 150 swallows. When annotated by swallowing experts mean RT of 11.16s±5.7 (SD) and 10.04s±5.74 were determined respectively, compared to model-generated values from 8.91s±3.71 to 10.87s±4.68 depending on model selection. The correlation score for the annotated RT of both examiners was 0.76 and 0.63 to 0.68 for comparison of model predicted values. Restitution time represents an important physiologic swallowing parameter not previously considered in HRM-studies of the UES, especially since disturbances of UES restitution may increase the risk of aspiration. The data presented here show that it takes approximately 9 to 11s for the UES to come to rest after swallowing. Based on maximal RT values, we demonstrate that an interval of 25-30s in between swallows is necessary until the

  12. Time-place learning and memory persist in mice lacking functional Per1 and Per2 clock genes.

    Science.gov (United States)

    Mulder, C; Van Der Zee, E A; Hut, R A; Gerkema, M P

    2013-12-01

    With time-place learning, animals link a stimulus with the location and the time of day. This ability may optimize resource localization and predator avoidance in daily changing environments. Time-place learning is a suitable task to study the interaction of the circadian system and memory. Previously, we showed that time-place learning in mice depends on the circadian system and Cry1 and/or Cry2 clock genes. We questioned whether time-place learning is Cry specific or also depends on other core molecular clock genes. Here, we show that Per1/Per2 double mutant mice, despite their arrhythmic phenotype, acquire time-place learning similar to wild-type mice. As well as an established role in circadian rhythms, Per genes have also been implicated in the formation and storage of memory. We found no deficiencies in short-term spatial working memory in Per mutant mice compared to wild-type mice. Moreover, both Per mutant and wild-type mice showed similar long-term memory for contextual features of a paradigm (a mild foot shock), measured in trained mice after a 2-month nontesting interval. In contrast, time-place associations were lost in both wild-type and mutant mice after these 2 months, suggesting a lack of maintained long-term memory storage for this type of information. Taken together, Cry-dependent time-place learning does not require Per genes, and Per mutant mice showed no PER-specific short-term or long-term memory deficiencies. These results limit the functional role of Per clock genes in the circadian regulation of time-place learning and memory.

  13. Numerical and machine learning simulation of parametric distributions of groundwater residence time in streams and wells

    Science.gov (United States)

    Starn, J. J.; Belitz, K.; Carlson, C.

    2017-12-01

    Groundwater residence-time distributions (RTDs) are critical for assessing susceptibility of water resources to contamination. This novel approach for estimating regional RTDs was to first simulate groundwater flow using existing regional digital data sets in 13 intermediate size watersheds (each an average of 7,000 square kilometers) that are representative of a wide range of glacial systems. RTDs were simulated with particle tracking. We refer to these models as "general models" because they are based on regional, as opposed to site-specific, digital data. Parametric RTDs were created from particle RTDs by fitting 1- and 2-component Weibull, gamma, and inverse Gaussian distributions, thus reducing a large number of particle travel times to 3 to 7 parameters (shape, location, and scale for each component plus a mixing fraction) for each modeled area. The scale parameter of these distributions is related to the mean exponential age; the shape parameter controls departure from the ideal exponential distribution and is partly a function of interaction with bedrock and with drainage density. Given the flexible shape and mathematical similarity of these distributions, any of them are potentially a good fit to particle RTDs. The 1-component gamma distribution provided a good fit to basin-wide particle RTDs. RTDs at monitoring wells and streams often have more complicated shapes than basin-wide RTDs, caused in part by heterogeneity in the model, and generally require 2-component distributions. A machine learning model was trained on the RTD parameters using features derived from regionally available watershed characteristics such as recharge rate, material thickness, and stream density. RTDs appeared to vary systematically across the landscape in relation to watershed features. This relation was used to produce maps of useful metrics with respect to risk-based thresholds, such as the time to first exceedance, time to maximum concentration, time above the threshold

  14. Simple synthesis of P(Cbz-alt-TBT) and PCDTBT by combining direct arylation with suzuki polycondensation of heteroaryl chlorides.

    Science.gov (United States)

    Lombeck, Florian; Matsidik, Rukiya; Komber, Hartmut; Sommer, Michael

    2015-01-01

    Direct arylation (DA) of 2-chlorothiophene and 2-chloro-3-hexylthiophene with 4,7-dibromo-2,1,3-benzothiadiazole is used to synthesize 4,7-bis(5-chloro-2-thienyl)-2,1,3-benzothiadiazole (TBTCl2) and 4,7-bis(5-chloro-4-hexyl-2-thienyl)-2,1,3-benzothiadiazole (DH-TBTCl2) in one step. Suitable conditions of the Suzuki polycondensations (SPC) of TBTCl2 and DH-TBTCl2 with the carbazole comonomer CbzPBE2 are established, furnishing PCDTBT and P(Cbz-alt-TBT) with high molecular weight and yield. Compared with control samples made from the corresponding dibromides, high-temperature NMR and UV-vis spectroscopy indicate similar properties for PCDTBT but an increased content of Cbz-Cbz homocouplings for P(Cbz-alt-TBT). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Clinico-biochemical factors to early predict biliary etiology of acute pancreatitis: age, female gender, and ALT.

    Science.gov (United States)

    Zarnescu, N O; Costea, R; Zarnescu Vasiliu, E C; Neagu, S

    2015-01-01

    Background/ Aims: Despite the existence of an easy tool to diagnose biliary tract disease as an etiology for acute pancreatitis (AP), the sensitivity of abdominal ultrasound is around 80%, which can be even lower in certain conditions. We have retrospectively reviewed data of 146 patients admitted for acute pancreatitis between 1999 and 2013. Bivariate analysis for clinical and biochemical variables was performed with respect to etiology of AP (biliary versus non-biliary). Multivariate analysis was performed by using binary logistic regression. There were 87 males (59.6%) and 59 females (40.4%), with a median age of 51. The etiology of acute pancreatitis was biliary in 71 patients (48.6%). Bivariate analysis found the following as significant association (p=0.001) with biliary pancreatitis: older age, female gender, and elevated AST, ALT. A binary logistic regression analysis identified as predictor factors for biliary etiology of acute pancreatitis: age OR = 1.031 (95% CI 1.004 - 1.059, p = 0.024), sex (female) OR = 2.34 (95% CI 1.022 - 5.359, p = 0.044) and ALT OR = 1.004 (95% CI 1.001 - 1.007, p =0.004). The two clinical scores included the three variables (A.S.ALT scores) in categorical format were generated and then checked with the ROC curves (areas under curve are 0.768 and 0.778). Age, female gender, and elevated ALT can help identifying cases with biliary etiology of acute pancreatitis.

  16. CASUÍSTICA DE ALTE NUM HOSPITAL TERCIÁRIO – O QUE MUDOU NOS ÚLTIMOS 5 ANOS?

    Directory of Open Access Journals (Sweden)

    Daniel Meireles

    2016-07-01

    Conclusões: O ALTE cursa frequentemente com evolução favorável. Apesar de não haver recomendação ou consensos na literatura para a investigação etiológica, esta mantém-se uma prática comum, sem aparente modificação na abordagem nos últimos anos, em comparação com estudo prévio realizado em 2009.

  17. Travel time tomography with local image regularization by sparsity constrained dictionary learning

    Science.gov (United States)

    Bianco, M.; Gerstoft, P.

    2017-12-01

    We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

  18. Executive Functions, Time Organization and Quality of Life among Adults with Learning Disabilities.

    Directory of Open Access Journals (Sweden)

    Kineret Sharfi

    Full Text Available This study compared the executive functions, organization in time and perceived quality of life (QoL of 55 adults with learning disabilities (LD with those of 55 matched controls (mean age 30 years. Furthermore, relationships and predictive relationships between these variables among the group with LD were examined.All participants completed the Behavioral Rating Inventory of Executive Functions (BRIEF-A, the Time Organization and Participation (TOPS, A-C and the World Health Organization Quality of Life (WHOQOL questionnaires. Chi-square tests, independent t-tests and MANOVA were used to examine group differences in each of the subscales scores and ratings of each instrument. Pearson correlations and regression predictive models were used to examine the relationships between the variables in the group with LD.Adults with LD had significantly poorer executive functions (BRIEF-A, deficient organization in time abilities (TOPS A-B, accompanied with negative emotional response (TOPS- C, and lower perceived QoL (physical, psychological, social and environmental in comparison to adults without LD. Regression analysis revealed that Initiation (BRIEF-A significantly predicted approximately 15% of the participants' organization in time abilities (TOPS A, B scores beyond group membership. Furthermore, initiation, emotional control (BRIEF-A subscales and emotional responses following unsuccessful organization of time (TOPS-C together accounted for 39% of the variance of psychological QoL beyond the contribution of group membership.Deficits in initiation and emotional executive functions as well as organization in time abilities and emotional responses to impairments in organizing time affect the QoL of adults with LD and thus should be considered in further research as well as in clinical applications.

  19. Executive Functions, Time Organization and Quality of Life among Adults with Learning Disabilities.

    Science.gov (United States)

    Sharfi, Kineret; Rosenblum, Sara

    2016-01-01

    This study compared the executive functions, organization in time and perceived quality of life (QoL) of 55 adults with learning disabilities (LD) with those of 55 matched controls (mean age 30 years). Furthermore, relationships and predictive relationships between these variables among the group with LD were examined. All participants completed the Behavioral Rating Inventory of Executive Functions (BRIEF-A), the Time Organization and Participation (TOPS, A-C) and the World Health Organization Quality of Life (WHOQOL) questionnaires. Chi-square tests, independent t-tests and MANOVA were used to examine group differences in each of the subscales scores and ratings of each instrument. Pearson correlations and regression predictive models were used to examine the relationships between the variables in the group with LD. Adults with LD had significantly poorer executive functions (BRIEF-A), deficient organization in time abilities (TOPS A-B), accompanied with negative emotional response (TOPS- C), and lower perceived QoL (physical, psychological, social and environmental) in comparison to adults without LD. Regression analysis revealed that Initiation (BRIEF-A) significantly predicted approximately 15% of the participants' organization in time abilities (TOPS A, B scores) beyond group membership. Furthermore, initiation, emotional control (BRIEF-A subscales) and emotional responses following unsuccessful organization of time (TOPS-C) together accounted for 39% of the variance of psychological QoL beyond the contribution of group membership. Deficits in initiation and emotional executive functions as well as organization in time abilities and emotional responses to impairments in organizing time affect the QoL of adults with LD and thus should be considered in further research as well as in clinical applications.

  20. Real-time lexical comprehension in young children learning American Sign Language.

    Science.gov (United States)

    MacDonald, Kyle; LaMarr, Todd; Corina, David; Marchman, Virginia A; Fernald, Anne

    2018-04-16

    When children interpret spoken language in real time, linguistic information drives rapid shifts in visual attention to objects in the visual world. This language-vision interaction can provide insights into children's developing efficiency in language comprehension. But how does language influence visual attention when the linguistic signal and the visual world are both processed via the visual channel? Here, we measured eye movements during real-time comprehension of a visual-manual language, American Sign Language (ASL), by 29 native ASL-learning children (16-53 mos, 16 deaf, 13 hearing) and 16 fluent deaf adult signers. All signers showed evidence of rapid, incremental language comprehension, tending to initiate an eye movement before sign offset. Deaf and hearing ASL-learners showed similar gaze patterns, suggesting that the in-the-moment dynamics of eye movements during ASL processing are shaped by the constraints of processing a visual language in real time and not by differential access to auditory information in day-to-day life. Finally, variation in children's ASL processing was positively correlated with age and vocabulary size. Thus, despite competition for attention within a single modality, the timing and accuracy of visual fixations during ASL comprehension reflect information processing skills that are important for language acquisition regardless of language modality. © 2018 John Wiley & Sons Ltd.

  1. Procedural learning is impaired in dyslexia: Evidence from a meta-analysis of serial reaction time studies☆

    Science.gov (United States)

    Lum, Jarrad A.G.; Ullman, Michael T.; Conti-Ramsden, Gina

    2013-01-01

    A number of studies have investigated procedural learning in dyslexia using serial reaction time (SRT) tasks. Overall, the results have been mixed, with evidence of both impaired and intact learning reported. We undertook a systematic search of studies that examined procedural learning using SRT tasks, and synthesized the data using meta-analysis. A total of 14 studies were identified, representing data from 314 individuals with dyslexia and 317 typically developing control participants. The results indicate that, on average, individuals with dyslexia have worse procedural learning abilities than controls, as indexed by sequence learning on the SRT task. The average weighted standardized mean difference (the effect size) was found to be 0.449 (CI95: .204, .693), and was significant (p dyslexia. PMID:23920029

  2. A Novel Real-Time Speech Summarizer System for the Learning of Sustainability

    Directory of Open Access Journals (Sweden)

    Hsiu-Wen Wang

    2015-04-01

    Full Text Available As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures. Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase real-time speech summarizer for the learning of sustainability (RTSSLS was proposed. Phase One involved selecting independent features (e.g., centrality, resemblance to the title, sentence length, term frequency, and thematic words and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and provide a summary. In practical results, the accuracies of macro-average and micro-average for the RTSSLS were 70% and 73%, respectively. Therefore, using a RTSSLS can enable users to acquire key speech information for the learning of sustainability.

  3. Distributed Cerebellar Motor Learning; a Spike-Timing-Dependent Plasticity Model

    Directory of Open Access Journals (Sweden)

    Niceto Rafael Luque

    2016-03-01

    Full Text Available Deep cerebellar nuclei neurons receive both inhibitory (GABAergic synaptic currents from Purkinje cells (within the cerebellar cortex and excitatory (glutamatergic synaptic currents from mossy fibres. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP located at different cerebellar sites (parallel fibres to Purkinje cells, mossy fibres to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibres to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP and inhibitory (i-STDP mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibres to Purkinje cells synapses and then transferred to mossy fibres to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation towards optimising its working range.

  4. Real-time Stereoscopic 3D for E-Robotics Learning

    Directory of Open Access Journals (Sweden)

    Richard Y. Chiou

    2011-02-01

    Full Text Available Following the design and testing of a successful 3-Dimensional surveillance system, this 3D scheme has been implemented into online robotics learning at Drexel University. A real-time application, utilizing robot controllers, programmable logic controllers and sensors, has been developed in the “MET 205 Robotics and Mechatronics” class to provide the students with a better robotic education. The integration of the 3D system allows the students to precisely program the robot and execute functions remotely. Upon the students’ recommendation, polarization has been chosen to be the main platform behind the 3D robotic system. Stereoscopic calculations are carried out for calibration purposes to display the images with the highest possible comfort-level and 3D effect. The calculations are further validated by comparing the results with students’ evaluations. Due to the Internet-based feature, multiple clients have the opportunity to perform the online automation development. In the future, students, in different universities, will be able to cross-control robotic components of different types around the world. With the development of this 3D ERobotics interface, automation resources and robotic learning can be shared and enriched regardless of location.

  5. An Investigation of the Effectiveness of Increasing Academic Learning Time for College Undergraduate Students' Achievement in Kuwait

    Science.gov (United States)

    Al-Shammari, Zaid; Mohammad, Anwar; Al-Shammari, Bandar

    2010-01-01

    The study investigated the effectiveness of increasing ALT for college students' achievement in Kuwait. In Phase 1, 37 students participated (22, experimental; 15, control); in Phase 2, 19 students participated (8, sub-experimental; 11, sub-control). Several experimental research methods used in conducting this study, including development of a…

  6. Radiation induced, raft mediated grafting of styrene onto poly(ethylene-alt-tetrafluoroethylene) (ETFE)

    International Nuclear Information System (INIS)

    Celik, G.; Barsbay, M.; Gueven, O.

    2011-01-01

    Complete text of publication follows. The development of cost-effective proton exchange membranes to replace the state-of-the-art and expensive perfluorinated membranes such as Nafion, Flemion, and Aciplex is one of the main challenges for commercialization of polymer electrolyte fuel cell technology. Recently, partially fluorinated poly(ethlyene-alt-tetrafluoroethylene) (ETFE) has been identified as a promising alternative base polymer film to prepare low-cost polymer electrolyte membranes because of its advantageous characters like superior mechanical properties and high resistance to radiation-induced damage. The radiation-induced grafting technique, based on the utilization of a polymer material such as ETFE in combination with further chemical modification steps (sulfonation) allows the functionalization of the base material and the introduction of the desired property (proton conductivity) for preparing a fuel cell membrane. However this simple conventional method suffers from one simple flaw: The molecular weight and the polydispersity of the grafted chains cannot be controlled. Predetermined molecular weights and low dispersities as well as homogeneous composition and desired architecture can be achieved by grafting of monomer onto base polymer under living/controlled free radical polymerization (CRP) conditions. Among the CRP methods, Reversible Addition Fragmentation-Chain Transfer (RAFT) is of particular interest as a very wide range of functional monomers can be polymerized in a controlled manner under non-demanding reaction conditions (e.g., tolerance to oxygen and low temperatures). The present study deals with the RAFT mediated radiation-induced (0.032 kGyh -1 , 60 Co) grafting of styrene on ETFE films followed by the sulfonation of the polystyrene grafts. The effect of monomer concentration, absorbed dose and RAFT agent concentration on the grafting were investigated. The synthesized films were characterized by ATR-FTIR, XPS, DSC and TGA methods

  7. A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

    Directory of Open Access Journals (Sweden)

    Quan Wang

    2017-08-01

    Full Text Available The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP and synaptic normalization (SN. When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that

  8. Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory

    Directory of Open Access Journals (Sweden)

    Stephen eGrossberg

    2014-10-01

    Full Text Available How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list

  9. Fast Mapping Across Time: Memory Processes Support Children's Retention of Learned Words

    Directory of Open Access Journals (Sweden)

    Haley eVlach

    2012-02-01

    Full Text Available Children's remarkable ability to map linguistic labels to objects in the world is referred to as fast mapping. The current study examined children's (N = 216 and adults’ (N = 54 retention of fast-mapped words over time (immediately, after a 1 week delay, and after a 1 month delay. The fast mapping literature often characterizes children's retention of words as consistently high across timescales. However, the current study demonstrates that learners forget word mappings at a rapid rate. Moreover, these patterns of forgetting parallel forgetting functions of domain general memory processes. Memory processes are critical to children's word learning and the role of one such process, forgetting, is discussed in detail—forgetting supports both word mapping and the generalization of words and categories.

  10. A combination of HARMONIE short time direct normal irradiance forecasts and machine learning: The #hashtdim procedure

    Science.gov (United States)

    Gastón, Martín; Fernández-Peruchena, Carlos; Körnich, Heiner; Landelius, Tomas

    2017-06-01

    The present work describes the first approach of a new procedure to forecast Direct Normal Irradiance (DNI): the #hashtdim that treats to combine ground information and Numerical Weather Predictions. The system is centered in generate predictions for the very short time. It combines the outputs from the Numerical Weather Prediction Model HARMONIE with an adaptive methodology based on Machine Learning. The DNI predictions are generated with 15-minute and hourly temporal resolutions and presents 3-hourly updates. Each update offers forecasts to the next 12 hours, the first nine hours are generated with 15-minute temporal resolution meanwhile the last three hours present hourly temporal resolution. The system is proved over a Spanish emplacement with BSRN operative station in south of Spain (PSA station). The #hashtdim has been implemented in the framework of the Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies (DNICast) project, under the European Union's Seventh Programme for research, technological development and demonstration framework.

  11. Surgical training and the European Working Time Directive: The role of informal workplace learning.

    Science.gov (United States)

    Giles, James A

    2010-01-01

    The introduction of European Working Time Directive, limiting doctors' working hours to 48 per week, has caused recent controversy within the profession. The Royal College of Surgeons of England in particular has been one of the loudest critics of the legislation. One of the main concerns is regarding the negative impact on training hours for those embarking on surgical careers. Simulation technology has been suggested as a method to overcome this reduction in hospital training hours, and research suggests that this is a good substitute for operative training in a theatre. However, modern educational theory emphasises the power of informal workplace learning in postgraduate education, and the essential role of experience in training future surgeons. Copyright 2010 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

  12. Real-time dosimetry system in catheterisation laboratory: utility as a learning tool in radiation protection

    International Nuclear Information System (INIS)

    Pinto Monedero, M.; Rodriguez Cobo, C.; Pifarre Martinez, X.; Ruiz Martin, J.; Barros Candelero, J.M.; Goicolea Ruigomez, J.; Diaz Blaires, G.; Garcia Lunar, I.

    2015-01-01

    Document available in abstract form only. Full text of publication follows: Workers at the catheter laboratory are among the most exposed to ionising radiation in hospitals. However, it is difficult to be certain of the radiation doses received by the staff, as personal dosemeters are often misused, and thus, the dose history is not reliable. Moreover, the information provided by personal dosemeters corresponds to the monthly accumulated dose, so corrective actions tends to be delayed. The purpose of this work is, on the one hand, to use a real-time dosimetry system to establish the occupational doses per procedure of workers at the catheter laboratories and, on the other hand, to evaluate its utility as a learning tool for radiation protection purposes with the simultaneous video recording of the interventions. (authors)

  13. A Just-in-Time Learning based Monitoring and Classification Method for Hyper/Hypocalcemia Diagnosis.

    Science.gov (United States)

    Peng, Xin; Tang, Yang; He, Wangli; Du, Wenli; Qian, Feng

    2017-01-20

    This study focuses on the classification and pathological status monitoring of hyper/hypo-calcemia in the calcium regulatory system. By utilizing the Independent Component Analysis (ICA) mixture model, samples from healthy patients are collected, diagnosed, and subsequently classified according to their underlying behaviors, characteristics, and mechanisms. Then, a Just-in-Time Learning (JITL) has been employed in order to estimate the diseased status dynamically. In terms of JITL, for the purpose of the construction of an appropriate similarity index to identify relevant datasets, a novel similarity index based on the ICA mixture model is proposed in this paper to improve online model quality. The validity and effectiveness of the proposed approach have been demonstrated by applying it to the calcium regulatory system under various hypocalcemic and hypercalcemic diseased conditions.

  14. Fast Mapping Across Time: Memory Processes Support Children's Retention of Learned Words.

    Science.gov (United States)

    Vlach, Haley A; Sandhofer, Catherine M

    2012-01-01

    Children's remarkable ability to map linguistic labels to referents in the world is commonly called fast mapping. The current study examined children's (N = 216) and adults' (N = 54) retention of fast-mapped words over time (immediately, after a 1-week delay, and after a 1-month delay). The fast mapping literature often characterizes children's retention of words as consistently high across timescales. However, the current study demonstrates that learners forget word mappings at a rapid rate. Moreover, these patterns of forgetting parallel forgetting functions of domain-general memory processes. Memory processes are critical to children's word learning and the role of one such process, forgetting, is discussed in detail - forgetting supports extended mapping by promoting the memory and generalization of words and categories.

  15. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding

    2014-03-01

    In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

  16. A confrontation with reality - Proceedings of the 19th Association for Learning Technology Conference

    NARCIS (Netherlands)

    Hawkridge, David; Verjans, Steven; Wilson, Gail

    2012-01-01

    Hawkridge, D., Verjans, S., & Wilson, G. (Eds.) (2012). A confrontation with reality - Proceedings of the 19th Association for Learning Technology Conference (ALT-C 2012). September, 11-14, 2012, Manchester, UK.

  17. "Our Journey through Time": An Oral History Project Carried out by Young People with Learning Disabilities

    Science.gov (United States)

    Bentley, Sarah; Nicholls, Rickie; Price, Maxine; Wilkinson, Aaron; Purcell, Matthew; Woodhall, Martin; Walmsley, Jan

    2011-01-01

    We are five young people with learning disabilities who found out about the history of hospitals for people with learning disabilities in our area, and made a film about the project. The project taught us what life had been like for some people with learning disabilities only 30 years ago. It was very different to our lives; we have more choice,…

  18. Infant Statistical-Learning Ability Is Related to Real-Time Language Processing

    Science.gov (United States)

    Lany, Jill; Shoaib, Amber; Thompson, Abbie; Estes, Katharine Graf

    2018-01-01

    Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests…

  19. Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

    Science.gov (United States)

    Niazmardi, S.; Safari, A.; Homayouni, S.

    2017-09-01

    Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

  20. Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream

    Science.gov (United States)

    Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika D.; Wang, Zhe; Lochner, Michelle; Matheson, Thomas; Saha, Abhijit; Yang, Shuo; Zhao, Zhenge; Kececioglu, John; Scheidegger, Carlos; Snodgrass, Richard T.; Axelrod, Tim; Jenness, Tim; Maier, Robert S.; Ridgway, Stephen T.; Seaman, Robert L.; Evans, Eric Michael; Singh, Navdeep; Taylor, Clark; Toeniskoetter, Jackson; Welch, Eric; Zhu, Songzhe; The ANTARES Collaboration

    2018-05-01

    The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demand that the astronomical community update its follow-up paradigm. Alert-brokers—automated software system to sift through, characterize, annotate, and prioritize events for follow-up—will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate, and retrospective classification of alerts. The first takes the form of variable versus transient categorization, the second a multiclass typing of the combined variable and transient data set, and the third a purity-driven subtyping of a transient class. Although several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress toward adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

  1. Machine Learning-based Transient Brokers for Real-time Classification of the LSST Alert Stream

    Science.gov (United States)

    Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika; ANTARES Collaboration

    2018-01-01

    The number of transient events discovered by wide-field time-domain surveys already far outstrips the combined followup resources of the astronomical community. This number will only increase as we progress towards the commissioning of the Large Synoptic Survey Telescope (LSST), breaking the community's current followup paradigm. Transient brokers - software to sift through, characterize, annotate and prioritize events for followup - will be a critical tool for managing alert streams in the LSST era. Developing the algorithms that underlie the brokers, and obtaining simulated LSST-like datasets prior to LSST commissioning, to train and test these algorithms are formidable, though not insurmountable challenges. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is a joint project of the National Optical Astronomy Observatory and the Department of Computer Science at the University of Arizona. We have been developing completely automated methods to characterize and classify variable and transient events from their multiband optical photometry. We describe the hierarchical ensemble machine learning algorithm we are developing, and test its performance on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, as well as our progress towards incorporating these into a real-time event broker working on live alert streams from time-domain surveys.

  2. Relations between the development of future time perspective in three life domains, investment in learning, and academic achievement

    NARCIS (Netherlands)

    Peetsma, T.; van der Veen, I.

    2011-01-01

    Relations between the development of future time perspectives in three life domains (i.e., school and professional career, social relations, and leisure time) and changes in students’ investment in learning and academic achievement were examined in this study. Participants were 584 students in the

  3. Relations between the Development of Future Time Perspective in Three Life Domains, Investment in Learning, and Academic Achievement

    Science.gov (United States)

    Peetsma, Thea; van der Veen, Ineke

    2011-01-01

    Relations between the development of future time perspectives in three life domains (i.e., school and professional career, social relations, and leisure time) and changes in students' investment in learning and academic achievement were examined in this study. Participants were 584 students in the first and 584 in the second year of the lower…

  4. Learning potentials and pitfalls working with animation aesthetics as leisure-time pedagogues

    DEFF Research Database (Denmark)

    Ringskou, Lea Thomsen; Ahm, Jacob Noer

    of ethnographic participant observations, accompanied by qualitative semi-structured focus group interviews.Findings:Aesthetic animation learning processes involve more playful and creative learning processes, acknowledging both sound, pictures, body and movement as signs of learning. Especially movement......, as a central part of animations aesthetics, offers both potentials and pittfalls when it comes to the learning processes of the children and calls for pedagogical attention. Overall, the research project constructs knowledge about the pedagogy emerging when working with the learning processes of animation...

  5. Sparse Bayesian learning machine for real-time management of reservoir releases

    Science.gov (United States)

    Khalil, Abedalrazq; McKee, Mac; Kemblowski, Mariush; Asefa, Tirusew

    2005-11-01

    Water scarcity and uncertainties in forecasting future water availabilities present serious problems for basin-scale water management. These problems create a need for intelligent prediction models that learn and adapt to their environment in order to provide water managers with decision-relevant information related to the operation of river systems. This manuscript presents examples of state-of-the-art techniques for forecasting that combine excellent generalization properties and sparse representation within a Bayesian paradigm. The techniques are demonstrated as decision tools to enhance real-time water management. A relevance vector machine, which is a probabilistic model, has been used in an online fashion to provide confident forecasts given knowledge of some state and exogenous conditions. In practical applications, online algorithms should recognize changes in the input space and account for drift in system behavior. Support vectors machines lend themselves particularly well to the detection of drift and hence to the initiation of adaptation in response to a recognized shift in system structure. The resulting model will normally have a structure and parameterization that suits the information content of the available data. The utility and practicality of this proposed approach have been demonstrated with an application in a real case study involving real-time operation of a reservoir in a river basin in southern Utah.

  6. Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

    Science.gov (United States)

    Gong, Xiajing; Hu, Meng; Zhao, Liang

    2018-05-01

    Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

  7. Upper Extremity Motor Learning among Individuals with Parkinson's Disease: A Meta-Analysis Evaluating Movement Time in Simple Tasks

    Directory of Open Access Journals (Sweden)

    K. Felix

    2012-01-01

    Full Text Available Motor learning has been found to occur in the rehabilitation of individuals with Parkinson's disease (PD. Through repetitive structured practice of motor tasks, individuals show improved performance, confirming that motor learning has probably taken place. Although a number of studies have been completed evaluating motor learning in people with PD, the sample sizes were small and the improvements were variable. The purpose of this meta-analysis was to determine the ability of people with PD to learn motor tasks. Studies which measured movement time in upper extremity reaching tasks and met the inclusion criteria were included in the analysis. Results of the meta-analysis indicated that people with PD and neurologically healthy controls both demonstrated motor learning, characterized by a decrease in movement time during upper extremity movements. Movement time improvements were greater in the control group than in individuals with PD. These results support the findings that the practice of upper extremity reaching tasks is beneficial in reducing movement time in persons with PD and has important implications for rehabilitation.

  8. The difference of delay time in monitoring system of facial acupressure learning media using bluetooth, wireless and ethernet

    Science.gov (United States)

    Agustin, Eny Widhia; Hangga, Arimaz; Fahrian, Muhammad Iqbal; Azhari, Anis Fikri

    2018-03-01

    The implementation of monitoring system in the facial acupressure learning media could increase the students' proficiency. However the common learning media still has not implemented a monitoring system in their learning process. This research was conducted to implement monitoring system in the mannequin head prototype as a learning media of facial acupressure using Bluetooth, wireless and Ethernet. The results of the implementation of monitoring system in the prototype showed that there were differences in the delay time between Bluetooth and wireless or Ethernet. The results data showed no difference in the average delay time between the use of Bluetooth with wireless and the use of Bluetooth with Ethernet in monitoring system of facial acupressure learning media. From all the facial acupressure points, the forehead facial acupressure point has the longest delay time of 11.93 seconds. The average delay time in all 3 class rooms was 1.96 seconds therefore the use of Bluetooth, wireless and Ethernet is highly recommended in the monitoring system of facial acupressure.

  9. Phase transitions between lower and higher level management learning in times of crisis: an experimental study based on synergetics.

    Science.gov (United States)

    Liening, Andreas; Strunk, Guido; Mittelstadt, Ewald

    2013-10-01

    Much has been written about the differences between single- and double-loop learning, or more general between lower level and higher level learning. Especially in times of a fundamental crisis, a transition between lower and higher level learning would be an appropriate reaction to a challenge coming entirely out of the dark. However, so far there is no quantitative method to monitor such a transition. Therefore we introduce theory and methods of synergetics and present results from an experimental study based on the simulation of a crisis within a business simulation game. Hypothesized critical fluctuations - as a marker for so-called phase transitions - have been assessed with permutation entropy. Results show evidence for a phase transition during the crisis, which can be interpreted as a transition between lower and higher level learning.

  10. The role of timing in the induction of neuromodulation in perceptual learning by transcranial electric stimulation.

    Science.gov (United States)

    Pirulli, Cornelia; Fertonani, Anna; Miniussi, Carlo

    2013-07-01

    Transcranial electric stimulation (tES) protocols are able to induce neuromodulation, offering important insights to focus and constrain theories of the relationship between brain and behavior. Previous studies have shown that different types of tES (i.e., direct current stimulation - tDCS, and random noise stimulation - tRNS) induce different facilitatory behavioral effects. However to date is not clear which is the optimal timing to apply tES in relation to the induction of robust facilitatory effects. The goal of this work was to investigate how different types of tES (tDCS and tRNS) can modulate behavioral performance in the healthy adult brain in relation to their timing of application. We applied tES protocols before (offline) or during (online) the execution of a visual perceptual learning (PL) task. PL is a form of implicit memory that is characterized by an improvement in sensory discrimination after repeated exposure to a particular type of stimulus and is considered a manifestation of neural plasticity. Our aim was to understand if the timing of tES is critical for the induction of differential neuromodulatory effects in the primary visual cortex (V1). We applied high-frequency tRNS, anodal tDCS and sham tDCS on V1 before or during the execution of an orientation discrimination task. The experimental design was between subjects and performance was measured in terms of d' values. The ideal timing of application varied depending on the stimulation type. tRNS facilitated task performance only when it was applied during task execution, whereas anodal tDCS induced a larger facilitation if it was applied before task execution. The main result of this study is the finding that the timing of identical tES protocols yields opposite effects on performance. These results provide important guidelines for designing neuromodulation induction protocols and highlight the different optimal timing of the two excitatory techniques. Copyright © 2013 Elsevier Inc. All

  11. TIME-ON-TASK IN PRIMARY CLASSROOMS, DURING DIFFERENT TEACHING-LEARNING APPROACHES

    OpenAIRE

    Sachin Mohite; Meenal Dashputre

    2017-01-01

    The entire education system is moving from the teacher-centered teaching-learning approaches towards student-centered teaching-learning approaches, with anticipation that it would increase the learning outcomes. This empirical study was carried out to compare the traditional and non-traditional classrooms. It also tried to understand the effectiveness of the Alternate Instructions in the Mathematics and Primary Language (Marathi) classrooms. This study collected about 8000 snapshots from the ...

  12. A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

    Science.gov (United States)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-03-01

    Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.

  13. Recognition of Time Stamps on Full-Disk Hα Images Using Machine Learning Methods

    Science.gov (United States)

    Xu, Y.; Huang, N.; Jing, J.; Liu, C.; Wang, H.; Fu, G.

    2016-12-01

    Observation and understanding of the physics of the 11-year solar activity cycle and 22-year magnetic cycle are among the most important research topics in solar physics. The solar cycle is responsible for magnetic field and particle fluctuation in the near-earth environment that have been found increasingly important in affecting the living of human beings in the modern era. A systematic study of large-scale solar activities, as made possible by our rich data archive, will further help us to understand the global-scale magnetic fields that are closely related to solar cycles. The long-time-span data archive includes both full-disk and high-resolution Hα images. Prior to the widely use of CCD cameras in 1990s, 35-mm films were the major media to store images. The research group at NJIT recently finished the digitization of film data obtained by the National Solar Observatory (NSO) and Big Bear Solar Observatory (BBSO) covering the period of 1953 to 2000. The total volume of data exceeds 60 TB. To make this huge database scientific valuable, some processing and calibration are required. One of the most important steps is to read the time stamps on all of the 14 million images, which is almost impossible to be done manually. We implemented three different methods to recognize the time stamps automatically, including Optical Character Recognition (OCR), Classification Tree and TensorFlow. The latter two are known as machine learning algorithms which are very popular now a day in pattern recognition area. We will present some sample images and the results of clock recognition from all three methods.

  14. Real-Time Strategy Video Game Experience and Visual Perceptual Learning.

    Science.gov (United States)

    Kim, Yong-Hwan; Kang, Dong-Wha; Kim, Dongho; Kim, Hye-Jin; Sasaki, Yuka; Watanabe, Takeo

    2015-07-22

    Visual perceptual learning (VPL) is defined as long-term improvement in performance on a visual-perception task after visual experiences or training. Early studies have found that VPL is highly specific for the trained feature and location, suggesting that VPL is associated with changes in the early visual cortex. However, the generality of visual skills enhancement attributable to action video-game experience suggests that VPL can result from improvement in higher cognitive skills. If so, experience in real-time strategy (RTS) video-game play, which may heavily involve cognitive skills, may also facilitate VPL. To test this hypothesis, we compared VPL between RTS video-game players (VGPs) and non-VGPs (NVGPs) and elucidated underlying structural and functional neural mechanisms. Healthy young human subjects underwent six training sessions on a texture discrimination task. Diffusion-tensor and functional magnetic resonance imaging were performed before and after training. VGPs performed better than NVGPs in the early phase of training. White-matter connectivity between the right external capsule and visual cortex and neuronal activity in the right inferior frontal gyrus (IFG) and anterior cingulate cortex (ACC) were greater in VGPs than NVGPs and were significantly correlated with RTS video-game experience. In both VGPs and NVGPs, there was task-related neuronal activity in the right IFG, ACC, and striatum, which was strengthened after training. These results indicate that RTS video-game experience, associated with changes in higher-order cognitive functions and connectivity between visual and cognitive areas, facilitates VPL in early phases of training. The results support the hypothesis that VPL can occur without involvement of only visual areas. Significance statement: Although early studies found that visual perceptual learning (VPL) is associated with involvement of the visual cortex, generality of visual skills enhancement by action video-game experience

  15. Just-in-Time Teaching in undergraduate physics courses: Implementation, learning, and perceptions

    Science.gov (United States)

    Dwyer, Jessica Hewitt

    Regardless of discipline, a decades-long battle has ensued within nearly every classroom in higher education: instructors getting students to come to class prepared to learn. In response to this clash between teacher expectations and frequent student neglect, a group of four physics education researchers developed a reformed instructional strategy called Just-in-Time Teaching (JiTT). This dissertation investigates the following three areas: 1) the fidelity with which undergraduate physics instructors implement JiTT, 2) whether student performance predicts student perception of their instructor's fidelity of JiTT implementation, and 3) whether student perception of their instructor's fidelity of JiTT implementation correlates with student views of their physics course. A blend of quantitative data (e.g., students grades, inventory scores, and questionnaire responses) are integrated with qualitative data (e.g., individual faculty interviews, student focus group discussions, and classroom observations). This study revealed no statistically significant relationship between instructors who spent time on a predefined JiTT critical component and their designation as a JiTT user or non-user. While JiTT users implemented the pedagogy in accordance with the creators' intended ideal vision, many also had trouble reconciling personal concerns about their role as a JiTT adopter and the anticipated demand of the innovation. I recommend that this population of faculty members can serve as a JiTT model for other courses, disciplines, and/or institutions. Student performance was not a predictor of student perception instructor fidelity of JiTT implementation. Additionally, the majority of students in this study reported they read their textbook prior to class and that JiTT assignments helped them prepare for in-class learning. I found evidence that exposure to the JiTT strategy may correlate with a more favorable student view of their physics course. Finally, according to students

  16. Europe: Strategies and agendas for lifelong learning at time of crisis

    DEFF Research Database (Denmark)

    Milana, Marcella

    2014-01-01

    A complete overview of lifelong learning strategies in Europe, at both international and national levels, calls for understanding the processes through which these strategies take shape. Accordingly, in this contribution, lifelong learning strategies are analyzed through a critical lens...... and the OECD, with important consequences for lifelong learning policy. Evidence is found, for instance, in the formation of a reductionist skills agenda, joint between the EU and the OECD; an agenda capable of influencing future governmental thinking about lifelong learning and adult education in Europe....

  17. Training haptic stiffness discrimination: time course of learning with or without visual information and knowledge of results.

    Science.gov (United States)

    Teodorescu, Kinneret; Bouchigny, Sylvain; Korman, Maria

    2013-08-01

    In this study, we explored the time course of haptic stiffness discrimination learning and how it was affected by two experimental factors, the addition of visual information and/or knowledge of results (KR) during training. Stiffness perception may integrate both haptic and visual modalities. However, in many tasks, the visual field is typically occluded, forcing stiffness perception to be dependent exclusively on haptic information. No studies to date addressed the time course of haptic stiffness perceptual learning. Using a virtual environment (VE) haptic interface and a two-alternative forced-choice discrimination task, the haptic stiffness discrimination ability of 48 participants was tested across 2 days. Each day included two haptic test blocks separated by a training block Additional visual information and/or KR were manipulated between participants during training blocks. Practice repetitions alone induced significant improvement in haptic stiffness discrimination. Between days, accuracy was slightly improved, but decision time performance was deteriorated. The addition of visual information and/or KR had only temporary effects on decision time, without affecting the time course of haptic discrimination learning. Learning in haptic stiffness discrimination appears to evolve through at least two distinctive phases: A single training session resulted in both immediate and latent learning. This learning was not affected by the training manipulations inspected. Training skills in VE in spaced sessions can be beneficial for tasks in which haptic perception is critical, such as surgery procedures, when the visual field is occluded. However, training protocols for such tasks should account for low impact of multisensory information and KR.

  18. A Styrene-alt-Maleic Acid Copolymer Is an Effective Inhibitor of R5 and X4 Human Immunodeficiency Virus Type 1 Infection

    Directory of Open Access Journals (Sweden)

    Vanessa Pirrone

    2010-01-01

    Full Text Available An alternating copolymer of styrene and maleic acid (alt-PSMA differs from other polyanionic antiviral agents in that the negative charges of alt-PSMA are provided by carboxylic acid groups instead of sulfate or sulfonate moieties. We hypothesized that alt-PSMA would have activity against human immunodeficiency virus type 1 (HIV-1 comparable to other polyanions, such as the related compound, poly(sodium 4-styrene sulfonate (PSS. In assays using cell lines and primary immune cells, alt-PSMA was characterized by low cytotoxicity and effective inhibition of infection by HIV-1 BaL and IIIB as well as clinical isolates of subtypes A, B, and C. In mechanism of action assays, in which each compound was added to cells and subsequently removed prior to HIV-1 infection (“washout” assay, alt-PSMA caused no enhancement of infection, while PSS washout increased infection 70% above control levels. These studies demonstrate that alt-PSMA is an effective HIV-1 inhibitor with properties that warrant further investigation.

  19. Politiken, Alt om Ikast Brande (web), Lemvig Folkeblad (Web), Politiken (web), Dabladet Ringkjøbing Skjern (web)

    DEFF Research Database (Denmark)

    Lauritsen, Jens

    2014-01-01

    Politiken 01.01.2014 14:16 Danskerne skød nytåret ind med et brag, men for enkeltes vedkommende gik det galt, da nytårskrudtet blev tændt. Skadestuerne har behandlet 73 personer for fyrværkeriskader mellem klokken 18 i aftes og klokken 06 i morges. Det viser en optælling, som Politiken har...... foretaget på baggrund af tal fra Ulykkes Analyse Gruppen på Odense Universitetshospital. Artiklen er også bragt i: Alt om Ikast Brande (web), Lemvig Folkeblad (web), Politiken (web), Dagbladet Ringkjøbing Skjern (web)....

  20. Sci-Fri AM: Quality, Safety, and Professional Issues 04: Predicting waiting times in Radiation Oncology using machine learning

    International Nuclear Information System (INIS)

    Joseph, Ackeem; Herrera, David; Hijal, Tarek; Hendren, Laurie; Leung, Alvin; Wainberg, Justin; Sawaf, Marya; Maxim, Gorshkov; Maglieri, Robert; Keshavarz, Mehryar; Kildea, John

    2016-01-01

    We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: Waiting at home for their treatment plan to be prepared Waiting in the waiting room for daily radiotherapy Waiting in the waiting room to see a physician in consultation or follow-up These waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously-treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.

  1. Sci-Fri AM: Quality, Safety, and Professional Issues 04: Predicting waiting times in Radiation Oncology using machine learning

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, Ackeem; Herrera, David; Hijal, Tarek; Hendren, Laurie; Leung, Alvin; Wainberg, Justin; Sawaf, Marya; Maxim, Gorshkov; Maglieri, Robert; Keshavarz, Mehryar; Kildea, John [McGill University Health Centre (Canada)

    2016-08-15

    We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: Waiting at home for their treatment plan to be prepared Waiting in the waiting room for daily radiotherapy Waiting in the waiting room to see a physician in consultation or follow-up These waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously-treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.

  2. Role of aging and hippocampus in Time-Place Learning: link to episodic-like memory?

    Directory of Open Access Journals (Sweden)

    Cornelis Kees Mulder

    2016-01-01

    Full Text Available Introduction: with time-place learning (TPL, animals link an event with the spatial location and the time of day. The what-where-when TPL components make the task putatively episodic-like in nature. Animals use an internal sense of time to master TPL, which is circadian system based. Finding indications for a role of the hippocampus and (early aging-sensitivity in TPL would strengthen the episodic-like memory nature of the paradigm. Methods: previously, we used C57Bl/6 mice for our TPL research. Here, we used CD1 mice which are less hippocampal-driven and age faster compared to C57Bl/6 mice. To demonstrate the low degree of hippocampal-driven performance in CD1 mice, a cross maze was used. The spontaneous alternation test was used to score spatial working memory in CD1 mice at four different age categories (young (3-6 months, middle-aged (7-11 months, aged (12-18 months and old (>19 months. TPL performance of middle-aged and aged CD1 mice was tested in a setup with either two or three time points per day (2-arm or 3-arm TPL task. Immunostainings was applied on brains of young and middle-aged C57Bl/6 mice that had successfully mastered the 3-arm TPL task. Results: in contrast to C57Bl/6 mice, middle-aged and aged CD1 mice were less hippocampus-driven and failed to master the 3-arm TPL task. They could, however, master the 2-arm TPL task primarily via an ordinal (non-circadian timing system. c-Fos, CRY2, vasopressin (AVP, and pCREB were investigated. We found no differences at the level of the suprachiasmatic nucleus (SCN; circadian master clock, whereas CRY2 expression was increased in the hippocampal dentate gyrus. The most pronounced difference between TPL trained and control mice was found in c-Fos expression in the paraventricular thalamic nucleus, a circadian system relay station. Conclusions: These results further indicate a key role of CRY proteins in TPL and confirm the limited role of the SCN in TPL. Based on the poor TPL performance of

  3. `Teaching What I Learned': Exploring students' Earth and Space Science learning experiences in secondary school with a particular focus on their comprehension of the concept of `geologic time'

    Science.gov (United States)

    Yoon, Sae Yeol; Peate, David W.

    2015-06-01

    According to the national survey of science education, science educators in the USA currently face many challenges such as lack of qualified secondary Earth and Space Science (ESS) teachers. Less qualified teachers may have difficulty teaching ESS because of a lack of conceptual understanding, which leads to diminished confidence in content knowledge. More importantly, teachers' limited conceptual understanding of the core ideas automatically leads to a lack of pedagogical content knowledge. This mixed methods study aims to explore the ways in which current secondary schooling, especially the small numbers of highly qualified ESS teachers in the USA, might influence students' learning of the discipline. To gain a better understanding of the current conditions of ESS education in secondary schools, in the first phase, we qualitatively examined a sample middle and high school ESS textbook to explore how the big ideas of ESS, particularly geological time, are represented. In the second phase, we quantitatively analyzed the participating college students' conceptual understanding of geological time by comparing those who had said they had had secondary school ESS learning experience with those who did not. Additionally, college students' perceptions on learning and teaching ESS are discussed. Findings from both the qualitative and quantitative phases indicate participating students' ESS learning experience in their secondary schools seemed to have limited or little influence on their conceptual understandings of the discipline. We believe that these results reflect the current ESS education status, connected with the declining numbers of highly qualified ESS teachers in secondary schools.

  4. A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

    Science.gov (United States)

    Bao, Wei; Yue, Jun; Rao, Yulei

    2017-01-01

    The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

  5. A deep learning framework for financial time series using stacked autoencoders and long-short term memory

    Science.gov (United States)

    Bao, Wei; Rao, Yulei

    2017-01-01

    The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance. PMID:28708865

  6. A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

    Directory of Open Access Journals (Sweden)

    Wei Bao

    Full Text Available The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT, stacked autoencoders (SAEs and long-short term memory (LSTM are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

  7. Aircraft Control Using Engine Thrust: A History of Learning TOC Real-Time

    Science.gov (United States)

    Cole, Jennifer H.; Batteas, Frank; Fullerton, Gordon

    2006-01-01

    A history of learning the operation of Throttles Only Control (TOC) to control an aircraft in real time using engine thrust is shown. The topics include: 1) Past TOC Accidents/Incidents; 2) 1972: DC-10 American Airlines; 3) May 1974: USAF B-52H; 4) April 1975: USAF C-5A; 5) April 1975: USAF C-5A; 6) 1981: USAF B-52G; 7) August 1985: JAL 123 B-747; 8) JAL 123 Survivor Story; 9) JAL 123 Investigation Findings; 10) July 1989: UAL 232 DC-10; 11) UAL 232 DC-10; 12) Eastwind 517 B-737; 13) November 2003: DHL A-300; 14) Historically, TOC has saved lives; 15) Automated Throttles-Only Control; 16) PCA Project; 17) Propulsion-Controlled Aircraft; 18) MD-11 PCA System and Flight Test Envelope; 19) MD-11 Simulation, PCA ILS-Soupled Landing Dispersion; 20) Throttles-Only Pitch and Roll Control Power; 21) PCA in Commercial Fleet; 22) Fall 2005: PCAR Project; 23) PCAR Background - TOC; and 24) PCAR Background - TOC.

  8. Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

    Science.gov (United States)

    D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R

    2010-03-09

    It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

  9. Reduction in training time of a deep learning model in detection of lesions in CT

    Science.gov (United States)

    Makkinejad, Nazanin; Tajbakhsh, Nima; Zarshenas, Amin; Khokhar, Ashfaq; Suzuki, Kenji

    2018-02-01

    Deep learning (DL) emerged as a powerful tool for object detection and classification in medical images. Building a well-performing DL model, however, requires a huge number of images for training, and it takes days to train a DL model even on a cutting edge high-performance computing platform. This study is aimed at developing a method for selecting a "small" number of representative samples from a large collection of training samples to train a DL model for the could be used to detect polyps in CT colonography (CTC), without compromising the classification performance. Our proposed method for representative sample selection (RSS) consists of a K-means clustering algorithm. For the performance evaluation, we applied the proposed method to select samples for the training of a massive training artificial neural network based DL model, to be used for the classification of polyps and non-polyps in CTC. Our results show that the proposed method reduce the training time by a factor of 15, while maintaining the classification performance equivalent to the model trained using the full training set. We compare the performance using area under the receiveroperating- characteristic curve (AUC).

  10. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

    International Nuclear Information System (INIS)

    Li, Q; Clifford, G D

    2012-01-01

    In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal. (paper)

  11. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals.

    Science.gov (United States)

    Li, Q; Clifford, G D

    2012-09-01

    In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.

  12. 2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism.

    Science.gov (United States)

    Wu, QingXiang; McGinnity, Thomas Martin; Maguire, Liam; Belatreche, Ammar; Glackin, Brendan

    2008-11-01

    In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.

  13. Time, Space and Structure in an E-Learning and E-Mentoring Project

    Science.gov (United States)

    Loureiro-Koechlin, Cecilia; Allan, Barbara

    2010-01-01

    This study focuses on a project, "EMPATHY Net-Works," which developed a learning community as a means of encouraging women to progress into employment and management positions in the logistics and supply chain industries (LaSCI). Learning activities were organised in the form of a taught module containing face-to-face and online elements and…

  14. Pink Time: Evidence of Self-Regulated Learning and Academic Motivation among Undergraduate Students

    Science.gov (United States)

    Baird, Timothy D.; Kniola, David J.; Lewis, Ashley L.; Fowler, Shelli B.

    2015-01-01

    This article describes and analyzes a classroom assignment to promote intrinsic motivation for learning in college students. Here, grades and instructor expectations for content are viewed as students' primary motivations for learning, and correspondingly present obstacles for improved critical thinking skills, student autonomy, and engagement.…

  15. Experience the city : analysis of space-time behavior and spatial learning

    NARCIS (Netherlands)

    Moiseeva, A.

    2013-01-01

    Learning plays an important role by coding information into individual cognitive maps that can be used to make decisions concerning individual behavior in space. Through traveling people learn about the urban environment and update their knowledge. In this regard, the growing concern in the field of

  16. Baby FaceTime: Can Toddlers Learn from Online Video Chat?

    Science.gov (United States)

    Myers, Lauren J.; LeWitt, Rachel B.; Gallo, Renee E.; Maselli, Nicole M.

    2017-01-01

    There is abundant evidence for the "video deficit": children under 2 years old learn better in person than from video. We evaluated whether these findings applied to video chat by testing whether children aged 12-25 months could form relationships with and learn from on-screen partners. We manipulated social contingency: children…

  17. Real time reinforcement learning control of dynamic systems applied to an inverted pendulum

    NARCIS (Netherlands)

    van Luenen, W.T.C.; van Luenen, W.T.C.; Stender, J.; Addis, T.

    1990-01-01

    Describes work started in order to investigate the use of neural networks for application in adaptive or learning control systems. Neural networks have learning capabilities and they can be used to realize non-linear mappings. These are attractive features which could make them useful building

  18. Pastors and the "Perpetuum Mobile": The Dynamics of Professional Learning in Times of Reform

    Science.gov (United States)

    Reite, Ingrid Chr.

    2015-01-01

    In a changing knowledge society, many workplaces experience a great number of reforms, implying improvement, new ways of working and professional learning. When a reform is introduced, however, does a professional act as an ever-moving machine--a "perpetuum mobile"--always learning with full energy? In this article, I ask the following:…

  19. Continuous-time on-policy neural reinforcement learning of working memory tasks

    NARCIS (Netherlands)

    D. Zambrano (Davide); P.R. Roelfsema; S.M. Bohte (Sander)

    2015-01-01

    htmlabstractAs living organisms, one of our primary characteristics is the ability to rapidly process and react to unknown and unexpected events. To this end, we are able to recognize an event or a sequence of events and learn to respond properly. Despite advances in machine learning, current

  20. Digital tomosynthesis for evaluating metastatic lung nodules: nodule visibility, learning curves, and reading times.

    Science.gov (United States)

    Lee, Kyung Hee; Goo, Jin Mo; Lee, Sang Min; Park, Chang Min; Bahn, Young Eun; Kim, Hyungjin; Song, Yong Sub; Hwang, Eui Jin

    2015-01-01

    To evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT). We included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined. Among 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, ≤ 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed. Most malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.

  1. Digital tomosynthesis for evaluating metastatic lung nodules: Nodule visibility, learning curves, and reading times

    International Nuclear Information System (INIS)

    Lee, Kyung Hee; Goo, Jin Mo; Lee, Sang Min; Park, Chang Min; Bahn, Young Eun; Kim, Hyung Jin; Song, Yong Sub; Hwang, Eui Jin

    2015-01-01

    To evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT). We included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined. Among 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, < or = 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p < 0.001). Area under the curve (AUC) values at the initial session were > 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed. Most malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.

  2. Digital tomosynthesis for evaluating metastatic lung nodules: Nodule visibility, learning curves, and reading times

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kyung Hee; Goo, Jin Mo; Lee, Sang Min; Park, Chang Min; Bahn, Young Eun; Kim, Hyung Jin; Song, Yong Sub; Hwang, Eui Jin [Dept. of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul (Korea, Republic of)

    2015-04-15

    To evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT). We included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined. Among 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, < or = 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p < 0.001). Area under the curve (AUC) values at the initial session were > 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed. Most malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.

  3. Enhancing Student Engagement and Active Learning through Just-in-Time Teaching and the Use of Powerpoint

    Science.gov (United States)

    Wanner, Thomas

    2015-01-01

    This instructional article is about an innovative teaching approach for enhancing student engagement and active learning in higher education through a combination of just-in-time teaching and the use of PowerPoint technology. The central component of this approach was students' pre-lecture preparation of a short PowerPoint presentation in which…

  4. Connections between Future Time Perspectives and Self-Regulated Learning for Mid-Year Engineering Students: A Multiple Case Study

    Science.gov (United States)

    Chasmar, Justine

    2017-01-01

    This dissertation presents multiple studies with the purpose of understanding the connections between undergraduate engineering students' motivations, specifically students' Future Time Perspectives (FTPs) and Self-Regulated Learning (SRL). FTP refers to the views students hold about the future and how their perceptions of current tasks are…

  5. Understanding the Association between Future Time Perspective and Self-Regulated Learning through the Lens of Self-Determination Theory

    Science.gov (United States)

    de Bilde, Jerissa; Vansteenkiste, Maarten; Lens, Willy

    2011-01-01

    The present cross-sectional research examined a process underlying the positive association between holding an extended future time perspective (FTP) and learning outcomes through the lens of self-determination theory. High school students and university students (N = 275) participated in the study. It was found that students with an extended FTP…

  6. Comprehension and Time Expended for a Doctoral Student with a Learning Disability when Reading with and without an Accommodation

    Science.gov (United States)

    Tanners, Adam; McDougall, Dennis; Skouge, Jim; Narkon, Drue

    2012-01-01

    The purpose of this alternating treatment, single-case research study was to compare reading comprehension and time expended reading, of a doctoral student with learning disabilities, under two reading conditions. In condition one, the student used a self-discovered accommodation, that is, listening, on an iPod, to an audiobook version…

  7. Reducing the Boundaries between the Community and the Academy with a Full-Time Service Learning Capstone

    Science.gov (United States)

    Ballard, Andy

    2013-01-01

    The purpose of this paper is to share my experiences as the instructor of a full-time, single semester, service-learning capstone course. In this innovative course students already volunteering in the Students in Free Enterprise (SIFE) organization work in teams to identify community needs and address them using their business skills and knowledge…

  8. The Effects of Textisms on Learning, Study Time, and Instructional Perceptions in an Online Artificial Intelligence Instructional Module

    Science.gov (United States)

    Beasley, Robert; Bryant, Nathan L.; Dodson, Phillip T.; Entwistle, Kevin C.

    2013-01-01

    The purpose of this study was to investigate the effects of textisms (i.e., abbreviated spellings, acronyms, and other shorthand notations) on learning, study time, and instructional perceptions in an online artificial intelligence instructional module. The independent variable in this investigation was experimental condition. For the control…

  9. Real-time tumor motion estimation using respiratory surrogate via memory-based learning

    Science.gov (United States)

    Li, Ruijiang; Lewis, John H.; Berbeco, Ross I.; Xing, Lei

    2012-08-01

    Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ∼15 mm and average duration of ∼115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95

  10. Real-time tumor motion estimation using respiratory surrogate via memory-based learning

    International Nuclear Information System (INIS)

    Li Ruijiang; Xing Lei; Lewis, John H; Berbeco, Ross I

    2012-01-01

    Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ∼15 mm and average duration of ∼115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95

  11. Ecologia: The Assumptions, Expectations, and Strategies of Modern Language Students Working in a Self-Access Learning Environment for the First Time.

    Science.gov (United States)

    Piper, Alison

    1994-01-01

    This study examined 29 second-year undergraduate students of Spanish using a self-access learning environment for the first time, focusing on their language attitudes and learning strategies. The results show that, even as modern languages majors, the students possessed a model of language and strategies for learning that were significantly…

  12. Coupling between Metacognition and Emotions during STEM Learning with Advanced Learning Technologies: A Critical Analysis, Implications for Future Research, and Design of Learning Systems

    Science.gov (United States)

    Azevedo, Roger; Mudrick, Nicholas; Taub, Michelle; Wortha, Franz

    2017-01-01

    Metacognition and emotions play a critical role in learners' ability to monitor and regulate their learning about 21st-century skills related to science, technology, engineering, and mathematics (STEM) content while using advanced learning technologies (ALTs; e.g., intelligent tutoring systems, serious games, hypermedia, augmented reality). In…

  13. Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

    Directory of Open Access Journals (Sweden)

    Zehui Kong

    Full Text Available To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM of power-request is derived. The reinforcement learning (RL is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

  14. Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

    Science.gov (United States)

    Kong, Zehui; Zou, Yuan; Liu, Teng

    2017-01-01

    To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

  15. #whitegenocide, the Alt-right and Conspiracy Theory: How Secrecy and Suspicion Contributed to the Mainstreaming of Hate

    Directory of Open Access Journals (Sweden)

    Andrew F. Wilson,

    2018-02-01

    Full Text Available This article considers the relationship between “hashtag activism” as it is currently being used by the alt-right and the tendency to draw on conspiracy theory that Richard Hofstadter identified as being prevalent among what he termed “pseudo-conservatives” half a century earlier. Both the alt-right and Hofstadter’s “pseudo-conservatives” can be characterised by a pronounced populist nationalism that understands its aims as protecting a particular way of life whilst drawing on an aggrieved sense of injustice at being conspired against by an unseen enemy. That this “enemy” is typically foreign in actuality or in spirit confirms the cultural dimension on which their politics is played out. It is argued here that this paranoid populist nationalism has been figuratively drawn upon in the rhetoric of Donald Trump and that this apparent openness to the “pseudo-conservative” discourse on nationalism has provided a bridging effect via which far right elements are seeking to normalize extremist viewpoints

  16. Frederick W. Alt received the 2015 Szent-Györgi Prize for Progress in Cancer Research.

    Science.gov (United States)

    Scully, Peter; Zhao, Jie; Ba, Sujuan

    2016-02-03

    The Szent-Györgyi Prize for Progress in Cancer Research is a prestigious scientific award established by the National Foundation for Cancer Research (NFCR)--a leading cancer research charitable organization in the United States that is committed to supporting scientific research and public education relating to the prevention, early diagnosis, better treatments, and ultimately, a cure for cancer. Each year, the Szent-Györgyi Prize honors an outstanding researcher, nominated by colleagues or peers, who has contributed outstanding, significant research to the fight against cancer, and whose accomplishments have helped improve treatment options for cancer patients. The Prize also promotes public awareness of the importance of basic cancer research and encourages the sustained investment needed to accelerate the translation of these research discoveries into new cancer treatments. This report highlights the pioneering work led by the 2015 Prize winner, Dr. Frederick Alt. Dr. Alt's work in the area of cancer genetics over four decades has helped to shape the very roots of modern cancer research. His work continues to profoundly impact the approaches that doctors around the globe use to diagnose and treat cancer. In particular, his seminal discoveries of gene amplification and his pioneering work on molecular mechanisms of DNA damage repair have helped to usher in the era of genetically targeted therapy and personalized medicine.

  17. Mastery learning: it is time for medical education to join the 21st century.

    Science.gov (United States)

    McGaghie, William C

    2015-11-01

    Clinical medical education in the 21st century is grounded in a 19th-century model that relies on longitudinal exposure to patients as the curriculum focus. The assumption is that medical students and postgraduate residents will learn from experience, that vicarious or direct involvement in patient care is the best teacher. The weight of evidence shows, however, that results from such traditional clinical education are uneven at best. Educational inertia endorsed until recently by medical school accreditation policies has maintained the clinical medical education status quo for decades.Mastery learning is a new paradigm for medical education. Basic principles of mastery learning are that educational excellence is expected and can be achieved by all learners and that little or no variation in measured outcomes will result. This Commentary describes the origins of mastery learning and presents its essential features. The Commentary then introduces the eight reports that comprise the mastery learning cluster for this issue of Academic Medicine. The reports are intended to help medical educators recognize advantages of the mastery model and begin to implement mastery learning at their own institutions. The Commentary concludes with brief statements about future directions for mastery learning program development and research in medical education.

  18. Timing matters: The impact of immediate and delayed feedback on artificial language learning

    Directory of Open Access Journals (Sweden)

    Bertram Opitz

    2011-02-01

    Full Text Available In the present experiment, we used event-related potentials (ERP to investigate the role of immediate and delayed feedback in an artificial grammar learning task. Two groups of participants were engaged in classifying non-word strings according to an underlying rule system, not known to the participants. Visual feedback was provided after each classification either immediately or with a short delay of one second. Both groups were able to learn the artificial grammar system as indicated by an increase in classification performance. However, the gain in performance was significantly larger for the group receiving immediate feedback as compared to the group receiving delayed feedback. Learning was accompanied by an increase in P300 activity in the ERP for delayed as compared to immediate feedback. Irrespective of feedback delay, both groups exhibited learning related decreases in the feedback-related positivity (FRP elicited by positive feedback only. The feedback-related negativity (FRN, however, remained constant over the course of learning. These results suggest, first, that delayed feedback is less effective for artificial grammar learning as task requirements are very demanding, and second, that the FRP elicited by positive prediction errors decreases with learning while the FRN to negative prediction errors is elicited in an all-or-none fashion by negative feedback throughout the entire experiment.

  19. Prediction and Real-Time Compensation of Qubit Decoherence Via Machine Learning (Open Access, Publisher’s Version)

    Science.gov (United States)

    2017-01-16

    accuracy increases with n, as the algorithm learns more about the temporal correlations in fA. For values of k]n, corresponding to prediction times...the identity. Diagnostic measurements are performed after a Noise injection a b c Stabilise up to tk Time forward tk (Δt) t–n Qubit Memory Future...supported by the ARC Centre of Excellence for Engineered Quantum Systems CE110001013, ARC Discovery Project DP130103823, the Intelligence Advanced

  20. Does Your Approach to Time Matter for Your Learning? The Role of Time Perspectives on Engagement and Achievement

    Science.gov (United States)

    King, Ronnel B.

    2016-01-01

    Time perspectives have been found to be related to a wide range of psychological phenomena. However, in the educational context, there remains to be a lack of research on how they relate to important academic outcomes. Therefore, the aim of this research was to examine how time perspectives are related to educational outcomes such as engagement,…

  1. Real-Time Probabilistic Structural Health Management Using Machine Learning and GPU Computing Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed project seeks to deliver an ultra-efficient, high-fidelity structural health management (SHM) framework using machine learning and graphics processing...

  2. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series

    OpenAIRE

    Chambon, Stanislas; Galtier, Mathieu; Arnal, Pierrick; Wainrib, Gilles; Gramfort, Alexandre

    2017-01-01

    Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or...

  3. A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

    OpenAIRE

    Wang, Haohan; Raj, Bhiksha

    2015-01-01

    This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On t...

  4. But science is international! Finding time and space to encourage intercultural learning in a content-driven physiology unit.

    Science.gov (United States)

    Etherington, Sarah J

    2014-06-01

    Internationalization of the curriculum is central to the strategic direction of many modern universities and has widespread benefits for student learning. However, these clear aspirations for internationalization of the curriculum have not been widely translated into more internationalized course content and teaching methods in the classroom, particularly in scientific disciplines. This study addressed one major challenge to promoting intercultural competence among undergraduate science students: finding time to scaffold such learning within the context of content-heavy, time-poor units. Small changes to enhance global and intercultural awareness were incorporated into existing assessments and teaching activities within a second-year biomedical physiology unit. Interventions were designed to start a conversation about global and intercultural perspectives on physiology, to embed the development of global awareness into the assessment and to promote cultural exchanges through peer interactions. In student surveys, 40% of domestic and 60% of international student respondents articulated specific learning about interactions in cross-cultural groups resulting from unit activities. Many students also identified specific examples of how cultural beliefs would impact on the place of biomedical physiology within the global community. In addition, staff observed more widespread benefits for student engagement and learning. It is concluded that a significant development of intercultural awareness and a more global perspective on scientific understanding can be supported among undergraduates with relatively modest, easy to implement adaptations to course content.

  5. Time and Effort Required by Persons with Spinal Cord Injury to Learn to Use a Powered Exoskeleton for Assisted Walking.

    Science.gov (United States)

    Kozlowski, Allan J; Bryce, Thomas N; Dijkers, Marcel P

    2015-01-01

    Powered exoskeletons have been demonstrated as being safe for persons with spinal cord injury (SCI), but little is known about how users learn to manage these devices. To quantify the time and effort required by persons with SCI to learn to use an exoskeleton for assisted walking. A convenience sample was enrolled to learn to use the first-generation Ekso powered exoskeleton to walk. Participants were given up to 24 weekly sessions of instruction. Data were collected on assistance level, walking distance and speed, heart rate, perceived exertion, and adverse events. Time and effort was quantified by the number of sessions required for participants to stand up, walk for 30 minutes, and sit down, initially with minimal and subsequently with contact guard assistance. Of 22 enrolled participants, 9 screen-failed, and 7 had complete data. All of these 7 were men; 2 had tetraplegia and 5 had motor-complete injuries. Of these, 5 participants could stand, walk, and sit with contact guard or close supervision assistance, and 2 required minimal to moderate assistance. Walk times ranged from 28 to 94 minutes with average speeds ranging from 0.11 to 0.21 m/s. For all participants, heart rate changes and reported perceived exertion were consistent with light to moderate exercise. This study provides preliminary evidence that persons with neurological weakness due to SCI can learn to walk with little or no assistance and light to somewhat hard perceived exertion using a powered exoskeleton. Persons with different severities of injury, including those with motor complete C7 tetraplegia and motor incomplete C4 tetraplegia, may be able to learn to use this device.

  6. Comparing Problem-Based Learning Students to Students in a Lecture-Based Curriculum: Learning Strategies and the Relation with Self-Study Time

    Science.gov (United States)

    Wijnen, Marit; Loyens, Sofie M. M.; Smeets, Guus; Kroeze, Maarten; van der Molen, Henk

    2017-01-01

    In educational theory, deep processing (i.e., connecting different study topics together) and self-regulation (i.e., taking control over one's own learning process) are considered effective learning strategies. These learning strategies can be influenced by the learning environment. Problem-based learning (PBL), a student-centered educational…

  7. Treatment with escitalopram but not desipramine decreases escape latency times in a learned helplessness model using juvenile rats.

    Science.gov (United States)

    Reed, Abbey L; Anderson, Jeffrey C; Bylund, David B; Petty, Frederick; El Refaey, Hesham; Happe, H Kevin

    2009-08-01

    The pharmacological treatment of depression in children and adolescents is different from that of adults due to the lack of efficacy of certain antidepressants in the pediatric age group. Our current understanding of why these differences occur is very limited. To develop more effective treatments, a juvenile animal model of depression was tested to validate it as a possible model to specifically study pediatric depression. Procedures for use with juvenile rats at postnatal day (PND) 21 and 28 were adapted from the adult learned helplessness model in which, 24 h after exposure to inescapable stress, animals are unable to remove themselves from an easily escapable stressor. Rats were treated for 7 days with either the selective serotonin reuptake inhibitor escitalopram at 10 mg/kg or the tricyclic antidepressant desipramine at 3, 10, or 15 mg/kg to determine if treatment could decrease escape latency times. Escitalopram treatment was effective at decreasing escape latency times in all ages tested. Desipramine treatment did not decrease escape latency times for PND 21 rats, but did decrease times for PND 28 and adult animals. The learned helplessness model with PND 21 rats predicts the efficacy of escitalopram and the lack of efficacy of desipramine seen in the treatment of pediatric depression. These findings suggest that the use of PND 21 rats in a modified learned helplessness procedure may be a valuable model of human pediatric depression that can predict pediatric antidepressant efficacy and be used to study antidepressant mechanisms involved in pediatric depression.

  8. Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

    Directory of Open Access Journals (Sweden)

    Ivana Šemanjski

    2015-12-01

    Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

  9. Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

    International Nuclear Information System (INIS)

    Wei Qing-Lai; Song Rui-Zhuo; Xiao Wen-Dong; Sun Qiu-Ye

    2015-01-01

    This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. (paper)

  10. The different time course of phonotactic constraint learning in children and adults: Evidence from speech errors.

    Science.gov (United States)

    Smalle, Eleonore H M; Muylle, Merel; Szmalec, Arnaud; Duyck, Wouter

    2017-11-01

    Speech errors typically respect the speaker's implicit knowledge of language-wide phonotactics (e.g., /t/ cannot be a syllable onset in the English language). Previous work demonstrated that adults can learn novel experimentally induced phonotactic constraints by producing syllable strings in which the allowable position of a phoneme depends on another phoneme within the sequence (e.g., /t/ can only be an onset if the medial vowel is /i/), but not earlier than the second day of training. Thus far, no work has been done with children. In the current 4-day experiment, a group of Dutch-speaking adults and 9-year-old children were asked to rapidly recite sequences of novel word forms (e.g., kieng nief siet hiem ) that were consistent with phonotactics of the spoken Dutch language. Within the procedure of the experiment, some consonants (i.e., /t/ and /k/) were restricted to the onset or coda position depending on the medial vowel (i.e., /i/ or "ie" vs. /øː/ or "eu"). Speech errors in adults revealed a learning effect for the novel constraints on the second day of learning, consistent with earlier findings. A post hoc analysis at the trial level showed that learning was statistically reliable after an exposure of 120 sequence trials (including a consolidation period). However, children started learning the constraints already on the first day. More precisely, the effect appeared significantly after an exposure of 24 sequences. These findings indicate that children are rapid implicit learners of novel phonotactics, which bears important implications for theorizing about developmental sensitivities in language learning. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  11. Climate policies and learning by doing: Impacts and timing of technology subsidies

    International Nuclear Information System (INIS)

    Kverndokk, Snorre; Rosendahl, Knut Einar

    2007-01-01

    We study the role of technology subsidies in climate policies, using a simple dynamic equilibrium model with learning by doing. The optimal subsidy rate of a carbon-free technology is high when the technology is first adopted, but falls significantly over the next decades. However, the efficiency costs of uniform instead of optimal subsidies, may be low if there are adjustment costs for a new technology. Finally, supporting existing energy technologies only, may lead to technology lock-in, and the impacts of lock-in increase with the learning potential of new technologies as well as the possibilities for early entry. (author)

  12. Preserved GLP-1 and exaggerated GIP secretion in type 2 diabetes and relationships with triglycerides and ALT

    DEFF Research Database (Denmark)

    Alssema, Marjan; Rijkelijkhuizen, Josina M; Holst, Jens Juul

    2013-01-01

    OBJECTIVE: To i) compare incretin responses to oral glucose and mixed meal of diabetic patients with the normoglycaemic population and ii) to investigate whether incretin responses are associated with hypertriglyceridaemia and alanine aminotransferase (ALT) as liver fat marker. DESIGN: A population......-based study. METHODS: A total of 163 persons with normal glucose metabolism (NGM), 20 with intermediate hyperglycaemia and 20 with type 2 diabetes aged 40-65 years participated. Participants received a mixed meal and oral glucose load on separate occasions. Glucagon-like peptide 1 (GLP-1), glucose......-dependent insulinotropic polypeptide (GIP) and glucagon profiles were analysed as total area under the curve (tAUC) and incremental area under the curve. RESULTS: In diabetic patients compared with persons with NGM, we found increased GLP-1 secretion (tAUC per hour) following oral glucose (23.2 pmol/l (95% CI 17...

  13. Chitosan: poly(N-vinylpyrrolidone-alt-itaconic anhydride) nanocapsules—a promising alternative for the lung cancer treatment

    Energy Technology Data Exchange (ETDEWEB)

    Raţă, Delia Mihaela, E-mail: iureadeliamihaela@yahoo.com [„Apollonia” University of Iasi, Faculty of Medical Dentistry, „Academician Ioan Haulică” Research Institute (Romania); Chailan, Jean-François, E-mail: chailan@univ-tln.fr [University of Sud Toulon-Var, « Matériaux-Polymères-Interfaces-Environnement Marin (MAPIEM) Laboratory (France); Peptu, Cătălina Anişoara, E-mail: catipeptu@yahoo.co.uk [“Gheorghe Asachi” Technical University of Iasi, Department of Natural and Synthetic Polymers, Faculty of Chemical Engineering and Environmental Protection (Romania); Costuleanu, Marcel, E-mail: mcostuleanu@yahoo.com [University of Medicine and Pharmacy “Grigore T. Popa”- Iaşi, Department of General Pathology, Faculty of Dental Medicine (Romania); Popa, Marcel, E-mail: marpopa2001@yahoo.fr [“Gheorghe Asachi” Technical University of Iasi, Department of Natural and Synthetic Polymers, Faculty of Chemical Engineering and Environmental Protection (Romania)

    2015-07-15

    This study reports the preparation of novel polymeric nanocapsules based on a natural polymer, chitosan and a synthetic one, poly(N-vinylpyrrolidone-alt-itaconic anhydride) [(poly(NVPAI)] using an interfacial condensation technique. The infrared spectroscopy studies confirmed the crosslinking through the presence of amide bonds, formed between the two polymers chains. The diameter of nanocapsules was found in the range of 126–214 nm and it was determined by dynamic light scattering method. Morphological characterization demonstrated their nano size, the spherical shape of the nanocapsules and the formation of hollow particles. The nanocapsules presented good swelling capacity in aqueous solutions. 5-Fluorouracil (5-FU) loading and release capacity was studied, the processes being controlled by the drug diffusion through the polymeric membrane. The obtained results were encouraging, showing that 5-FU-loaded nanocapsules had 70 % higher apoptotic effect on A549 tumour cells than the drug in free state or mixed with the nanocapsules.

  14. Grafting amino drugs to poly(styrene-alt-maleic anhydride) as a potential method for drug release

    Energy Technology Data Exchange (ETDEWEB)

    Khazaei, Ardeshir; Saednia, Shahnaz; Saien, Javad; Abbasi, Fatemeh, E-mail: Khazaei_1326@yahoo.com, E-mail: ssaednia@gmail.com [Faculty of Chemistry, Bu-Ali Sina University, Hamedan (Iran, Islamic Republic of); Kazem-Rostami, Masoud [Young Researchers Club and Elite, Takestan Branch, Islamic Azad University, Takestan (Iran, Islamic Republic of); Sadeghpour, Mahdieh [Department of Chemistry, Takestan Branch, Islamic Azad University, Takestan (Iran, Islamic Republic of); Borazjani, Maryam Kiani [Faculty of Science, Department of Chemistry, Bushehr Payame Noor University (PNU), Bushehr (Iran, Islamic Republic of)

    2013-07-15

    Drug delivery systems based on polymer-drug conjugates give an improved treatment with lower toxicity or side effects and be used for the treatment of different diseases. Conjugates of biodegradable poly(styrene-alt-maleic anhydride) (PSMA), with a therapeutic agents such as amantadine hydrochloride, amlodipine, gabapentin, zonisamide and mesalamine, were afforded by the formation of the amide bonds of the amino drugs that reacted with the PSMA anhydride groups. The amounts of covalently conjugated drugs were determined by a {sup 1}H NMR spectroscopic method, and the in vitro release rate in buffer solution (pH 1.3) was studied at body temperature 37 Degree-Sign C. In kinetic studies, different dissolution models were examined to obtain drug release data and the collected data were well-fitted to the Korsmeyer-Peppas equation, revealing a dominant Fickian diffusion mechanism for drug release under the in vitro conditions. (author)

  15. Chitosan: poly( N-vinylpyrrolidone- alt-itaconic anhydride) nanocapsules—a promising alternative for the lung cancer treatment

    Science.gov (United States)

    Raţă, Delia Mihaela; Chailan, Jean-François; Peptu, Cătălina Anişoara; Costuleanu, Marcel; Popa, Marcel

    2015-07-01

    This study reports the preparation of novel polymeric nanocapsules based on a natural polymer, chitosan and a synthetic one, poly( N-vinylpyrrolidone- alt-itaconic anhydride) [(poly(NVPAI)] using an interfacial condensation technique. The infrared spectroscopy studies confirmed the crosslinking through the presence of amide bonds, formed between the two polymers chains. The diameter of nanocapsules was found in the range of 126-214 nm and it was determined by dynamic light scattering method. Morphological characterization demonstrated their nano size, the spherical shape of the nanocapsules and the formation of hollow particles. The nanocapsules presented good swelling capacity in aqueous solutions. 5-Fluorouracil (5-FU) loading and release capacity was studied, the processes being controlled by the drug diffusion through the polymeric membrane. The obtained results were encouraging, showing that 5-FU-loaded nanocapsules had 70 % higher apoptotic effect on A549 tumour cells than the drug in free state or mixed with the nanocapsules.

  16. Chitosan: poly(N-vinylpyrrolidone-alt-itaconic anhydride) nanocapsules—a promising alternative for the lung cancer treatment

    International Nuclear Information System (INIS)

    Raţă, Delia Mihaela; Chailan, Jean-François; Peptu, Cătălina Anişoara; Costuleanu, Marcel; Popa, Marcel

    2015-01-01

    This study reports the preparation of novel polymeric nanocapsules based on a natural polymer, chitosan and a synthetic one, poly(N-vinylpyrrolidone-alt-itaconic anhydride) [(poly(NVPAI)] using an interfacial condensation technique. The infrared spectroscopy studies confirmed the crosslinking through the presence of amide bonds, formed between the two polymers chains. The diameter of nanocapsules was found in the range of 126–214 nm and it was determined by dynamic light scattering method. Morphological characterization demonstrated their nano size, the spherical shape of the nanocapsules and the formation of hollow particles. The nanocapsules presented good swelling capacity in aqueous solutions. 5-Fluorouracil (5-FU) loading and release capacity was studied, the processes being controlled by the drug diffusion through the polymeric membrane. The obtained results were encouraging, showing that 5-FU-loaded nanocapsules had 70 % higher apoptotic effect on A549 tumour cells than the drug in free state or mixed with the nanocapsules

  17. Data Mining and Machine Learning in Time-Domain Discovery and Classification

    Science.gov (United States)

    Bloom, Joshua S.; Richards, Joseph W.

    2012-03-01

    The changing heavens have played a central role in the scientific effort of astronomers for centuries. Galileo's synoptic observations of the moons of Jupiter and the phases of Venus starting in 1610, provided strong refutation of Ptolemaic cosmology. These observations came soon after the discovery of Kepler's supernova had challenged the notion of an unchanging firmament. In more modern times, the discovery of a relationship between period and luminosity in some pulsational variable stars [41] led to the inference of the size of the Milky way, the distance scale to the nearest galaxies, and the expansion of the Universe (see Ref. [30] for review). Distant explosions of supernovae were used to uncover the existence of dark energy and provide a precise numerical account of dark matter (e.g., [3]). Repeat observations of pulsars [71] and nearby main-sequence stars revealed the presence of the first extrasolar planets [17,35,44,45]. Indeed, time-domain observations of transient events and variable stars, as a technique, influences a broad diversity of pursuits in the entire astronomy endeavor [68]. While, at a fundamental level, the nature of the scientific pursuit remains unchanged, the advent of astronomy as a data-driven discipline presents fundamental challenges to the way in which the scientific process must now be conducted. Digital images (and data cubes) are not only getting larger, there are more of them. On logistical grounds, this taxes storage and transport systems. But it also implies that the intimate connection that astronomers have always enjoyed with their data - from collection to processing to analysis to inference - necessarily must evolve. Figure 6.1 highlights some of the ways that the pathway to scientific inference is now influenced (if not driven by) modern automation processes, computing, data-mining, and machine-learning (ML). The emerging reliance on computation and ML is a general one - a central theme of this book - but the time

  18. Altıntop Dilim Konservesi Üretiminde Enzim Kullanımı: I. Kabuk Soyma

    Directory of Open Access Journals (Sweden)

    Osman Kola

    2015-02-01

    Full Text Available Bu çalışmada; altıntop dilim konservesi yapımında kabuk soyma ve dilim zarının uzaklaştırılması amaçlarıyla enzim çözeltisi kullanılmasının etkileri araştırılmıştır. Bunun için: önce, çözeltinin meyve kabuğu içine nüfuz ettirilmesi amacıyla uygulanabilecek uygun işlemler, sonra da, enzim çözeltisi uygulamasının kabuk soyma işlemleri üzerindeki etkileri araştırılmıştır. Kabuk soyma işleminde meyvenin; 95 °C sıcaklıktaki suda 5 dakika bekletildikten sonra kapak açma ve kabuk çizme işlemlerini takiben, su ya da %0.5 düzeyindeki Peelzym II çözeltisi içine konulan meyvelerin 1.76 kg/cm2 (25 psi basınç altında 60 saniye süreyle işleme tabi tutulmasının, hem işlemin etkinliği hem de son ürünün kalitesi açısından uygun olabileceği sonucuna varılmıştır.

  19. AMPA receptor phosphorylation and recognition memory: learning-related, time-dependent changes in the chick brain following filial imprinting.

    Science.gov (United States)

    Solomonia, Revaz O; Meparishvili, Maia; Mikautadze, Ekaterine; Kunelauri, Nana; Apkhazava, David; McCabe, Brian J

    2013-04-01

    There is strong evidence that a restricted part of the chick forebrain, the intermediate medial mesopallium (IMM), stores information acquired through the learning process of visual imprinting. We have previously demonstrated that at 1 h but not 24 h after imprinting training, a learning-specific increase in the amount of membrane Thr286-autophosphorylated α-calcium/calmodulin-dependent protein kinase II (αCaMKII), and in the proportion of total αCaMKII that is phosphorylated, occurs in the IMM but not in a control brain region, the posterior pole of the nidopallium (PPN). αCaMKII directly phosphorylates Ser831 in the GluA1 subunit of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor. In the present study we have inquired whether the learning-related increase in αCaMKII autophosphorylation is followed by changes in the Ser831 phosphorylation of GluA1 (P-GluA1) and in the total amount of this subunit (T-GluA1). Trained chicks together with untrained control chicks were killed either 1 or 24 h after training. Tissue was removed from the IMM together with tissue from the PPN as a control. Amounts of P-GluA1 and T-GluA1 were measured. In the left IMM of the 1 h group the P-GluA1/T-GluA1 ratio increased in a learning-specific way. No learning-related changes were observed in other brain regions at 1 h or in any region 24 h after training. The results indicate that a time- and regionally-dependent, learning-specific increase in GluA1 phosphorylation occurs early in recognition memory formation.

  20. Whole brain radiation-induced impairments in learning and memory are time-sensitive and reversible by systemic hypoxia.

    Directory of Open Access Journals (Sweden)

    Junie P Warrington

    Full Text Available Whole brain radiation therapy (WBRT is commonly used for treatment of primary and metastatic brain tumors; however, cognitive impairment occurs in 40-50% of brain tumor survivors. The etiology of the cognitive impairment following WBRT remains elusive. We recently reported that radiation-induced cerebrovascular rarefaction within hippocampal subregions could be completely reversed by systemic hypoxia. However, the effects of this intervention on learning and memory have not been reported. In this study, we assessed the time-course for WBRT-induced impairments in contextual and spatial learning and the capacity of systemic hypoxia to reverse WBRT-induced deficits in spatial memory. A clinical fractionated series of 4.5Gy WBRT was administered to mice twice weekly for 4 weeks, and after various periods of recovery, behavioral analyses were performed. To study the effects of systemic hypoxia, mice were subjected to 11% (hypoxia or 21% oxygen (normoxia for 28 days, initiated 1 month after the completion of WBRT. Our results indicate that WBRT induces a transient deficit in contextual learning, disruption of working memory, and progressive impairment of spatial learning. Additionally, systemic hypoxia completely reversed WBRT-induced impairments in learning and these behavioral effects as well as increased vessel density persisted for at least 2 months following hypoxia treatment. Our results provide critical support for the hypothesis that cerebrovascular rarefaction is a key component of cognitive impairment post-WBRT and indicate that processes of learning and memory, once thought to be permanently impaired after WBRT, can be restored.

  1. Comparing problem-based learning students to students in a lecture-based curriculum: learning strategies and the relation with self-study time

    OpenAIRE

    Wijnen, Marit; Loyens, Sofie; Smeets, Guus; Kroeze, Maarten; Molen, Henk

    2017-01-01

    textabstractIn educational theory, deep processing (i.e., connecting different study topics together) and self-regulation (i.e., taking control over one’s own learning process) are considered effective learning strategies. These learning strategies can be influenced by the learning environment. Problem-based learning (PBL), a student-centered educational method, is believed to stimulate the use of these effective learning strategies. Several aspects of PBL such as discussions of real-life pro...

  2. Development of a Computer-aided Learning System for Graphical Analysis of Continuous-Time Control Systems

    Directory of Open Access Journals (Sweden)

    J. F. Opadiji

    2010-06-01

    Full Text Available We present the development and deployment process of a computer-aided learning tool which serves as a training aid for undergraduate control engineering courses. We show the process of algorithm construction and implementation of the software which is also aimed at teaching software development at undergraduate level. The scope of this project is limited to graphical analysis of continuous-time control systems.

  3. Training and learning robotic surgery, time for a more structured approach: a systematic review

    NARCIS (Netherlands)

    Schreuder, H. W. R.; Wolswijk, R.; Zweemer, R. P.; Schijven, M. P.; Verheijen, R. H. M.

    2012-01-01

    Background Robotic assisted laparoscopic surgery is growing rapidly and there is an increasing need for a structured approach to train future robotic surgeons. Objectives To review the literature on training and learning strategies for robotic assisted laparoscopic surgery. Search strategy A

  4. The Logarithmic-to-Linear Shift: One Learning Sequence, Many Tasks, Many Time Scales

    Science.gov (United States)

    Siegler, Robert S.; Thompson, Clarissa A.; Opfer, John E.

    2009-01-01

    The relation between short-term and long-term change (also known as learning and development) has been of great interest throughout the history of developmental psychology. Werner and Vygotsky believed that the two involved basically similar progressions of qualitatively distinct knowledge states; behaviorists such as Kendler and Kendler believed…

  5. The Perfect Mix: With Blended Professional Learning, Learners Choose Time, Place, Path, and Pace

    Science.gov (United States)

    Cieminski, Amie; Andrews, Deagan

    2018-01-01

    The Greeley-Evans School District in Colorado began moving toward innovation several years ago as district leaders searched for ways to improve student achievement, leverage technology, and stay within a very limited budget. In 2014, district leaders created a five-year blended learning implementation plan to increase student achievement that…

  6. Transformative, transgressive social learning: rethinking higher education pedagogy in times of systemic global dysfunction

    NARCIS (Netherlands)

    Lotz-Sisitka, Heila; Wals, A.E.J.; Kronlid, David; McGarry, Dylan

    2015-01-01

    The nature of the sustainability challenges currently at hand is such that dominant pedagogies and forms of learning that characterize higher education need to be reconsidered to enable students and staff to deal with accelerating change, increasing complexity, contested knowledge claims and

  7. Just-in-Time Teaching, Just-in-Need Learning: Designing towards Optimized Pedagogical Outcomes

    Science.gov (United States)

    Killi, Steinar; Morrison, Andrew

    2015-01-01

    Teaching methods are constantly being changed, new ones are developed and old methods have undergone a renaissance. Two main approaches to teaching prevail: a) lecture-based and project-based and b) an argumentative approach to known knowledge or learning by exploration. Today, there is a balance between these two approaches, and they are more…

  8. Targeted, Timely, Learning Support for International Students: One Australian University's Approach

    Science.gov (United States)

    Baird, Craig

    2012-01-01

    This paper documents the approach taken by an Australian University to enhance student study skills, development of academic language, and writing skills. The Curtin Business School (CBS) has the only fully faculty-based student learning support centre at Curtin University in Western Australia. Called the CBS Communication Skills Centre (CSC) it…

  9. Education as a "Risky Business": Theorising Student and Teacher Learning in Complex Times

    Science.gov (United States)

    Hardy, Ian

    2015-01-01

    This paper employs sociological literature on risk and the commodification of education to explain current schooling practices in a context of increased concerns about students' behaviour and results on standardised tests of achievement. Drawing upon teacher and student learning practices in three school sites in south-east Queensland, Australia,…

  10. Iterative Learning Control design for uncertain and time-windowed systems

    NARCIS (Netherlands)

    Wijdeven, van de J.J.M.

    2008-01-01

    Iterative Learning Control (ILC) is a control strategy capable of dramatically increasing the performance of systems that perform batch repetitive tasks. This performance improvement is achieved by iteratively updating the command signal, using measured error data from previous trials, i.e., by

  11. "Scaffolding" of Action Learning within a Part-Time Management Development Module

    Science.gov (United States)

    Joesbury, Mark

    2015-01-01

    This Account of Practice describes the introduction and development of action learning within a level 5 module of "Communications at Work" delivered as part of a Business & Technology Education Council (BTEC) Professional Certificate in Management (CMS) between 2005/2006 and 2009/2010. This will commence with a personal narrative and…

  12. Zones of Intervention: Teaching and Learning at All Places and at All Times

    Science.gov (United States)

    Taylor, Jonathan E.; McKissac, Jonathan C.

    2014-01-01

    This article identifies four distinct zones in which workplace problems can be addressed through education and training. These zones enable educators to address workplace learning more widely and broadly. Very often, problems arising in the workplace are dealt with through training in the classroom, but other options exist. The theoretical…

  13. It Takes Time and Experience to Learn How to Interpret Gaze in Mentalistic Terms

    Science.gov (United States)

    Leavens, David A.

    2006-01-01

    What capabilities are required for an organism to evince an "explicit" understanding of gaze as a mentalistic phenomenon? One possibility is that mentalistic interpretations of gaze, like concepts of unseen, supernatural beings, are culturally-specific concepts, acquired through cultural learning. These abstract concepts may either require a…

  14. Problem-Based Learning, Scaffolding, and Coaching: Improving Student Outcomes through Structured Group Time

    Science.gov (United States)

    Murray, Lynn M.

    2012-01-01

    Live-client projects are increasingly used in marketing coursework. However, students, instructors, and clients are often disappointed by the results. This paper reports an approach drawn from the problem-based learning, scaffolding, and team formation and coaching literatures that uses favor of a series of workshops designed to guide students in…

  15. Changes in performance over time while learning to use a myoelectric prosthesis

    NARCIS (Netherlands)

    Bouwsema, Hanneke; van der Sluis, Corry K.; Bongers, Raoul M.

    2014-01-01

    Background: Training increases the functional use of an upper limb prosthesis, but little is known about how people learn to use their prosthesis. The aim of this study was to describe the changes in performance with an upper limb myoelectric prosthesis during practice. The results provide a basis

  16. A Measure of Student Involvement in Learning: Time-on-Task.

    Science.gov (United States)

    Anderson, Lorin W.

    The importance of appropriate task relevant behaviors as a necessary condition for school learning has long been noted. This paper suggests a multiple measure of one set of student classroom behaviors, presents a brief theoretical basis for the measure, provides some empirical support for the use of the measure, and indicates some educational…

  17. An Overview on Evaluation of E-Learning/Training Response Time Considering Artificial Neural Networks Modeling

    Science.gov (United States)

    Mustafa, Hassan M. H.; Tourkia, Fadhel Ben; Ramadan, Ramadan Mohamed

    2017-01-01

    The objective of this piece of research is to interpret and investigate systematically an observed brain functional phenomenon which is associated with proceeding of e-learning processes. More specifically, this work addresses an interesting and challenging educational issue concerned with dynamical evaluation of elearning performance considering…

  18. It's Story Time!: Exploring the Potential of Multimodality in Oral Storytelling to Support Children's Vocabulary Learning

    Science.gov (United States)

    Lwin, Soe Marlar

    2016-01-01

    Although many studies have been done on the benefits of parent/teacher-child interactions during shared storybook reading or read'aloud sessions, very few have examined the potential of professional storytellers' oral discourse to support children's vocabulary learning. In those storytelling sessions conducted by professional storytellers, the…

  19. From "When the Bell Say" to "In the Almost Dark": Learning from Children's Concepts of Time.

    Science.gov (United States)

    Smith, M. Lynne

    Collaborative, participatory, and empowerment research (D. Fetterman and others, 1996) presents particular challenges and special rewards for evaluators working with children. This paper describes some of what one evaluator learned from the process of designing and using in data collection and analysis a structured interview for children (ages…

  20. Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.

    Science.gov (United States)

    Terunuma, Toshiyuki; Tokui, Aoi; Sakae, Takeji

    2018-03-01

    Robustness to obstacles is the most important factor necessary to achieve accurate tumor tracking without fiducial markers. Some high-density structures, such as bone, are enhanced on X-ray fluoroscopic images, which cause tumor mistracking. Tumor tracking should be performed by controlling "importance recognition": the understanding that soft-tissue is an important tracking feature and bone structure is unimportant. We propose a new real-time tumor-contouring method that uses deep learning with importance recognition control. The novelty of the proposed method is the combination of the devised random overlay method and supervised deep learning to induce the recognition of structures in tumor contouring as important or unimportant. This method can be used for tumor contouring because it uses deep learning to perform image segmentation. Our results from a simulated fluoroscopy model showed accurate tracking of a low-visibility tumor with an error of approximately 1 mm, even if enhanced bone structure acted as an obstacle. A high similarity of approximately 0.95 on the Jaccard index was observed between the segmented and ground truth tumor regions. A short processing time of 25 ms was achieved. The results of this simulated fluoroscopy model support the feasibility of robust real-time tumor contouring with fluoroscopy. Further studies using clinical fluoroscopy are highly anticipated.