International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor

2016-01-01

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems. The International Work-Conferences on Time Series (ITISE) provide a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

Applying ARIMA model for annual volume time series of the Magdalena River

OpenAIRE

Gloria Amaris; Humberto Ávila; Thomas Guerrero

2017-01-01

Context: Climate change effects, human interventions, and river characteristics are factors that increase the risk on the population and the water resources. However, negative impacts such as flooding, and river droughts may be previously identified using appropriate numerical tools. Objectives: The annual volume (Millions of m3/year) time series of the Magdalena River was analyzed by an ARIMA model, using the historical time series of the Calamar station (Instituto de Hidrología, Meteoro...

High-Performance Home Technologies: Solar Thermal & Photovoltaic Systems; Volume 6 Building America Best Practices Series

Energy Technology Data Exchange (ETDEWEB)

None

2007-06-01

The sixth volume of the Building America Best Practices Series presents information that is useful throughout the U.S. for enhancing the energy efficiency practices in the specific climate zones that are presented in each of the volumes.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis.

Science.gov (United States)

Mohammed, Emad A; Naugler, Christopher

2017-01-01

Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. This tool will allow anyone with historic test volume data to model future demand.

Open-source software for demand forecasting of clinical laboratory test volumes using time-series analysis

Directory of Open Access Journals (Sweden)

Emad A Mohammed

2017-01-01

Full Text Available Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand.

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor; Valenzuela, Olga

2017-01-01

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting.  It focuses on interdisciplinary and multidisciplinary rese arch encompassing the disciplines of comput...

Regional hippocampal volumes and development predict learning and memory.

Science.gov (United States)

Tamnes, Christian K; Walhovd, Kristine B; Engvig, Andreas; Grydeland, Håkon; Krogsrud, Stine K; Østby, Ylva; Holland, Dominic; Dale, Anders M; Fjell, Anders M

2014-01-01

The hippocampus is an anatomically and functionally heterogeneous structure, but longitudinal studies of its regional development are scarce and it is not known whether protracted maturation of the hippocampus in adolescence is related to memory development. First, we investigated hippocampal subfield development using 170 longitudinally acquired brain magnetic resonance imaging scans from 85 participants aged 8-21 years. Hippocampal subfield volumes were estimated by the use of automated segmentation of 7 subfields, including the cornu ammonis (CA) sectors and the dentate gyrus (DG), while longitudinal subfield volumetric change was quantified using a nonlinear registration procedure. Second, associations between subfield volumes and change and verbal learning/memory across multiple retention intervals (5 min, 30 min and 1 week) were tested. It was hypothesized that short and intermediate memory would be more closely related to CA2-3/CA4-DG and extended, remote memory to CA1. Change rates were significantly different across hippocampal subfields, but nearly all subfields showed significant volume decreases over time throughout adolescence. Several subfield volumes were larger in the right hemisphere and in males, while for change rates there were no hemisphere or sex differences. Partly in support of the hypotheses, greater volume of CA1 and CA2-3 was related to recall and retention after an extended delay, while longitudinal reduction of CA2-3 and CA4-DG was related to learning. This suggests continued regional development of the hippocampus across adolescence and that volume and volume change in specific subfields differentially predict verbal learning and memory over different retention intervals, but future high-resolution studies are called for. © 2014 S. Karger AG, Basel.

Grammar-based feature generation for time-series prediction

CERN Document Server

De Silva, Anthony Mihirana

2015-01-01

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

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.

Analyzing temporal changes in maximum runoff volume series of the Danube River

International Nuclear Information System (INIS)

Halmova, Dana; Pekarova, Pavla; Onderka, Milan; Pekar, Jan

2008-01-01

Several hypotheses claim that more extremes in climatic and hydrologic phenomena are anticipated. In order to verify such hypotheses it is inevitable to examine the past periods by thoroughly analyzing historical data. In the present study, the annual maximum runoff volumes with t-day durations were calculated for a 130-year series of mean daily discharge of Danube River at Bratislava gauge (Slovakia). Statistical methods were used to clarify how the maximum runoff volumes of the Danube River changed over two historical periods (1876-1940 and 1941-2005). The conclusion is that the runoff volume regime during floods has not changed significantly during the last 130 years.

Learning Organic Chemistry Through Natural Products

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 2. Learning Organic Chemistry Through Natural Products Determination of Absolute Stereochemistry. N R Krishnaswamy. Series Article Volume 1 Issue 2 February 1996 pp 40-46 ...

Learning Organic Chemistry Through Natural Products

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 10. Learning Organic Chemistry Through Natural Products Architectural Designs in Molecular Constructions. N R Krishnaswamy. Series Article Volume 1 Issue 10 October 1996 pp 37-43 ...

Learning Organic Chemistry Through Natural Products

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 7. Learning Organic Chemistry Through Natural engine Products - Structure and Biological Functions. N R Krishnaswamy. Series Article Volume 1 Issue 7 July 1996 pp 23-30 ...

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.

Learning Organic Chemistry Through Natural Products

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 5. Learning Organic Chemistry Through Natural Products From Molecular and Electronic Structures to Reactivity. N R Krishnaswamy. Series Article Volume 1 Issue 5 May 1996 pp 12-18 ...

Building Sustainable Leadership Capacity. The Soul of Educational Leadership Series. Volume 5

Science.gov (United States)

Blankstein, Alan M.; Houston, Paul D.; Cole, Robert W.

2009-01-01

Today's rapidly changing schools and educational trends present administrators and school leaders with unique challenges. This fifth volume in the "Soul of Educational Leadership" series offers inspiring articles that examine how to sustain the achievements of school communities while building shared leadership to carry on the work of school…

Reading Right: A Text for Reading, Volume 1. English for Special Purposes Series: Nursing Aide.

Science.gov (United States)

Hubbard, Katherine E.

This is the first of four volumes devoted to reading instruction, in a series of materials for teaching English as a second language to adult nursing aide students. The two units included deal principally with survival skills. The first unit is an introduction to Mahimahi Island, the imaginary quasi-Hawaiian locale used throughout the series. The…

National Low-Level Waste Management Program radionuclide report series. Volume 2, Niobium-94

International Nuclear Information System (INIS)

Adams, J.P.; Carboneau, M.L.

1995-04-01

The Purpose of the National Low-Level Waste Management Program Radionuclide Report Series is to provide information to, state representatives and developers of low-level radioactive waste disposal facilities about the radiological chemical, and physical characteristics of selected radionuclides and their behavior in the low-level radioactive waste disposal facility environment. Extensive surveys of available literature provided information used to produce this series of reports and an introductory report. This report is Volume 11 of the series. It outlines the basic radiological, chemical, and physical characteristics of niobium-94, waste types and forms that contain it, and its behavior in environmental media such as soils, plants, groundwater, air, animals and the human body

Constellation Program: Lessons Learned. Volume 1; Executive Summary

Science.gov (United States)

Rhatigan, Jennifer L. (Editor)

2011-01-01

This document (Volume I) provides an executive summary of the lessons learned from the Constellation Program. A companion Volume II provides more detailed analyses for those seeking further insight and information. In this volume, Section 1.0 introduces the approach in preparing and organizing the content to enable rapid assimilation of the lessons. Section 2.0 describes the contextual framework in which the Constellation Program was formulated and functioned that is necessary to understand most of the lessons. Context of a former program may seem irrelevant in the heady days of new program formulation. However, readers should take some time to understand the context. Many of the lessons would be different in a different context, so the reader should reflect on the similarities and differences in his or her current circumstances. Section 3.0 summarizes key findings developed from the significant lessons learned at the program level that appear in Section 4.0. Readers can use the key findings in Section 3.0 to peruse for particular topics, and will find more supporting detail and analyses in Section 4.0 in a topical format. Appendix A contains a white paper describing the Constellation Program formulation that may be of use to readers wanting more context or background information. The reader will no doubt recognize some very similar themes from previous lessons learned, blue-ribbon committee reviews, National Academy reviews, and advisory panel reviews for this and other large-scale human spaceflight programs; including Apollo, Space Shuttle, Shuttle/Mir, and the ISS. This could represent an inability to learn lessons from previous generations; however, it is more likely that similar challenges persist in the Agency structure and approach to program formulation, budget advocacy, and management. Perhaps the greatest value of these Constellation lessons learned can be found in viewing them in context with these previous efforts to guide and advise the Agency and its

Data Mining Foundations and Intelligent Paradigms Volume 2 Statistical, Bayesian, Time Series and other Theoretical Aspects

CERN Document Server

Jain, Lakhmi

2012-01-01

Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 2 of this three volume series, we have brought together contributions from some of the most prestigious researchers in theoretical data mining. Each of the chapters is self contained. Statisticians and applied scientists/ engineers will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in data mining.

Lasers. Technology Learning Activity. Teacher Edition. Technology Education Series.

Science.gov (United States)

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This document contains the materials required for presenting an 8-day competency-based technology learning activity (TLA) designed to introduce students in grades 6-10 to advances and career opportunities in the field of laser technology. The guide uses a series of hands-on exploratory experiences into which activities to help students develop…

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.

Researching into Learning Resources in Colleges and Universities. The Practical Research Series.

Science.gov (United States)

Higgins, Chris; Reading, Judy; Taylor, Paul

This book examines issues and methods for conducting research into the educational resource environment in colleges and universities. That environment is defined as whatever is used to facilitate the learning process, including learning space, support staff, and teaching staff. Chapter 1 is an introduction to the series and lays out the process of…

Glacier Volume Change Estimation Using Time Series of Improved Aster Dems

Science.gov (United States)

Girod, Luc; Nuth, Christopher; Kääb, Andreas

2016-06-01

Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be

National low-level waste management program radionuclide report series, Volume 15: Uranium-238

International Nuclear Information System (INIS)

Adams, J.P.

1995-09-01

This report, Volume 15 of the National Low-Level Waste Management Program Radionuclide Report Series, discusses the radiological and chemical characteristics of uranium-238 ( 238 U). The purpose of the National Low-Level Waste Management Program Radionuclide Report Series is to provide information to state representatives and developers of low-level radioactive waste disposal facilities about the radiological, chemical, and physical characteristics of selected radionuclides and their behavior in the waste disposal facility environment. This report also includes discussions about waste types and forms in which 238 U can be found, and 238 U behavior in the environment and in the human body

National low-level waste management program radionuclide report series, Volume 14: Americium-241

International Nuclear Information System (INIS)

Winberg, M.R.; Garcia, R.S.

1995-09-01

This report, Volume 14 of the National Low-Level Waste Management Program Radionuclide Report Series, discusses the radiological and chemical characteristics of americium-241 ( 241 Am). This report also includes discussions about waste types and forms in which 241 Am can be found and 241 Am behavior in the environment and in the human body

National Low-Level Waste Management Program radionuclide report series. Volume 13, Curium-242

International Nuclear Information System (INIS)

Adams, J.P.

1995-08-01

This report, Volume 13 of the National Low-Level Waste Management Program Radionuclide Report Series, discusses the radiological and chemical characteristics of curium-242 ( 242 Cm). This report also includes discussions about waste types and forms in which 242 Cm can be found and 242 Cm behavior in the environment and in the human body

Learning Organic Chemistry Through Natural Products A Practical ...

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 9. Learning Organic Chemistry Through Natural Products A Practical Approach. N R Krishnaswamy. Series Article Volume 1 Issue 9 September 1996 pp 25-33. Fulltext. Click here to view fulltext PDF. Permanent link:

National Low-Level Waste Management Program radionuclide report series. Volume 13, Curium-242

Energy Technology Data Exchange (ETDEWEB)

Adams, J.P.

1995-08-01

This report, Volume 13 of the National Low-Level Waste Management Program Radionuclide Report Series, discusses the radiological and chemical characteristics of curium-242 ({sup 242}Cm). This report also includes discussions about waste types and forms in which {sup 242}Cm can be found and {sup 242}Cm behavior in the environment and in the human body.

GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

Directory of Open Access Journals (Sweden)

L. Girod

2016-06-01

Full Text Available Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER system embarked on the Terra (EOS AM-1 satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter

National Low-Level Waste Management Program Radionuclide Report Series, Volume 17: Plutonium-239

International Nuclear Information System (INIS)

Adams, J.P.; Carboneau, M.L.

1999-01-01

This report, Volume 17 of the National Low-Level Waste Management Program Radionuclide Report Series, discusses the radiological and chemical characteristics of plutonium-239 (Pu-239). This report also discusses waste types and forms in which Pu-239 can be found, waste and disposal information on Pu-239, and Pu-239 behavior in the environment and in the human body

National Low-Level Waste Management Program Radionuclide Report Series, Volume 17: Plutonium-239

Energy Technology Data Exchange (ETDEWEB)

J. P. Adams; M. L. Carboneau

1999-03-01

This report, Volume 17 of the National Low-Level Waste Management Program Radionuclide Report Series, discusses the radiological and chemical characteristics of plutonium-239 (Pu-239). This report also discusses waste types and forms in which Pu-239 can be found, waste and disposal information on Pu-239, and Pu-239 behavior in the environment and in the human body.

Transition of Youth with Disabilities to Postsecondary Education. DADD Prism Series. Volume 5

Science.gov (United States)

Stodden, Robert, Ed.; Zucker, Stanley, Ed.

2004-01-01

This volume presents an organized collection of peer-reviewed articles focused upon issues faced by young persons with intellectual disabilities and those who support them as they prepare for and transition to postsecondary education and other life-long learning activities. The reader is provided with an overview of this field of work, a range of…

Blending Learning: The Evolution of Online and Face-to-Face Education from 2008-2015. Promising Practices in Blended and Online Learning Series

Science.gov (United States)

Powell, Allison; Watson, John; Staley, Patrick; Patrick, Susan; Horn, Michael; Fetzer, Leslie; Hibbard, Laura; Oglesby, Jonathan; Verma, Sue

2015-01-01

In 2008, the International Association for K-12 Online Learning (iNACOL) produced a series of papers documenting promising practices identified throughout the field of K-12 online learning. Since then, we have witnessed a tremendous acceleration of transformative policy and practice driving personalized learning in the K-12 education space. State,…

Mathematics education and students with learning disabilities: introduction to the special series.

Science.gov (United States)

Rivera, D P

1997-01-01

influences on the field of mathematics education. Reform efforts have shaped the field significantly since the 1950s, contributing to the curriculum offered in mathematics textbooks and the pedagogical practices taught in higher education courses. Mathematics educators continue to search for a better understanding of how children learn mathematics; this process is shaped by the prevailing theoretical orientations and research methodologies. This special series in mathematics special education provides readers with information about the characteristics of students with mathematics learning disabilities, assessment procedures, mathematics programming, teacher preparation, and future directions for the field. The series originated as a result of discussions with Dr. Lee Wiederholt and Dr. Judith K. Voress, who saw a need for the compilation of recent research and best practices in mathematics special education. I thank them for their support of and thoughtful insights about the development of this series. I also appreciate the support of Dr. George Hynd and his editorial assistant, Kathryn Black, in finalizing the details for publication. Finally, I am most appreciative of the authors' contributions to this series; their work continues to significantly influence the development of the field of mathematics special education and programming for students with mathematics learning disabilities.

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.

Energy landscapes for a machine learning application to series data

Energy Technology Data Exchange (ETDEWEB)

Ballard, Andrew J.; Stevenson, Jacob D.; Das, Ritankar; Wales, David J., E-mail: dw34@cam.ac.uk [University Chemical Laboratories, Lensfield Road, Cambridge CB2 1EW (United Kingdom)

2016-03-28

Methods developed to explore and characterise potential energy landscapes are applied to the corresponding landscapes obtained from optimisation of a cost function in machine learning. We consider neural network predictions for the outcome of local geometry optimisation in a triatomic cluster, where four distinct local minima exist. The accuracy of the predictions is compared for fits using data from single and multiple points in the series of atomic configurations resulting from local geometry optimisation and for alternative neural networks. The machine learning solution landscapes are visualised using disconnectivity graphs, and signatures in the effective heat capacity are analysed in terms of distributions of local minima and their properties.

Energy landscapes for a machine learning application to series data

International Nuclear Information System (INIS)

Ballard, Andrew J.; Stevenson, Jacob D.; Das, Ritankar; Wales, David J.

2016-01-01

Methods developed to explore and characterise potential energy landscapes are applied to the corresponding landscapes obtained from optimisation of a cost function in machine learning. We consider neural network predictions for the outcome of local geometry optimisation in a triatomic cluster, where four distinct local minima exist. The accuracy of the predictions is compared for fits using data from single and multiple points in the series of atomic configurations resulting from local geometry optimisation and for alternative neural networks. The machine learning solution landscapes are visualised using disconnectivity graphs, and signatures in the effective heat capacity are analysed in terms of distributions of local minima and their properties.

Transportation life cycle assessment (LCA) synthesis : life cycle assessment learning module series.

Science.gov (United States)

2015-03-12

The Life Cycle Assessment Learning Module Series is a set of narrated, self-advancing slideshows on : various topics related to environmental life cycle assessment (LCA). This research project produced the first 27 of such modules, which : are freely...

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

Learning musculoskeletal imaging

Energy Technology Data Exchange (ETDEWEB)

Vilanova, Joan C. (eds.) [Girona Univ. (Spain). Clinica Girona; Ribes, Ramon

2010-07-01

This introduction to musculoskeletal imaging is a further volume in the Learning Imaging series. Written in a user-friendly format, it takes into account that musculoskeletal radiology is a subspecialty which has widely expanded its scope and imaging capabilities with the advent of ultrasound, MRI, multidetector CT, and PET. The book is divided into ten sections covering: infection and arthritis, tumors, tendons and muscles, bone marrow, spine, shoulder, elbow, hand and wrist, hip and pelvis, knee, and ankle and foot. Each chapter is presented with an introduction and ten case studies with illustrations and comments from anatomical, physiopathological and radiological standpoints along with bibliographic recommendations. Learning Imaging is a unique case-based series for those in professional education in general and for physicians in particular. (orig.)

Learning musculoskeletal imaging

International Nuclear Information System (INIS)

Vilanova, Joan C.; Ribes, Ramon

2010-01-01

This introduction to musculoskeletal imaging is a further volume in the Learning Imaging series. Written in a user-friendly format, it takes into account that musculoskeletal radiology is a subspecialty which has widely expanded its scope and imaging capabilities with the advent of ultrasound, MRI, multidetector CT, and PET. The book is divided into ten sections covering: infection and arthritis, tumors, tendons and muscles, bone marrow, spine, shoulder, elbow, hand and wrist, hip and pelvis, knee, and ankle and foot. Each chapter is presented with an introduction and ten case studies with illustrations and comments from anatomical, physiopathological and radiological standpoints along with bibliographic recommendations. Learning Imaging is a unique case-based series for those in professional education in general and for physicians in particular. (orig.)

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

Characteristic research on Hong Kong "I learned" series computer textbooks

Science.gov (United States)

Hu, Jinyan; Liu, Zhongxia; Li, Yuanyuan; Lu, Jianheng; Zhang, Lili

2011-06-01

Currently, the construction of information technology textbooks in the primary and middle schools is an important content of the information technology curriculum reform. The article expect to have any inspire and reference on inland China school information technology teaching material construction and development through the analyzing and refining the characteristics of the Hong Kong quality textbook series - "I learn . elementary school computer cognitive curriculum".

ORGANIZATIONS AND STRATEGIES IN ASTRONOMY VOLUME 7

CERN Document Server

HECK, ANDRÉ

2006-01-01

This book is the seventh volume under the title Organizations and Strategies in Astronomy (OSA). The OSA series covers a large range of fields and themes: in practice, one could say that all aspects of astronomy-related life and environment are considered in the spirit of sharing specific expertise and lessons learned. The chapters of this book are dealing with socio-dynamical aspects of the astronomy (and related space sciences) community: characteristics of organizations, strategies for development, operational techniques, observing practicalities, journal and magazine profiles, public outreach, publication studies, relationships with the media, research communication, series of conferences, evaluation and selection procedures, research indicators, national specificities, contemporary history, and so on. The experts contributing to this volume have done their best to write in a way understandable to readers not necessarily hyperspecialized in astronomy while providing specific detailed information and somet...

Regional Patterns of Ethnicity in Nova Scotia: A Geographical Study. Ethnic Heritage Series, Volume VI.

Science.gov (United States)

Millward, Hugh A.

In this sixth volume of the Ethnic Heritage Series, the pattern of ethnicity in Nova Scotia (Canada) is examined by deriving indices of diversity for counties and larger towns. The historical development of ethnic patterns from 1767 to 1971 and recent changes in the ethnic pattern are discussed. Ethnic origin data is mapped for 1871 and 1971 and…

The Implementation of Cumulative Learning Theory in Calculating Triangular Prism and Tube Volumes

Science.gov (United States)

Muklis, M.; Abidin, C.; Pamungkas, M. D.; Masriyah

2018-01-01

This study aims at describing the application of cumulative learning theory in calculating the volume of a triangular prism and a tube as well as revealing the students’ responses toward the learning. The research method used was descriptive qualitative with elementary school students as the subjects of the research. Data obtained through observation, field notes, questionnaire, tests, and interviews. The results from the application of cumulative learning theory obtained positive students’ responses in following the learning and students’ learning outcomes was dominantly above the average. This showed that cumulative learning could be used as a reference to be implemented in learning, so as to improve the students’ achievement.

The "Tse Tsa Watle" Speaker Series: An Example of Ensemble Leadership and Generative Adult Learning

Science.gov (United States)

McKendry, Virginia

2017-01-01

This chapter examines an Indigenous speaker series formed to foster intercultural partnerships at a Canadian university. Using ensemble leadership and generative learning theories to make sense of the project, the author argues that ensemble leadership is key to designing the generative learning adult learners need in an era of ambiguity.

Using Technology to Improve Student Learning. NCREL Viewpoints, Volume 12

Science.gov (United States)

Gahala, Jan, Ed.

2004-01-01

"Viewpoints" is a multimedia package containing two audio CDs and a short, informative booklet. This volume of "Viewpoints" focuses on how technology can help improve student learning. The audio CDs provide the voices, or viewpoints, of various leaders from the education field who work closely with technology issues. Their…

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.

Forecasting of exported volume for brazilian fruits by time series analysis: an arima/garch approach

Directory of Open Access Journals (Sweden)

Abdinardo Moreira Barreto de Oliveira

2015-06-01

Full Text Available The aim of this paper was to offer econometric forecasting models to the Brazilian exported volume fruits, with a view to assisting the planning and production control, also motivated by the existence of a few published papers dealing with this issue. In this sense, it was used the ARIMA/GARCH models, considering, likewise, the occurrence of a multiplicative stochastic seasonality in these series. They were collected 300 observations of exported net weight (kg between Jan/1989 and Dec/2013 of the following fruits: pineapple, banana, orange, lemon, apple, papaya, mango, watermelon, melon and grape, which selection criteria was its importance in the exported basket fruit, because they represented 97% of total received dollars, and 99% of total volume sold in 2010, of a population about 28 kinds of exported fruits. The results showed that it was not only observed the existence of a 12 month multiplicative seasonality in banana and mango. On the other hand, they were identified two fruits groups: (1 those which are continuously exported, and (2 those which have export peaks. On the quality of the models, they were considered satisfactory for six of the ten fruits analyzed. On the volatility, it was seen a high persistence in banana and papaya series, pointing to the existence of a structural break in time series, which could be linked to the economic crises happened in the last 17 years.

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...

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.

Reasoning with Paper and Pencil: The Role of Inscriptions in Student Learning of Geometric Series

Science.gov (United States)

Carlsen, Martin

2009-01-01

The purpose of this article is to analyse how students use inscriptions as tools for thinking and learning in mathematical problem-solving activities. The empirical context is that of learning about geometric series in a small group setting. What has been analysed is how students made use of inscriptions, self-made as well as those provided by…

Exploring the Digital Library: A Guide for Online Teaching and Learning

Science.gov (United States)

Johnson, Kay; Magusin, Elaine

2005-01-01

This book, which is a volume in The Jossey-Bass Online Teaching and Learning series, addresses the key issue of library services for faculty and their students in the online learning environment. Written by librarians at Athabasca University, a leading institution in distance education, this book shows how faculty can effectively use digital…

The Department of Energy's Rocky Flats Plant: A guide to record series useful for health-related research. Volume 5: Waste management

International Nuclear Information System (INIS)

1995-01-01

This is the fifth in a series of seven volumes which constitute a guide to records of the Rocky Flats Plant useful for conducting health-related research. The primary purpose of Volume 5 is to describe record series pertaining to waste management activities at the Department of Energy's (DOE) Rocky Flats Plant, now named the Rocky Flats Environmental Technology Site, near Denver, Colorado. History Associates Incorporated (HAI) prepared this guide as part of its work as the support services contractor for DOE's Epidemiologic Records Inventory Project. This introduction briefly describes the Epidemiologic Records Inventory Project and HAI's role in the project, provides a history of waste management practices at Rocky Flats, and identifies organizations contributing to waste management policies and activities. Other topics include the scope and arrangement of this volume and the organization to contact for access to these records

Numbers and Measuring, Learning With TOR: MINNEMAST Coordinated Mathematics - Science Series, Unit 16.

Science.gov (United States)

Vogt, Elaine E., Ed.

This volume is the sixteenth in a series of 29 coordinated MINNEMAST units in mathematics and science for kindergarten and the primary grades. Intended for use by second-grade teachers, this unit guide provides a summary and overview of the unit, a list of materials needed, and descriptions of five groups of lessons. The purposes and procedures…

Time series analysis time series analysis methods and applications

CERN Document Server

Rao, Tata Subba; Rao, C R

2012-01-01

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respect...

Effectiveness of Cooperative Learning Instructional Tools With Predict-Observe-Explain Strategy on the Topic of Cuboid and Cube Volume

Science.gov (United States)

Nurhuda; Lukito, A.; Masriyah

2018-01-01

This study aims to develop instructional tools and implement it to see the effectiveness. The method used in this research referred to Designing Effective Instruction. Experimental research with two-group pretest-posttest design method was conducted. The instructional tools have been developed is cooperative learning model with predict-observe-explain strategy on the topic of cuboid and cube volume which consist of lesson plans, POE tasks, and Tests. Instructional tools were of good quality by criteria of validity, practicality, and effectiveness. These instructional tools was very effective for teaching the volume of cuboid and cube. Cooperative instructional tool with predict-observe-explain (POE) strategy was good of quality because the teacher was easy to implement the steps of learning, students easy to understand the material and students’ learning outcomes completed classically. Learning by using this instructional tool was effective because learning activities were appropriate and students were very active. Students’ learning outcomes were completed classically and better than conventional learning. This study produced a good instructional tool and effectively used in learning. Therefore, these instructional tools can be used as an alternative to teach volume of cuboid and cube topics.

BOX MEDIA MODEL THROUGH THE USE OF CONTEXTUAL UNDERSTANDING TO IMPROVE STUDENT LEARNING CONCEPTS IN VOLUME BEAM

Directory of Open Access Journals (Sweden)

Dede Rohaeni

2016-05-01

Full Text Available Abstract. This research is motivated Cilengkrang Elementary School fifth grade students in the learning of the beam volume is still experiencing difficulties. This happens because the learning process that takes place is conventional. Learning by applying a contextual model chosen researchers by reason students will know if the learning is associated with the real world of students. The method used in this research is a classroom action research methods to the design of the research procedure refers to the spiral model Kemmis and MC. Tujuanpenelitianini is to obtain an overview of the planning, implementation and improvement of students' understanding of the results of the application of the concept model of contextual learning in the classroom beam volume V Elementary School Cilengkrang. The method used in this research is a classroom action research methods to the design of the research procedure refers to the spiral model Kemmis and MC. Taggart. Based on the implementation of the actions performed by three cycles, as a whole has shown an increase from the initial data, both process and outcomes of learning. So that the application of contextual models can enhance students' understanding of class V SDN Cilengkrang Northern District of Sumedang Sumedang district of the concept of the beam volume.   Keywords: Contextual Model, Mathematics, Mathematics Learning Objectives     Abstrak. Penelitian ini dilatarbelakangi siswa kelas V SDN Cilengkrang dalam pembelajaran volume balok masih mengalami kesulitan. Ini terjadi karena proses pembelajaran yang berlangsung bersifat konvensional. Pembelajaran dengan menerapkan model kontekstual dipilih peneliti dengan alasan siswa akan paham jika pembelajaran dikaitkan dengan dunia nyata siswa. Metode penelitian yang digunakan dalam penelitian ini adalah metode penelitian tindakan kelas dengan rancangan prosedur penelitiannya mengacu pada model spiral Kemmis dan MC. Tujuanpenelitianini yaitu untuk memperoleh

Methodologies and intelligent systems for technology enhanced learning

CERN Document Server

Gennari, Rosella; Vitorini, Pierpaolo; Vicari, Rosa; Prieta, Fernando

2014-01-01

This volume presents recent research on Methodologies and Intelligent Systems for Technology Enhanced Learning. It contains the contributions of ebuTEL 2013 conference which took place in Trento, Italy, on September, 16th 2013 and of mis4TEL 2014 conference, which took take place in Salamanca, Spain, on September, 4th-6th 2014 This conference series are an open forum for discussing intelligent systems for Technology Enhanced Learning and empirical methodologies for its design or evaluation.

Impaired Verbal Learning Is Associated with Larger Caudate Volumes in Early Onset Schizophrenia Spectrum Disorders.

Directory of Open Access Journals (Sweden)

Monica Juuhl-Langseth

Full Text Available Both brain structural abnormalities and neurocognitive impairments are core features of schizophrenia. We have previously reported enlargements in subcortical brain structure volumes and impairment of neurocognitive functioning as measured by the MATRICS Cognitive Consensus Battery (MCCB in early onset schizophrenia spectrum disorders (EOS. To our knowledge, no previous study has investigated whether neurocognitive performance and volumetric abnormalities in subcortical brain structures are related in EOS.Twenty-four patients with EOS and 33 healthy controls (HC were included in the study. Relationships between the caudate nucleus, the lateral and fourth ventricles volumes and neurocognitive performance were investigated with multivariate linear regression analyses. Intracranial volume, age, antipsychotic medication and IQ were included as independent predictor-variables.The caudate volume was negatively correlated with verbal learning performance uniquely in the EOS group (r=-.454, p=.034. There were comparable positive correlations between the lateral ventricular volume and the processing speed, attention and reasoning and problem solving domains for both the EOS patients and the healthy controls. Antipsychotic medication was related to ventricular enlargements, but did not affect the brain structure-function relationship.Enlargement of the caudate volume was related to poorer verbal learning performance in patients with EOS. Despite a 32% enlargement of the lateral ventricles in the EOS group, associations to processing speed, attention and reasoning and problem solving were similar for both the EOS and the HC groups.

Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling.

Science.gov (United States)

Nguyen, Hien D; Ullmann, Jeremy F P; McLachlan, Geoffrey J; Voleti, Venkatakaushik; Li, Wenze; Hillman, Elizabeth M C; Reutens, David C; Janke, Andrew L

2018-02-01

Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.

Science Library of Test Items. Volume Eight. Mastery Testing Program. Series 3 & 4 Supplements to Introduction and Manual.

Science.gov (United States)

New South Wales Dept. of Education, Sydney (Australia).

Continuing a series of short tests aimed at measuring student mastery of specific skills in the natural sciences, this supplementary volume includes teachers' notes, a users' guide and inspection copies of test items 27 to 50. Answer keys and test scoring statistics are provided. The items are designed for grades 7 through 10, and a list of the…

Innovative Language Teaching and Learning at University: Enhancing Employability

Science.gov (United States)

Álvarez-Mayo, Carmen, Ed.; Gallagher-Brett, Angela, Ed.; Michel, Franck, Ed.

2017-01-01

This second volume in this series of papers dedicated to innovative language teaching and learning at university focuses on enhancing employability. Throughout the book, which includes a selection of 14 peer-reviewed and edited short papers, authors share good practices drawing on research; reflect on their experience to promote student…

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.

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)

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.

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.

The Department of Energy`s Rocky Flats Plant: A guide to record series useful for health-related research. Volume VII. Employee occupational exposure and health

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-08-01

This is the seventh in a series of seven volumes which constitute a guide to records of the Rocky Flats Plant useful for conducting health-related research. The primary purpose of Volume VII is to describe record series pertaining to employee occupational exposure and health at the Department of Energy`s (DOE) Rocky Flats Plant, now named the Rocky Flats Environmental Technology Site, near Denver, Colorado. History Associates Incorporated (HAI) prepared this guide as part of its work as the support services contractor for DOE`s Epidemiologic Records Inventory Project. This introduction briefly describes the Epidemiologic Records Inventory Project and HAI`s role in the project, provides a history of occupational exposure monitoring and health practices at Rocky Flats, and identifies organizations contributing to occupational exposure monitoring and health policies and activities. Other topics include the scope and arrangement of the guide and the organization to contact for access to these records. Comprehensive introductory and background information is available in Volume 1. Other volumes in the guide pertain to administrative and general subjects, facilities and equipment, production and materials handling, environmental and workplace monitoring, and waste management. In addition, HAI has produced a subject-specific guide, titled The September 1957 Rocky Flats Fire: A Guide to Record Series of the Department of Energy and Its Contractors, which researchers should consult for further information about records related to this incident.

Aerobic fitness relates to learning on a virtual morris water task and hippocampal volume in adolescents

Science.gov (United States)

Herting, Megan M.; Nagel, Bonnie J.

2012-01-01

In rodents, exercise increases hippocampal neurogenesis and allows for better learning and memory performance on water maze tasks. While exercise has also been shown to be beneficial for the brain and behavior in humans, no study has examined how exercise impacts spatial learning using a directly translational water maze task, or if these relationships exist during adolescence – a developmental period which the animal literature has shown to be especially vulnerable to exercise effects. In this study, we investigated the influence of aerobic fitness on hippocampal size and subsequent learning and memory, including visuospatial memory using a human analogue of the Morris Water Task, in 34 adolescents. Results showed that higher aerobic fitness predicted better learning on the virtual Morris Water Task and larger hippocampal volumes. No relationship between virtual Morris Water Task memory recall and aerobic fitness was detected. Aerobic fitness, however, did not relate to global brain volume, or verbal learning, which might suggest some specificity of the influence of aerobic fitness on the adolescent brain. This study provides a direct translational approach to the existing animal literature on exercise, as well as adds to the sparse research that exists on how aerobic exercise impacts the developing human brain and memory. PMID:22610054

Aerobic fitness relates to learning on a virtual Morris Water Task and hippocampal volume in adolescents.

Science.gov (United States)

Herting, Megan M; Nagel, Bonnie J

2012-08-01

In rodents, exercise increases hippocampal neurogenesis and allows for better learning and memory performance on water maze tasks. While exercise has also been shown to be beneficial for the brain and behavior in humans, no study has examined how exercise impacts spatial learning using a directly translational water maze task, or if these relationships exist during adolescence--a developmental period which the animal literature has shown to be especially vulnerable to exercise effects. In this study, we investigated the influence of aerobic fitness on hippocampal size and subsequent learning and memory, including visuospatial memory using a human analogue of the Morris Water Task, in 34 adolescents. Results showed that higher aerobic fitness predicted better learning on the virtual Morris Water Task and larger hippocampal volumes. No relationship between virtual Morris Water Task memory recall and aerobic fitness was detected. Aerobic fitness, however, did not relate to global brain volume or verbal learning, which might suggest some specificity of the influence of aerobic fitness on the adolescent brain. This study provides a direct translational approach to the existing animal literature on exercise, as well as adds to the sparse research that exists on how aerobic exercise impacts the developing human brain and memory. Published by Elsevier B.V.

Civic Education and Deeper Learning. Deeper Learning Research Series

Science.gov (United States)

Levine, Peter; Kawashima-Ginsberg, Kei

2015-01-01

This report proposes that the turn toward deeper learning in education reform should go hand in hand with a renewed emphasis on high-quality civics education. Not only does deeper learning have great potential to promote civic outcomes and strengthen our democracy but, at the same time, civic education exemplifies deeper learning, in that it…

Organizations and Strategies in Astronomy Volume 6

CERN Document Server

Heck, André

2006-01-01

This book is the sixth volume under the title Organizations and Strategies in Astronomy (OSA). The OSA series is intended to cover a large range of fields and themes. In practice, one could say that all aspects of astronomy-related life and environment are considered in the spirit of sharing specific expertise and lessons learned. The chapters of this book are dealing with socio-dynamical aspects of the astronomy (and related space sciences) community: characteristics of organizations, strategies for development, legal issues, operational techniques, observing practicalities, educational policies, journal and magazine profiles, public outreach, publication studies, relationships with the media, research communication, evaluation and selection procedures, research indicators, national specificities, contemporary history, and so on. The experts contributing to this volume have done their best to write in a way understandable to readers not necessarily hyperspecialized in astronomy while providing specific detai...

What is the fundamental ion-specific series for anions and cations? Ion specificity in standard partial molar volumes of electrolytes and electrostriction in water and non-aqueous solvents.

Science.gov (United States)

Mazzini, Virginia; Craig, Vincent S J

2017-10-01

The importance of electrolyte solutions cannot be overstated. Beyond the ionic strength of electrolyte solutions the specific nature of the ions present is vital in controlling a host of properties. Therefore ion specificity is fundamentally important in physical chemistry, engineering and biology. The observation that the strengths of the effect of ions often follows well established series suggests that a single predictive and quantitative description of specific-ion effects covering a wide range of systems is possible. Such a theory would revolutionise applications of physical chemistry from polymer precipitation to drug design. Current approaches to understanding specific-ion effects involve consideration of the ions themselves, the solvent and relevant interfaces and the interactions between them. Here we investigate the specific-ion effects trends of standard partial molar volumes and electrostrictive volumes of electrolytes in water and eleven non-aqueous solvents. We choose these measures as they relate to bulk properties at infinite dilution, therefore they are the simplest electrolyte systems. This is done to test the hypothesis that the ions alone exhibit a specific-ion effect series that is independent of the solvent and unrelated to surface properties. The specific-ion effects trends of standard partial molar volumes and normalised electrostrictive volumes examined in this work show a fundamental ion-specific series that is reproduced across the solvents, which is the Hofmeister series for anions and the reverse lyotropic series for cations, supporting the hypothesis. This outcome is important in demonstrating that ion specificity is observed at infinite dilution and demonstrates that the complexity observed in the manifestation of specific-ion effects in a very wide range of systems is due to perturbations of solvent, surfaces and concentration on the underlying fundamental series. This knowledge will guide a general understanding of specific

On the series

Indian Academy of Sciences (India)

Home; Journals; Proceedings – Mathematical Sciences; Volume 115; Issue 4. On the Series ∑ k = 1 ∞ ( 3 k k ) − 1 k − n x k. Necdet Batir. Volume 115 Issue 4 November 2005 pp 371- ... Author Affiliations. Necdet Batir1. Department of Mathematics, Faculty of Arts and Sciences, Yüzüncü Yil University, 65080 Van, Turkey ...

75 FR 13259 - NOAA Is Hosting a Series of Informational Webinars for Individuals and Organizations To Learn...

Science.gov (United States)

2010-03-19

... DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration NOAA Is Hosting a Series of Informational Webinars for Individuals and Organizations To Learn About the Proposed NOAA Climate Service AGENCY: Office of Oceanic and Atmospheric Research, National Oceanic and Atmospheric Administration (NOAA...

Unsupervised Learning Through Randomized Algorithms for High-Volume High-Velocity Data (ULTRA-HV).

Energy Technology Data Exchange (ETDEWEB)

Pinar, Ali [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kolda, Tamara G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Carlberg, Kevin Thomas [Wake Forest Univ., Winston-Salem, MA (United States); Ballard, Grey [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mahoney, Michael [Univ. of California, Berkeley, CA (United States)

2018-01-01

Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.

Iterative volume morphing and learning for mobile tumor based on 4DCT.

Science.gov (United States)

Mao, Songan; Wu, Huanmei; Sandison, George; Fang, Shiaofen

2017-02-21

During image-guided cancer radiation treatment, three-dimensional (3D) tumor volumetric information is important for treatment success. However, it is typically not feasible to image a patient's 3D tumor continuously in real time during treatment due to concern over excessive patient radiation dose. We present a new iterative morphing algorithm to predict the real-time 3D tumor volume based on time-resolved computed tomography (4DCT) acquired before treatment. An offline iterative learning process has been designed to derive a target volumetric deformation function from one breathing phase to another. Real-time volumetric prediction is performed to derive the target 3D volume during treatment delivery. The proposed iterative deformable approach for tumor volume morphing and prediction based on 4DCT is innovative because it makes three major contributions: (1) a novel approach to landmark selection on 3D tumor surfaces using a minimum bounding box; (2) an iterative morphing algorithm to generate the 3D tumor volume using mapped landmarks; and (3) an online tumor volume prediction strategy based on previously trained deformation functions utilizing 4DCT. The experimental performance showed that the maximum morphing deviations are 0.27% and 1.25% for original patient data and artificially generated data, which is promising. This newly developed algorithm and implementation will have important applications for treatment planning, dose calculation and treatment validation in cancer radiation treatment.

Sums of Generalized Harmonic Series

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 20; Issue 9. Sums of Generalized Harmonic Series: For Kids from Five to Fifteen. Zurab Silagadze. General Article Volume 20 Issue 9 September 2015 pp 822-843. Fulltext. Click here to view fulltext PDF. Permanent link:

Implementation of machine learning for high-volume manufacturing metrology challenges (Conference Presentation)

Science.gov (United States)

Timoney, Padraig; Kagalwala, Taher; Reis, Edward; Lazkani, Houssam; Hurley, Jonathan; Liu, Haibo; Kang, Charles; Isbester, Paul; Yellai, Naren; Shifrin, Michael; Etzioni, Yoav

2018-03-01

In recent years, the combination of device scaling, complex 3D device architecture and tightening process tolerances have strained the capabilities of optical metrology tools to meet process needs. Two main categories of approaches have been taken to address the evolving process needs. In the first category, new hardware configurations are developed to provide more spectral sensitivity. Most of this category of work will enable next generation optical metrology tools to try to maintain pace with next generation process needs. In the second category, new innovative algorithms have been pursued to increase the value of the existing measurement signal. These algorithms aim to boost sensitivity to the measurement parameter of interest, while reducing the impact of other factors that contribute to signal variability but are not influenced by the process of interest. This paper will evaluate the suitability of machine learning to address high volume manufacturing metrology requirements in both front end of line (FEOL) and back end of line (BEOL) sectors from advanced technology nodes. In the FEOL sector, initial feasibility has been demonstrated to predict the fin CD values from an inline measurement using machine learning. In this study, OCD spectra were acquired after an etch process that occurs earlier in the process flow than where the inline CD is measured. The fin hard mask etch process is known to impact the downstream inline CD value. Figure 1 shows the correlation of predicted CD vs downstream inline CD measurement obtained after the training of the machine learning algorithm. For BEOL, machine learning is shown to provide an additional source of information in prediction of electrical resistance from structures that are not compatible for direct copper height measurement. Figure 2 compares the trench height correlation to electrical resistance (Rs) and the correlation of predicted Rs to the e-test Rs value for a far back end of line (FBEOL) metallization level

Electroanalytical chemistry. Volume 14

International Nuclear Information System (INIS)

Bard, A.J.

1986-01-01

This volume is part of a series aimed at authoritative reviews of electroanalytical techniques and related areas of investigation. Volume 14 clearly maintains the high standards and proven usefulness of the series. Topics covered include conformation change and isomerization associated with electrode reactions, infrared vibrational spectroscopy of the electrode-solution interface, and precision in linear sweep and cyclic voltametry. A short history of electrochemical techniques which include the term square wave is provided

The Department of Energy's Rocky Flats Plant: A guide to record series useful for health related research. Volume 4: Production and materials handling

International Nuclear Information System (INIS)

1995-01-01

This is the fourth in a series of seven volumes which constitute a guide to records of the Rocky Flats Plant useful for conducting health-related research. The primary purpose of Volume 4 is to describe record series pertaining to production and materials handling activities at the Department of Energy's (DOE) Rocky Flats Plant, now named the Rocky Flats Environmental Technology Site, near Denver, Colorado. History Associates Incorporated (HAI) prepared this guide as part of its work as the support services contractor for DOE's Epidemiologic Records Inventory Project. This introduction briefly describes the Epidemiologic Records Inventory Project and HAI's role in the project, provides a history of production and materials handling practices at Rocky Flats, and identifies organizations contributing to production and materials handling policies and activities. Other topics include the scope and arrangement of the guide and the organization to contact for access to these records

Introduction to Time Series Modeling

CERN Document Server

Kitagawa, Genshiro

2010-01-01

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f

Fourier Series Optimization Opportunity

Science.gov (United States)

Winkel, Brian

2008-01-01

This note discusses the introduction of Fourier series as an immediate application of optimization of a function of more than one variable. Specifically, it is shown how the study of Fourier series can be motivated to enrich a multivariable calculus class. This is done through discovery learning and use of technology wherein students build the…

Active learning through a debate series in a first-year pharmacy self-care course.

Science.gov (United States)

Lampkin, Stacie J; Collins, Christine; Danison, Ryan; Lewis, Michelle

2015-03-25

To evaluate the usefulness of formal debates in the pharmacy classroom as a way to learn course material and as a tool for developing competency in essential skills including critical thinking, communication, public speaking, research methods, and teamwork. Debates were incorporated into a self-care course, where students were assigned different debate topics focused on controversial issues. Quantitative analysis was completed to assess debate style learning, knowledge about the subjects presented, and the impact on necessary skills. Quizzes given before and after debates showed up to a 36% improvement in grades and up to a 31% change in opinions on the topic. Students assessed themselves as more competent in the skill sets at the completion of the debate series. Incorporation of debates into didactic style courses offers students an opportunity to improve upon skills that will help them succeed as pharmacists.

Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

Directory of Open Access Journals (Sweden)

Kai Wang

2016-01-01

Full Text Available Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.

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.

Striatal volume predicts level of video game skill acquisition.

Science.gov (United States)

Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Prakash, Ruchika S; Voss, Michelle W; Graybiel, Ann M; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

2010-11-01

Video game skills transfer to other tasks, but individual differences in performance and in learning and transfer rates make it difficult to identify the source of transfer benefits. We asked whether variability in initial acquisition and of improvement in performance on a demanding video game, the Space Fortress game, could be predicted by variations in the pretraining volume of either of 2 key brain regions implicated in learning and memory: the striatum, implicated in procedural learning and cognitive flexibility, and the hippocampus, implicated in declarative memory. We found that hippocampal volumes did not predict learning improvement but that striatal volumes did. Moreover, for the striatum, the volumes of the dorsal striatum predicted improvement in performance but the volumes of the ventral striatum did not. Both ventral and dorsal striatal volumes predicted early acquisition rates. Furthermore, this early-stage correlation between striatal volumes and learning held regardless of the cognitive flexibility demands of the game versions, whereas the predictive power of the dorsal striatal volumes held selectively for performance improvements in a game version emphasizing cognitive flexibility. These findings suggest a neuroanatomical basis for the superiority of training strategies that promote cognitive flexibility and transfer to untrained tasks.

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

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.

AIED 2009 Workshops Proceeedings Volume 10: Natural Language Processing in Support of Learning: Metrics, Feedback and Connectivity

NARCIS (Netherlands)

Dessus, Philippe; Trausan-Matu, Stefan; Van Rosmalen, Peter; Wild, Fridolin

2009-01-01

Dessus, P., Trausan-Matu, S., Van Rosmalen, P., & Wild, F. (Eds.) (2009). AIED 2009 Workshops Proceedings Volume 10 Natural Language Processing in Support of Learning: Metrics, Feedback and Connectivity. In S. D. Craig & D. Dicheva (Eds.), AIED 2009: 14th International Conference in Artificial

Price-volume multifractal analysis and its application in Chinese stock markets

Science.gov (United States)

Yuan, Ying; Zhuang, Xin-tian; Liu, Zhi-ying

2012-06-01

An empirical research on Chinese stock markets is conducted using statistical tools. First, the multifractality of stock price return series, ri(ri=ln(Pt+1)-ln(Pt)) and trading volume variation series, vi(vi=ln(Vt+1)-ln(Vt)) is confirmed using multifractal detrended fluctuation analysis. Furthermore, a multifractal detrended cross-correlation analysis between stock price return and trading volume variation in Chinese stock markets is also conducted. It is shown that the cross relationship between them is also found to be multifractal. Second, the cross-correlation between stock price Pi and trading volume Vi is empirically studied using cross-correlation function and detrended cross-correlation analysis. It is found that both Shanghai stock market and Shenzhen stock market show pronounced long-range cross-correlations between stock price and trading volume. Third, a composite index R based on price and trading volume is introduced. Compared with stock price return series ri and trading volume variation series vi, R variation series not only remain the characteristics of original series but also demonstrate the relative correlation between stock price and trading volume. Finally, we analyze the multifractal characteristics of R variation series before and after three financial events in China (namely, Price Limits, Reform of Non-tradable Shares and financial crisis in 2008) in the whole period of sample to study the changes of stock market fluctuation and financial risk. It is found that the empirical results verified the validity of R.

Accuracy evaluation of Fourier series analysis and singular spectrum analysis for predicting the volume of motorcycle sales in Indonesia

Science.gov (United States)

Sasmita, Yoga; Darmawan, Gumgum

2017-08-01

This research aims to evaluate the performance of forecasting by Fourier Series Analysis (FSA) and Singular Spectrum Analysis (SSA) which are more explorative and not requiring parametric assumption. Those methods are applied to predicting the volume of motorcycle sales in Indonesia from January 2005 to December 2016 (monthly). Both models are suitable for seasonal and trend component data. Technically, FSA defines time domain as the result of trend and seasonal component in different frequencies which is difficult to identify in the time domain analysis. With the hidden period is 2,918 ≈ 3 and significant model order is 3, FSA model is used to predict testing data. Meanwhile, SSA has two main processes, decomposition and reconstruction. SSA decomposes the time series data into different components. The reconstruction process starts with grouping the decomposition result based on similarity period of each component in trajectory matrix. With the optimum of window length (L = 53) and grouping effect (r = 4), SSA predicting testing data. Forecasting accuracy evaluation is done based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The result shows that in the next 12 month, SSA has MAPE = 13.54 percent, MAE = 61,168.43 and RMSE = 75,244.92 and FSA has MAPE = 28.19 percent, MAE = 119,718.43 and RMSE = 142,511.17. Therefore, to predict volume of motorcycle sales in the next period should use SSA method which has better performance based on its accuracy.

Software Engineering Laboratory Series: Collected Software Engineering Papers. Volume 14

Science.gov (United States)

1996-01-01

The Software Engineering Laboratory (SEL) is an organization sponsored by NASA/GSFC and created to investigate the effectiveness of software engineering technologies when applied to the development of application software. The activities, findings, and recommendations of the SEL are recorded in the Software Engineering Laboratory Series, a continuing series of reports that includes this document.

Software Engineering Laboratory Series: Collected Software Engineering Papers. Volume 15

Science.gov (United States)

1997-01-01

The Software Engineering Laboratory (SEL) is an organization sponsored by NASA/GSFC and created to investigate the effectiveness of software engineering technologies when applied to the development of application software. The activities, findings, and recommendations of the SEL are recorded in the Software Engineering Laboratory Series, a continuing series of reports that includes this document.

Software Engineering Laboratory Series: Collected Software Engineering Papers. Volume 13

Science.gov (United States)

1995-01-01

The Software Engineering Laboratory (SEL) is an organization sponsored by NASA/GSFC and created to investigate the effectiveness of software engineering technologies when applied to the development of application software. The activities, findings, and recommendations of the SEL are recorded in the Software Engineering Laboratory Series, a continuing series of reports that includes this document.

The "E" Is for Everything: E-Commerce, E-Business, and E-Learning in Higher Education. EDUCAUSE Leadership Strategies, No. 2. Jossey-Bass Higher and Adult Education Series.

Science.gov (United States)

Katz, Richard N., Ed.; Oblinger, Diana G., Ed.

The Educause Leadership Strategies series addresses themes related to information technology's influence on higher education. This second volume in the series explores how the "e-revolution" will affect higher education, particularly how higher education can participate in anticipated changes in ways that strengthen the best of what…

On the Behavior of Eisenstein Series Through Elliptic Degeneration

Science.gov (United States)

Garbin, D.; Pippich, A.-M. V.

2009-12-01

Let Γ be a Fuchsian group of the first kind acting on the hyperbolic upper half plane {mathbb{H}}, and let {M = Γbackslash mathbb{H}} be the associated finite volume hyperbolic Riemann surface. If γ is a primitive parabolic, hyperbolic, resp. elliptic element of Γ, there is an associated parabolic, hyperbolic, resp. elliptic Eisenstein series. In this article, we study the limiting behavior of these Eisenstein series on an elliptically degenerating family of finite volume hyperbolic Riemann surfaces. In particular, we prove the following result. The elliptic Eisenstein series associated to a degenerating elliptic element converges up to a factor to the parabolic Eisenstein series associated to the parabolic element which fixes the newly developed cusp on the limit surface.

A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications

NARCIS (Netherlands)

Johnson, Corinne; Price, Gareth; Khalifa, Jonathan; Faivre-Finn, Corinne; Dekker, Andre; Moore, Christopher; van Herk, Marcel

2017-01-01

The gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV). We present and validate a method to synthesise GTV data from the iGTV,

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.

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

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.

Divergent series, summability and resurgence II simple and multiple summability

CERN Document Server

Loday-Richaud, Michèle

2016-01-01

Addressing the question how to “sum” a power series in one variable when it diverges, that is, how to attach to it analytic functions, the volume gives answers by presenting and comparing the various theories of k-summability and multisummability. These theories apply in particular to all solutions of ordinary differential equations. The volume includes applications, examples and revisits, from a cohomological point of view, the group of tangent-to-identity germs of diffeomorphisms of C studied in volume 1. With a view to applying the theories to solutions of differential equations, a detailed survey of linear ordinary differential equations is provided which includes Gevrey asymptotic expansions, Newton polygons, index theorems and Sibuya’s proof of the meromorphic classification theorem that characterizes the Stokes phenomenon for linear differential equations. This volume is the second of a series of three entitled Divergent Series, Summability and Resurgence. It is aimed at graduate students and res...

Still to Learn from Vicarious Learning

Science.gov (United States)

Mayes, J. T.

2015-01-01

The term "vicarious learning" was introduced in the 1960s by Bandura, who demonstrated how learning can occur through observing the behaviour of others. Such social learning is effective without the need for the observer to experience feedback directly. More than twenty years later a series of studies on vicarious learning was undertaken…

The Motivational Enhancement Therapy and Cognitive Behavioral Therapy Supplement: 7 Sessions of Cognitive Behavioral Therapy for Adolescent Cannabis Users, Cannabis Youth Treatment (CYT) Series, Volume 2.

Science.gov (United States)

Webb, Charles; Scudder, Meleney; Kaminer, Yifrah; Kaden, Ron

This manual, a supplement to "Motivational Enhancement Therapy and Cognitive Behavioral Therapy for Adolescent Cannabis Users: 5 Sessions, Cannabis Youth Treatment (CYT) Series, Volume 1", presents a seven-session cognitive behavioral treatment (CBT7) approach designed especially for adolescent cannabis users. It addresses the implementation and…

Exposures series

OpenAIRE

Stimson, Blake

2011-01-01

Reaktion Books’ Exposures series, edited by Peter Hamilton and Mark Haworth-Booth, is comprised of 13 volumes and counting, each less than 200 pages with 80 high-quality illustrations in color and black and white. Currently available titles include Photography and Australia, Photography and Spirit, Photography and Cinema, Photography and Literature, Photography and Flight, Photography and Egypt, Photography and Science, Photography and Africa, Photography and Italy, Photography and the USA, P...

Efficient Approximate OLAP Querying Over Time Series

DEFF Research Database (Denmark)

Perera, Kasun Baruhupolage Don Kasun Sanjeewa; Hahmann, Martin; Lehner, Wolfgang

2016-01-01

The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of business intelligence and decision making. As OLAP...... queries play a major role in these domains, it is desirable to also execute them on time series data. While this is not a problem on the conceptual level, it can become a bottleneck with regards to query run-time. In general, processing OLAP queries gets more computationally intensive as the volume...... of data grows. This is a particular problem when querying time series data, which generally contains multiple measures recorded at fine time granularities. Usually, this issue is addressed either by scaling up hardware or by employing workload based query optimization techniques. However, these solutions...

Data catalog series for space science and applications flight missions. Volume 1B: Descriptions of data sets from planetary and heliocentric spacecraft and investigations

Science.gov (United States)

Horowitz, Richard (Compiler); Jackson, John E. (Compiler); Cameron, Winifred S. (Compiler)

1987-01-01

The main purpose of the data catalog series is to provide descriptive references to data generated by space science flight missions. The data sets described include all of the actual holdings of the Space Science Data Center (NSSDC), all data sets for which direct contact information is available, and some data collections held and serviced by foreign investigators, NASA and other U.S. government agencies. This volume contains narrative descriptions of planetary and heliocentric spacecraft and associated experiments. The following spacecraft series are included: Mariner, Pioneer, Pioneer Venus, Venera, Viking, Voyager, and Helios. Separate indexes to the planetary and interplanetary missions are also provided.

Fundamental Concepts in Biophysics Volume 1

CERN Document Server

Jue, Thomas

2009-01-01

HANDBOOK OF MODERN BIOPHYSICS Series Editor Thomas Jue, PhD Handbook of Modern Biophysics brings current biophysics topics into focus, so that biology, medical, engineering, mathematics, and physical-science students or researchers can learn fundamental concepts and the application of new techniques in addressing biomedical challenges. Chapters explicate the conceptual framework of the physics formalism and illustrate the biomedical applications. With the addition of problem sets, guides to further study, and references, the interested reader can continue to explore independently the ideas presented. Volume I: Fundamental Concepts in Biophysics Editor Thomas Jue, PhD In Fundamental Concepts in Biophysics, prominent professors have established a foundation for the study of biophysics related to the following topics: Mathematical Methods in Biophysics Quantum Mechanics Basic to Biophysical Methods Computational Modeling of Receptor–Ligand Binding and Cellular Signaling Processes Fluorescence Spectroscopy Elec...

Time-series prediction and applications a machine intelligence approach

CERN Document Server

Konar, Amit

2017-01-01

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

Basic Quechua. Volume I: Quechua Reader. Volume II: Quechua Grammar and Dictionary.

Science.gov (United States)

Aitken-Soux, Percy G.; Crapo, Richley H.

Volume I, the reader, has 86 lessons consisting of short passages and vocabulary lists. The language and the stories presented were learned and collected at the Indian community and Hacienda of Cayara near Potosi, Bolivia. Translations of the passages are provided in a separate section. The second volume presents the grammar and phonology of the…

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. BEDARTHA GOSWAMI. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 51-60 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Inferring interdependencies from short ...

Predicting chaotic time series

International Nuclear Information System (INIS)

Farmer, J.D.; Sidorowich, J.J.

1987-01-01

We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow

The Future of STEM Curriculum and Instructional Design: A Research and Development Agenda for Learning Designers. Report of a Workshop Series

Science.gov (United States)

Center for the Study of Mathematics Curriculum, 2012

2012-01-01

In 2009-10 a series of Workshops was organized to focus on STEM (science, technology, engineering, and mathematics) learning design for young students and adolescents. The objective was to provide visionary leadership to the education community by: (a) identifying and analyzing the needs and opportunities for future STEM curriculum development and…

Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns

International Nuclear Information System (INIS)

Chou, Jui-Sheng; Ngo, Ngoc-Tri

2016-01-01

Highlights: • This study develops a novel time-series sliding window forecast system. • The system integrates metaheuristics, machine learning and time-series models. • Site experiment of smart grid infrastructure is installed to retrieve real-time data. • The proposed system accurately predicts energy consumption in residential buildings. • The forecasting system can help users minimize their electricity usage. - Abstract: Smart grids are a promising solution to the rapidly growing power demand because they can considerably increase building energy efficiency. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning system for predicting real-time building energy consumption data collected by a smart grid. The proposed system integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed system fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. Real-time data retrieved from an experimental smart grid installed in a building were used to evaluate the efficacy and effectiveness of the proposed system. A k-week sliding window approach is proposed for employing historical data as input for the novel time-series forecasting system. The prediction system yielded high and reliable accuracy rates in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kW h. Notably, the system demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially

Environmental Pollution: Noise Pollution - Sonic Boom. Volume I.

Science.gov (United States)

Defense Documentation Center, Alexandria, VA.

The unclassified, annotated bibliography is Volume I of a two-volume set on Noise Pollution - Sonic Boom in a series of scheduled bibliographies on Environmental Pollution. Volume II is Confidential. Corporate author-monitoring agency, subject, title, contract, and report number indexes are included. (Author/JR)

Variable Selection in Time Series Forecasting Using Random Forests

Directory of Open Access Journals (Sweden)

Hristos Tyralis

2017-10-01

Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. SERGEY P KUZNETSOV. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 117-132 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Chaos in three coupled rotators: ...

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Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. PRIYANKA SHUKLA. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 133-143 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Grad-type fourteen-moment theory for ...

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Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. F FAMILY. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 221-224 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Transport in ratchets with single-file constraint.

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Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. GIOVANNA ZIMATORE. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 35-41 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. RQA correlations on real business cycles ...

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Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. SUDHARSANA V IYENGAR. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 93-99 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Missing cycles: Effect of climate ...

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Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. NORBERT MARWAN. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 51-60 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Inferring interdependencies from short time ...

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Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. JANAKI BALAKRISHNAN. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 93-99 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Missing cycles: Effect of climate change ...

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. PAUL SCHULTZ. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 51-60 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Inferring interdependencies from short time ...

Student understanding of Taylor series expansions in statistical mechanics

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Trevor I. Smith

2013-08-01

Full Text Available One goal of physics instruction is to have students learn to make physical meaning of specific mathematical expressions, concepts, and procedures in different physical settings. As part of research investigating student learning in statistical physics, we are developing curriculum materials that guide students through a derivation of the Boltzmann factor using a Taylor series expansion of entropy. Using results from written surveys, classroom observations, and both individual think-aloud and teaching interviews, we present evidence that many students can recognize and interpret series expansions, but they often lack fluency in creating and using a Taylor series appropriately, despite previous exposures in both calculus and physics courses.

Student understanding of Taylor series expansions in statistical mechanics

Science.gov (United States)

Smith, Trevor I.; Thompson, John R.; Mountcastle, Donald B.

2013-12-01

One goal of physics instruction is to have students learn to make physical meaning of specific mathematical expressions, concepts, and procedures in different physical settings. As part of research investigating student learning in statistical physics, we are developing curriculum materials that guide students through a derivation of the Boltzmann factor using a Taylor series expansion of entropy. Using results from written surveys, classroom observations, and both individual think-aloud and teaching interviews, we present evidence that many students can recognize and interpret series expansions, but they often lack fluency in creating and using a Taylor series appropriately, despite previous exposures in both calculus and physics courses.

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series; Volume 1; Issue 1. Missing cycles: Effect of climate change on population dynamics. JANAKI BALAKRISHNAN SUDHARSANA V IYENGAR JÜRGEN KURTHS. Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016 Volume 1 Issue 1 ...

Handbook on the physics and chemistry of rare earths: Volume 17

International Nuclear Information System (INIS)

Gschneidner, K.A. Jr; Eyring, L.; Choppin, G.R.; Lander, G.H.

1993-10-01

This volume of the handbook is the first of a three volume set of reviews devoted to the interrelationships, similarities, differences and contrasts of the lanthanide and actinide series of elements. The volume contains eight chapters (numbered 110-117) concerned with some of the physical aspects of lanthanide and actinide series. The first three chapters are theoretical in nature and the last five are more heavily oriented towards experimental studies

Price-volume multifractal analysis of the Moroccan stock market

Science.gov (United States)

El Alaoui, Marwane

2017-11-01

In this paper, we analyzed price-volume multifractal cross-correlations of Moroccan Stock Exchange. We chose the period from January 1st 2000 to January 20th 2017 to investigate the multifractal behavior of price change and volume change series. Then, we used multifractal detrended cross-correlations analysis method (MF-DCCA) and multifractal detrended fluctuation analysis (MF-DFA) to analyze the series. We computed bivariate generalized Hurst exponent, Rényi exponent and spectrum of singularity for each pair of indices to measure quantitatively cross-correlations. Furthermore, we used detrended cross-correlations coefficient (DCCA) and cross-correlation test (Q(m)) to analyze cross-correlation quantitatively and qualitatively. By analyzing results, we found existence of price-volume multifractal cross-correlations. The spectrum width has a strong multifractal cross-correlation. We remarked that volume change series is anti-persistent when we analyzed the generalized Hurst exponent for all moments q. The cross-correlation test showed the presence of a significant cross-correlation. However, DCCA coefficient had a small positive value, which means that the level of correlation is not very significant. Finally, we analyzed sources of multifractality and their degree of contribution in the series.

Stochastic models for time series

CERN Document Server

Doukhan, Paul

2018-01-01

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit ...

Basketball training increases striatum volume.

Science.gov (United States)

Park, In Sung; Lee, Kea Joo; Han, Jong Woo; Lee, Nam Joon; Lee, Won Teak; Park, Kyung Ah; Rhyu, Im Joo

2011-02-01

The striatum is associated with the learning and retention of motor skills. Several studies have shown that motor learning induces neuronal changes in the striatum. We investigated whether macroscopic change in striatum volume occurs in a segment of the human population who learned basketball-related motor skills and practiced them throughout their entire athletic life. Three-dimensional magnetic resonance imaging volumetry was performed in basketball players and healthy controls, and striatum volumes were compared based on basketball proficiency, region and side. We identified morphological enlargement in the striatum of basketball players in comparison with controls. Our results suggest that continued practice and repetitive performance of basketball-related motor skills may induce plastic structural changes in the human striatum. Copyright © 2010 Elsevier B.V. All rights reserved.

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. F REVUELTA. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 145-155 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Rate calculation in two-dimensional barriers with ...

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. JOYDEEP SINGHA. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 195-203 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Spatial splay states in coupled map lattices ...

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series. MURILO S BAPTISTA. Articles written in Indian Academy of Sciences Conference Series. Volume 1 Issue 1 December 2017 pp 17-23 Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016. Interpreting physical flows in networks as a ...

Collaborative Learning: Cognitive and Computational Approaches. Advances in Learning and Instruction Series.

Science.gov (United States)

Dillenbourg, Pierre, Ed.

Intended to illustrate the benefits of collaboration between scientists from psychology and computer science, namely machine learning, this book contains the following chapters, most of which are co-authored by scholars from both sides: (1) "Introduction: What Do You Mean by 'Collaborative Learning'?" (Pierre Dillenbourg); (2)…

Data catalog series for space science and applications flight missions. Volume 3B: Descriptions of data sets from low- and medium-altitude scientific spacecraft and investigations

Science.gov (United States)

Jackson, John E. (Editor); Horowitz, Richard (Editor)

1986-01-01

The main purpose of the data catalog series is to provide descriptive references to data generated by space science flight missions. The data sets described include all of the actual holdings of the Space Science Data Center (NSSDC), all data sets for which direct contact information is available, and some data collections held and serviced by foreign investigators, NASA and other U.S. government agencies. This volume contains narrative descriptions of data sets from low and medium altitude scientific spacecraft and investigations. The following spacecraft series are included: Mariner, Pioneer, Pioneer Venus, Venera, Viking, Voyager, and Helios. Separate indexes to the planetary and interplanetary missions are also provided.

Data Catalog Series for Space Science and Applications Flight Missions. Volume 2B; Descriptions of Data Sets from Geostationary and High-Altitude Scientific Spacecraft and Investigations

Science.gov (United States)

Schofield, Norman J. (Editor); Parthasarathy, R. (Editor); Hills, H. Kent (Editor)

1988-01-01

The main purpose of the data catalog series is to provide descriptive references to data generated by space science flight missions. The data sets described include all of the actual holdings of the Space Science Data Center (NSSDC), all data sets for which direct contact information is available, and some data collections held and serviced by foreign investigators, NASA and other U.S. government agencies. This volume contains narrative descriptions of data sets from geostationary and high altitude scientific spacecraft and investigations. The following spacecraft series are included: Mariner, Pioneer, Pioneer Venus, Venera, Viking, Voyager, and Helios. Separate indexes to the planetary and interplanetary missions are also provided.

Rheumatoid Arthritis Educational Video Series

Medline Plus

Full Text Available ... and what other conditions are associated with RA. Learning more about your condition will allow you to ... Arthritis Educational Video Series Psoriatic Arthritis 101 2010 E.S.C.A.P.E. Study Patient Update Transitioning ...

Changes in Search Path Complexity and Length During Learning of a Virtual Water Maze: Age Differences and Differential Associations with Hippocampal Subfield Volumes.

Science.gov (United States)

Daugherty, Ana M; Bender, Andrew R; Yuan, Peng; Raz, Naftali

2016-06-01

Impairment of hippocampus-dependent cognitive processes has been proposed to underlie age-related deficits in navigation. Animal studies suggest a differential role of hippocampal subfields in various aspects of navigation, but that hypothesis has not been tested in humans. In this study, we examined the association between volume of hippocampal subfields and age differences in virtual spatial navigation. In a sample of 65 healthy adults (age 19-75 years), advanced age was associated with a slower rate of improvement operationalized as shortening of the search path over 25 learning trials on a virtual Morris water maze task. The deficits were partially explained by greater complexity of older adults' search paths. Larger subiculum and entorhinal cortex volumes were associated with a faster decrease in search path complexity, which in turn explained faster shortening of search distance. Larger Cornu Ammonis (CA)1-2 volume was associated with faster distance shortening, but not in path complexity reduction. Age differences in regional volumes collectively accounted for 23% of the age-related variance in navigation learning. Independent of subfield volumes, advanced age was associated with poorer performance across all trials, even after reaching the asymptote. Thus, subiculum and CA1-2 volumes were associated with speed of acquisition, but not magnitude of gains in virtual maze navigation. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Profiling and Utilizing Learning Style. NASSP Learning Style Series.

Science.gov (United States)

Keefe, James W., Ed.

In 1986, the National Association of Secondary School Principals, with the assistance of a national task force, published the NASSP Learning Style Profile (LSP) for diagnosis of the cognitive styles, perceptual response tendencies, and instructional preferences of middle level and senior high school students. This monograph offers a short course…

Conditional time series forecasting with convolutional neural networks

NARCIS (Netherlands)

A. Borovykh (Anastasia); S.M. Bohte (Sander); C.W. Oosterlee (Cornelis)

2017-01-01

textabstractForecasting financial time series using past observations has been a significant topic of interest. While temporal relationships in the data exist, they are difficult to analyze and predict accurately due to the non-linear trends and noise present in the series. We propose to learn these

Data catalog series for space science and applications flight missions. Volume 5A: Descriptions of astronomy, astrophysics, and solar physics spacecraft and investigations. Volume 5B: Descriptions of data sets from astronomy, astrophysics, and solar physics spacecraft and investigations

Science.gov (United States)

Kim, Sang J. (Editor)

1988-01-01

The main purpose of the data catalog series is to provide descriptive references to data generated by space science flight missions. The data sets described include all of the actual holdings of the Space Science Data Center (NSSDC), all data sets for which direct contact information is available, and some data collections held and serviced by foreign investigators, NASA and other U.S. government agencies. This volume contains narrative descriptions of data sets of astronomy, astrophysics, solar physics spacecraft and investigations. The following spacecraft series are included: Mariner, Pioneer, Pioneer Venus, Venera, Viking, Voyager, and Helios. Separate indexes to the planetary and interplanetary missions are also provided.

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

Developmental Physical Education Accountability; Volume I.

Science.gov (United States)

Guarnieri, Barbara; Sandeen, Cecile

Presented in the first of a two volume series is a developmental physical education checklist which provides teachers of trainable mentally retarded students with a permanent and accountable record of pupil progress and needs. The checklist is intended to be used with the accompanying volume of curricular activities in a nongraded enviroment for…

Adult Education and Lifelong Learning in Europe and Beyond: Comparative Perspectives from the 2015 Würzburg Winter School. Studies in Pedagogy, Andragogy, and Gerontagogy. Volume 67

Science.gov (United States)

Egetenmeyer, Regina, Ed.

2016-01-01

This volume presents comparisons of adult education and lifelong learning with a focus on educational policies, professionalization in adult education, participation in adult learning and education, quality in adult education, and educational guidance and counselling. The essays are based on comparisons discussed at the international Winter School…

Small-Volume Injections: Evaluation of Volume Administration Deviation From Intended Injection Volumes.

Science.gov (United States)

Muffly, Matthew K; Chen, Michael I; Claure, Rebecca E; Drover, David R; Efron, Bradley; Fitch, William L; Hammer, Gregory B

2017-10-01

In the perioperative period, anesthesiologists and postanesthesia care unit (PACU) nurses routinely prepare and administer small-volume IV injections, yet the accuracy of delivered medication volumes in this setting has not been described. In this ex vivo study, we sought to characterize the degree to which small-volume injections (≤0.5 mL) deviated from the intended injection volumes among a group of pediatric anesthesiologists and pediatric postanesthesia care unit (PACU) nurses. We hypothesized that as the intended injection volumes decreased, the deviation from those intended injection volumes would increase. Ten attending pediatric anesthesiologists and 10 pediatric PACU nurses each performed a series of 10 injections into a simulated patient IV setup. Practitioners used separate 1-mL tuberculin syringes with removable 18-gauge needles (Becton-Dickinson & Company, Franklin Lakes, NJ) to aspirate 5 different volumes (0.025, 0.05, 0.1, 0.25, and 0.5 mL) of 0.25 mM Lucifer Yellow (LY) fluorescent dye constituted in saline (Sigma Aldrich, St. Louis, MO) from a rubber-stoppered vial. Each participant then injected the specified volume of LY fluorescent dye via a 3-way stopcock into IV tubing with free-flowing 0.9% sodium chloride (10 mL/min). The injected volume of LY fluorescent dye and 0.9% sodium chloride then drained into a collection vial for laboratory analysis. Microplate fluorescence wavelength detection (Infinite M1000; Tecan, Mannedorf, Switzerland) was used to measure the fluorescence of the collected fluid. Administered injection volumes were calculated based on the fluorescence of the collected fluid using a calibration curve of known LY volumes and associated fluorescence.To determine whether deviation of the administered volumes from the intended injection volumes increased at lower injection volumes, we compared the proportional injection volume error (loge [administered volume/intended volume]) for each of the 5 injection volumes using a linear

Learning and Celebrating: The Glamour of Design Lecture Series

Science.gov (United States)

Popov, Lubomir

2013-01-01

The purpose of this paper is to highlight the celebratory aspect of the Design Lecture Series, a tradition in architecture schools and interior design programs, its meaning for all constituent parties, and its contributions to creating professional identity and community. The Design Lecture Series is a public event popular in design programs,…

A Mobile Gamification Learning System for Improving the Learning Motivation and Achievements

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Su, C-H.; Cheng, C-H.

2015-01-01

This paper aims to investigate how a gamified learning approach influences science learning, achievement and motivation, through a context-aware mobile learning environment, and explains the effects on motivation and student learning. A series of gamified learning activities, based on MGLS (Mobile Gamification Learning System), was developed and…

Time series modeling, computation, and inference

CERN Document Server

Prado, Raquel

2010-01-01

The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit

A Unified Method of Finding Laplace Transforms, Fourier Transforms, and Fourier Series. [and] An Inversion Method for Laplace Transforms, Fourier Transforms, and Fourier Series. Integral Transforms and Series Expansions. Modules and Monographs in Undergraduate Mathematics and Its Applications Project. UMAP Units 324 and 325.

Science.gov (United States)

Grimm, C. A.

This document contains two units that examine integral transforms and series expansions. In the first module, the user is expected to learn how to use the unified method presented to obtain Laplace transforms, Fourier transforms, complex Fourier series, real Fourier series, and half-range sine series for given piecewise continuous functions. In…

Interracial America. Opposing Viewpoints Series.

Science.gov (United States)

Szumski, Bonnie, Ed.

Books in the Opposing Viewpoints Series present debates about current issues that can be used to teach critical reading and thinking skills. The varied opinions in each book examine different aspects of a single issue. The topics covered in this volume explore the racial and ethnic tensions that concern many Americans today. The racial divide…

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory

Directory of Open Access Journals (Sweden)

Haimin Yang

2017-01-01

Full Text Available Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam, for long short-term memory (LSTM to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory.

Science.gov (United States)

Yang, Haimin; Pan, Zhisong; Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

Foundations of Sequence-to-Sequence Modeling for Time Series

OpenAIRE

Kuznetsov, Vitaly; Mariet, Zelda

2018-01-01

The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

PENERAPAN PENDEKATAN KONTEKSTUAL TERHADAP PENGUASAAN KONSEP DASAR MATERI VOLUME BENDA PUTAR

Directory of Open Access Journals (Sweden)

Alpha Galih Adirakasiwi

2018-02-01

Full Text Available ABSTRACTThe purpose of this study was determine the application of contextual teaching learning approach to improve the mastery of concepts volume of the rotary object. The study design was nonequivalent pretest-posttest control group design. Data collection technique used pretest and posttest to determine the mastery of concepts of volume of the rotary object. Hypothesis testing using t-test to compare the the mastery of concepts volume of the rotary object in the experimental class and control class, then a further test by comparing the mean (compare means and average absolute gain Based on the research result the data show that it can be concluded that the approach of contextual learning significantly distinction of concepts volume of the rotary object. Keywords:  Contextual Teaching Learning Approach, The Mastery of Concepts, Volume of The Rotary Object

Facilitation of learning: part 1.

Science.gov (United States)

Warburton, Tyler; Trish, Houghton; Barry, Debbie

2016-04-06

This article, the fourth in a series of 11, discusses the context for the facilitation of learning. It outlines the main principles and theories for understanding the process of learning, including examples which link these concepts to practice. The practical aspects of using these theories in a practice setting will be discussed in the fifth article of this series. Together, these two articles will provide mentors and practice teachers with knowledge of the learning process, which will enable them to meet the second domain of the Nursing and Midwifery Council's Standards to Support Learning and Assessment in Practice on facilitation of learning.

Divergent series, summability and resurgence III resurgent methods and the first Painlevé equation

CERN Document Server

Delabaere, Eric

2016-01-01

The aim of this volume is two-fold. First, to show how the resurgent methods introduced in volume 1 can be applied efficiently in a non-linear setting; to this end further properties of the resurgence theory must be developed. Second, to analyze the fundamental example of the First Painlevé equation. The resurgent analysis of singularities is pushed all the way up to the so-called “bridge equation”, which concentrates all information about the non-linear Stokes phenomenon at infinity of the First Painlevé equation. The third in a series of three, entitled Divergent Series, Summability and Resurgence, this volume is aimed at graduate students, mathematicians and theoretical physicists who are interested in divergent power series and related problems, such as the Stokes phenomenon. The prerequisites are a working knowledge of complex analysis at the first-year graduate level and of the theory of resurgence, as presented in volume 1. .

Learning through Debate during Problem-Based Learning: An Active Learning Strategy

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Mumtaz, Sadaf; Latif, Rabia

2017-01-01

We explored medical student's views and perceptions of a series of debates conducted during problem-based learning (PBL) practiced as a part of the Spiral curriculum at the Imam Abdulrahman Bin Faisal University, Saudi Arabia. A series of debates were employed during PBL sessions for second-year female medical students, over the period 2014-2016.…

How Should Students Learn in the School Science Laboratory? The Benefits of Cooperative Learning

Science.gov (United States)

Raviv, Ayala; Cohen, Sarit; Aflalo, Ester

2017-07-01

Despite the inherent potential of cooperative learning, there has been very little research into its effectiveness in middle school laboratory classes. This study focuses on an empirical comparison between cooperative learning and individual learning in the school science laboratory, evaluating the quality of learning and the students' attitudes. The research included 67 seventh-grade students who undertook four laboratory experiments on the subject of "volume measuring skills." Each student engaged both in individual and cooperative learning in the laboratory, and the students wrote individual or group reports, accordingly. A total of 133 experiment reports were evaluated, 108 of which also underwent textual analysis. The findings show that the group reports were superior, both in terms of understanding the concept of "volume" and in terms of acquiring skills for measuring volume. The students' attitudes results were statistically significant and demonstrated that they preferred cooperative learning in the laboratory. These findings demonstrate that science teachers should be encouraged to implement cooperative learning in the laboratory. This will enable them to improve the quality and efficiency of laboratory learning while using a smaller number of experimental kits. Saving these expenditures, together with the possibility to teach a larger number of students simultaneously in the laboratory, will enable greater exposure to learning in the school science laboratory.

Sandia Software Guidelines, Volume 2. Documentation

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-09-01

This volume is one in a series of Sandia Software Guidelines intended for use in producing quality software within Sandia National Laboratories. In consonance with the IEEE Standards for software documentation, this volume provides guidance in the selection of an adequate document set for a software project and example formats for many types of software documentation. A tutorial on life cycle documentation is also provided. Extended document thematic outlines and working examples of software documents are available on electronic media as an extension of this volume.

Advances in time series methods and applications the A. Ian McLeod festschrift

CERN Document Server

Stanford, David; Yu, Hao

2016-01-01

This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.

Painting Cloud Nine: A Study of Magritte's Bottle Series.

Science.gov (United States)

Turner, Dianne

2000-01-01

Provides background information on Rene Magritte and his work. Offers an activity in which elementary and middle school students can learn about Magritte's sky and silhouette series of painted wine bottles. Explains that the lesson should be used when students are learning about poetry in language arts classes. (CMK)

Working memory supports inference learning just like classification learning.

Science.gov (United States)

Craig, Stewart; Lewandowsky, Stephan

2013-08-01

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.

Evaluation of dose-volume histograms after prostate seed implantation. 4-year experience

International Nuclear Information System (INIS)

Hoinkis, C.; Lehmann, D.; Winkler, C.; Herrmann, T.; Hakenberg, O.W.; Wirth, M.P.

2004-01-01

Background and purpose: permanent interstitial brachytherapy by seed implantation is a treatment alternative for low-volume low-risk prostate cancer and a complex interdisciplinary treatment with a learning curve. Dose-volume histograms are used to assess postimplant quality. The authors evaluated their learning curve based on dose-volume histograms and analyzed factors influencing implantation quality. Patients and methods: since 1999, 38 patients with a minimum follow-up of 6 months were treated at the authors' institution with seed implantation using palladium-103 or iodine-125, initially using the preplan method and later real-time planning. Postimplant CT was performed after 4 weeks. The dose-volume indices D90, V100, V150, the D max of pre- and postplans, and the size and position of the volume receiving the prescribed dose (high-dose volume) of the postplans were evaluated. In six patients, postplan imaging both by CT and MRI was used and prostate volumes were compared with preimplant transrectal ultrasound volumes. The first five patients were treated under external supervision. Results: patients were divided into three consecutive groups for analysis of the learning curve (group 1: n = 5 patients treated under external supervision; group 2: n = 13 patients; group 3: n = 20 patients). D90 post for the three groups were 79.3%, 74.2%, and 99.9%, the V100 post were 78.6%, 73.5%, and 88.2%, respectively. The relationship between high-dose volume and prostate volume showed a similar increase as the D90, while the relationship between high-dose volume lying outside the prostate and prostate volume remained constant. The ratio between prostate volumes from transrectal ultrasound and CT imaging decreased with increasing D90 post , while the preplanning D90 and V100 remained constant. The different isotopes used, the method of planning, and the implanted activity per prostate volume did not influence results. Conclusion: a learning curve characterized by an increase

Liberating Learning Object Design from the Learning Style of Student Instructional Designers

Science.gov (United States)

Akpinar, Yavuz

2007-01-01

Learning objects are a new form of learning resource, and the design of these digital environments has many facets. To investigate senior instructional design students' use of reflection tools in designing learning objects, a series of studies was conducted using the Reflective Action Instructional Design and Learning Object Review Instrument…

Carotid wall volume quantification from magnetic resonance images using deformable model fitting and learning-based correction of systematic errors

International Nuclear Information System (INIS)

Hameeteman, K; Niessen, W J; Klein, S; Van 't Klooster, R; Selwaness, M; Van der Lugt, A; Witteman, J C M

2013-01-01

We present a method for carotid vessel wall volume quantification from magnetic resonance imaging (MRI). The method combines lumen and outer wall segmentation based on deformable model fitting with a learning-based segmentation correction step. After selecting two initialization points, the vessel wall volume in a region around the bifurcation is automatically determined. The method was trained on eight datasets (16 carotids) from a population-based study in the elderly for which one observer manually annotated both the lumen and outer wall. An evaluation was carried out on a separate set of 19 datasets (38 carotids) from the same study for which two observers made annotations. Wall volume and normalized wall index measurements resulting from the manual annotations were compared to the automatic measurements. Our experiments show that the automatic method performs comparably to the manual measurements. All image data and annotations used in this study together with the measurements are made available through the website http://ergocar.bigr.nl. (paper)

Multi length-scale characterisation inorganic materials series

CERN Document Server

Bruce, Duncan W; Walton, Richard I

2013-01-01

Whereas the first five volumes in the Inorganic Materials Series focused on particular classes of materials (synthesis, structures, chemistry, and properties), it is now very timely to provide complementary volumes that introduce and review state-of-the-art techniques for materials characterization. This is an important way of emphasizing the interplay of chemical synthesis and physical characterization. The methods reviewed include spectroscopic, diffraction, and surface techniques that examine the structure of materials on all length scales, from local atomic structure to long-range crystall

Arts-based Methods and Organizational Learning

DEFF Research Database (Denmark)

This thematic volume explores the relationship between the arts and learning in various educational contexts and across cultures, but with a focus on higher education and organizational learning. Arts-based interventions are at the heart of this volume, which addresses how they are conceived, des...

Classification of time-series images using deep convolutional neural networks

Science.gov (United States)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

The didactic situation in geometry learning based on analysis of learning obstacles and learning trajectory

Science.gov (United States)

Sulistyowati, Fitria; Budiyono, Slamet, Isnandar

2017-12-01

This study aims to design a didactic situation based on the analysis of learning obstacles and learning trajectory on prism volume. The type of this research is qualitative and quantitative research with steps: analyzing the learning obstacles and learning trajectory, preparing the didactic situation, applying the didactic situation in the classroom, mean difference test of problem solving ability with t-test statistic. The subjects of the study were 8th grade junior high school students in Magelang 2016/2017 selected randomly from eight existing classes. The result of this research is the design of didactic situations that can be implemented in prism volume learning. The effectiveness of didactic situations that have been designed is shown by the mean difference test that is the problem solving ability of the students after the application of the didactic situation better than before the application. The didactic situation that has been generated is expected to be a consideration for teachers to design lessons that match the character of learners, classrooms and teachers themselves, so that the potential thinking of learners can be optimized to avoid the accumulation of learning obstacles.

A Literature Survey of Early Time Series Classification and Deep Learning

OpenAIRE

Santos, Tiago; Kern, Roman

2017-01-01

This paper provides an overview of current literature on time series classification approaches, in particular of early time series classification. A very common and effective time series classification approach is the 1-Nearest Neighbor classier, with different distance measures such as the Euclidean or dynamic time warping distances. This paper starts by reviewing these baseline methods. More recently, with the gain in popularity in the application of deep neural networks to the eld of...

Sandia software guidelines, Volume 4: Configuration management

Energy Technology Data Exchange (ETDEWEB)

1992-06-01

This volume is one in a series of Sandia Software Guidelines for use in producing quality software within Sandia National Laboratories. This volume is based on the IEEE standard and guide for software configuration management. The basic concepts and detailed guidance on implementation of these concepts are discussed for several software project types. Example planning documents for both projects and organizations are included.

A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction.

Science.gov (United States)

Chen, C P; Wan, J Z

1999-01-01

A fast learning algorithm is proposed to find an optimal weights of the flat neural networks (especially, the functional-link network). Although the flat networks are used for nonlinear function approximation, they can be formulated as linear systems. Thus, the weights of the networks can be solved easily using a linear least-square method. This formulation makes it easier to update the weights instantly for both a new added pattern and a new added enhancement node. A dynamic stepwise updating algorithm is proposed to update the weights of the system on-the-fly. The model is tested on several time-series data including an infrared laser data set, a chaotic time-series, a monthly flour price data set, and a nonlinear system identification problem. The simulation results are compared to existing models in which more complex architectures and more costly training are needed. The results indicate that the proposed model is very attractive to real-time processes.

E-learning for Nuclear Newcomers

International Nuclear Information System (INIS)

2013-01-01

We have created an interactive e-learning series explaining the IAEA's Milestones Approach to introducing a nuclear power programme. This approach is based on three phases and covers the 19 infrastructure issues that need to be addressed, and brings decades of expertise to life. Both newcomers and those expanding their nuclear power programmes may benefit from the e-learning series.

Music! Young Discovery Library Series: 25.

Science.gov (United States)

Laurencin, Genevieve

Part of an international series of amply illustrated, colorful, small size books for children ages 5 to 10, this volume presents stories about different aspects of music. The text explains how to listen to music, the main families of musical instruments, the importance of musical instruments in other cultures, and how a violin is constructed. Each…

Assessment: Continuous Learning. Strategies for Teaching and Learning Professional Library.

Science.gov (United States)

Bridges, Lois

This publication is part of a series of monographs on the art of teaching. Each volume, focusing on a specific discipline, explores theory in the context of teaching strategies Three techniques for using the series: dialogues (as self-evaluation and in study groups), shop talk (review of current professional literature), and teacher-to-teacher…

Segmentation of Nonstationary Time Series with Geometric Clustering

DEFF Research Database (Denmark)

Bocharov, Alexei; Thiesson, Bo

2013-01-01

We introduce a non-parametric method for segmentation in regimeswitching time-series models. The approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree, where regime switches are modeled by oblique splits. Such models can be learned efficiently...... from data, where clustering is used to propose one single split candidate at each split level. We use the class of ART time series models to serve as illustration, but because of the non-parametric nature of our segmentation approach, it readily generalizes to a wide range of time-series models that go...

Do Mobile Learning Devices Enhance Learning in Higher Education Anatomy Classrooms?

Science.gov (United States)

Wilkinson, Kate; Barter, Phil

2015-01-01

Recently there has been an increased volume of research and practice of mobile Learning (mLearning) and in particular of the tablet device. The question of how, when and where to best incorporate the tablet device into the learning environment in Higher Education remains largely unanswered. The article presents the findings of an empirical study…

Classroom Management. TESOL Classroom Practice Series

Science.gov (United States)

Farrell, Thomas S. C., Ed.

2008-01-01

This series captures the dynamics of the contemporary ESOL classroom. It showcases state-of-the-art curricula, materials, tasks, and activities reflecting emerging trends in language education and seeks to build localized language teaching and learning theories based on teachers' and students' unique experiences in and beyond the classroom. Each…

Mathematical methods in time series analysis and digital image processing

CERN Document Server

Kurths, J; Maass, P; Timmer, J

2008-01-01

The aim of this volume is to bring together research directions in theoretical signal and imaging processing developed rather independently in electrical engineering, theoretical physics, mathematics and the computer sciences. In particular, mathematically justified algorithms and methods, the mathematical analysis of these algorithms, and methods as well as the investigation of connections between methods from time series analysis and image processing are reviewed. An interdisciplinary comparison of these methods, drawing upon common sets of test problems from medicine and geophysical/enviromental sciences, is also addressed. This volume coherently summarizes work carried out in the field of theoretical signal and image processing. It focuses on non-linear and non-parametric models for time series as well as on adaptive methods in image processing.

Egyptians, Maya, Minoans. Learning Works Enrichment Series.

Science.gov (United States)

Matthies, Susanna

The activities in this instructional resource book are designed to be used by gifted 4-8th grade students as independent research guides or in guided or cooperative learning environments. The activities are organized in three sections which focus the ancient civilizations of Egypt, Maya, and Minoa. The activities presented encourage development of…

Teaching & Learning Tips 1: Teaching perspectives - an introduction.

Science.gov (United States)

Rana, Jasmine; Burgin, Susan

2017-11-01

Challenge: Clinical and research responsibilities often leave little or no time to plan thoughtful teaching encounters with trainees. This "Teaching & Learning Tips" series is designed to be an accessible guide for dermatologists who want to improve their teaching skills. It is comprised of 12 articles about how to enhance teaching in various settings informed by research about how people learn and expert-derived or data-driven best practices for teaching. The series begins with a review of principles to optimize learning in any setting, including cognitive load theory, active learning strategies, and the impact of motivation and emotion on learning. It transitions into a practical "how to" guide format for common teaching scenarios in dermatology, such as lecturing, case-based teaching, and teaching procedures, among others. Herein, we kickoff the series by unpacking assumptions about teaching and learning. What does it mean to teach and learn? © 2017 The International Society of Dermatology.

Strategy for Alternative Occupant Volume Testing

Science.gov (United States)

2009-10-20

This paper describes plans for a series of quasi-static : compression tests of rail passenger equipment. These tests are : designed to evaluate the strength of the occupant volume under : static loading conditions. The research plan includes a detail...

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Indian Academy of Sciences Conference Series; Volume 1; Issue 1. Chimera-like states generated by large perturbation of synchronous state of coupled metronomes. SERGEY BREZETSKIY DAWID DUDKOWSKI PATRYCJA JAROS JERZY WOJEWODA KRZYSZTOF CZOLCZYNSKI YURI MAISTRENKO ...

Machine Learning for Neuroimaging with Scikit-Learn

Directory of Open Access Journals (Sweden)

Alexandre eAbraham

2014-02-01

Full Text Available Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn.

Science.gov (United States)

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Designing and Evaluating an Interprofessional Experiential Course Series Involving Medical and Pharmacy Students

Science.gov (United States)

Dueñas, Gladys G.; Zanoni, Aileen; Grover, Anisha B.

2016-01-01

Objective. To prepare first-year and second-year pharmacy and medical students to build effective collaborative health care teams by participating in an interprofessional experiential 6-semester course series. Design. An interprofessional experiential course series was designed using a variety of teaching methods to achieve both interprofessional and experiential learning outcomes. A standardized objective behavioral assessment was developed to measure team performance of interprofessional communication and teamwork. In addition, student perceptions were measured using a validated instrument. Assessment. A majority of teams demonstrated appropriate competence with respect to interprofessional communication and teamwork. Additionally, a majority of students expressed positive perceptions of interprofessional collaboration with respect to teamwork, roles and responsibilities, and patient outcomes. Conclusion. An interprofessional experiential course series can be successfully implemented to achieve both interprofessional and experiential learning outcomes. Highly collaborative teams and positive student perceptions provide evidence of achievement of interprofessional education learning outcomes. PMID:27402988

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

RQA correlations on real business cycles time series ... Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016 Volume 1 Issue 1 ... di Bari “Aldo Moro” - Department of Economics and Finance, Via C. Rosalba 53, Bari, ...

Fourier Series, the DFT and Shape Modelling

DEFF Research Database (Denmark)

Skoglund, Karl

2004-01-01

This report provides an introduction to Fourier series, the discrete Fourier transform, complex geometry and Fourier descriptors for shape analysis. The content is aimed at undergraduate and graduate students who wish to learn about Fourier analysis in general, as well as its application to shape...

Bare-Hand Volume Cracker for Raw Volume Data Analysis

Directory of Open Access Journals (Sweden)

Bireswar Laha

2016-09-01

Full Text Available Analysis of raw volume data generated from different scanning technologies faces a variety of challenges, related to search, pattern recognition, spatial understanding, quantitative estimation, and shape description. In a previous study, we found that the Volume Cracker (VC 3D interaction (3DI technique mitigated some of these problems, but this result was from a tethered glove-based system with users analyzing simulated data. Here, we redesigned the VC by using untethered bare-hand interaction with real volume datasets, with a broader aim of adoption of this technique in research labs. We developed symmetric and asymmetric interfaces for the Bare-Hand Volume Cracker (BHVC through design iterations with a biomechanics scientist. We evaluated our asymmetric BHVC technique against standard 2D and widely used 3D interaction techniques with experts analyzing scanned beetle datasets. We found that our BHVC design significantly outperformed the other two techniques. This study contributes a practical 3DI design for scientists, documents lessons learned while redesigning for bare-hand trackers, and provides evidence suggesting that 3D interaction could improve volume data analysis for a variety of visual analysis tasks. Our contribution is in the realm of 3D user interfaces tightly integrated with visualization, for improving the effectiveness of visual analysis of volume datasets. Based on our experience, we also provide some insights into hardware-agnostic principles for design of effective interaction techniques.

Dose-volume considerations in stereotaxic brain radiation therapy

International Nuclear Information System (INIS)

Houdek, P.V.; Schwade, J.G.; Pisciotta, V.J.; Medina, A.J.; Lewin, A.A.; Abitbol, A.A.; Serago, C.F.

1988-01-01

Although brain radiation therapy experience suggests that a gain in the therapeutic ratio may be achieved by optimizing the dose-volume relationship, no practical system for quantitative assessment of dose-volume data has been developed. This presentation describes the rationale for using the integral dose function for this purpose and demonstrates that with the use of a conventional treatment planning computer and a series of computed tomographic scans, first-order optimization of the dose-volume function can be accomplished in two steps: first, high-dose volume is minimized by selecting an appropriate treatment technique and tumor margin, and then dosage is maximized by calculating the brain tolerance dose as a function of the irradiated volume

SPATIOTEMPORAL VISUALIZATION OF TIME-SERIES SATELLITE-DERIVED CO2 FLUX DATA USING VOLUME RENDERING AND GPU-BASED INTERPOLATION ON A CLOUD-DRIVEN DIGITAL EARTH

Directory of Open Access Journals (Sweden)

S. Wu

2017-10-01

Full Text Available The ocean carbon cycle has a significant influence on global climate, and is commonly evaluated using time-series satellite-derived CO2 flux data. Location-aware and globe-based visualization is an important technique for analyzing and presenting the evolution of climate change. To achieve realistic simulation of the spatiotemporal dynamics of ocean carbon, a cloud-driven digital earth platform is developed to support the interactive analysis and display of multi-geospatial data, and an original visualization method based on our digital earth is proposed to demonstrate the spatiotemporal variations of carbon sinks and sources using time-series satellite data. Specifically, a volume rendering technique using half-angle slicing and particle system is implemented to dynamically display the released or absorbed CO2 gas. To enable location-aware visualization within the virtual globe, we present a 3D particlemapping algorithm to render particle-slicing textures onto geospace. In addition, a GPU-based interpolation framework using CUDA during real-time rendering is designed to obtain smooth effects in both spatial and temporal dimensions. To demonstrate the capabilities of the proposed method, a series of satellite data is applied to simulate the air-sea carbon cycle in the China Sea. The results show that the suggested strategies provide realistic simulation effects and acceptable interactive performance on the digital earth.

Long-term prediction of chaotic time series with multi-step prediction horizons by a neural network with Levenberg-Marquardt learning algorithm

International Nuclear Information System (INIS)

Mirzaee, Hossein

2009-01-01

The Levenberg-Marquardt learning algorithm is applied for training a multilayer perception with three hidden layer each with ten neurons in order to carefully map the structure of chaotic time series such as Mackey-Glass time series. First the MLP network is trained with 1000 data, and then it is tested with next 500 data. After that the trained and tested network is applied for long-term prediction of next 120 data which come after test data. The prediction is such a way that, the first inputs to network for prediction are the four last data of test data, then the predicted value is shifted to the regression vector which is the input to the network, then after first four-step of prediction, the input regression vector to network is fully predicted values and in continue, each predicted data is shifted to input vector for subsequent prediction.

Update on alternative occupant volume testing

Science.gov (United States)

2010-04-27

This paper describes the conduct of the first of a series of quasi-static compression tests of rail passenger equipment being done to examine occupant volume strength. Budd Pioneer car 244 has been chosen as the test article for examination of altern...

Learning chest imaging

Energy Technology Data Exchange (ETDEWEB)

Pedrozo Pupo, John C. (ed.) [Magdalena Univ., Santa Maria (Colombia). Respire - Inst. for Respiratory Care

2013-03-01

Useful learning tool for practitioners and students. Overview of the imaging techniques used in chest radiology. Aid to the correct interpretation of chest X-ray images. Radiology of the thorax forms an indispensable element of the basic diagnostic process for many conditions and is of key importance in a variety of medical disciplines. This user-friendly book provides an overview of the imaging techniques used in chest radiology and presents numerous instructive case-based images with accompanying explanatory text. A wide range of clinical conditions and circumstances are covered with the aim of enabling the reader to confidently interpret chest images by correctly identifying structures of interest and the causes of abnormalities. This book, which will be an invaluable learning tool, forms part of the Learning Imaging series for medical students, residents, less experienced radiologists, and other medical staff. Learning Imaging is a unique case-based series for those in professional education in general and for physicians in prarticular.

Young Gifted Children: Meeting Their Needs. Research in Practice Series. Volume 12, Number 3

Science.gov (United States)

Porter, Louise

2005-01-01

The "Research in Practice Series" is published four times each year by Early Childhood Australia. The series aims to provide practical, easy to read, up-to-date information and support to a growing national readership of early childhood workers. The books bring together the best information available on wide-ranging topics and are an…

Barriers and Strategies on Adoption of E-Learning in Tanzanian Higher Learning Institutions: Lessons for Adopters

Science.gov (United States)

Kisanga, Dalton; Ireson, Gren

2015-01-01

Tanzanian Higher learning institutions (HLIs) are faced with challenges of adopting e-learning in education. This study involved experts in e-learning to examine barriers of adopting e-learning and the best strategies to address them. Data were gathered from a series of semi-structured interviews with e-learning experts from two HLIs in Tanzania.…

RAIL TRAFFIC VOLUME ESTIMATION BASED ON WORLD DEVELOPMENT INDICATORS

Directory of Open Access Journals (Sweden)

Luka Lazarević

2015-08-01

Full Text Available European transport policy, defined in the White Paper, supports shift from road to rail and waterborne transport. The hypothesis of the paper is that changes in the economic environment influence rail traffic volume. Therefore, a model for prediction of rail traffic volume applied in different economic contexts could be a valuable tool for the transport planners. The model was built using common Machine Learning techniques that learn from the past experience. In the model preparation, world development indicators defined by the World Bank were used as input parameters.

What is the fundamental ion-specific series for anions and cations? Ion specificity in standard partial molar volumes of electrolytes and electrostriction in water and non-aqueous solvents† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02691a Click here for additional data file.

Science.gov (United States)

Mazzini, Virginia

2017-01-01

The importance of electrolyte solutions cannot be overstated. Beyond the ionic strength of electrolyte solutions the specific nature of the ions present is vital in controlling a host of properties. Therefore ion specificity is fundamentally important in physical chemistry, engineering and biology. The observation that the strengths of the effect of ions often follows well established series suggests that a single predictive and quantitative description of specific-ion effects covering a wide range of systems is possible. Such a theory would revolutionise applications of physical chemistry from polymer precipitation to drug design. Current approaches to understanding specific-ion effects involve consideration of the ions themselves, the solvent and relevant interfaces and the interactions between them. Here we investigate the specific-ion effects trends of standard partial molar volumes and electrostrictive volumes of electrolytes in water and eleven non-aqueous solvents. We choose these measures as they relate to bulk properties at infinite dilution, therefore they are the simplest electrolyte systems. This is done to test the hypothesis that the ions alone exhibit a specific-ion effect series that is independent of the solvent and unrelated to surface properties. The specific-ion effects trends of standard partial molar volumes and normalised electrostrictive volumes examined in this work show a fundamental ion-specific series that is reproduced across the solvents, which is the Hofmeister series for anions and the reverse lyotropic series for cations, supporting the hypothesis. This outcome is important in demonstrating that ion specificity is observed at infinite dilution and demonstrates that the complexity observed in the manifestation of specific-ion effects in a very wide range of systems is due to perturbations of solvent, surfaces and concentration on the underlying fundamental series. This knowledge will guide a general understanding of specific

Koopman Operator Framework for Time Series Modeling and Analysis

Science.gov (United States)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

Divergent series, summability and resurgence I monodromy and resurgence

CERN Document Server

Mitschi, Claude

2016-01-01

Providing an elementary introduction to analytic continuation and monodromy, the first part of this volume applies these notions to the local and global study of complex linear differential equations, their formal solutions at singular points, their monodromy and their differential Galois groups. The Riemann-Hilbert problem is discussed from Bolibrukh’s point of view. The second part expounds 1-summability and Ecalle’s theory of resurgence under fairly general conditions. It contains numerous examples and presents an analysis of the singularities in the Borel plane via “alien calculus”, which provides a full description of the Stokes phenomenon for linear or non-linear differential or difference equations. The first of a series of three, entitled Divergent Series, Summability and Resurgence, this volume is aimed at graduate students, mathematicians and theoretical physicists interested in geometric, algebraic or local analytic properties of dynamical systems. It includes useful exercises with solution...

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...

Forecasting daily patient volumes in the emergency department.

Science.gov (United States)

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by

The influence of attention, learning, and motivation on visual search.

Science.gov (United States)

Dodd, Michael D; Flowers, John H

2012-01-01

The 59th Annual Nebraska Symposium on Motivation (The Influence of Attention, Learning, and Motivation on Visual Search) took place April 7-8, 2011, on the University of Nebraska-Lincoln campus. The symposium brought together leading scholars who conduct research related to visual search at a variety levels for a series of talks, poster presentations, panel discussions, and numerous additional opportunities for intellectual exchange. The Symposium was also streamed online for the first time in the history of the event, allowing individuals from around the world to view the presentations and submit questions. The present volume is intended to both commemorate the event itself and to allow our speakers additional opportunity to address issues and current research that have since arisen. Each of the speakers (and, in some cases, their graduate students and post docs) has provided a chapter which both summarizes and expands on their original presentations. In this chapter, we sought to a) provide additional context as to how the Symposium came to be, b) discuss why we thought that this was an ideal time to organize a visual search symposium, and c) to briefly address recent trends and potential future directions in the field. We hope you find the volume both enjoyable and informative, and we thank the authors who have contributed a series of engaging chapters.

The Digital Learning Imperative: How Technology and Teaching Meet Today's Education Challenges. Digital Learning Series

Science.gov (United States)

Schwartzbeck, Terri Duggan; Wolf, Mary Ann

2012-01-01

This report outlines how digital learning can connect middle and high school students with better teaching and learning experiences while also addressing three major challenges facing the nation's education system--access to good teaching, tight budgets, and boosting student achievement. But simply slapping a netbook on top of a textbook will not…

Clinical time series prediction: Toward a hierarchical dynamical system framework.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Clinical time series prediction: towards a hierarchical dynamical system framework

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

SU-C-BRA-05: Delineating High-Dose Clinical Target Volumes for Head and Neck Tumors Using Machine Learning Algorithms

Energy Technology Data Exchange (ETDEWEB)

Cardenas, C [Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States); The University of Texas Graduate School of Biomedical Sciences, Houston, TX (United States); Wong, A [Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States); School of Medicine, The University of Texas Health Sciences Center at San Antonio, San Antonio, TX (United States); Mohamed, A; Fuller, C [Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States); Yang, J; Court, L; Aristophanous, M [Department of Radiation Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States); Rao, A [Department of Bioinformatics and Computational Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX (United States)

2016-06-15

Purpose: To develop and test population-based machine learning algorithms for delineating high-dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter-physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy. Methods: Twenty-five node-negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil). All patients had GTV and CTVs manually contoured by an experienced radiation oncologist prior to treatment. This contouring process, which is driven by anatomical, pathological, and patient specific information, typically results in non-uniform margin expansions about the GTV. Therefore, we tested two methods to delineate high-dose CTV given a manually-contoured GTV: (1) regression-support vector machines(SVM) and (2) classification-SVM. These models were trained and tested on each patient group using leave-one-out cross-validation. The volume difference(VD) and Dice similarity coefficient(DSC) between the manual and auto-contoured CTV were calculated to evaluate the results. Distances from GTV-to-CTV were computed about each patient’s GTV and these distances, in addition to distances from GTV to surrounding anatomy in the expansion direction, were utilized in the regression-SVM method. The classification-SVM method used categorical voxel-information (GTV, selected anatomical structures, else) from a 3×3×3cm3 ROI centered about the voxel to classify voxels as CTV. Results: Volumes for the auto-contoured CTVs ranged from 17.1 to 149.1cc and 17.4 to 151.9cc; the average(range) VD between manual and auto-contoured CTV were 0.93 (0.48–1.59) and 1.16(0.48–1.97); while average(range) DSC values were 0.75(0.59–0.88) and 0.74(0.59–0.81) for the regression-SVM and classification-SVM methods, respectively. Conclusion: We developed two novel machine learning methods to delineate

SU-C-BRA-05: Delineating High-Dose Clinical Target Volumes for Head and Neck Tumors Using Machine Learning Algorithms

International Nuclear Information System (INIS)

Cardenas, C; Wong, A; Mohamed, A; Fuller, C; Yang, J; Court, L; Aristophanous, M; Rao, A

2016-01-01

Purpose: To develop and test population-based machine learning algorithms for delineating high-dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter-physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy. Methods: Twenty-five node-negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil). All patients had GTV and CTVs manually contoured by an experienced radiation oncologist prior to treatment. This contouring process, which is driven by anatomical, pathological, and patient specific information, typically results in non-uniform margin expansions about the GTV. Therefore, we tested two methods to delineate high-dose CTV given a manually-contoured GTV: (1) regression-support vector machines(SVM) and (2) classification-SVM. These models were trained and tested on each patient group using leave-one-out cross-validation. The volume difference(VD) and Dice similarity coefficient(DSC) between the manual and auto-contoured CTV were calculated to evaluate the results. Distances from GTV-to-CTV were computed about each patient’s GTV and these distances, in addition to distances from GTV to surrounding anatomy in the expansion direction, were utilized in the regression-SVM method. The classification-SVM method used categorical voxel-information (GTV, selected anatomical structures, else) from a 3×3×3cm3 ROI centered about the voxel to classify voxels as CTV. Results: Volumes for the auto-contoured CTVs ranged from 17.1 to 149.1cc and 17.4 to 151.9cc; the average(range) VD between manual and auto-contoured CTV were 0.93 (0.48–1.59) and 1.16(0.48–1.97); while average(range) DSC values were 0.75(0.59–0.88) and 0.74(0.59–0.81) for the regression-SVM and classification-SVM methods, respectively. Conclusion: We developed two novel machine learning methods to delineate

Validation of Left Atrial Volume Estimation by Left Atrial Diameter from the Parasternal Long-Axis View.

Science.gov (United States)

Canciello, Grazia; de Simone, Giovanni; Izzo, Raffaele; Giamundo, Alessandra; Pacelli, Filomena; Mancusi, Costantino; Galderisi, Maurizio; Trimarco, Bruno; Losi, Maria-Angela

2017-03-01

Measurement of left atrial (LA) volume (LAV) is recommended for quantification of LA size. Only LA anteroposterior diameter (LAd) is available in a number of large cohorts, trials, or registries. The aim of this study was to evaluate whether LAV may be reasonably estimated from LAd. One hundred forty consecutive patients referred to our outpatient clinics were prospectively enrolled to measure LAd from the long-axis view on two-dimensional echocardiography. LA orthogonal dimensions were also taken from apical four- and two-chamber views. LAV was measured using the Simpson, area-length, and ellipsoid (LAV e ) methods. The first 70 patients were the learning series and the last 70 the testing series (TeS). In the learning series, best-fitting regression analysis of LAV-LAd was run using all LAV methods, and the highest values of F were chosen among the regression equations. In the TeS, the best-fitting regressions were used to estimate LAV from LAd. In the learning series, the best-fitting regression was linear for the Spearman method (r 2  = 0.62, F = 111.85, P = .0001) and area-length method (r 2  = 0.62, F = 112.24, P = .0001) and powered for the LAV e method (r 2  = 0.81, F = 288.41, P = .0001). In the TeS, the r 2 value for LAV prediction was substantially better using the LAV e method (r 2  = 0.89) than the Simpson (r 2  = 0.72) or area-length (r 2  = 0.70) method, as was the intraclass correlation (ρ = 0.96 vs ρ = 0.89 and ρ = 0.89, respectively). In the TeS, the sensitivity and specificity of LA dilatation by the estimated LAV e method were 87% and 90%, respectively. LAV can be estimated from LAd using a nonlinear equation with an elliptical model. The proposed method may be used in retrospective analysis of existing data sets in which determination of LAV was not programmed. Copyright © 2016 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.

Service-Learning Instructional Design Considerations

Science.gov (United States)

Maddrell, Jennifer

2014-01-01

This paper explores the design of "service-learning" experiences to engage college students in the real-world application of course subject matter. Service learning is an educational approach that combines community service, academic coursework, and work-based applied learning. Based on data gathered during a series of recent interviews…

Sandia software guidelines: Volume 5, Tools, techniques, and methodologies

Energy Technology Data Exchange (ETDEWEB)

1989-07-01

This volume is one in a series of Sandia Software Guidelines intended for use in producing quality software within Sandia National Laboratories. This volume describes software tools and methodologies available to Sandia personnel for the development of software, and outlines techniques that have proven useful within the Laboratories and elsewhere. References and evaluations by Sandia personnel are included. 6 figs.

Thermophysical properties of freons methane series, pt.1

CERN Document Server

1987-01-01

These are the succeeding volumes of a series of books on thermodynamic properties of engineering materials prepared under the auspices of the State Service of Standard Reference data of the Soviet Union. Each volume is set up in the same way: Part I deals with a study of all necessary aspects of experimental data interpretation and analysis; Part II then presents the fundamental constants, symbols with units, and data tables. Researchers and engineers in the fields of process design, equipment development, custody transfer and safety will find these book valuable and reliable reference sources for their respective tasks.

Refining Markov state models for conformational dynamics using ensemble-averaged data and time-series trajectories

Science.gov (United States)

Matsunaga, Y.; Sugita, Y.

2018-06-01

A data-driven modeling scheme is proposed for conformational dynamics of biomolecules based on molecular dynamics (MD) simulations and experimental measurements. In this scheme, an initial Markov State Model (MSM) is constructed from MD simulation trajectories, and then, the MSM parameters are refined using experimental measurements through machine learning techniques. The second step can reduce the bias of MD simulation results due to inaccurate force-field parameters. Either time-series trajectories or ensemble-averaged data are available as a training data set in the scheme. Using a coarse-grained model of a dye-labeled polyproline-20, we compare the performance of machine learning estimations from the two types of training data sets. Machine learning from time-series data could provide the equilibrium populations of conformational states as well as their transition probabilities. It estimates hidden conformational states in more robust ways compared to that from ensemble-averaged data although there are limitations in estimating the transition probabilities between minor states. We discuss how to use the machine learning scheme for various experimental measurements including single-molecule time-series trajectories.

Indian Academy of Sciences Conference Series | Indian Academy of ...

Indian Academy of Sciences (India)

Annual Meetings · Mid Year Meetings · Discussion Meetings · Public Lectures · Lecture Workshops · Refresher Courses · Symposia · Live Streaming. Home; Journals; Indian Academy of Sciences Conference Series; Volume 1; Issue 1. Organizing Commitee. Proceedings of the Conference on Perspectives in Nonlinear ...

TechEdSat Nano-Satellite Series Fact Sheet

Science.gov (United States)

Murbach, Marcus; Martinez, Andres; Guarneros Luna, Ali

2014-01-01

TechEdSat-3p is the second generation in the TechEdSat-X series. The TechEdSat Series uses the CubeSat standards established by the California Polytechnic State University Cal Poly), San Luis Obispo. With typical blocks being constructed from 1-unit (1U 10x10x10 cm) increments, the TechEdSat-3p has a 3U volume with a 30 cm length. The project uniquely pairs advanced university students with NASA researchers in a rapid design-to-flight experience lasting 1-2 semesters.The TechEdSat Nano-Satellite Series provides a rapid platform for testing technologies for future NASA Earth and planetary missions, as well as providing students with an early exposure to flight hardware development and management.

Using learning analytics to evaluate a video-based lecture series.

Science.gov (United States)

Lau, K H Vincent; Farooque, Pue; Leydon, Gary; Schwartz, Michael L; Sadler, R Mark; Moeller, Jeremy J

2018-01-01

The video-based lecture (VBL), an important component of the flipped classroom (FC) and massive open online course (MOOC) approaches to medical education, has primarily been evaluated through direct learner feedback. Evaluation may be enhanced through learner analytics (LA) - analysis of quantitative audience usage data generated by video-sharing platforms. We applied LA to an experimental series of ten VBLs on electroencephalography (EEG) interpretation, uploaded to YouTube in the model of a publicly accessible MOOC. Trends in view count; total percentage of video viewed and audience retention (AR) (percentage of viewers watching at a time point compared to the initial total) were examined. The pattern of average AR decline was characterized using regression analysis, revealing a uniform linear decline in viewership for each video, with no evidence of an optimal VBL length. Segments with transient increases in AR corresponded to those focused on core concepts, indicative of content requiring more detailed evaluation. We propose a model for applying LA at four levels: global, series, video, and feedback. LA may be a useful tool in evaluating a VBL series. Our proposed model combines analytics data and learner self-report for comprehensive evaluation.

Automated Feature Design for Time Series Classification by Genetic Programming

OpenAIRE

Harvey, Dustin Yewell

2014-01-01

Time series classification (TSC) methods discover and exploit patterns in time series and other one-dimensional signals. Although many accurate, robust classifiers exist for multivariate feature sets, general approaches are needed to extend machine learning techniques to make use of signal inputs. Numerous applications of TSC can be found in structural engineering, especially in the areas of structural health monitoring and non-destructive evaluation. Additionally, the fields of process contr...

The accuracy of MRI-determined synovial membrane and joint effusion volumes in arthritis. A comparison of pre- and post-aspiration volumes

DEFF Research Database (Denmark)

Østergaard, Mikkel; Stoltenberg, M; Henriksen, O

1995-01-01

Magnetic resonance imaging (MRI) of 18 knees of patients with arthritis was performed before and immediately after arthrocentesis. Pre- and post-aspiration volumes were calculated by adding the outlined areas of synovium/effusion from a continuous series of gadolinium-DTPA-enhanced 5 mm transversal...... T1-weighted MR-images. The difference between MRI-determined and syringe-determined volumes of aspirated joint fluid was 0-7 ml, median 2 ml, corresponding to 0-18%, median 7%, of the pre-aspiration effusion volume. Synovial membrane volumes, determined before and after arthrocentesis varied 0-10 ml......, median 3 ml (0-17%, median 7%). No significant systematic misinterpretation of the borderline between joint fluid and synovium was found. We conclude that effusion volumes and in all probability also synovial membrane volumes, can be determined by MRI with a maximal analytical error of approximately 20...

Measurement of the deuterium Balmer series line emission on EAST

Energy Technology Data Exchange (ETDEWEB)

Wu, C. R.; Xu, Z.; Jin, Z.; Zhang, P. F. [Institute of Plasma Physics, Chinese Academy of Sciences, P.O. Box 1126, Hefei, Anhui 230031 (China); Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, Anhui 230031 (China); Huang, J., E-mail: juan.huang@ipp.ac.cn; Gao, W.; Gao, W.; Chang, J. F.; Xu, J. C.; Duan, Y. M.; Chen, Y. J.; Zhang, L.; Wu, Z. W.; Li, J. G. [Institute of Plasma Physics, Chinese Academy of Sciences, P.O. Box 1126, Hefei, Anhui 230031 (China); Hou, Y. M. [Institute of Plasma Physics, Chinese Academy of Sciences, P.O. Box 1126, Hefei, Anhui 230031 (China); School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230026 (China)

2016-11-15

Volume recombination plays an important role towards plasma detachment for magnetically confined fusion devices. High quantum number states of the Balmer series of deuterium are used to study recombination. On EAST (Experimental Advanced Superconducting Tokamak), two visible spectroscopic measurements are applied for the upper/lower divertor with 13 channels, respectively. Both systems are coupled with Princeton Instruments ProEM EMCCD 1024B camera: one is equipped on an Acton SP2750 spectrometer, which has a high spectral resolution ∼0.0049 nm with 2400 gr/mm grating to measure the D{sub α}(H{sub α}) spectral line and with 1200 gr/mm grating to measure deuterium molecular Fulcher band emissions and another is equipped on IsoPlane SCT320 using 600 gr/mm to measure high-n Balmer series emission lines, allowing us to study volume recombination on EAST and to obtain the related line averaged plasma parameters (T{sub e}, n{sub e}) during EAST detached phases. This paper will present the details of the measurements and the characteristics of deuterium Balmer series line emissions during density ramp-up L-mode USN plasma on EAST.

SOCIAL COMPLEXITY AND LEARNING FORAGING TASKS IN BEES

Directory of Open Access Journals (Sweden)

AMAYA-MÁRQUEZ MARISOL

2008-12-01

Full Text Available Social complexity and models concerning central place foraging were tested with respect to learning predictions using the social honey bee (Apis mellifera and solitary blue orchard bee (Osmia lignaria when given foraging problems. Both species were presented the same foraging problems, where 1 only reward molarity varied between flower morphs, and 2 only reward volume varied between flower morphs. Experiments utilized blue vs. white flower patches to standardize rewards in each experimental situation. Although honey bees learned faster than blue orchard bees when given a molarity difference reward problem, there was no significant difference in learning rate when presented a volume difference reward problem. Further, the rate at which blue orchard bees learned the volume difference problem was not significantly different from that with which honey bees learned about reward molarity differences. The results do not support the predictions of the social complexity theory, but do support those of the central place model

A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

Science.gov (United States)

Ngan, Chun-Kit

2013-01-01

Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

Natural Computing in Computational Finance Volume 4

CERN Document Server

O’Neill, Michael; Maringer, Dietmar

2012-01-01

This book follows on from Natural Computing in Computational Finance  Volumes I, II and III.   As in the previous volumes of this series, the  book consists of a series of  chapters each of  which was selected following a rigorous, peer-reviewed, selection process.  The chapters illustrate the application of a range of cutting-edge natural  computing and agent-based methodologies in computational finance and economics.  The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading,  corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  While describing cutting edge applications, the chapters are  written so that they are accessible to a wide audience. Hence, they should be of interest  to academics, students and practitioners in the fields of computational finance and  economics.  

Reduced hippocampal volume is associated with overgeneralization of negative context in individuals with PTSD.

Science.gov (United States)

Levy-Gigi, Einat; Szabo, Csilla; Richter-Levin, Gal; Kéri, Szabolcs

2015-01-01

Previous studies demonstrated reduced hippocampal volume in individuals with posttraumatic stress disorder (PTSD). However, the functional role the hippocampus plays in PTSD symptomatology is still unclear. The aim of the present study was to explore generalization learning and its connection to hippocampal volume in individuals with and without PTSD. Animal and human models argue that hippocampal deficit may result in failure to process contextual information. Therefore we predicted associations between reduced hippocampal volume and overgeneralization of context in individuals with PTSD. We conducted MRI scans of bilateral hippocampal and amygdala formations as well as intracranial and total brain volumes. Generalization was measured using a novel-learning paradigm, which separately evaluates generalization of cue and context in conditions of negative and positive outcomes. As expected, MRI scans indicated reduced hippocampal volume in PTSD compared to non-PTSD participants. Behavioral results revealed a selective deficit in context generalization learning in individuals with PTSD, F(1, 43) = 8.27, p < .01, η(p)² = .16. Specifically, as predicted, while generalization of cue was spared in both groups, individuals with PTSD showed overgeneralization of negative context. Hence, they could not learn that a previously negative context is later associated with a positive outcome, F(1, 43) = 7.33, p = .01, η(p)² = .15. Most importantly, overgeneralization of negative context significantly correlated with right and left hippocampal volume (r = .61, p = .000; r = .5, p = .000). Finally, bilateral hippocampal volume provided the strongest prediction of overgeneralization of negative context. Reduced hippocampal volume may account for the difficulty of individuals with PTSD to differentiate negative and novel conditions and hence may facilitate reexperiencing symptoms. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Bears, Big and Little. Young Discovery Library Series.

Science.gov (United States)

Pfeffer, Pierre

This book is written for children 5 through 10. Part of a series designed to develop their curiosity, fascinate them and educate them, this volume describes: (1) the eight species of bears, including black bear, brown bear, grizzly bear, spectacled bear, sun bear, sloth bear, polar bear, and giant panda; (2) geographical habitats of bears; (3)…

Law and Learning in the Middle Ages

DEFF Research Database (Denmark)

This volume contains papers presented at the conference on "Law and Learning in the Middle Ages" held at the Carlsberg Academy in Copenhagen in May 2005. Here, a group of European and American scholars give their contribution to the examination of the theological and legal schooling...... that the 'creators' of the laws received at the major centres of learning in Europe, and address a number of important questions concerning the creation and development of legal professions and the dynamics between legal practice and theoretical, learned approaches to jurisprudence. Contributors to this volume...

Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective

CERN Document Server

Chen, Shyi-Ming

2013-01-01

Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological a...

Learning Foodchain with Calotropis procera

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 12; Issue 1. Learning Foodchain with Calotropis procera. Dilip Amritphale Santosh K Sharma. Classroom Volume 12 Issue 1 January 2007 pp 67-75. Fulltext. Click here to view fulltext PDF. Permanent link:

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data

Science.gov (United States)

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size. PMID:28045443

Hippocampal dose volume histogram predicts Hopkins Verbal Learning Test scores after brain irradiation

Directory of Open Access Journals (Sweden)

Catherine Okoukoni, PhD

2017-10-01

Full Text Available Purpose: Radiation-induced cognitive decline is relatively common after treatment for primary and metastatic brain tumors; however, identifying dosimetric parameters that are predictive of radiation-induced cognitive decline is difficult due to the heterogeneity of patient characteristics. The memory function is especially susceptible to radiation effects after treatment. The objective of this study is to correlate volumetric radiation doses received by critical neuroanatomic structures to post–radiation therapy (RT memory impairment. Methods and materials: Between 2008 and 2011, 53 patients with primary brain malignancies were treated with conventionally fractionated RT in prospectively accrued clinical trials performed at our institution. Dose-volume histogram analysis was performed for the hippocampus, parahippocampus, amygdala, and fusiform gyrus. Hopkins Verbal Learning Test-Revised scores were obtained at least 6 months after RT. Impairment was defined as an immediate recall score ≤15. For each anatomic region, serial regression was performed to correlate volume receiving a given dose (VD(Gy with memory impairment. Results: Hippocampal V53.4Gy to V60.9Gy significantly predicted post-RT memory impairment (P < .05. Within this range, the hippocampal V55Gy was the most significant predictor (P = .004. Hippocampal V55Gy of 0%, 25%, and 50% was associated with tumor-induced impairment rates of 14.9% (95% confidence interval [CI], 7.2%-28.7%, 45.9% (95% CI, 24.7%-68.6%, and 80.6% (95% CI, 39.2%-96.4%, respectively. Conclusions: The hippocampal V55Gy is a significant predictor for impairment, and a limiting dose below 55 Gy may minimize radiation-induced cognitive impairment.

Time series prediction by feedforward neural networks - is it difficult?

International Nuclear Information System (INIS)

Rosen-Zvi, Michal; Kanter, Ido; Kinzel, Wolfgang

2003-01-01

The difficulties that a neural network faces when trying to learn from a quasi-periodic time series are studied analytically using a teacher-student scenario where the random input is divided into two macroscopic regions with different variances, 1 and 1/γ 2 (γ >> 1). The generalization error is found to decrease as ε g ∝ exp(-α/γ 2 ), where α is the number of examples per input dimension. In contradiction to this very slow vanishing generalization error, the next output prediction is found to be almost free of mistakes. This picture is consistent with learning quasi-periodic time series produced by feedforward neural networks, which is dominated by enhanced components of the Fourier spectrum of the input. Simulation results are in good agreement with the analytical results

Industrial Learning Curves Series Production of the LHC Main Superconduting Dipoles

CERN Document Server

Fessia, Paolo; Rossi, Lucio

2007-01-01

By mid August 2006, 1160 of the 1232 of LHC main dipoles have been delivered to CERN by the three suppliers in charge of the production. The training of the staff, mostly hired just for this manufacture, and the improvement of the procedures with the acquired experience, naturally decrease the time necessary for the assembly of a unit. The aim of this paper is to apply methodologies like the cost-based learning curves and the time-based learning curves to the LHC Main Dipole production comparing the estimated learning percentage to the ones experienced in other industries. This type of analysis, already presented on 500 units is here extended to more than 1000 completed units. The work also tries to identify which type of industry presents the learning percentages that are the most similar to our case and to investigate the impact of the production strategy on the process efficiency.

Developing Enterprise E-Learning at Kodak.

Science.gov (United States)

Gold, Martha

2003-01-01

The third in a five-part series of case studies on enterprisewide electronic learning describes how Kodak's approach to a global learning management system integrated 80 discrete human resource systems into one. (JOW)

NPR hazards review: (Phase 1, Production only appendixes). Volume 2

Energy Technology Data Exchange (ETDEWEB)

Miller, N.R.; Trumble, R.E.

1962-08-15

The NPR Hazards Review is being issued in a series of volumes. Volume 1, which has already been published, was of the nature of an expanded summary. It included the results of hazards analyses with some explanatory material to put the results in context. Volume 2 presents results of reviews made after the preparation of Volume 1. It also contains supporting material and details not included in Volume 1. Volumes 1 and 2 together provide a nearly complete ``Design Hazards Review of the NPR.`` However, certain remaining problems still exist and are to be the subject of a continuing R&D program. These problems and programs are discussed in Appendix H. Neither Volume 1 nor Volume 2 treat operational aspects of reactor hazards in detail. This area of concern will be the primary subject of a third volume of the NPR Hazards Review. This third volume, to be prepared and issued at a later date, may also contain information supplementing Volumes 1 and 2.

Calibration of a large volume argon-41 gas-effluent monitor

International Nuclear Information System (INIS)

Wilson, William E.; Lovas, Thomas A.

1976-01-01

In September of 1975, a large volume Argon-41 sampler was calibrated using a series connected calibration chamber of known sensitivity and a constant flow of activated Argon gas. The calibration included analysis of the effects of flow rate through the large volume sampler and yielded a calibration constant of 2.34 x 10 -8 μc/cm 3 /CPM. (author)

Human choice and climate change. Volume 1: The societal framework

International Nuclear Information System (INIS)

Raynor, S.; Malone, E.

1998-01-01

This book is Volume 1 of a four-volume set which assesses social science research that is relevant to global climate change from a wide-ranging interdisciplinary perspective. Attention is focused on the societal framework as it relates to climate change. This series is indispensable reading for scientists and engineers wishing to make an effective contribution to the climate change policy debate

Consumer Learning and Brand Equity

NARCIS (Netherlands)

S.M.J. van Osselaer (Stijn); J.W. Alba (Joseph)

2000-01-01

textabstractA series of experiments illustrates a learning process that enhances brand equity at the expense of quality-determining attributes. When the relationship between brand name and product quality is learned prior to the relationship between product attributes and quality, inhibition of the

Integrating transformative learning and action learning approaches to enhance ethical leadership for supervisors in the hotel business

OpenAIRE

Boonyuen Saranya; Charungkaittikul Suwithida; Ratana-ubol Archanya

2016-01-01

Ethical leadership is now increasingly focused in leadership development. The main purpose of this study is to explore two methods of adult learning, action learning and transformative learning, and to use the methods to enhance ethical leadership. Building ethical leadership requires an approach that focuses on personal values, beliefs, or frames of references, which is transformative learning. Transformative learning requires a series of meetings to conduct critical discourse and to follow ...

Invasion Ecology. Student Edition. Cornell Scientific Inquiry Series.

Science.gov (United States)

Krasny, Marianne E.; Trautmann, Nancy; Carlsen, William; Cunningham, Christine

This book contains the student edition of the Environmental Inquiry curriculum series developed at Cornell University. It is designed to teach learning skills for investigating the behaviors of non-native and native species and demonstrate how to apply scientific knowledge to solve real-life problems. This book focuses on strange intruders…

Manhattan Project Technical Series: The Chemistry of Uranium (I)

International Nuclear Information System (INIS)

Rabinowitch, E. I.; Katz, J. J.

1947-01-01

This constitutes Chapters 11 through 16, inclusive, of the Survey Volume on Uranium Chemistry prepared for the Manhattan Project Technical Series. Chapters are titled: Uranium Oxides, Sulfides, Selenides, and Tellurides; The Non-Volatile Fluorides of Uranium; Uranium Hexafluoride; Uranium-Chlorine Compounds; Bromides, Iodides, and Pseudo-Halides of Uranium; and Oxyhalides of Uranium.

National Low-Level Waste Management Program Radionuclide Report Series

International Nuclear Information System (INIS)

Rudin, M.J.; Garcia, R.S.

1992-02-01

This report, Volume 3 of the National Low-Level Radioactive Waste Management Program Radionuclide Report Series, discusses the radiological and chemical characteristics of carbon-14. The report also discusses waste streams that contain carbon-14, waste forms that contain carbon-14, and carbon-14 behavior in the environment and in the human body

Manhattan Project Technical Series: The Chemistry of Uranium (I)

Energy Technology Data Exchange (ETDEWEB)

Rabinowitch, E. I. [Argonne National Lab. (ANL), Argonne, IL (United States); Katz, J. J. [Argonne National Lab. (ANL), Argonne, IL (United States)

1947-03-10

This constitutes Chapters 11 through 16, inclusive, of the Survey Volume on Uranium Chemistry prepared for the Manhattan Project Technical Series. Chapters are titled: Uranium Oxides, Sulfides, Selenides, and Tellurides; The Non-Volatile Fluorides of Uranium; Uranium Hexafluoride; Uranium-Chlorine Compounds; Bromides, Iodides, and Pseudo-Halides of Uranium; and Oxyhalides of Uranium.

Learning Organic Chemistry Through Natural Products

Indian Academy of Sciences (India)

Higher Learning. ... The Series on "learning Organic Chemistry Through Natural Products". Nature is a remarkable ... skeletal structure to the interior electronic configu- ration ... Among the advantages of this approach are the fact that unlike the.

Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming.

Science.gov (United States)

Ostrowski, M; Paulevé, L; Schaub, T; Siegel, A; Guziolowski, C

2016-11-01

Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Employ Simulation Techniques. Second Edition. Module C-5 of Category C--Instructional Execution. Professional Teacher Education Module Series.

Science.gov (United States)

Ohio State Univ., Columbus. National Center for Research in Vocational Education.

One of a series of performance-based teacher education learning packages focusing upon specific professional competencies of vocational teachers, this learning module deals with employing simulation techniques. It consists of an introduction and four learning experiences. Covered in the first learning experience are various types of simulation…

Revisiting the returns-volume relationship: Time variation, alternative measures and the financial crisis

Science.gov (United States)

Cook, Steve; Watson, Duncan

2017-03-01

Following its introduction in the seminal study of Osborne (1959), a voluminous literature has emerged examining the returns-volume relationship for financial assets. The present paper revisits this relationship in an examination of the FTSE100 which extends the existing literature in two ways. First, alternative daily measures of the FTSE100 index are used to create differing returns and absolute returns series to employ in an examination of returns-volume causality. Second, rolling regression analysis is utilised to explore potential time variation in the returns-volume relationship. The findings obtained depict a hitherto unconsidered complexity in this relationship with the type of returns series considered and financial crisis found to be significant underlying factors. The implications of the newly derived results for both the understanding of the nature of the returns-volume relationship and the development of theories in connection to it are discussed.

Learning Organic Chemistry Through Natural Products

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 16; Issue 12. Learning Organic Chemistry Through Natural Products - Architectural Designs in Molecular Constructions. N R Krishnaswamy. Volume 16 Issue 12 December 2011 pp 1287-1293 ...

Spatiotemporal alignment of in utero BOLD-MRI series.

Science.gov (United States)

Turk, Esra Abaci; Luo, Jie; Gagoski, Borjan; Pascau, Javier; Bibbo, Carolina; Robinson, Julian N; Grant, P Ellen; Adalsteinsson, Elfar; Golland, Polina; Malpica, Norberto

2017-08-01

To present a method for spatiotemporal alignment of in-utero magnetic resonance imaging (MRI) time series acquired during maternal hyperoxia for enabling improved quantitative tracking of blood oxygen level-dependent (BOLD) signal changes that characterize oxygen transport through the placenta to fetal organs. The proposed pipeline for spatiotemporal alignment of images acquired with a single-shot gradient echo echo-planar imaging includes 1) signal nonuniformity correction, 2) intravolume motion correction based on nonrigid registration, 3) correction of motion and nonrigid deformations across volumes, and 4) detection of the outlier volumes to be discarded from subsequent analysis. BOLD MRI time series collected from 10 pregnant women during 3T scans were analyzed using this pipeline. To assess pipeline performance, signal fluctuations between consecutive timepoints were examined. In addition, volume overlap and distance between manual region of interest (ROI) delineations in a subset of frames and the delineations obtained through propagation of the ROIs from the reference frame were used to quantify alignment accuracy. A previously demonstrated rigid registration approach was used for comparison. The proposed pipeline improved anatomical alignment of placenta and fetal organs over the state-of-the-art rigid motion correction methods. In particular, unexpected temporal signal fluctuations during the first normoxia period were significantly decreased (P quantitative studies of placental function by improving spatiotemporal alignment across placenta and fetal organs. 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:403-412. © 2017 International Society for Magnetic Resonance in Medicine.

Interpretable Categorization of Heterogeneous Time Series Data

Science.gov (United States)

Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

2017-01-01

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Synovial volume--a marker of disease severity in rheumatoid arthritis? Quantification by MRI

DEFF Research Database (Denmark)

Østergaard, Mikkel; Gideon, P; Henriksen, O

1994-01-01

Volumes of synovial membrane and joint effusion were determined by magnetic resonance imaging (MRI) in patients with inflammatory gonarthritis. Volumes were calculated by adding the outlined areas of synovium/effusion from a continuous series of gadolinium-DTPA-enhanced 5 mm transversal T1-weighted...

Perspectives on ontology learning

CERN Document Server

Lehmann, J

2014-01-01

Perspectives on Ontology Learning brings together researchers and practitioners from different communities − natural language processing, machine learning, and the semantic web − in order to give an interdisciplinary overview of recent advances in ontology learning.Starting with a comprehensive introduction to the theoretical foundations of ontology learning methods, the edited volume presents the state-of-the-start in automated knowledge acquisition and maintenance. It outlines future challenges in this area with a special focus on technologies suitable for pushing the boundaries beyond the c

E-Learning Systems, Environments and Approaches

OpenAIRE

Isaias, P.; Spector, J.M.; Ifenthaler, D.; Sampson, D.G.

2015-01-01

The volume consists of twenty-five chapters selected from among peer-reviewed papers presented at the CELDA (Cognition and Exploratory Learning in the Digital Age) 2013 Conference held in Fort Worth, Texas, USA, in October 2013 and also from world class scholars in e-learning systems, environments and approaches. The following sub-topics are included: Exploratory Learning Technologies (Part I), e-Learning social web design (Part II), Learner communities through e-Learning implementations (Par...

The open corpus challenge in eLearning

Directory of Open Access Journals (Sweden)

Mahantesh K. Pattanshetti

2018-03-01

Full Text Available Learning has transcended into a life-long endeavor in the information age. It is no longer restricted to confines of formal classrooms. Consequently, a student is not restricted to traditional learning resources like teachers, textbooks or printed content. Digital resources available on the Internet form a very significant component of self-learning. Copious volumes of learning resources without legal barriers to self-learning reside in digital repositories, educational institution portals and on numerous websites. Learners wishing to utilize the web for personalized learning are faced with a daunting array of content to wade through and select the suitable ones to fulfill his/her learning objectives. Therefore, it is not a question of availability; it is one of relevance and suitability. Typically, in addition to time constraints, learners lack the expertise to screen content for effective eLearning. Adaptive hypermedia systems (AHSs offer a path to harnessing this large volume of learning resources for personalized learning. This review paper provides a concise and coherent discussion about the evolution of AHSs along with the challenges that need to be addressed for effectively harnessing openly available educational resources referred to as open corpus resources (OCRs.

Volume determination of organs using NMR-CT images

International Nuclear Information System (INIS)

Matsumoto, Kunihiko; Hyodo, Kazuyuki; Ikehira, Hiroo; Fukuda, Nobuo; Tateno, Yukio.

1986-01-01

Water phantoms with the volume of 10, 50, 100, 200 and 300 ml surrounded by salad oil were made. The basic experiments were achieved with these phantoms to investigate the accuracy of volume determination and the influence of RF pulse series. NMR - CT employed was Asahi Mark - J. The magnetic field was 0.1T (conductive magnet). The slice thickness were 15 mm. The contour of the phantoms was determined manually using truck - ball and/or automatically by a computer program developed by us. The volume was calculated by the summation of contour area multiplied by the slice pitch. At volumes < 50 ml the error is quite significant but at larger volumes greater than 300 ml the error is reduced to ± 10 %. The volumes of the liver and spleen were measured using both coronal and transverse scans. The error in volume measurement between the scans taken in different planes was found to be 7.0 ± 4.1 % for the liver and 12.4 ± 4.65 % for the spleen. (author)

Machine Learning for Hackers

CERN Document Server

Conway, Drew

2012-01-01

If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyz

Teaching Graphical Simulations of Fourier Series Expansion of Some Periodic Waves Using Spreadsheets

Science.gov (United States)

Singh, Iqbal; Kaur, Bikramjeet

2018-01-01

The present article demonstrates a way of programming using an Excel spreadsheet to teach Fourier series expansion in school/colleges without the knowledge of any typical programming language. By using this, a student learns to approximate partial sum of the n terms of Fourier series for some periodic signals such as square wave, saw tooth wave,…

Students creative thinking skills in solving two dimensional arithmetic series through research-based learning

Science.gov (United States)

Tohir, M.; Abidin, Z.; Dafik; Hobri

2018-04-01

Arithmetics is one of the topics in Mathematics, which deals with logic and detailed process upon generalizing formula. Creativity and flexibility are needed in generalizing formula of arithmetics series. This research aimed at analyzing students creative thinking skills in generalizing arithmetic series. The triangulation method and research-based learning was used in this research. The subjects were students of the Master Program of Mathematics Education in Faculty of Teacher Training and Education at Jember University. The data was collected by giving assignments to the students. The data collection was done by giving open problem-solving task and documentation study to the students to arrange generalization pattern based on the dependent function formula i and the function depend on i and j. Then, the students finished the next problem-solving task to construct arithmetic generalization patterns based on the function formula which depends on i and i + n and the sum formula of functions dependent on i and j of the arithmetic compiled. The data analysis techniques operative in this study was Miles and Huberman analysis model. Based on the result of data analysis on task 1, the levels of students creative thinking skill were classified as follows; 22,22% of the students categorized as “not creative” 38.89% of the students categorized as “less creative” category; 22.22% of the students categorized as “sufficiently creative” and 16.67% of the students categorized as “creative”. By contrast, the results of data analysis on task 2 found that the levels of students creative thinking skills were classified as follows; 22.22% of the students categorized as “sufficiently creative”, 44.44% of the students categorized as “creative” and 33.33% of the students categorized as “very creative”. This analysis result can set the basis for teaching references and actualizing a better teaching model in order to increase students creative thinking skills.

The Blue Planet: Seas & Oceans. Young Discovery Library Series.

Science.gov (United States)

de Beauregard, Diane Costa

This book is written for children ages 5 through 10. Part of a series designed to develop their curiosity, facinate them and educate them, this volume explores the physical and environmental characteristics of the world's oceans. Topics are: (1) human exploration; (2) the food chain; (3) coral reefs; (4) currents and tides; (5) waves; (6)…

National Low-Level Waste Management Program Radionuclide Report Series

International Nuclear Information System (INIS)

Rudin, M.J.; Stanton, C.; Patterson, R.G.; Garcia, R.S.

1992-02-01

This report, Volume 2 of the National Low-Level Radioactive Waste Management Program Radionuclide Report Series, discusses radiological and chemical characteristics of technetium-99. This report also includes discussions about waste streams in which technetium-99 can be found, waste forms that contain technetium-99, and technetium-99's behavior in the environment and in the human body

Living with the Eskimos. Young Discovery Library Series: 12.

Science.gov (United States)

Planche, Bernard

Part of an international series of amply illustrated, colorful, small size books for children ages 5 to 10, this volume describes daily Eskimo life in Greenland: hunting, fishing, what they eat, how they combat the cold, their dogs, and igloos. The book also talks about the animals of the Arctic area: polar bears, seals, and birds. The document…

A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2017-11-01

Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

Application examples of EFPACS series

International Nuclear Information System (INIS)

Tsuchiya, Yasunori; Aoki, Makoto; Yamahata, Noboru

1989-01-01

This paper introduces some application examples of picture archiving and communications system EFPACS series which achieves efficient management of a volume of image data generated in a hospital, and powerfully support image diagnosis using multi-modality. EFPACS can be applied to various objectives of system installation, and can meet the scale of a hospital and the way of image filing. EFPACS has been installed in a middle-scale hospital for image conference, in a general hospital for long-term archiving of MRI data and for referring in the outpatient clinic, in a dental hospital for dental image processing, and so on. (author)

The accuracy of MRI-determined synovial membrane and joint effusion volumes in arthritis. A comparison of pre- and post-aspiration volumes

DEFF Research Database (Denmark)

Østergaard, Mikkel; Stoltenberg, M; Henriksen, O

1995-01-01

Magnetic resonance imaging (MRI) of 18 knees of patients with arthritis was performed before and immediately after arthrocentesis. Pre- and post-aspiration volumes were calculated by adding the outlined areas of synovium/effusion from a continuous series of gadolinium-DTPA-enhanced 5 mm transversal...

Series paralelas al Rorschach: validación en nuestro medio de la serie de Parisi-Pes y del Test de Zulliger Rorschach parallel series: local validation of the Parisi-Pes series and the Z Test

Directory of Open Access Journals (Sweden)

Ana María Núñez

2009-12-01

Full Text Available El presente artículo surge de la necesidad de validar, en nuestro medio, series paralelas al Test de Rorschach con el fin de poder reemplazarlo en aquellos casos en que se lo requiera. El incremento de la difusión de esta técnica, fuera del ámbito de la comunidad psicológica, puede derivar en un efecto de aprendizaje que dificulte el uso de la herramienta psicodiagnóstica. En esta publicación se realiza un recorrido a través de las diferentes series propuestas como paralelas al Test de Rorschach y se exponen los resultados de dos investigaciones: una de las cuales corresponde a la serie de Parisi-Pes, creada por la Escuela Romana de Rorschach, poco difundida en nuestro medio pero validada en uno con características socioculturales similares al nuestro (Proyecto UBACyT P039; y la otra, el Test de Zulliger, que se aplica con frecuencia en el ámbito laboral, en ambas versiones, individual y colectiva (Proyecto UBACyT P005.This article stems from the need to validate Rorschach parallel series at our social environment, in order to replace it when required. The increase in the dissemination of this technique, outside the psychological community, can lead to a learning effect which may prevent this psychodiagnostic tool from being used. This publication is a journey through the different Rorschach parallel series, and the results from two previous researches are being exposed: the first one of those, belongs to the Parisi-Pes series, created by the Roman Rorschach School, not much locally known but it had been validated in a similar social environment (Project UBACyT P039; the other one, the Z Test, is often used at Labor Psychology in both versions, individual and group administrations (Project UBACyT P005.

Exploitation of linkage learning in evolutionary algorithms

CERN Document Server

Chen, Ying-ping

2010-01-01

The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.

Neural substrates underlying stimulation-enhanced motor skill learning after stroke.

Science.gov (United States)

Lefebvre, Stéphanie; Dricot, Laurence; Laloux, Patrice; Gradkowski, Wojciech; Desfontaines, Philippe; Evrard, Frédéric; Peeters, André; Jamart, Jacques; Vandermeeren, Yves

2015-01-01

Motor skill learning is one of the key components of motor function recovery after stroke, especially recovery driven by neurorehabilitation. Transcranial direct current stimulation can enhance neurorehabilitation and motor skill learning in stroke patients. However, the neural mechanisms underlying the retention of stimulation-enhanced motor skill learning involving a paretic upper limb have not been resolved. These neural substrates were explored by means of functional magnetic resonance imaging. Nineteen chronic hemiparetic stroke patients participated in a double-blind, cross-over randomized, sham-controlled experiment with two series. Each series consisted of two sessions: (i) an intervention session during which dual transcranial direct current stimulation or sham was applied during motor skill learning with the paretic upper limb; and (ii) an imaging session 1 week later, during which the patients performed the learned motor skill. The motor skill learning task, called the 'circuit game', involves a speed/accuracy trade-off and consists of moving a pointer controlled by a computer mouse along a complex circuit as quickly and accurately as possible. Relative to the sham series, dual transcranial direct current stimulation applied bilaterally over the primary motor cortex during motor skill learning with the paretic upper limb resulted in (i) enhanced online motor skill learning; (ii) enhanced 1-week retention; and (iii) superior transfer of performance improvement to an untrained task. The 1-week retention's enhancement driven by the intervention was associated with a trend towards normalization of the brain activation pattern during performance of the learned motor skill relative to the sham series. A similar trend towards normalization relative to sham was observed during performance of a simple, untrained task without a speed/accuracy constraint, despite a lack of behavioural difference between the dual transcranial direct current stimulation and sham

Human Resource Management in Virtual Organizations. Research in Human Resource Management Series.

Science.gov (United States)

Heneman, Robert L., Ed.; Greenberger, David B., Ed.

This document contains 14 papers on human resources (HR) and human resource management (HRM) in virtual organizations. The following papers are included: "Series Preface" (Rodger Griffeth); "Volume Preface" (Robert L. Heneman, David B. Greenberger); "The Virtual Organization: Definition, Description, and…

Institutional Perspectives: The Challenges of E-Learning Diffusion

Science.gov (United States)

Nichols, Mark

2008-01-01

There has been significant recent interest in the dynamics of institutional change and e-learning. This paper reports on the findings from a series of discussions about e-learning diffusion held with institutional e-learning representatives from across the globe. In the course of discussion it became clear that in some institutions e-learning was…

Using probabilistic finite automata to simulate hourly series of global radiation

Energy Technology Data Exchange (ETDEWEB)

Mora-Lopez, L. [Universidad de Malaga (Spain). Dpto. Lenguajes y Computacion; Sidrach-de-Cardona, M. [Universidad de Malaga (Spain). Dpto. Fisica Aplicada II

2003-03-01

A model to generate synthetic series of hourly exposure of global radiation is proposed. This model has been constructed using a machine learning approach. It is based on the use of a subclass of probabilistic finite automata which can be used for variable-order Markov processes. This model allows us to represent the different relationships and the representative information observed in the hourly series of global radiation; the variable-order Markov process can be used as a natural way to represent different types of days, and to take into account the ''variable memory'' of cloudiness. A method to generate new series of hourly global radiation, which incorporates the randomness observed in recorded series, is also proposed. As input data this method only uses the mean monthly value of the daily solar global radiation. We examine if the recorded and simulated series are similar. It can be concluded that both series have the same statistical properties. (author)

Becoming Life-Long Learners--"A Pedagogy for Learning about Visionary Leadership"

Science.gov (United States)

McNeil, Mary, Ed.; Nevin, Ann, Ed.

2014-01-01

In this volume we apply a personal narrative methodology to understanding what we have learned about visionary leadership. Authors in this volume developed their reflections of life-long learning as they investigated existing leadership theories and theories about future leadership. Graduate program faculty and authors read and critically reviewed…

Partial Molar Volumes of Aqua Ions from First Principles.

Science.gov (United States)

Wiktor, Julia; Bruneval, Fabien; Pasquarello, Alfredo

2017-08-08

Partial molar volumes of ions in water solution are calculated through pressures obtained from ab initio molecular dynamics simulations. The correct definition of pressure in charged systems subject to periodic boundary conditions requires access to the variation of the electrostatic potential upon a change of volume. We develop a scheme for calculating such a variation in liquid systems by setting up an interface between regions of different density. This also allows us to determine the absolute deformation potentials for the band edges of liquid water. With the properly defined pressures, we obtain partial molar volumes of a series of aqua ions in very good agreement with experimental values.

Bringing It All Together Through Group Learning

OpenAIRE

Chance, Shannon

2014-01-01

Interpersonal and trans-disciplinary collaboration can facilitate and amplify the benefits of learning. Drawing from ideas presented throughout this volume, this culminating chapter describes ways to enhance collaborative learning within and among various stakeholder groups.

Student Learning in the Information Age. American Council on Education Series on Higher Education.

Science.gov (United States)

Breivik, Patricia Senn

This book discusses resource-based learning in higher education. One premise of resource-based learning is that as students become able to select their own learning materials from information resources, they become active, independent learners, while professors become learning facilitators in cooperation with librarians and other information…

Volume of hydration in terminal cancer patients.

Science.gov (United States)

Bruera, E; Belzile, M; Watanabe, S; Fainsinger, R L

1996-03-01

In this retrospective study we reviewed the volume and modality of hydration of consecutive series of terminal cancer patients in two different settings. In a palliative care unit 203/290 admitted patients received subcutaneous hydration for 12 +/- 8 days at a daily volume of 1015 +/- 135 ml/day. At the cancer center, 30 consecutive similar patients received intravenous hydration for 11.5 +/- 5 days (P > 0.2) but at a daily volume of 2080 +/- 720 ml/day (P palliative care unit patients required discontinuation of hydration because of complications. Hypodermoclysis was administered mainly as a continuous infusion, an overnight infusion, or in one to three 1-h boluses in 62 (31%), 98 (48%) and 43 (21%) patients, respectively. Our findings suggest that, in some settings, patients may be receiving excessive volumes of hydration by less comfortable routes such as the intravenous route. Increased education and research in this area are badly needed.

Series: Practical guidance to qualitative research. Part 1: Introduction.

Science.gov (United States)

Moser, Albine; Korstjens, Irene

2017-12-01

In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called Frequently Asked Questions. This journal series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By 'novice' we mean Master's students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of papers reporting on qualitative research. This first article describes the key features of qualitative research, provides publications for further learning and reading, and gives an outline of the series.

Two cycles of cisplatin-based chemotherapy for low-volume stage II seminoma: results of a retrospective, single-center case series.

Science.gov (United States)

Pichler, Renate; Leonhartsberger, Nicolai; Stöhr, Brigitte; Horninger, Wolfgang; Steiner, Hannes

2012-01-01

To report on the oncological outcome and toxicity of patients treated with 2 cycles of cisplatin-based chemotherapy for low-volume metastatic stage II seminoma. We retrospectively identified a case series of 15 patients with seminoma stage IIA (26.7%) and IIB (73.3%) who underwent chemotherapy consisting of 2 cycles of cisplatin, etoposide and bleomycin (PEB) (cisplatin 20 mg/m(2) on days 1-5, etoposide 100 mg/m(2) on days 1-5, bleomycin 30 mg on days 1, 8 and 15) according to patient preference (refusing a 3rd cycle of PEB) or institutional practice in the last decades. Complete staging before chemotherapy was available in all patients. Patient age, the side and diameter of the primary tumor, the size of the lymph nodes before and after chemotherapy, acute and late toxicity of chemotherapy, the incidence of second malignancies, the relapse-free rate and cancer-specific mortality were recorded. Chemotherapy was well tolerated and no episode of febrile neutropenia occurred. Thrombocytopenia grade 4 was not seen in any patient, while leukopenia grade 4 was observed in 4 (26.6%) patients. The mean (range) lymph node size decreased significantly from 2.54 cm (1.1-4.0) before chemotherapy to 0.75 cm (0.4-2.2) after chemotherapy (p < 0.001). After a median (range) follow-up of 60 (13-185) months, no patient had relapsed, no patient had died as a result of seminoma and second malignancy was seen in only 1 (6.6%) patient. These excellent long-term results from a retrospective case series of 2 cycles of PEB in stage IIA/IIB seminoma patients represent a hint for further research with a view to reducing treatment burden. However, these incidental findings should be studied in prospective trials prior to drawing any conclusions. Copyright © 2013 S. Karger AG, Basel.

Ripe for Change: Garden-Based Learning in Schools. Harvard Education Letter Impact Series

Science.gov (United States)

Hirschi, Jane S.

2015-01-01

"Ripe for Change: Garden-Based Learning in Schools" takes a big-picture view of the school garden movement and the state of garden-based learning in public K--8 education. The book frames the garden movement for educators and shows how school gardens have the potential to be a significant resource for teaching and learning. In this…

A Time Series Forecasting Method

Directory of Open Access Journals (Sweden)

Wang Zhao-Yu

2017-01-01

Full Text Available This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

Pharmacists' perception of synchronous versus asynchronous distance learning for continuing education programs.

Science.gov (United States)

Buxton, Eric C

2014-02-12

To evaluate and compare pharmacists' satisfaction with the content and learning environment of a continuing education program series offered as either synchronous or asynchronous webinars. An 8-lecture series of online presentations on the topic of new drug therapies was offered to pharmacists in synchronous and asynchronous webinar formats. Participants completed a 50-question online survey at the end of the program series to evaluate their perceptions of the distance learning experience. Eighty-two participants completed the survey instrument (41 participants from the live webinar series and 41 participants from the asynchronous webinar series.) Responses indicated that while both groups were satisfied with the program content, the asynchronous group showed greater satisfaction with many aspects of the learning environment. The synchronous and asynchronous webinar participants responded positively regarding the quality of the programming and the method of delivery, but asynchronous participants rated their experience more positively overall.

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

Science.gov (United States)

Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

Directory of Open Access Journals (Sweden)

Waddah Waheeb

Full Text Available Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN and the Dynamic Ridge Polynomial Neural Network (DRPNN. Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Hanford 67-series: a volume of atmospheric field diffusion measurements

International Nuclear Information System (INIS)

Nickola, P.W.

1977-11-01

This volume documents atmospheric diffusion experiments carried out at the Hanford reservation during the period 1967 to 1973. A total of 103 tracer releases during 54 release periods is tabulated. Multi-tracer releases (generally from different elevations) were made during most of the experimental periods. Release heights varied from ground level to an elevation of 111 m. Tracers were sampled simultaneously on as many as 10 arcs at distances of up to 12.8 km from the tracer release point. As many as 718 field sampling locations were employed during some of the experiments. Vertical profiles of concentration were monitored on towers during 23 of the 54 release periods. Concurrent vertical profiles of mean temperature, of mean wind speed and direction, and of direction standard deviation are also tabled for elevations up to 122 m

Cooperation in Japan. Grades Kindergarten-Third. Elementary Literature Series, Part 1.

Science.gov (United States)

Mukai, Gary

The Stanford Program on International and Cross-Cultural Education (SPICE) represents a long-term effort by Stanford University to improve international and cross-cultural education in elementary and secondary schools. This volume of the elementary literature series focuses on the primary grades; utilizes primary source literature from Japan;…

A DDC Bibliography on Computers in Information Sciences. Volume II. Information Sciences Series.

Science.gov (United States)

Defense Documentation Center, Alexandria, VA.

The unclassified and unlimited bibliography compiles references dealing specifically with the role of computers in information sciences. The volume contains 239 annotated references grouped under three major headings: Artificial and Programming Languages, Computer Processing of Analog Data, and Computer Processing of Digital Data. The references…

Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters

Directory of Open Access Journals (Sweden)

Chao Lu

2016-08-01

Full Text Available Reinforcement Learning (RL has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simulation-based experiments were designed and conducted by using a macroscopic traffic flow model. Simulation results showed that, compared with the discount rate, the learning rate and action selection parameter made more remarkable impacts on the algorithm performance. Based on the analysis, some suggestionsabout how to select suitable parameter values that can achieve a superior performance were provided.

Module Seven: Combination Circuits and Voltage Dividers; Basic Electricity and Electronics Individualized Learning System.

Science.gov (United States)

Bureau of Naval Personnel, Washington, DC.

In this module the student will learn to apply the rules previously learned for series and parallel circuits to more complex circuits called series-parallel circuits, discover the utility of a common reference when making reference to voltage values, and learn how to obtain a required voltage from a voltage divider network. The module is divided…

Changes in apparent molar water volume and DKP solubility yield insights on the Hofmeister effect.

Science.gov (United States)

Payumo, Alexander Y; Huijon, R Michael; Mansfield, Deauna D; Belk, Laurel M; Bui, Annie K; Knight, Anne E; Eggers, Daryl K

2011-12-15

This study examines the properties of a 4 × 2 matrix of aqueous cations and anions at concentrations up to 8.0 M. The apparent molar water volume, as calculated by subtracting the mass and volume of the ions from the corresponding solution density, was found to exceed the molar volume of ice in many concentrated electrolyte solutions, underscoring the nonideal behavior of these systems. The solvent properties of water were also analyzed by measuring the solubility of diketopiperazine (DKP) in 2.000 M salt solutions prepared from the same ion combinations. Solution rankings for DKP solubility were found to parallel the Hofmeister series for both cations and anions, whereas molar water volume concurred with the cation series only. The results are discussed within the framework of a desolvation energy model that attributes solute-specific changes in equilibria to solute-dependent changes in the free energy of bulk water.

feets: feATURE eXTRACTOR for tIME sERIES

Science.gov (United States)

Cabral, Juan; Sanchez, Bruno; Ramos, Felipe; Gurovich, Sebastián; Granitto, Pablo; VanderPlas, Jake

2018-06-01

feets characterizes and analyzes light-curves from astronomical photometric databases for modelling, classification, data cleaning, outlier detection and data analysis. It uses machine learning algorithms to determine the numerical descriptors that characterize and distinguish the different variability classes of light-curves; these range from basic statistical measures such as the mean or standard deviation to complex time-series characteristics such as the autocorrelation function. The library is not restricted to the astronomical field and could also be applied to any kind of time series. This project is a derivative work of FATS (ascl:1711.017).

Recommender Systems in Technology Enhanced Learning

NARCIS (Netherlands)

Manouselis, Nikos; Drachsler, Hendrik; Verbert, Katrien; Santos, Olga

2010-01-01

Manouselis, N., Drachsler, H., Verbert, K., & Santos, C. S. (Eds.) (2010). Recommender System in Technology Enhanced Learning. Elsevier Procedia Computer Science: Volume 1, Issue 2. Proceedings of the 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL). September, 29-30,

Teaching graphical simulations of Fourier series expansion of some periodic waves using spreadsheets

Science.gov (United States)

Singh, Iqbal; Kaur, Bikramjeet

2018-05-01

The present article demonstrates a way of programming using an Excel spreadsheet to teach Fourier series expansion in school/colleges without the knowledge of any typical programming language. By using this, a student learns to approximate partial sum of the n terms of Fourier series for some periodic signals such as square wave, saw tooth wave, half wave rectifier and full wave rectifier signals.

Flexible Pedagogies: Technology-Enhanced Learning. Flexible Pedagogies: Preparing for the Future Series

Science.gov (United States)

Gordon, Neil

2014-01-01

This publication is part of our five-strand research project "Flexible Pedagogies: preparing for the future". It focuses on a better understanding of technology-enhanced learning (TEL) and: (1) identifies key international drivers in the move towards technology-enhanced learning; (2) highlights some of the challenges and opportunities…

Subcortical intelligence: caudate volume predicts IQ in healthy adults.

Science.gov (United States)

Grazioplene, Rachael G; G Ryman, Sephira; Gray, Jeremy R; Rustichini, Aldo; Jung, Rex E; DeYoung, Colin G

2015-04-01

This study examined the association between size of the caudate nuclei and intelligence. Based on the central role of the caudate in learning, as well as neuroimaging studies linking greater caudate volume to better attentional function, verbal ability, and dopamine receptor availability, we hypothesized the existence of a positive association between intelligence and caudate volume in three large independent samples of healthy adults (total N = 517). Regression of IQ onto bilateral caudate volume controlling for age, sex, and total brain volume indicated a significant positive correlation between caudate volume and intelligence, with a comparable magnitude of effect across each of the three samples. No other subcortical structures were independently associated with IQ, suggesting a specific biological link between caudate morphology and intelligence. © 2014 Wiley Periodicals, Inc.

Non-invasive breast biopsy method using GD-DTPA contrast enhanced MRI series and F-18-FDG PET/CT dynamic image series

Science.gov (United States)

Magri, Alphonso William

This study was undertaken to develop a nonsurgical breast biopsy from Gd-DTPA Contrast Enhanced Magnetic Resonance (CE-MR) images and F-18-FDG PET/CT dynamic image series. A five-step process was developed to accomplish this. (1) Dynamic PET series were nonrigidly registered to the initial frame using a finite element method (FEM) based registration that requires fiducial skin markers to sample the displacement field between image frames. A commercial FEM package (ANSYS) was used for meshing and FEM calculations. Dynamic PET image series registrations were evaluated using similarity measurements SAVD and NCC. (2) Dynamic CE-MR series were nonrigidly registered to the initial frame using two registration methods: a multi-resolution free-form deformation (FFD) registration driven by normalized mutual information, and a FEM-based registration method. Dynamic CE-MR image series registrations were evaluated using similarity measurements, localization measurements, and qualitative comparison of motion artifacts. FFD registration was found to be superior to FEM-based registration. (3) Nonlinear curve fitting was performed for each voxel of the PET/CT volume of activity versus time, based on a realistic two-compartmental Patlak model. Three parameters for this model were fitted; two of them describe the activity levels in the blood and in the cellular compartment, while the third characterizes the washout rate of F-18-FDG from the cellular compartment. (4) Nonlinear curve fitting was performed for each voxel of the MR volume of signal intensity versus time, based on a realistic two-compartment Brix model. Three parameters for this model were fitted: rate of Gd exiting the compartment, representing the extracellular space of a lesion; rate of Gd exiting a blood compartment; and a parameter that characterizes the strength of signal intensities. Curve fitting used for PET/CT and MR series was accomplished by application of the Levenburg-Marquardt nonlinear regression

A DDC Bibliography on Computers in Information Sciences. Volume I. Information Sciences Series.

Science.gov (United States)

Defense Documentation Center, Alexandria, VA.

The unclassified and unlimited bibliography compiles references dealing specifically with the role of computers in information sciences. The volume contains 249 annotated references grouped under two major headings: Time Shared, On-Line, and Real Time Systems, and Computer Components. The references are arranged in accesion number (AD-number)…

National Low-Level Waste Management Program Radionuclide Report Series

International Nuclear Information System (INIS)

Rudin, M.J.; Garcia, R.S.

1992-02-01

This volume serves as an introduction to the National Low-Level Radioactive Waste Management Program Radionuclide Report Series. This report includes discussions of radionuclides listed in Title 10 of the Code of Federal Regulations Part 61.55, Tables 1 and 2 (including alpha-emitting transuranics with half-lives greater than five years). Each report includes information regarding radiological and chemical characteristics of specific radionuclides. Information is also included discussing waste streams and waste forms that may contain each radionuclide, and radionuclide behavior in the environment and in the human body. Not all radionuclides commonly found at low-level radioactive waste sites are included in this report. The discussion in this volume explains the rationale of the radionuclide selection process

Learning and instruction with computer simulations

NARCIS (Netherlands)

de Jong, Anthonius J.M.

1991-01-01

The present volume presents the results of an inventory of elements of such a computer learning environment. This inventory was conducted within a DELTA project called SIMULATE. In the project a learning environment that provides intelligent support to learners and that has a simulation as its

A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2015-01-01

Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs' spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets.

How School Districts Can Support Deeper Learning: The Need for Performance Alignment. Executive Summary. Deeper Learning Research Series

Science.gov (United States)

Honig, Meredith I.; Rainey, Lydia R.

2015-01-01

School district leaders nationwide aspire to help their schools become vibrant places for learning--where students have meaningful academic opportunities "and" develop critical thinking and problem-solving skills, the ability to communicate effectively, and other deeper learning capacities that are essential to success in later life.…

Sensors, Volume 4, Thermal Sensors

Science.gov (United States)

Scholz, Jorg; Ricolfi, Teresio

1996-12-01

'Sensors' is the first self-contained series to deal with the whole area of sensors. It describes general aspects, technical and physical fundamentals, construction, function, applications and developments of the various types of sensors. This volume describes the construction and applicational aspects of thermal sensors while presenting a rigorous treatment of the underlying physical principles. It provides a unique overview of the various categories of sensors as well as of specific groups, e.g. temperature sensors (resistance thermometers, thermocouples, and radiation thermometers), noise and acoustic thermometers, heat-flow and mass-flow sensors. Specific facettes of applications are presented by specialists from different fields including process control, automotive technology and cryogenics. This volume is an indispensable reference work and text book for both specialists and newcomers, researchers and developers.

A New Approach to Group Learning

Science.gov (United States)

Parsons, Jerry

1976-01-01

To help teachers plan strategy for working with a learning group, 12 factors affecting a learning group are discussed and a series of check points are identified as criteria for evaluation. Concepts and principles of group dynamics are drawn from sociology and the work of Carl Rogers. (Author/AJ)

Cultivating Institutional Capacities for Learning Analytics

Science.gov (United States)

Lonn, Steven; McKay, Timothy A.; Teasley, Stephanie D.

2017-01-01

This chapter details the process the University of Michigan developed to build institutional capacity for learning analytics. A symposium series, faculty task force, fellows program, research grants, and other initiatives are discussed, with lessons learned for future efforts and how other institutions might adapt such efforts to spur cultural…

Ensemble Machine Learning Methods and Applications

CERN Document Server

Ma, Yunqian

2012-01-01

It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...

Evaluation of the e-Learning material developed by EMERALD and EMIT for diagnostic imaging and radiotherapy.

Science.gov (United States)

Aitken, Victoria; Tabakov, Slavik

2005-09-01

Two Leonardo projects, EMERALD and EMIT, have developed in a partnershipof university and hospital departments (the consortia) e-Learning materials in X-ray diagnostic radiology, nuclear medicine, radiotherapy, ultrasound and magnetic resonance imaging for medical physics graduates and other healthcare professionals. These e-Learning materials are described in a separate paper in this issue. To assess the effectiveness and relevance of the e-Learning material, a series of evaluations by student users groups plus experts in medical physics education and training were undertaken. The students, with backgrounds in physics and clinical ultrasound, reviewed the e-Learning material using an evaluation form developed by the consortia. The student feedback was favourable with students commenting that their level of knowledge had increased having completed the tasks. Areas identified for development were a reduction in text volume and an increase in the time allowed for completion of some tasks. The feedback from the experts was positive with an overall appreciation of the value of the learning material as a resource for students in medical physics field across Europe and identified other disciplines in which the access to the learning material could be useful contribution to their learning. Suggestions made for improvements ranged from grading the tasks into basic and advanced topics to increasing the interactive nature of the material. These early evaluation of the e-Learning material look promising and provide a framework for further developments in the field. Insight into users and providers views is important if developers are to provide relevant and worthwhile educational learning opportunities.

Intraoperative stroke volume optimization using stroke volume, arterial pressure, and heart rate: closed-loop (learning intravenous resuscitator) versus anesthesiologists.

Science.gov (United States)

Rinehart, Joseph; Chung, Elena; Canales, Cecilia; Cannesson, Maxime

2012-10-01

The authors compared the performance of a group of anesthesia providers to closed-loop (Learning Intravenous Resuscitator [LIR]) management in a simulated hemorrhage scenario using cardiac output monitoring. A prospective cohort study. In silico simulation. University hospital anesthesiologists and the LIR closed-loop fluid administration system. Using a patient simulator, a 90-minute simulated hemorrhage protocol was run, which included a 1,200-mL blood loss over 30 minutes. Twenty practicing anesthesiology providers were asked to manage this scenario by providing fluids and vasopressor medication at their discretion. The simulation program was also run 20 times with the LIR closed-loop algorithm managing fluids and an additional 20 times with no intervention. Simulated patient weight, height, heart rate, mean arterial pressure, and cardiac output (CO) were similar at baseline. The mean stroke volume, the mean arterial pressure, CO, and the final CO were higher in the closed-loop group than in the practitioners group, and the coefficient of variance was lower. The closed-loop group received slightly more fluid (2.1 v 1.9 L, p closed-loop maintained more stable hemodynamics than the practitioners primarily because the fluid was given earlier in the protocol and CO optimized before the hemorrhage began, whereas practitioners tended to resuscitate well but only after significant hemodynamic change indicated the need. Overall, these data support the potential usefulness of this closed-loop algorithm in clinical settings in which dynamic predictors are not available or applicable. Published by Elsevier Inc.

Changes in Apparent Molar Water Volume and DKP Solubility Yield Insights on the Hofmeister Effect

Science.gov (United States)

Payumo, Alexander Y.; Huijon, R. Michael; Mansfield, Deauna D.; Belk, Laurel M.; Bui, Annie K.; Knight, Anne E.; Eggers, Daryl K.

2011-01-01

This study examines the properties of a 4 × 2 matrix of aqueous cations and anions at concentrations up to 8.0 M. The apparent molar water volume, as calculated by subtracting the mass and volume of the ions from the corresponding solution density, was found to exceed the molar volume of ice in many concentrated electrolyte solutions, underscoring the non-ideal behavior of these systems. The solvent properties of water were also analyzed by measuring the solubility of diketopiperazine (DKP) in 2.000 M salt solutions prepared from the same ion combinations. Solution rankings for DKP solubility were found to parallel the Hofmeister series for both cations and anions, whereas molar water volume concurred with the cation series only. The results are discussed within the framework of a desolvation energy model that attributes solute-specific changes in equilibria to solute-dependent changes in the free energy of bulk water. PMID:22029390

Technical support for GEIS: radioactive waste isoltaion in geologic formations. Volume 19. Thermal analyses

International Nuclear Information System (INIS)

1978-04-01

This volume, Y/OWI/TM-36/19, ''Thermal Analyses,'' is one of a 23-volume series, ''Technical Support for GEIS: Radioactive Waste Isolation in Geologic Formations,'' Y/OWI/TM-36, which supplements the ''Contribution to Draft Generic Environmental Impact Statement on Commercial Waste Management: Radioactive Waste Isolation in Geologic Formations,'' Y/OWI/TM-44. The series provides a more complete technical basis for the preconceptual designs, resource requirements, and environmental source terms associated with isolating commercial LWR wastes in underground repositories in salt, granite, shale and basalt. Wastes are considered from three fuel cycles: uranium and plutonium recycling, no recycling of spent fuel and uranium-only recycling. This volume discusses the thermal impacts of the isolated high level and spent-fuel wastes in geologic formations. A detailed account of the methodologies employed is given as well as selected results of the analyses

Chemical thermodynamics of iron - Part 1 - Chemical thermodynamics volume 13a

International Nuclear Information System (INIS)

Lemire, Robert J.; Berner, Urs; Musikas, Claude; Palmer, Donald A.; Taylor, Peter; Tochiyama, Osamu; Perrone, Jane

2013-01-01

Volume 13a of the 'Chemical Thermodynamics' (TDB) series, is the first of two volumes describing the selection of chemical thermodynamic data for species of iron. Because of the voluminous information in the literature, it has been more efficient to prepare the review in two (unequal) parts. This larger first part contains assessments of data for the metal, simple ions, aqueous hydroxido, chlorido, sulfido, sulfato and carbonato complexes, and for solid oxides and hydroxides, halides, sulfates, carbonates and simple silicates. The second part will provide assessments of data for other aqueous halido species, sulfide solids, and solid and solution species with nitrate, phosphate and arsenate, as well as some aspects of solid solutions in iron-oxide and iron-sulfide systems. The database system developed at the OECD/NEA Data Bank ensures consistency not only within the recommended data sets of iron, but also among all the data sets published in the series. This volume will be of particular interest to scientists carrying out performance assessments of deep geological disposal sites for radioactive waste

Director`s series on proliferation

Energy Technology Data Exchange (ETDEWEB)

Bailey, K.C.; Price, M.E. [eds.

1994-12-27

The Director`s Series on Proliferation is an occasional publication of essays on the topics of nuclear, chemical, biological, and missile proliferation. The seven papers presented in this issue cover the following topics: Should the Treaty on the Nonproliferation of Nuclear Weapons (NPT) be amended?; NPT extension - Legal and procedural issues; An Indonesian view of NPT review conference issues; The treaty of Tlatelolco and the NPT - Tools for peace and development; Perspectives on cut-off, weapons dismantlement, and security assurances; Belarus and NPT challenges; A perspective on the chemical weapons convention - Lessons learned from the preparatory commission.

Air Pollution Translations: A Bibliography with Abstracts - Volume 4.

Science.gov (United States)

Environmental Protection Agency, Research Triangle Park, NC. Air Pollution Technical Information Center.

This volume is the fourth in a series of compilations presenting abstracts and indexes of translations of technical air pollution literature. The entries are grouped into 12 subject categories: Emission Sources, Control Methods, Measurement Methods, Air Quality Measurements, Atmospheric Interaction, Basic Science and Technology, Effects--Human…

Air Pollution Translations: A Bibliography with Abstracts - Volume 2.

Science.gov (United States)

National Air Pollution Control Administration (DHEW), Raleigh, NC.

This volume is the second in a series of compilations presenting abstracts and indexes of translations of technical air pollution literature. The 444 entries are grouped into 12 subject categories: General; Emission Sources; Atmospheric Interaction; Measurement Methods; Control Methods; Effects--Human Health; Effects--Plants and Livestock;…

Michigan Journal of Community Service Learning. Volume 13, Number 1, Fall 2006

Science.gov (United States)

Howard, Jeffrey, Ed.

2006-01-01

The "Michigan Journal of Community Service Learning" ("MJCSL") is a national, peer-reviewed journal consisting of articles written by faculty and service-learning educators on research, theory, pedagogy, and issues pertinent to the service-learning community. The "MJCSL" aims to: (1) widen the community of…

Electron beams, lenses, and optics. Volume 2

International Nuclear Information System (INIS)

El-Kareh, A.B.; El-Kareh, J.C.J.

1970-01-01

This volume presents a systematic coverage of aberrations. It analyzes the geometrical aberrations and treats the spherical and chromatic aberrations in great detail. The coefficients of spherical and chromatic aberration have been computed for a series of electrostatic and magnetic lenses and are listed in table form. The book also covers space charge and its effect on highly focused electron beams

Readings on American Society. The Audio-Lingual Literary Series II.

Science.gov (United States)

Imamura, Shigeo; Ney, James W.

This text contains 11 lessons based on an adaptation of the 1964 essay "Automation: Road to Lifetime Jobs" by A.H. Raskin and 14 lessons based on an adaptation of John Fischer's 1948 essay "Unwritten Rules of American Politics." The format of the book and the lessons is the same as that of the other volumes of "The Audio-Lingual Literary Series."…

Use of the challenge point framework to guide motor learning of stepping reactions for improved balance control in people with stroke: a case series.

Science.gov (United States)

Pollock, Courtney L; Boyd, Lara A; Hunt, Michael A; Garland, S Jayne

2014-04-01

Stepping reactions are important for walking balance and community-level mobility. Stepping reactions of people with stroke are characterized by slow reaction times, poor coordination of motor responses, and low amplitude of movements, which may contribute to their decreased ability to recover their balance when challenged. An important aspect of rehabilitation of mobility after stroke is optimizing the motor learning associated with retraining effective stepping reactions. The Challenge Point Framework (CPF) is a model that can be used to promote motor learning through manipulation of conditions of practice to modify task difficulty, that is, the interaction of the skill of the learner and the difficulty of the task to be learned. This case series illustrates how the retraining of multidirectional stepping reactions may be informed by the CPF to improve balance function in people with stroke. Four people (53-68 years of age) with chronic stroke (>1 year) and mild to moderate motor recovery received 4 weeks of multidirectional stepping reaction retraining. Important tenets of motor learning were optimized for each person during retraining in accordance with the CPF. Participants demonstrated improved community-level walking balance, as determined with the Community Balance and Mobility Scale. These improvements were evident 1 year later. Aspects of balance-related self-efficacy and movement kinematics also showed improvements during the course of the intervention. The application of CPF motor learning principles in the retraining of stepping reactions to improve community-level walking balance in people with chronic stroke appears to be promising. The CPF provides a plausible theoretical framework for the progression of functional task training in neurorehabilitation.

Learning through debate during problem-based learning: an active learning strategy.

Science.gov (United States)

Mumtaz, Sadaf; Latif, Rabia

2017-09-01

We explored medical student's views and perceptions of a series of debates conducted during problem-based learning (PBL) practiced as a part of the Spiral curriculum at the Imam Abdulrahman Bin Faisal University, Saudi Arabia. A series of debates were employed during PBL sessions for second-year female medical students, over the period 2014-2016. Each cohort of students was randomly split into 10 small PBL groups and exposed to weekly PBL activity. Within each group, the students were divided into a proposition half and an opposition half. Students were given 1 wk for debate preparation. The students' responses were recorded on a formulated questionnaire. Descriptive statistics were used to analyze quantitative data, and results are presented as percentages. The usefulness of debate in alleviating potential difficulties in communicating with patients was agreed to by 69% ( n = 126) of participants. That these sessions evoked critical thinking among students was reported by 78% ( n = 142). This series of debates helped 61% ( n = 111) of students to learn effectively about controversial issues. Seventy-one percent ( n = 130) considered that debate promoted argument generation and interpretation skills. Enhanced ability to analyze and research evidence was reported by 59% ( n = 108) of students. One hundred and thirteen students (62%) agreed that debate helped them to improve clinical decision-making, and 75% of students agreed that debates encouraged tolerance toward diverse viewpoints/convincing strategies. The majority of our medical students found debating enhanced analytic decision-making, communication, and critical thinking skills. Copyright © 2017 the American Physiological Society.

Materials Research Society Symposium Proceedings Volume 635. Anisotropic Nanoparticles - Synthesis, Characterization and Applications

National Research Council Canada - National Science Library

Lyon, L

2000-01-01

This volume contains a series of papers originally presented at Symposium C, "Anisotropic Nanoparticles Synthesis, Characterization and Applications," at the 2000 MRS Fall Meeting in Boston, Massachusetts...

Back-end interconnection. A generic concept for high volume manufacturing

Energy Technology Data Exchange (ETDEWEB)

Bosman, J.; Budel, T.; De Kok, C.J.G.M.

2013-10-15

The general method to realize series connection in thin film PV modules is monolithical interconnection through a sequence of laser scribes (P1, P2 and P3) and layer depositions. This method however implies that the deposition processes are interrupted several times, an undesirable situation in high volume processing. In order to eliminate this drawback we focus our developments on the so called 'back-end interconnection concept' in which series interconnection takes place AFTER the deposition of the functional layers of the thin film PV device. The process of making a back-end interconnection combines laser scribing, curing, sintering and inkjet processes. These different processes interacts with each other and are investigated in order to create processing strategies that are robust to ensure high volume production. The generic approach created a technology base that can be applied to any thin film PV technology.

Hanford spent nuclear fuel project recommended path forward, volume III: Alternatives and path forward evaluation supporting documentation

International Nuclear Information System (INIS)

Fulton, J.C.

1994-10-01

Volume I of the Hanford Spent Nuclear Fuel Project - Recommended Path Forward constitutes an aggressive series of projects to construct and operate systems and facilities to safely retrieve, package, transport, process, and store K Basins fuel and sludge. Volume II provided a comparative evaluation of four Alternatives for the Path Forward and an evaluation for the Recommended Path Forward. Although Volume II contained extensive appendices, six supporting documents have been compiled in Volume III to provide additional background for Volume II

OSA Trends in Optics and Photonics Series, Volume 14 Spatial Light Modulators

Science.gov (United States)

1998-05-26

controlled optical diffraction by volume holograms in electrooptic crystals," Sov. Tech. Phys. Lett. v. 3, pp. 36-38, 1977. 9. A. J. Agranat and M. Silberg ...the BCB layer using CF4 + 02 (average etch rate 0.21 fim per minute) or SF6 + 02 gas chemistries (0.25 [im per minute). For a 2 /im patterned via...5,000 angstroms of pure aluminum is then deposited as the upper mirror layer. The aluminum is dry etched in a chlorine-based gas chemistry . The

Personal Coaching: Reflection on a Model for Effective Learning

Science.gov (United States)

Griffiths, Kerryn

2015-01-01

The article "Personal Coaching: A Model for Effective Learning" (Griffiths, 2006) appeared in the "Journal of Learning Design" Volume 1, Issue 2 in 2006. Almost ten years on, Kerryn Griffiths reflects upon her original article. Specifically, Griffiths looks back at the combined coaching-learning model she suggested in her…

Inventory of Federal energy-related environment and safety research for FY 1978. Volume 1. Executive summary

International Nuclear Information System (INIS)

1979-12-01

The FY 1978 Federal Inventory is a compilation of 3225 federally funded energy-related environmental and safety reserch projects. It consists of three volumes: an executive summary providing an overview of the data (Volume I), a catalog listing each Inventory project followed by series of indexes (Volume II), and an interactive terminal guide giving instructions for on-line data retrieval (Volume III). Volume I reviews the inventory data as a whole and also within each of three major categories: biomedical and environmental research, environmental control technology research, and operational safety research

Rhetorical ways of thinking Vygotskian theory and mathematical learning

CERN Document Server

Albert, Lillie R; Macadino, Vittoria

2012-01-01

Combining Vygotskian theory with current teaching and learning practices, this volume focuses on how the co-construction of learning models the interpretation of a mathematical situation, providing educationalists with a valuable practical methodology.

Analysis of increasing trend of mortgage volume in the Czech Republic

Directory of Open Access Journals (Sweden)

Petra Střelcová

2009-01-01

Full Text Available The aim of this paper is an empirical analysis of mortgage volume in the Czech Republic and factors identification of the increasing trend of the mortgage volume in the period from 2001 to 2007. Firstly, analysis of quarterly time series of mortgage volume and average mortgage rate are performed. Consequently, causality between mortgage volume and average mortgage rate is analysed. The morgage rate is the most important factor for economic subjects decision of residential investment. Afterwards, it is analysed causality between mortgage volume and selected factors via multiple regression analysis. Based on this analysis, influencing factors for multiple regression analysis describing mortgage volume are selected. Our empirical analysis validate the causality between mortgage volume and mortgage rate, unemployment rate and price level of real estates. Part of this paper is also economic eduction of causality and estimation of expect progress of mortgage volume especially in connection with present economic and business recession.

PUBLIC KNOWLEDGE, LEARNING ORGANIZATIONS AND KNOWLEDGE MANAGEMENT AS STRATEGIC LEVERS FOR A NEW PUBLIC ADMINISTRATION IN ITALY

Directory of Open Access Journals (Sweden)

Pierfranco Malizia

2012-12-01

Full Text Available In one of the most interesting volumes of an equally interesting series entitled “Proposals for a change in the public administrations” concerning the Italian P.A. (VV. AA., 2002, edited by the Civil Service Department of the Italian government and realised with the collaboration of public and private partners to stimulate processes of change in the P.A., a precise and carefully explained reference is made to the absolute importance for the public administrations of the promotion of know-how development by means of the creation, valorisation and sharing of the knowledge-competence patrimony necessary to back the innovation processes like the logic of learning organizations and knowledge management.

Time series clustering in large data sets

Directory of Open Access Journals (Sweden)

Jiří Fejfar

2011-01-01

Full Text Available The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009 it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

Learning Earthquake Design and Construction–Why are Open ...

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 10; Issue 10. Learning Earthquake Design and Construction – Why are Open-Ground Storey Buildings Vulnerable in Earthquakes? C V R Murty. Classroom Volume 10 Issue 10 October 2005 pp 84-87 ...

Design and testing of an energy-absorbing crewseat for the F/FB-111 aircraft. Volume 2: Data from seat testing

Science.gov (United States)

Shane, S. J.

1985-01-01

The unacceptably high injury rate during the escape sequence (including the ejection and ground impact) of the crew module for F/FB-111 aircraft is reviewed. A program to determine if the injury potential could be reduced by replacing the existing crewseats with energy absorbing crewseats is presented. An energy absorbing test seat is designed using much of the existing seat hardware. An extensive dynamic seat test series, designed to duplicate various crew module ground impact conditions is conducted at a sled test facility. Comparative tests with operational F-111 crewseats are also conducted. After successful dynamic testing of the seat, more testing is conducted with the seats mounted in an F-111 crew module. Both swing tests and vertical drop tests are conducted. The vertical drop tests are used to obtain comparative data between the energy absorbing and operational seats. Volume 1 describes the energy absorbing test seat and testing conducted, and evaluates the data from both test series. Volume 2 presents the data obtained during the seat test series, while Volume 3 presents the data from the crew module test series.

Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function.

Science.gov (United States)

Cardenas, Carlos E; McCarroll, Rachel E; Court, Laurence E; Elgohari, Baher A; Elhalawani, Hesham; Fuller, Clifton D; Kamal, Mona J; Meheissen, Mohamed A M; Mohamed, Abdallah S R; Rao, Arvind; Williams, Bowman; Wong, Andrew; Yang, Jinzhong; Aristophanous, Michalis

2018-06-01

Automating and standardizing the contouring of clinical target volumes (CTVs) can reduce interphysician variability, which is one of the largest sources of uncertainty in head and neck radiation therapy. In addition to using uniform margin expansions to auto-delineate high-risk CTVs, very little work has been performed to provide patient- and disease-specific high-risk CTVs. The aim of the present study was to develop a deep neural network for the auto-delineation of high-risk CTVs. Fifty-two oropharyngeal cancer patients were selected for the present study. All patients were treated at The University of Texas MD Anderson Cancer Center from January 2006 to August 2010 and had previously contoured gross tumor volumes and CTVs. We developed a deep learning algorithm using deep auto-encoders to identify physician contouring patterns at our institution. These models use distance map information from surrounding anatomic structures and the gross tumor volume as input parameters and conduct voxel-based classification to identify voxels that are part of the high-risk CTV. In addition, we developed a novel probability threshold selection function, based on the Dice similarity coefficient (DSC), to improve the generalization of the predicted volumes. The DSC-based function is implemented during an inner cross-validation loop, and probability thresholds are selected a priori during model parameter optimization. We performed a volumetric comparison between the predicted and manually contoured volumes to assess our model. The predicted volumes had a median DSC value of 0.81 (range 0.62-0.90), median mean surface distance of 2.8 mm (range 1.6-5.5), and median 95th Hausdorff distance of 7.5 mm (range 4.7-17.9) when comparing our predicted high-risk CTVs with the physician manual contours. These predicted high-risk CTVs provided close agreement to the ground-truth compared with current interobserver variability. The predicted contours could be implemented clinically, with only

St. Louis FUSRAP Lessons Learned

International Nuclear Information System (INIS)

Eberlin, J.; Williams, D.; Mueller, D.

2003-01-01

The purpose of this paper is to present lessons learned from fours years' experience conducting Remedial Investigation and Remedial Action activities at the St. Louis Downtown Site (SLDS) under the Formerly Utilized Sites Remedial Action Program (FUSRAP). Many FUSRAP sites are experiencing challenges conducting Remedial Actions within forecasted volume and budget estimates. The St. Louis FUSRAP lessons learned provide insight to options for cost effective remediation at FUSRAP sites. The lessons learned are focused on project planning (budget and schedule), investigation, design, and construction

Learning Analytics: drivers, developments and challenges

Directory of Open Access Journals (Sweden)

Rebecca Ferguson

2014-12-01

Full Text Available Learning analytics is a significant area of Technology-Enhanced Learning (TEL that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learning-focused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.

Regional differences in brain volume predict the acquisition of skill in a complex real-time strategy videogame.

Science.gov (United States)

Basak, Chandramallika; Voss, Michelle W; Erickson, Kirk I; Boot, Walter R; Kramer, Arthur F

2011-08-01

Previous studies have found that differences in brain volume among older adults predict performance in laboratory tasks of executive control, memory, and motor learning. In the present study we asked whether regional differences in brain volume as assessed by the application of a voxel-based morphometry technique on high resolution MRI would also be useful in predicting the acquisition of skill in complex tasks, such as strategy-based video games. Twenty older adults were trained for over 20 h to play Rise of Nations, a complex real-time strategy game. These adults showed substantial improvements over the training period in game performance. MRI scans obtained prior to training revealed that the volume of a number of brain regions, which have been previously associated with subsets of the trained skills, predicted a substantial amount of variance in learning on the complex game. Thus, regional differences in brain volume can predict learning in complex tasks that entail the use of a variety of perceptual, cognitive and motor processes. Copyright © 2011 Elsevier Inc. All rights reserved.

Radioactive decay properties of CANDU fuel. Volume 1: the natural uranium fuel cycle

International Nuclear Information System (INIS)

Clegg, L.J.; Coady, J.R.

1977-01-01

The two books of Volume 1 comprise the first in a three-volume series of compilations on the radioactive decay propertis of CANDU fuel and deal with the natural uranium fuel cycle. Succeeding volumes will deal with fuel cycles based on plutonium recycle and thorium. In Volume 1 which is divided into three parts, the computer code CANIGEN was used to obtain the mass, activity, decay heat and toxicity of CANDU fuel and its component isotopes. Data are also presented on gamma spectra and neutron emissions. Part 3 contains the data relating to the plutonium product and the high level wastes produced during fuel reprocessing. (author)

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

Science.gov (United States)

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Intelligent and Adaptive Educational-Learning Systems Achievements and Trends

CERN Document Server

2013-01-01

The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form.  This book is devoted to the “Intelligent and Adaptive Educational-Learning Systems”. It privileges works that highlight key achievements and outline trends to inspire future research.  After a rigorous revision process twenty manuscripts were accepted and organized into four parts as follows: ·     Modeling: The first part embraces five chapters oriented to: 1) shape the affective behavior; 2) depict the adaptive learning curriculum; 3) predict learning achievements; 4) mine learner models to outcome optimized and adaptive e-learning objects; 5) classify learning preferences of learners. ·     Content: The second part encompas...

Evaluating ILI Advanced Series through Bloom's Revised Taxonomy

OpenAIRE

MAHDIPOUR, Nasim; SADEGHI, Bahador

2015-01-01

Abstract. This study investigated Iran Language Institute Advanced Series in terms of learning objectives based on Bloom's Revised Taxonomy. It examined the cognitive, affective and psychomotor domains to see how the critical thinking skills are used and to what extent these books are different from each other. For these purposes, the frequencies, percentages and Standard Residual were analyzed. Results revealed that the lower-order cognitive skills (i.e. remembering, understanding and applyi...

Enhancing Learning Outcomes through Application Driven Activities in Marketing

Science.gov (United States)

Stegemann, Nicole; Sutton-Brady, Catherine

2013-01-01

This paper introduces an activity used in class to allow students to apply previously acquired information to a hands-on task. As the authors have previously shown active learning is a way to effectively facilitate and improve students' learning outcomes. As a result to improve learning outcomes we have overtime developed a series of learning…

Spontaneous recombination volumes of Frenkel defects in neutron-irradiated non-fcc metals

International Nuclear Information System (INIS)

Nakagawa, M.; Mansel, W.; Boening, K.; Rosner, P.; Vogl, G.

1979-01-01

Production and production-rate curves for the non-fcc metals Fe, Mo, Ta, W, Zr, and Sn are obtained by electrical-resistivity measurements taken at 4.6 K during reactor neutron irradiations. The saturation concentration of Frenkel defects, c/sub s/, and the recombination volume v/sub o/ are evaluated. A parabolic relation between the spontaneous recombination volume v 0 and the compressibility kappa for a series of bcc metals is found

Face-name association learning and brain structural substrates in alcoholism.

Science.gov (United States)

Pitel, Anne-Lise; Chanraud, Sandra; Rohlfing, Torsten; Pfefferbaum, Adolf; Sullivan, Edith V

2012-07-01

Associative learning is required for face-name association and is impaired in alcoholism, but the cognitive processes and brain structural components underlying this deficit remain unclear. It is also unknown whether prompting alcoholics to implement a deep level of processing during face-name encoding would enhance performance. Abstinent alcoholics and controls performed a levels-of-processing face-name learning task. Participants indicated whether the face was that of an honest person (deep encoding) or that of a man (shallow encoding). Retrieval was examined using an associative (face-name) recognition task and a single-item (face or name only) recognition task. Participants also underwent 3T structural MRI. Compared with controls, alcoholics had poorer associative and single-item learning and performed at similar levels. Level of processing at encoding had little effect on recognition performance but affected reaction time (RT). Correlations with brain volumes were generally modest and based primarily on RT in alcoholics, where the deeper the processing at encoding, the more restricted the correlations with brain volumes. In alcoholics, longer control task RTs correlated modestly with smaller tissue volumes across several anterior to posterior brain regions; shallow encoding correlated with calcarine and striatal volumes; deep encoding correlated with precuneus and parietal volumes; and associative recognition RT correlated with cerebellar volumes. In controls, poorer associative recognition with deep encoding correlated significantly with smaller volumes of frontal and striatal structures. Despite prompting, alcoholics did not take advantage of encoding memoranda at a deep level to enhance face-name recognition accuracy. Nonetheless, conditions of deeper encoding resulted in faster RTs and more specific relations with regional brain volumes than did shallow encoding. The normal relation between associative recognition and corticostriatal volumes was not

Invasion Ecology. Teacher's Guide [and Student Edition]. Cornell Scientific Inquiry Series.

Science.gov (United States)

Krasny, Marianne E.; Trautmann, Nancy; Carlsen, William; Cunningham, Christine

This book contains the teacher's guide of the Environmental Inquiry curriculum series developed at Cornell University. It is designed to teach learning skills for investigating the behaviors of non-native and native species and demonstrate how to apply scientific knowledge to solve real-life problems. This book focuses on strange intruders…

Unsteady force estimation using a Lagrangian drift-volume approach

Science.gov (United States)

McPhaden, Cameron J.; Rival, David E.

2018-04-01

A novel Lagrangian force estimation technique for unsteady fluid flows has been developed, using the concept of a Darwinian drift volume to measure unsteady forces on accelerating bodies. The construct of added mass in viscous flows, calculated from a series of drift volumes, is used to calculate the reaction force on an accelerating circular flat plate, containing highly-separated, vortical flow. The net displacement of fluid contained within the drift volumes is, through Darwin's drift-volume added-mass proposition, equal to the added mass of the plate and provides the reaction force of the fluid on the body. The resultant unsteady force estimates from the proposed technique are shown to align with the measured drag force associated with a rapid acceleration. The critical aspects of understanding unsteady flows, relating to peak and time-resolved forces, often lie within the acceleration phase of the motions, which are well-captured by the drift-volume approach. Therefore, this Lagrangian added-mass estimation technique opens the door to fluid-dynamic analyses in areas that, until now, were inaccessible by conventional means.

Fourier series

CERN Document Server

Tolstov, Georgi P

1962-01-01

Richard A. Silverman's series of translations of outstanding Russian textbooks and monographs is well-known to people in the fields of mathematics, physics, and engineering. The present book is another excellent text from this series, a valuable addition to the English-language literature on Fourier series.This edition is organized into nine well-defined chapters: Trigonometric Fourier Series, Orthogonal Systems, Convergence of Trigonometric Fourier Series, Trigonometric Series with Decreasing Coefficients, Operations on Fourier Series, Summation of Trigonometric Fourier Series, Double Fourie

Occupational dose reduction at nuclear power plants: Annotated bibliography of selected readings in radiation protection and ALARA. Volume 7

Energy Technology Data Exchange (ETDEWEB)

Kaurin, D.G.; Khan, T.A.; Sullivan, S.G.; Baum, J.W. [Brookhaven National Lab., Upton, NY (United States)

1993-07-01

The ALARA Center at Brookhaven National Laboratory publishes a series of bibliographies of selected readings in radiation protection and ALARA in the continuing effort to collect and disseminate information on radiation dose reduction at nuclear power plants. This is volume 7 of the series. The abstracts in this bibliography were selected from proceedings of technical meetings and conferences, journals, research reports, and searches of the Energy Science and Technology database of the US Department of Energy. The subject material of these abstracts relates to radiation protection and dose reduction, and ranges from use of robotics to operational health physics, to water chemistry. Material on the design, planning, and management of nuclear power stations is included, as well as information on decommissioning and safe storage efforts. Volume 7 contains 293 abstract, an author index, and a subject index. The author index is specific for this volume. The subject index is cumulative and lists all abstract numbers from volumes 1 to 7. The numbers in boldface indicate the abstracts in this volume; the numbers not in boldface represent abstracts in previous volumes.

Occupational dose reduction at nuclear power plants: Annotated bibliography of selected readings in radiation protection and ALARA. Volume 8

Energy Technology Data Exchange (ETDEWEB)

Sullivan, S.G.; Khan, T.A.; Xie, J.W. [Brookhaven National Lab., Upton, NY (United States)

1995-05-01

The ALARA Center at Brookhaven National Laboratory publishes a series of bibliographies of selected readings in radiation protection and ALARA in a continuing effort to collect and disseminate information on radiation dose reduction at nuclear power plants. This volume 8 of the series. The abstracts in this bibliography were selected form proceedings of technical meetings and conference journals, research reports, and searches of the Energy Science and Technology database of the US Department of Energy. The subject material of these abstracts relates to the many aspects of radiation protection and dose reduction, and ranges form use of robotics, to operational health physics, to water chemistry. Material on the design, planning, and management of nuclear power stations is included, as well as information on decommissioning and safe storage efforts. Volume 8 contains 232 abstracts, an author index, and a subject index. The author index is specific for this volume. The subject index is cumulative and lists all abstract numbers from volumes 1 to 8. The numbers in boldface indicate the abstracts in this volume; the numbers not in boldface represent abstracts in previous volumes.

Occupational dose reduction at nuclear power plants: Annotated bibliography of selected readings in radiation protection and ALARA. Volume 8

International Nuclear Information System (INIS)

Sullivan, S.G.; Khan, T.A.; Xie, J.W.

1995-05-01

The ALARA Center at Brookhaven National Laboratory publishes a series of bibliographies of selected readings in radiation protection and ALARA in a continuing effort to collect and disseminate information on radiation dose reduction at nuclear power plants. This volume 8 of the series. The abstracts in this bibliography were selected form proceedings of technical meetings and conference journals, research reports, and searches of the Energy Science and Technology database of the US Department of Energy. The subject material of these abstracts relates to the many aspects of radiation protection and dose reduction, and ranges form use of robotics, to operational health physics, to water chemistry. Material on the design, planning, and management of nuclear power stations is included, as well as information on decommissioning and safe storage efforts. Volume 8 contains 232 abstracts, an author index, and a subject index. The author index is specific for this volume. The subject index is cumulative and lists all abstract numbers from volumes 1 to 8. The numbers in boldface indicate the abstracts in this volume; the numbers not in boldface represent abstracts in previous volumes

The FITS model: an improved Learning by Design approach

OpenAIRE

Michels, Koen; Vries, de, Marc; Breukelen, van, Dave; Schure, Frank

2016-01-01

Learning by Design (LBD) is a project-based inquiry approach for interdisciplinary teaching that uses design contexts to learn skills and conceptual knowledge. Research around the year 2000 showed that LBD students achieved high skill performances but disappointing conceptual learning gains. A series of exploratory studies, previous to the study in this paper, indicated how to enhance concept learning. Small-scale tested modifications, based on explicit teaching and scaffolding, were promisin...

Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction.

Science.gov (United States)

Miranian, A; Abdollahzade, M

2013-02-01

Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.

Time Series Analysis Using Geometric Template Matching.

Science.gov (United States)

Frank, Jordan; Mannor, Shie; Pineau, Joelle; Precup, Doina

2013-03-01

We present a novel framework for analyzing univariate time series data. At the heart of the approach is a versatile algorithm for measuring the similarity of two segments of time series called geometric template matching (GeTeM). First, we use GeTeM to compute a similarity measure for clustering and nearest-neighbor classification. Next, we present a semi-supervised learning algorithm that uses the similarity measure with hierarchical clustering in order to improve classification performance when unlabeled training data are available. Finally, we present a boosting framework called TDEBOOST, which uses an ensemble of GeTeM classifiers. TDEBOOST augments the traditional boosting approach with an additional step in which the features used as inputs to the classifier are adapted at each step to improve the training error. We empirically evaluate the proposed approaches on several datasets, such as accelerometer data collected from wearable sensors and ECG data.

Human semi-supervised learning.

Science.gov (United States)

Gibson, Bryan R; Rogers, Timothy T; Zhu, Xiaojin

2013-01-01

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. Copyright © 2013 Cognitive Science Society, Inc.

Vehicle speed prediction via a sliding-window time series analysis and an evolutionary least learning machine: A case study on San Francisco urban roads

Directory of Open Access Journals (Sweden)

Ladan Mozaffari

2015-06-01

Full Text Available The main goal of the current study is to take advantage of advanced numerical and intelligent tools to predict the speed of a vehicle using time series. It is clear that the uncertainty caused by temporal behavior of the driver as well as various external disturbances on the road will affect the vehicle speed, and thus, the vehicle power demands. The prediction of upcoming power demands can be employed by the vehicle powertrain control systems to improve significantly the fuel economy and emission performance. Therefore, it is important to systems design engineers and automotive industrialists to develop efficient numerical tools to overcome the risk of unpredictability associated with the vehicle speed profile on roads. In this study, the authors propose an intelligent tool called evolutionary least learning machine (E-LLM to forecast the vehicle speed sequence. To have a practical evaluation regarding the efficacy of E-LLM, the authors use the driving data collected on the San Francisco urban roads by a private Honda Insight vehicle. The concept of sliding window time series (SWTS analysis is used to prepare the database for the speed forecasting process. To evaluate the performance of the proposed technique, a number of well-known approaches, such as auto regressive (AR method, back-propagation neural network (BPNN, evolutionary extreme learning machine (E-ELM, extreme learning machine (ELM, and radial basis function neural network (RBFNN, are considered. The performances of the rival methods are then compared in terms of the mean square error (MSE, root mean square error (RMSE, mean absolute percentage error (MAPE, median absolute percentage error (MDAPE, and absolute fraction of variances (R2 metrics. Through an exhaustive comparative study, the authors observed that E-LLM is a powerful tool for predicting the vehicle speed profiles. The outcomes of the current study can be of use for the engineers of automotive industry who have been

Assess Student Performance: Skills. Second Edition. Module D-4 of Category D--Instructional Evaluation. Professional Teacher Education Module Series.

Science.gov (United States)

Ohio State Univ., Columbus. National Center for Research in Vocational Education.

This module, one of a series of 127 performance-based teacher education learning packages focusing upon specific professional competencies of vocational education teachers, deals with assessing student performance of psychomotor skills. Included in the module are learning experiences that address the following topics: important considerations…

Learning Earthquake Design and Construction 20. How do Beam ...

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 10; Issue 6. Learning Earthquake Design and Construction – How do Beam–Column Joints in RC Buildings Resist Earthquakes? C V R Murty. Classroom Volume 10 Issue 6 June 2005 pp 82-85 ...

East African Journal of Sciences (2015) Volume 9 (1) 41-48 Effect of ...

African Journals Online (AJOL)

tosheba

East African Journal of Sciences (2015). Volume 9 (1) 41-48 ... but no significant difference was observed amongst them. Queen .... used method of termite management in western. Ethiopia ..... Ethiopia. Working Document Series 68, ICRA,.

New Directions for Self-Regulation of Learning in Postsecondary Education

Science.gov (United States)

Bembenutty, Hefer

2011-01-01

This chapter highlights the major contributions of this volume on self-regulation of learning and provides new directions for cutting-edge theoretical and empirical work that could serve to facilitate self-regulation of learning in postsecondary education. "Self-regulation of learning" refers to learners' beliefs about their ability to engage in…

Deep learning with Python

CERN Document Server

Chollet, Francois

2018-01-01

DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES • Practical code examples • In-depth introduction to Keras • Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural ...

Auction dynamics: A volume constrained MBO scheme

Science.gov (United States)

Jacobs, Matt; Merkurjev, Ekaterina; Esedoǧlu, Selim

2018-02-01

We show how auction algorithms, originally developed for the assignment problem, can be utilized in Merriman, Bence, and Osher's threshold dynamics scheme to simulate multi-phase motion by mean curvature in the presence of equality and inequality volume constraints on the individual phases. The resulting algorithms are highly efficient and robust, and can be used in simulations ranging from minimal partition problems in Euclidean space to semi-supervised machine learning via clustering on graphs. In the case of the latter application, numerous experimental results on benchmark machine learning datasets show that our approach exceeds the performance of current state-of-the-art methods, while requiring a fraction of the computation time.

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

Science.gov (United States)

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

`Indoor` series vending machines; `Indoor` series jido hanbaiki

Energy Technology Data Exchange (ETDEWEB)

Gensui, T.; Kida, A. [Fuji Electric Co. Ltd., Tokyo (Japan); Okumura, H. [Fuji Denki Reiki Co. Ltd., Tokyo (Japan)

1996-07-10

This paper introduces three series of vending machines that were designed to match the interior of an office building. The three series are vending machines for cups, paper packs, cans, and tobacco. Among the three series, `Interior` series has a symmetric design that was coated in a grain pattern. The inside of the `Interior` series is coated by laser satin to ensure a sense of superior quality and a refined style. The push-button used for product selection is hot-stamped on the plastic surface to ensure the hair-line luster. `Interior Phase II` series has a bay window design with a sense of superior quality and lightness. The inside of the `Interior Phase II` series is coated by laser satin. `Interior 21` series is integrated with the wall except the sales operation panel. The upper and lower dress panels can be detached and attached. The door lock is a wire-type structure with high operativity. The operation block is coated by titanium color. The dimensions of three series are standardized. 6 figs., 1 tab.

A Review of the Literature on Social and Emotional Learning for Students Ages 3-8: Teacher and Classroom Strategies that Contribute to Social and Emotional Learning (Part 3 of 4). REL 2017-247

Science.gov (United States)

O'Conner, Rosemarie; De Feyter, Jessica; Carr, Alyssa; Luo, Jia Lisa; Romm, Helen

2017-01-01

Social and emotional learning (SEL) is the process by which children and adults learn to understand and manage emotions, maintain positive relationships, and make responsible decisions. This is the third in a series of four related reports about what is known about SEL programs for students ages 3-8. The report series addresses four issues raised…

Integrating transformative learning and action learning approaches to enhance ethical leadership for supervisors in the hotel business

Directory of Open Access Journals (Sweden)

Boonyuen Saranya

2016-01-01

Full Text Available Ethical leadership is now increasingly focused in leadership development. The main purpose of this study is to explore two methods of adult learning, action learning and transformative learning, and to use the methods to enhance ethical leadership. Building ethical leadership requires an approach that focuses on personal values, beliefs, or frames of references, which is transformative learning. Transformative learning requires a series of meetings to conduct critical discourse and to follow up the learning of learners. By organizing such action learning, human resource developers can optimize their time and effort more effectively. The authors have created a comprehensive model to integrate the two learning approaches in a general way that focuses not only on ethical leadership, but also on all kinds of behavioral transformation in the workplace in the hotel business or even other types of business.

Applications of Task-Based Learning in TESOL

Science.gov (United States)

Shehadeh, Ali, Ed.; Coombe, Christine, Ed.

2010-01-01

Why are many teachers around the world moving toward task-based learning (TBL)? This shift is based on the strong belief that TBL facilitates second language acquisition and makes second language learning and teaching more principled and effective. Based on insights gained from using tasks as research tools, this volume shows how teachers can use…

The Relationship between Logistics and Economic Development in Indonesia: Analysis of Time Series Data

Directory of Open Access Journals (Sweden)

Mohammad Reza

2013-01-01

Full Text Available This paper investigates the relationship between logistics and economic development in Indonesia using time series data on traffic volume and economic growth for the period from 1988 to 2010. Literature reviews were conducted to find the most applicable econometric model. The data of cargo volume that travels through sea, air and rail is used as the logistics index, while GDP is used for the economic index. The time series data was tested using stationarity and co-integration tests. Granger causality tests were employed, and then a proposed logistic model is presented. This study showed that logistics plays an important role in supporting and sustaining economic growth, in a form where the economic growth is the significant demand-pull effect towards logistics. Although the model is developed in the context of Indonesia, the overall statistical analysis can be generalized to other developing economies. Based on the model, this paper presented the importance of sustaining economic development with regards continuously improving the logistics infrastructure.

Immersive volume rendering of blood vessels

Science.gov (United States)

Long, Gregory; Kim, Han Suk; Marsden, Alison; Bazilevs, Yuri; Schulze, Jürgen P.

2012-03-01

In this paper, we present a novel method of visualizing flow in blood vessels. Our approach reads unstructured tetrahedral data, resamples it, and uses slice based 3D texture volume rendering. Due to the sparse structure of blood vessels, we utilize an octree to efficiently store the resampled data by discarding empty regions of the volume. We use animation to convey time series data, wireframe surface to give structure, and utilize the StarCAVE, a 3D virtual reality environment, to add a fully immersive element to the visualization. Our tool has great value in interdisciplinary work, helping scientists collaborate with clinicians, by improving the understanding of blood flow simulations. Full immersion in the flow field allows for a more intuitive understanding of the flow phenomena, and can be a great help to medical experts for treatment planning.

Learning Earthquake Design and Construction – 23. Why are ...

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 10; Issue 11. Learning Earthquake Design and Construction – 23. Why are Buildings with Shear Walls Preferred in Seismic Regions? C V R Murty. Classroom Volume 10 Issue 11 November 2005 pp 85-88 ...

Convergence of the Distorted Wave Born series

International Nuclear Information System (INIS)

MacMillan, D.S.

1981-01-01

The aim of this thesis is to begin to understand the idea of reaction mechanisms in nonrelativistic scattering systems. If we have a complete reaction theory of a particular scattering system, then we claim that the theory itself must contain information about important reaction mechanisms in the system. This information can be used to decide what reaction mechanisms should be included in an approximate calculation. To investigate this claim, we studied several solvable models. The primary concept employed in studying our models is the convergence of the multistep series generated by iterating the corresponding scattering integral equation. We known that the eigenvalues of the kernel of the Lippmann-Schwinger equation for potential scattering determine the rate of convergence of the Born series. The Born series will converge only if these eigenvalues all life within the unit circle. We extend these results to a study of the distorted wave Born series for inelastic scattering. The convergence criterion tells us when approximations are valid. We learn how the convergence of the distorted wave series depends upon energy, coupling constants, angular momentum, and angular momentum transfer. In one of our models, we look at several possible distorting potentials to see which one gives the best convergence. We have also applied our results to several actual DWBA or coupled channel calculations in the literature. In addition to the study of models of two-body scattering systems, we have considered the case of rearrangement scattering. We have discussed the formulation of (N greater than or equal to 3)-body distorted wave equations in which the interior dynamics have been redistributed by introducing compact N-body distortion potentials

Learning in a sheltered Internet environment: The use of WebQuests

NARCIS (Netherlands)

Segers, P.C.J.; Verhoeven, L.T.W.

2009-01-01

The present study investigated the effects on learning in a sheltered Internet environment using so-called WebQuests in elementary school classrooms in the Netherlands. A WebQuest is an assignment presented together with a series of web pages to help guide children's learning. The learning gains and

Explicit teaching and scaffolding to enhance concept learning by design challenges

NARCIS (Netherlands)

MEd Maurice Smeets; MEd Dave van Breukelen; Prof. Dr. Marc de Vries

2016-01-01

This paper presents a mixed methods study in which 21 first-year student teachers took part that investigated learning outcomes of a modified learning by design task. The study is part of a series of studies that aims to improve student learning, teaching skills and teacher training. Design-based

Prediction about chaotic times series of natural circulation flow under rolling motion

International Nuclear Information System (INIS)

Yuan Can; Cai Qi; Guo Li; Yan Feng

2014-01-01

The paper have proposed a chaotic time series prediction model, which combined phase space reconstruction with support vector machines. The model has been used to predict the coolant volume flow, in which a synchronous parameter optimization method was brought up based on particle swarm optimization algorithm, since the numerical value selection of related parameter was a key factor for the prediction precision. The average relative error of prediction values and actual observation values was l,5% and relative precision was 0.9879. The result indicated that the model could apply for the natural circulation coolant volume flow prediction under rolling motion condition with high accuracy and robustness. (authors)

Cuadernos de Autoformacion en Participacion Social. Principios y Valores. Volumen 1 (Self Instructional Notebooks on Social Participation. Principles and Values. Volume 1).

Science.gov (United States)

Instituto Nacional para la Educacion de los Adultos, Mexico City (Mexico).

The series "Self-instructional Notes on Social Participation" is a six-volume series intended as teaching aids for adult educators. The theoretical, methodological, informative and practical elements of this series will assist professionals in their work and help them achieve greater success. The specific purpose of each notebook is…

Cuadernos de Autoformacion en Participacion Social: Metodologia. Volumen 2. Primera Edicion (Self-Instructional Notebooks on Social Participation: Methodology. Volume 2. First Edition).

Science.gov (United States)

Instituto Nacional para la Educacion de los Adultos, Mexico City (Mexico).

The series "Self-instructional Notes on Social Participation" is a six-volume series intended as teaching aids for adult educators. The theoretical, methodological, informative and practical elements of this series will assist professionals in their work and help them achieve greater success. The specific purpose of each notebook is…

Technical support for GEIS: radioactive waste isolation in geologic formations. Volume 23. Environmental effluent analyses

International Nuclear Information System (INIS)

1978-04-01

This volume, Y/OWI/TM-36/23, ''Environmental Effluent Analysis,'' is one of a 23-volume series, ''Technical Support for GEIS: Radioactive Waste Isolation in Geologic Formations,'' Y/OWI/TM-36, which supplements the ''Contribution to Drat Generic Environmental Impact Statement on Commercial Waste Management: Radioactive Waste Isolation in Geologic Formations,'' Y/OWI/TM-44. The series provides a more complete technical basis for the preconceptual designs, resource requirements, and environmental source terms associated with isolating commercial LWR wastes in underground repositories in salt, granite, shale and basalt. Wastes are considered from three fuel cycles: uranium and plutonium recycling, no recycling of spent fuel and uranium-only recycling. This volume discusses the releases to the environment of radioactive and non-radioactive materials that arise during facility construction and waste handling operations, as well as releases that could occur in the event of an operational accident. The results of the analyses are presented along with a detailed description of the analytical methodologies employed

Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models

DEFF Research Database (Denmark)

Yang, Bin; Guo, Chenjuan; Jensen, Christian S.

2013-01-01

of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each...... road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending...... with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies...

Statistical assessment of the learning curves of health technologies.

Science.gov (United States)

Ramsay, C R; Grant, A M; Wallace, S A; Garthwaite, P H; Monk, A F; Russell, I T

2001-01-01

(1) To describe systematically studies that directly assessed the learning curve effect of health technologies. (2) Systematically to identify 'novel' statistical techniques applied to learning curve data in other fields, such as psychology and manufacturing. (3) To test these statistical techniques in data sets from studies of varying designs to assess health technologies in which learning curve effects are known to exist. METHODS - STUDY SELECTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): For a study to be included, it had to include a formal analysis of the learning curve of a health technology using a graphical, tabular or statistical technique. METHODS - STUDY SELECTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): For a study to be included, it had to include a formal assessment of a learning curve using a statistical technique that had not been identified in the previous search. METHODS - DATA SOURCES: Six clinical and 16 non-clinical biomedical databases were searched. A limited amount of handsearching and scanning of reference lists was also undertaken. METHODS - DATA EXTRACTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): A number of study characteristics were abstracted from the papers such as study design, study size, number of operators and the statistical method used. METHODS - DATA EXTRACTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): The new statistical techniques identified were categorised into four subgroups of increasing complexity: exploratory data analysis; simple series data analysis; complex data structure analysis, generic techniques. METHODS - TESTING OF STATISTICAL METHODS: Some of the statistical methods identified in the systematic searches for single (simple) operator series data and for multiple (complex) operator series data were illustrated and explored using three data sets. The first was a case series of 190 consecutive laparoscopic fundoplication procedures performed by a single surgeon; the second

Normal radiographic heart volume in the neonate. Pt. 3

International Nuclear Information System (INIS)

Dahlstroem, A.; Ringertz, H.G.; Sachsska Pediatric Hospital, Stockholm

1985-01-01

The diagnostic power of the heart volume and the cardiothoracic ratio in congenital heart disease in neonates has been compared. A consecutive series of 130 children with suspection of heart disease examined radiologically at between 48 h and 15 days of age were followed for 14+-10 months. Of these, 16 (12%) were diagnosed as having congenital heart disease. The number of false positive and negative diagnoses was less for heart volume than for cardiothoracic ratio using +2 SD as limit for pathology. Accuracy, sensitivity and specificity was 84, 75, and 85% respectively for heart volume and 73, 57, and 75% for cardiothoracic ratio. Cases that were false positive with both methods were significantly more often examined between 48 and 72 hours of age indicating that the explanation might be a somewhat late closure of the ductus arteriosus. (orig.)

Incorporating technology-based learning tools into teaching and learning of optimization problems

Science.gov (United States)

Yang, Irene

2014-07-01

The traditional approach of teaching optimization problems in calculus emphasizes more on teaching the students using analytical approach through a series of procedural steps. However, optimization normally involves problem solving in real life problems and most students fail to translate the problems into mathematic models and have difficulties to visualize the concept underlying. As an educator, it is essential to embed technology in suitable content areas to engage students in construction of meaningful learning by creating a technology-based learning environment. This paper presents the applications of technology-based learning tool in designing optimization learning activities with illustrative examples, as well as to address the challenges in the implementation of using technology in teaching and learning optimization. The suggestion activities in this paper allow flexibility for educator to modify their teaching strategy and apply technology to accommodate different level of studies for the topic of optimization. Hence, this provides great potential for a wide range of learners to enhance their understanding of the concept of optimization.

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.

Shifting Sands, Firm Foundations: Proceedings of the 2009 Annual International Conference of the Association of Tertiary Learning Advisors of Aotearoa/New Zealand (ATLAANZ) (Auckland, New Zealand, November 18-20, 2009). Volume 5

Science.gov (United States)

van der Ham, Vanessa, Ed.; Sevillano, Lilia, Ed.; George, Lily, Ed.

2010-01-01

The 15 articles in this volume comprise the refereed proceedings of the 2009 ATLAANZ (Association of Tertiary Learning Advisors Aotearoa/New Zealand) conference. The first three chapters focus on collaborative work. In Chapter 1, Ann Pocock shares her experiences of working with other university support services. In Chapter 2, Berni Cooper and…

Deep Learning in Neuroradiology.

Science.gov (United States)

Zaharchuk, G; Gong, E; Wintermark, M; Rubin, D; Langlotz, C P

2018-02-01

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method. © 2018 by American Journal of Neuroradiology.

Petroleum industry in Latin America: volume III Argentina, Bolivia, Mexico, Peru

International Nuclear Information System (INIS)

Reinsch, A.E.; Tissot, R.R.

1995-01-01

As the previous volume in this series, this concluding volume was divided into separately paged sections, one for each of Argentina, Bolivia, Mexico and Peru, each section being complete in itself. For each of the countries dealt with, there was a brief historical introduction, followed by a detailed analysis of its energy sector, a description of the physical and market characteristics, the transportation and infrastructure systems, the legal and regulatory issues pertaining to the petroleum industry, especially as regards investment and environmental requirements, and an analysis of the prevailing political climate. figs., tabs., refs

The Role of Higher Education in Promoting Lifelong Learning. UIL Publication Series on Lifelong Learning Policies and Strategies: No. 3

Science.gov (United States)

Yang, Jin, Ed.; Schneller, Chripa, Ed.; Roche, Stephen, Ed.

2015-01-01

There is no doubt that universities have a vital role to play in promoting lifelong learning. This publication presents possible ways of expanding and transforming higher education to facilitate lifelong learning in different socio-economic contexts. Nine articles address the various dimensions of the role of higher education in promoting lifelong…

Learning process for performing and analyzing 3D/4D transperineal ultrasound imaging and interobserver reliability study.

Science.gov (United States)

Siafarikas, F; Staer-Jensen, J; Braekken, I H; Bø, K; Engh, M Ellström

2013-03-01

To evaluate the learning process for acquiring three- and four-dimensional (3D/4D) transperineal ultrasound volumes of the levator hiatus (LH) dimensions at rest, during pelvic floor muscle (PFM) contraction and on Valsalva maneuver, and for analyzing the ultrasound volumes, as well as to perform an interobserver reliability study between two independent ultrasound examiners. This was a prospective study including 22 women. We monitored the learning process of an inexperienced examiner (IE) performing 3D/4D transperineal ultrasonography and analyzing the volumes. The examination included acquiring volumes during three PFM contractions and three Valsalva maneuvers. LH dimensions were determined in the axial plane. The learning process was documented by estimating agreement between the IE and an experienced examiner (E) using the intraclass correlation coefficient. Agreement was calculated in blocks of 10 ultrasound examinations and analyzed volumes. After the learning process was complete the interobserver reliability for the technique was calculated between these two independent examiners. For offline analysis of the first 10 ultrasound volumes obtained by E, good to very good agreement between E and IE was achieved for all LH measurements except for the left and right levator-urethra gap and pubic arc. For the next 10 analyzed volumes, agreement improved for all LH measurements. Volumes that had been obtained by IE and E were then re-evaluated by IE, and good to very good agreement was found for all LH measurements indicating consistency in volume acquisition. The interobserver reliability study showed excellent ICC values (ICC, 0.81-0.97) for all LH measurements except the pubic arc (ICC = 0.67). 3D/4D transperineal ultrasound is a reliable technique that can be learned in a short period of time. Copyright © 2012 ISUOG. Published by John Wiley & Sons, Ltd.

Open and Distance Learning Today. Routledge Studies in Distance Education Series.

Science.gov (United States)

Lockwood, Fred, Ed.

This book contains the following papers on open and distance learning today: "Preface" (Daniel); "Big Bang Theory in Distance Education" (Hawkridge); "Practical Agenda for Theorists of Distance Education" (Perraton); "Trends, Directions and Needs: A View from Developing Countries" (Koul); "American…

Energy perspectives 2035 - Volume 4, side-notes

International Nuclear Information System (INIS)

2007-01-01

This comprehensive report published by the Swiss Federal Office of Energy (SFOE) presents a number of side-notes pertaining to the first three volumes of the Energy Perspectives series of reports. Various topics are discussed by the authors of the first three volumes of the perspectives in the meetings held by the Energy Perspectives Working Group. The sixteen side-notes presented here cover the following topics: General conditions, fossil resources, the influence of climate warming, CO 2 emissions trading (Joint Implementation JI and Clean Development Mechanism CDM), definition of potentials, air traffic, imported renewable electricity, hydro power, electricity cost calculation, sensitivity analysis of centralised power production facilities, heat-pumps and their power consumption, cold spells and heat-waves, risk and its perception, the 2000-Watt society and international and national energy perspectives

Predicting the Market Potential Using Time Series Analysis

Directory of Open Access Journals (Sweden)

Halmet Bradosti

2015-12-01

Full Text Available The aim of this analysis is to forecast a mini-market sales volume for the period of twelve months starting August 2015 to August 2016. The study is based on the monthly sales in Iraqi Dinar for a private local mini-market for the month of April 2014 to July 2015. As revealed on the graph and of course if the stagnant economic condition continues, the trend of future sales is down-warding. Based on time series analysis, the business may continue to operate and generate small revenues until August 2016. However, due to low sales volume, low profit margin and operating expenses, the revenues may not be adequate enough to produce positive net income and the business may not be able to operate afterward. The principal question rose from this is the forecasting sales in the region will be difficult where the business cycle so dynamic and revolutionary due to systematic risks and unforeseeable future.

An estimate of the glacier ice volume in the Swiss Alps

Science.gov (United States)

Farinotti, Daniel; Huss, Matthias; Bauder, Andreas; Funk, Martin

2009-08-01

Changes in glacier volume are important for questions linked to sea-level rise, water resource management, and tourism industry. With the ongoing climate warming, the retreat of mountain glaciers is a major concern. Predictions of glacier changes, necessarily need the present ice volume as initial condition, and for transient modelling, the ice thickness distribution has to be known. In this paper, a method based on mass conservation and principles of ice flow dynamics is applied to 62 glaciers located in the Swiss Alps for estimating their ice thickness distribution. All available direct ice thickness measurements are integrated. The ice volumes are referenced to the year 1999 by means of a mass balance time series. The results are used to calibrate a volume-area scaling relation, and the coefficients obtained show good agreement with values reported in the literature. We estimate the total ice volume present in the Swiss Alps in the year 1999 to be 74 ± 9 km 3. About 12% of this volume was lost between 1999 and 2008, whereas the extraordinarily warm summer 2003 caused a volume loss of about 3.5%.

Improving Neuromuscular Monitoring and Reducing Residual Neuromuscular Blockade With E-Learning: Protocol for the Multicenter Interrupted Time Series INVERT Study.

Science.gov (United States)

Thomsen, Jakob Louis Demant; Mathiesen, Ole; Hägi-Pedersen, Daniel; Skovgaard, Lene Theil; Østergaard, Doris; Engbaek, Jens; Gätke, Mona Ring

2017-10-06

Muscle relaxants facilitate endotracheal intubation under general anesthesia and improve surgical conditions. Residual neuromuscular blockade occurs when the patient is still partially paralyzed when awakened after surgery. The condition is associated with subjective discomfort and an increased risk of respiratory complications. Use of an objective neuromuscular monitoring device may prevent residual block. Despite this, many anesthetists refrain from using the device. Efforts to increase the use of objective monitoring are time consuming and require the presence of expert personnel. A neuromuscular monitoring e-learning module might support consistent use of neuromuscular monitoring devices. The aim of the study is to assess the effect of a neuromuscular monitoring e-learning module on anesthesia staff's use of objective neuromuscular monitoring and the incidence of residual neuromuscular blockade in surgical patients at 6 Danish teaching hospitals. In this interrupted time series study, we are collecting data repeatedly, in consecutive 3-week periods, before and after the intervention, and we will analyze the effect using segmented regression analysis. Anesthesia departments in the Zealand Region of Denmark are included, and data from all patients receiving a muscle relaxant are collected from the anesthesia information management system MetaVision. We will assess the effect of the module on all levels of potential effect: staff's knowledge and skills, patient care practice, and patient outcomes. The primary outcome is use of neuromuscular monitoring in patients according to the type of muscle relaxant received. Secondary outcomes include last recorded train-of-four value, administration of reversal agents, and time to discharge from the postanesthesia care unit as well as a multiple-choice test to assess knowledge. The e-learning module was developed based on a needs assessment process, including focus group interviews, surveys, and expert opinions. The e-learning

The SEA of the Future: Building the Productivity Infrastructure. Volume 3

Science.gov (United States)

Gross, Betheny, Ed.; Jochim, Ashley, Ed.

2014-01-01

"The SEA of the Future" is an education publication series examining how state education agencies can shift from a compliance to a performance-oriented organization through strategic planning and performance management tools to meet growing demands to support education reform while improving productivity. This volume, the third in the…

Recursive Bayesian recurrent neural networks for time-series modeling.

Science.gov (United States)

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

Improving your genetic literacy in epilepsy-A new series.

Science.gov (United States)

Tan, Nigel C K; Lowenstein, Daniel H

2015-11-01

Advances in epilepsy genetics have been rapid, and it is challenging for clinicians on the ground to keep pace with these advances. The International League Against Epilepsy (ILAE) Genetics Commission has thus crafted a new Genetic Literacy series targeted at busy clinicians. Our goal is to help provide a concise, accessible resource on epilepsy genetics for the busy, on-the-ground clinician so that he/she can apply that knowledge at point-of-care to help patients. This new series is grounded in educational theories and evidence to ensure that learning is effective and efficient. We hope that by promoting and encouraging continuing medical education in epilepsy genetics, this eventually translates to better patient management and therefore better patient health outcomes. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Context-Aware Writing Support for SNS: Connecting Formal and Informal Learning

Science.gov (United States)

Waragai, Ikumi; Kurabayashi, Shuichi; Ohta, Tatsuya; Raindl, Marco; Kiyoki, Yasushi; Tokuda, Hideyuki

2014-01-01

This paper presents another stage in a series of research efforts by the authors to develop an experience-connected mobile language learning environment, bridging formal and informal learning. Building on a study in which the authors tried to connect classroom learning (of German in Japan) with learners' real life experiences abroad by having…

How Children Learn to Use Language - An Overview of R ...

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 13; Issue 5. How Children Learn to Use Language - An Overview of R. Narasimhan's Ideas on Child Language Acquisition. Raman Chandrasekar. General Article Volume 13 Issue 5 May 2008 pp 430-439 ...

European Science Notes. Volume 40, Number 1.

Science.gov (United States)

1986-01-01

Mass Spectrometry mers and copolymers of polyacrylate salt series edited by Professor J.F.J. Todd latex) rather than an inorganic or or- (University...changes in the popu- cy with two potassium dihydrogen phos- lation of a vibrational manifold were phate (KDP) crystals. Following a fil- determined by...AD-A162 235 EUROPEAN SCIENCE NOTES VOLUME 48 NUMBER I(U) OFFICE OF i/1 NAVAL RESEARCH LONDON (ENGLAND) L E SHAFFER JAN 86 UNCLASSIFIED F/G 5/2

Failure and reliability prediction by support vector machines regression of time series data

International Nuclear Information System (INIS)

Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique

2011-01-01

Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques. - Highlights: → Realistic modeling of reliability demands complex mathematical formulations. → SVM is proper when the relation input/output is unknown or very costly to be obtained. → Results indicate the potential of SVM for reliability time series prediction. → Reliability estimates support the establishment of adequate maintenance strategies.

Cuadernos de Autoformacion en Participacion Social: Normatividad. Volumen 5. Primera Edicion (Self-Instructional Notebooks on Social Participation: Legal Issues. Volume 5. First Edition).

Science.gov (United States)

Instituto Nacional para la Educacion de los Adultos, Mexico City (Mexico).

The series "Self-instructional Notes on Social Participation" is a six volume series intended as teaching aids for adult educators. The theoretical, methodological, informative and practical elements of this series will assist professionals in their work and help them achieve greater success. The specific purpose of each notebook is…

Teaching for Engagement: Part 3: Designing for Active Learning

Science.gov (United States)

Hunter, William J.

2015-01-01

In the first two parts of this series, ("Teaching for Engagement: Part 1: Constructivist Principles, Case-Based Teaching, and Active Learning") and ("Teaching for Engagement: Part 2: Technology in the Service of Active Learning"), William J. Hunter sought to outline the theoretical rationale and research basis for such active…

A Decade of Chais Conferences: Introduction to the IJELL Special Series of Chais Conference 2015 Best Papers

Directory of Open Access Journals (Sweden)

Nitza Geri

2015-12-01

This preface presents the mission and activities of the Research Center for Innovation in Learning Technologies at the Open University of Israel. It describes the objectives and themes of the Chais conference 2015, explains the special series synergies with IJELL and the Informing Science Institute, chronicles the topics that have been published in the series, and introduces the papers included in this special selection.

Facilitating Learning at Conferences

DEFF Research Database (Denmark)

Ravn, Ib; Elsborg, Steen

2011-01-01

The typical conference consists of a series of PowerPoint presentations that tend to render participants passive. Students of learning have long abandoned the transfer model that underlies such one-way communication. We propose an al-ternative theory of conferences that sees them as a forum...... for learning, mutual inspiration and human flourishing. We offer five design principles that specify how conferences may engage participants more and hence increase their learning. In the research-and-development effort reported here, our team collaborated with conference organizers in Denmark to introduce...... and facilitate a variety of simple learning techniques at thirty one- and two-day conferences of up to 300 participants each. We present ten of these techniques and data evaluating them. We conclude that if conference organizers allocate a fraction of the total conference time to facilitated processes...

Systemic or Intra-Amygdala Infusion of the Benzodiazepine, Midazolam, Impairs Learning, but Facilitates Re-Learning to Inhibit Fear Responses in Extinction

Science.gov (United States)

Hart, Genevra; Harris, Justin A.; Westbrook, R. Frederick

2010-01-01

A series of experiments used rats to study the effect of a systemic or intra-amygdala infusion of the benzodiazepine, midazolam, on learning and re-learning to inhibit context conditioned fear (freezing) responses. Rats were subjected to two context-conditioning episodes followed by extinction under drug or vehicle, or to two cycles of context…

Cases on Challenges Facing E-Learning and National Development: Institutional Studies and Practices. e-Learning Practices. Volume II

Science.gov (United States)

Demiray, Ugur, Ed.

2010-01-01

E-Learning offers many opportunities for individuals and institutions all over the world. Individuals can access to education they need almost anytime and anywhere they are ready to. Institutions are able to provide more cost-effective training to their employees. E-learning context is very important. It is common to find educators who perceive…

Mutual learning and reverse innovation–where next?

Science.gov (United States)

2014-01-01

There is a clear and evident need for mutual learning in global health systems. It is increasingly recognized that innovation needs to be sourced globally and that we need to think in terms of co-development as ideas are developed and spread from richer to poorer countries and vice versa. The Globalization and Health journal’s ongoing thematic series, “Reverse innovation in global health systems: learning from low-income countries” illustrates how mutual learning and ideas about so-called "reverse innovation" or "frugal innovation" are being developed and utilized by researchers and practitioners around the world. The knowledge emerging from the series is already catalyzing change and challenging the status quo in global health. The path to truly “global innovation flow”, although not fully established, is now well under way. Mobilization of knowledge and resources through continuous communication and awareness raising can help sustain this movement. Global health learning laboratories, where partners can support each other in generating and sharing lessons, have the potential to construct solutions for the world. At the heart of this dialogue is a focus on creating practical local solutions and, simultaneously, drawing out the lessons for the whole world. PMID:24673828

Mutual learning and reverse innovation--where next?

Science.gov (United States)

Crisp, Nigel

2014-03-28

There is a clear and evident need for mutual learning in global health systems. It is increasingly recognized that innovation needs to be sourced globally and that we need to think in terms of co-development as ideas are developed and spread from richer to poorer countries and vice versa. The Globalization and Health journal's ongoing thematic series, "Reverse innovation in global health systems: learning from low-income countries" illustrates how mutual learning and ideas about so-called "reverse innovation" or "frugal innovation" are being developed and utilized by researchers and practitioners around the world. The knowledge emerging from the series is already catalyzing change and challenging the status quo in global health. The path to truly "global innovation flow", although not fully established, is now well under way. Mobilization of knowledge and resources through continuous communication and awareness raising can help sustain this movement. Global health learning laboratories, where partners can support each other in generating and sharing lessons, have the potential to construct solutions for the world. At the heart of this dialogue is a focus on creating practical local solutions and, simultaneously, drawing out the lessons for the whole world.

Learning with Literature in the EFL Classroom. Anglo-American Studies. Volume 49

Science.gov (United States)

Delanoy, Werner, Ed.; Eisenmann, Maria, Ed.; Matz, Frauke, Ed.

2015-01-01

"Learning with Literature in the EFL Classroom" provides a comprehensive, in-depth and state-of-the-art introduction to literature learning in EFL contexts. Paying attention to both theoretical and practical concerns, the study focuses on a wide range of literary genres, different age and ability groups and new topics for literature…

A deep learning approach for pose estimation from volumetric OCT data.

Science.gov (United States)

Gessert, Nils; Schlüter, Matthias; Schlaefer, Alexander

2018-05-01

Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects. Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label's resolution. We achieve a mean average error of 14.89 ± 9.3 µm and 0.096 ± 0.072° for position and orientation learning, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

Multidetector-row computed tomography in the preoperative diagnosis of intestinal complications caused by clinically unsuspected ingested dietary foreign bodies: a case series emphasizing the use of volume rendering techniques

Energy Technology Data Exchange (ETDEWEB)

Teixeira, Augusto Cesar Vieira; Torres, Ulysses dos Santos; Oliveira, Eduardo Portela de; Gual, Fabiana; Bauab Junior, Tufik, E-mail: usantor@yahoo.com.br [Faculdade de Medicina de Sao Jose do Rio Preto (FAMERP), SP (Brazil). Hospital de Base. Serv. de Radiologia e Diagnostico por Imagem; Westin, Carlos Eduardo Garcia [Faculdade de Medicina de Sao Jose do Rio Preto (FAMERP), SP (Brazil). Hospital de Base. Cirurgia Geral; Cardoso, Luciana Vargas [Faculdade de Medicina de Sao Jose do Rio Preto (FAMERP), SP (Brazil). Hospital de Base. Setor de Tomografia Computadorizada

2013-11-15

Objective: the present study was aimed at describing a case series where a preoperative diagnosis of intestinal complications secondary to accidentally ingested dietary foreign bodies was made by multidetector-row computed tomography (MDCT), with emphasis on complementary findings yielded by volume rendering techniques (VRT) and curved multiplanar reconstructions (MPR). Materials and Methods: The authors retrospectively assessed five patients with surgically confirmed intestinal complications (perforation and/or obstruction) secondary to unsuspected ingested dietary foreign bodies, consecutively assisted in their institution between 2010 and 2012. Demographic, clinical, laboratory and radiological data were analyzed. VRT and curved MPR were subsequently performed. Results: preoperative diagnosis of intestinal complications was originally performed in all cases. In one case the presence of a foreign body was not initially identified as the causal factor, and the use of complementary techniques facilitated its retrospective identification. In all cases these tools allowed a better depiction of the entire foreign bodies on a single image section, contributing to the assessment of their morphology. Conclusion: although the use of complementary techniques has not had a direct impact on diagnostic performance in most cases of this series, they may provide a better depiction of foreign bodies' morphology on a single image section. (author)

How the Japanese Learn To Work. Second Edition. Nissan Institute/Routledge Japanese Studies Series.

Science.gov (United States)

Dore, Ronald; Sako, Mari

This book examines how the Japanese learn to work by exploring the following topics: common assumptions about vocational education and training (VET) that Japan brings into question; Japan's general education system and the moral quality and prestige status of the teaching and learning process; screening processes within Japan's education system…

E-learning programs in oncology

DEFF Research Database (Denmark)

Degerfält, Jan; Sjöstedt, Staffan; Fransson, Per

2017-01-01

BACKGROUND: E-learning is an established concept in oncological education and training. However, there seems to be a scarcity of long-term assessments of E-learning programs in oncology vis-á-vis their structural management and didactic value. This study presents descriptive, nationwide data from...... 2005 to 2014. E-learning oncology programs in chemotherapy, general oncology, pain management, palliative care, psycho-social-oncology, and radiotherapy, were reviewed from our databases. Questionnaires of self-perceived didactic value of the programs were examined 2008-2014. RESULTS: The total number.......6% (MDs: 64.9%; RNs: 66.8%; SHCAs: 77.7%) and as good by 30.6% (MDs: 34.5%; RNs: 32.4%; SHCAs: 21.5%) of the responders. CONCLUSIONS: This descriptive study, performed in a lengthy timeframe, presents high-volume data from multi-professional, oncological E-learning programs. While the E-learning paradigm...

Modeling climate change impacts on combined sewer overflow using synthetic precipitation time series.

Science.gov (United States)

Bendel, David; Beck, Ferdinand; Dittmer, Ulrich

2013-01-01

In the presented study climate change impacts on combined sewer overflows (CSOs) in Baden-Wuerttemberg, Southern Germany, were assessed based on continuous long-term rainfall-runoff simulations. As input data, synthetic rainfall time series were used. The applied precipitation generator NiedSim-Klima accounts for climate change effects on precipitation patterns. Time series for the past (1961-1990) and future (2041-2050) were generated for various locations. Comparing the simulated CSO activity of both periods we observe significantly higher overflow frequencies for the future. Changes in overflow volume and overflow duration depend on the type of overflow structure. Both values will increase at simple CSO structures that merely divide the flow, whereas they will decrease when the CSO structure is combined with a storage tank. However, there is a wide variation between the results of different precipitation time series (representative for different locations).

Computer-Mediated Collaborative Learning

Science.gov (United States)

Beatty, Ken; Nunan, David

2004-01-01

The study reported here investigates collaborative learning at the computer. Ten pairs of students were presented with a series of comprehension questions about Mary Shelley's novel "Frankenstein or a Modern Prometheus" along with a CD-ROM, "Frankenstein Illuminated," containing the novel and a variety of source material. Five students worked with…

Real Rainfall Time Series for Storm Sewer Design

DEFF Research Database (Denmark)

Larsen, Torben

The paper describes a simulation method for the design of retention storages, overflows etc. in storm sewer systems. The method is based on computer simulation with real rainfall time series as input ans with the aply of a simple transfer model of the ARMA-type (autoregressiv moving average model......) as the model of the storm sewer system. The output of the simulation is the frequency distribution of the peak flow, overflow volume etc. from the overflow or retention storage. The parameters in the transfer model is found either from rainfall/runoff measurements in the catchment or from one or a few...

On Sums of Numerical Series and Fourier Series

Science.gov (United States)

Pavao, H. Germano; de Oliveira, E. Capelas

2008-01-01

We discuss a class of trigonometric functions whose corresponding Fourier series, on a conveniently chosen interval, can be used to calculate several numerical series. Particular cases are presented and two recent results involving numerical series are recovered. (Contains 1 note.)

Test-Enhanced Learning in an Immunology and Infectious Disease Medicinal Chemistry/Pharmacology Course.

Science.gov (United States)

Hernick, Marcy

2015-09-25

Objective. To develop a series of active-learning modules that would improve pharmacy students' performance on summative assessments. Design. A series of optional online active-learning modules containing questions with multiple formats for topics in a first-year (P1) course was created using a test-enhanced learning approach. A subset of module questions was modified and included on summative assessments. Assessment. Student performance on module questions improved with repeated attempts and was predictive of student performance on summative assessments. Performance on examination questions was higher for students with access to modules than for those without access to modules. Module use appeared to have the most impact on low performing students. Conclusion. Test-enhanced learning modules with immediate feedback provide pharmacy students with a learning tool that improves student performance on summative assessments and also may improve metacognitive and test-taking skills.

Cuadernos de Autoformacion en Participacion Social: Orientaciones Practicas. Volumen 4. Primera Edicion (Self-Instructional Notebooks on Social Participation: Practical Orientations. Volume 4. First Edition).

Science.gov (United States)

Instituto Nacional para la Educacion de los Adultos, Mexico City (Mexico).

The series "Self-instructional Notes on Social Participation" is a six volume series intended as teaching aids for adult educators. The theoretical, methodological, informative and practical elements of this series will assist professionals in their work and help them achieve greater success. The specific purpose of each notebook is…

Learning in a Sheltered Internet Environment: The Use of WebQuests

Science.gov (United States)

Segers, Eliane; Verhoeven, Ludo

2009-01-01

The present study investigated the effects on learning in a sheltered Internet environment using so-called WebQuests in elementary school classrooms in the Netherlands. A WebQuest is an assignment presented together with a series of web pages to help guide children's learning. The learning gains and quality of the work of 229 sixth graders…

Reverse hypothesis machine learning a practitioner's perspective

CERN Document Server

Kulkarni, Parag

2017-01-01

This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as ...

On-the-Job Training and Social Learning Theory. A Literature Review

Science.gov (United States)

1980-05-01

and discussed by Albert Bandura (47). The principles of social learning theory and learning from models are first described. Then a series of rules...developed by Bandura and his students (47, 48, 49) to be the most useful theory to account for observational learning and to provide a basis for...Learning Theory and Its Application 47. Bandura , A. Principles of Behavior Modification, New York: Holt, Rinehart & Winston, 1969. 48. Bandura , A

Quantifying Golgi structure using EM: combining volume-SEM and stereology for higher throughput.

Science.gov (United States)

Ferguson, Sophie; Steyer, Anna M; Mayhew, Terry M; Schwab, Yannick; Lucocq, John Milton

2017-06-01

Investigating organelles such as the Golgi complex depends increasingly on high-throughput quantitative morphological analyses from multiple experimental or genetic conditions. Light microscopy (LM) has been an effective tool for screening but fails to reveal fine details of Golgi structures such as vesicles, tubules and cisternae. Electron microscopy (EM) has sufficient resolution but traditional transmission EM (TEM) methods are slow and inefficient. Newer volume scanning EM (volume-SEM) methods now have the potential to speed up 3D analysis by automated sectioning and imaging. However, they produce large arrays of sections and/or images, which require labour-intensive 3D reconstruction for quantitation on limited cell numbers. Here, we show that the information storage, digital waste and workload involved in using volume-SEM can be reduced substantially using sampling-based stereology. Using the Golgi as an example, we describe how Golgi populations can be sensed quantitatively using single random slices and how accurate quantitative structural data on Golgi organelles of individual cells can be obtained using only 5-10 sections/images taken from a volume-SEM series (thereby sensing population parameters and cell-cell variability). The approach will be useful in techniques such as correlative LM and EM (CLEM) where small samples of cells are treated and where there may be variable responses. For Golgi study, we outline a series of stereological estimators that are suited to these analyses and suggest workflows, which have the potential to enhance the speed and relevance of data acquisition in volume-SEM.

Volume-editing tools for three-dimensional imaging of CT data

International Nuclear Information System (INIS)

Ney, D.R.; Fishman, E.K.

1989-01-01

Three-dimensional imaging of complex structures relies heavily on the ability to edit the routine CT scans to provide an optimal view of the area in question. The authors present a series of strategies for defining the volume editing tools. The authors have developed a series of editing tools that allow the operator to edit critical areas out of an image. The tools are based on a variety of imaging strategies that are implemented depending on the difficulty of separating two structures. The tools combine rectangular masking, threshold base filling, arbitrary curve-based masking, masking, threshold base filling, arbitrary curve-based masking, and object definition via edge detection

Infinite series

CERN Document Server

Hirschman, Isidore Isaac

2014-01-01

This text for advanced undergraduate and graduate students presents a rigorous approach that also emphasizes applications. Encompassing more than the usual amount of material on the problems of computation with series, the treatment offers many applications, including those related to the theory of special functions. Numerous problems appear throughout the book.The first chapter introduces the elementary theory of infinite series, followed by a relatively complete exposition of the basic properties of Taylor series and Fourier series. Additional subjects include series of functions and the app

Relationships between volume, efficiency, and quality in surgery--a delicate balance from managerial perspectives.

Science.gov (United States)

Kraus, Thomas W; Büchler, Markus W; Herfarth, Christian

2005-10-01

Volume, efficiency, and quality in hospital care are often mixed in debate. We analyze how these dimensions are interrelated in surgical hospital management, with particular focus on volume effects: under financial constraints, efficiency is the best form of cost control. External perception of quality is important to attract patients and gain volumes. There are numerous explicit and implicit notions of surgical quality. The relevance of implicit criteria (functionality, reliability, consistency, customaziability, convenience) can change in the time course of hospital competition. Outcome data theoretically are optimal measures of quality, but surgical quality is multifactorially influenced by case mix, surgical technique, indication, process designs, organizational structures, and volume. As quality of surgery is hard to grade, implicit criteria such as customizability currently often overrule functionality (outcome) as the dominant market driver. Activities and volumes are inputs to produce quality. Capability does not translate to ability in a linear function. Adequate process design is important to realize efficiency and quality. Volumes of activities, degree of standardization, specialization, and customer involvement are relevant estimates for process design in services. Flow-orientated management focuses primarily on resource utilization and efficiency, not on surgical quality. The relationship between volume and outcome in surgery is imperfectly understood. Factors involve learning effects both on process efficiency and quality, increased standardization and task specialization, process flow homogeneity, and potential for process integration. Volume is a structural component to develop efficiency and quality. The specific capabilities and process characteristics that contribute to surgical outcome improvement should be defined and exported. Adequate focus should allow even small institutions to benefit from volume-associated effects. All volumes

Sensors, Volume 1, Fundamentals and General Aspects

Science.gov (United States)

Grandke, Thomas; Ko, Wen H.

1996-12-01

'Sensors' is the first self-contained series to deal with the whole area of sensors. It describes general aspects, technical and physical fundamentals, construction, function, applications and developments of the various types of sensors. This volume deals with the fundamentals and common principles of sensors and covers the wide areas of principles, technologies, signal processing, and applications. Contents include: Sensor Fundamentals, e.g. Sensor Parameters, Modeling, Design and Packaging; Basic Sensor Technologies, e.g. Thin and Thick Films, Integrated Magnetic Sensors, Optical Fibres and Intergrated Optics, Ceramics and Oxides; Sensor Interfaces, e.g. Signal Processing, Multisensor Signal Processing, Smart Sensors, Interface Systems; Sensor Applications, e.g. Automotive: On-board Sensors, Traffic Surveillance and Control, Home Appliances, Environmental Monitoring, etc. This volume is an indispensable reference work and text book for both specialits and newcomers, researchers and developers.

Creative Learning Environments in Education--A Systematic Literature Review

Science.gov (United States)

Davies, Dan; Jindal-Snape, Divya; Collier, Chris; Digby, Rebecca; Hay, Penny; Howe, Alan

2013-01-01

This paper reports on a systematic review of 210 pieces of educational research, policy and professional literature relating to creative environments for learning in schools, commissioned by Learning and Teaching Scotland (LTS). Despite the volume of academic literature in this field, the team of six reviewers found comparatively few empirical…

Design Framework for an Adaptive MOOC Enhanced by Blended Learning

DEFF Research Database (Denmark)

Gynther, Karsten

2016-01-01

The research project has developed a design framework for an adaptive MOOC that complements the MOOC format with blended learning. The design framework consists of a design model and a series of learning design principles which can be used to design in-service courses for teacher professional...

IEEE International Workshop on Machine Learning for Signal Processing: Preface

DEFF Research Database (Denmark)

Tao, Jianhua

The 21st IEEE International Workshop on Machine Learning for Signal Processing will be held in Beijing, China, on September 18–21, 2011. The workshop series is the major annual technical event of the IEEE Signal Processing Society's Technical Committee on Machine Learning for Signal Processing...

Neuroanatomical and cognitive mediators of age-related differences in perceptual priming and learning

OpenAIRE

Kennedy, Kristen M.; Rodrigue, Karen M.; Head, Denise; Gunning-Dixon, Faith; Raz, Naftali

2009-01-01

Our objectives were to assess age differences in perceptual repetition priming and perceptual skill learning, and to determine whether they are mediated by cognitive resources and regional cerebral volume differences. Fragmented picture identification paradigm allows the study of both priming and learning within the same task. We presented this task to 169 adults (ages 18–80), assessed working memory and fluid intelligence, and measured brain volumes of regions that were deemed relevant to th...

Determining the efficiency of a commercial belly board device in reducing small bowel volume in rectal cancer patients

International Nuclear Information System (INIS)

Lukarski, Dusko; Petkovska, Sonja; Angelovska, Natalija; Grozdanovska, Biljana; Mitrevski, Nenad

2010-01-01

The purpose of this treatment planning study was to evaluate the efficiency of a commercial belly board device in reducing the irradiated volume of the small bowel. In this study 10 patients with rectal carcinoma receiving postoperative radiotherapy were included. For each of them we made two computer tomography series in prone position. In the first one the patients were lying on the flat table top, and in the second one they were lying on the belly board device which is under investigation. On both series we calculated and optimized plans according to the standing protocol of our department. From the dose-volume histograms of these plans we compared the volumes of the small bowel irradiated to three dose levels 15, 30 and 45 Gy. The results showed that the absolute irradiated volumes were significantly smaller in the plans with the belly board device. Based on these results we believe that the employment of this belly board device will reduce the acute and late small bowel toxicity. This should be verified with a clinical study.(Author)

Determining the efficiency of a commercial belly board device in reducing small bowel volume in rectal cancer patients

Energy Technology Data Exchange (ETDEWEB)

Lukarski, Dusko; Petkovska, Sonja; Angelovska, Natalija; Grozdanovska, Biljana; Mitrevski, Nenad [University Clinic of Radiotherapy and Oncology, Skopje(Macedonia, The Former Yugoslav Republic of)

2010-07-01

The purpose of this treatment planning study was to evaluate the efficiency of a commercial belly board device in reducing the irradiated volume of the small bowel. In this study 10 patients with rectal carcinoma receiving postoperative radiotherapy were included. For each of them we made two computer tomography series in prone position. In the first one the patients were lying on the flat table top, and in the second one they were lying on the belly board device which is under investigation. On both series we calculated and optimized plans according to the standing protocol of our department. From the dose-volume histograms of these plans we compared the volumes of the small bowel irradiated to three dose levels 15, 30 and 45 Gy. The results showed that the absolute irradiated volumes were significantly smaller in the plans with the belly board device. Based on these results we believe that the employment of this belly board device will reduce the acute and late small bowel toxicity. This should be verified with a clinical study.(Author)

Grooming. Learning Activity Package.

Science.gov (United States)

Stark, Pamela

This learning activity package on grooming for health workers is one of a series of 12 titles developed for use in health occupations education programs. Materials in the package include objectives, a list of materials needed, information sheets, reviews (self evaluations) of portions of the content, and answers to reviews. These topics are…

Generation and prediction of time series by a neural network

International Nuclear Information System (INIS)

Eisenstein, E.; Kanter, I.; Kessler, D.A.; Kinzel, W.

1995-01-01

Generation and prediction of time series are analyzed for the case of a bit generator: a perceptron where in each time step the input units are shifted one bit to the right with the state of the leftmost input unit set equal to the output unit in the previous time step. The long-time dynamical behavior of the bit generator consists of cycles whose typical period scales polynomially with the size of the network and whose spatial structure is periodic with a typical finite wavelength. The generalization error on a cycle is zero for a finite training set, and global dynamical behaviors can also be learned in a finite time. Hence, a projection of a rule can be learned in a finite time

Learning Physics through Play in an Augmented Reality Environment

Science.gov (United States)

Enyedy, Noel; Danish, Joshua A.; Delacruz, Girlie; Kumar, Melissa

2012-01-01

The Learning Physics through Play Project (LPP) engaged 6-8-year old students (n = 43) in a series of scientific investigations of Newtonian force and motion including a series of augmented reality activities. We outline the two design principles behind the LPP curriculum: 1) the use of socio-dramatic, embodied play in the form of participatory…

Evolving progress in oncologic and operative outcomes for esophageal and junctional cancer: lessons from the experience of a high-volume center.

LENUS (Irish Health Repository)

Reynolds, John V

2012-05-01

Modern series from high-volume esophageal centers report an approximate 40% 5-year survival in patients treated with curative intent and postoperative mortality rates of less than 4%. An objective analysis of factors that underpin current benchmarks within high-volume centers has not been performed.

The Value of Children: A Cross-National Study, Volume Three. Hawaii.

Science.gov (United States)

Arnold, Fred; Fawcett, James T.

The document, one in a series of seven reports from the Value of Children Project, discusses results of the survey in Hawaii. Specifically, the study investigated the social, psychological, and economic costs and benefits associated with having children. The volume is presented in seven chapters. Chapter I describes the background of the study and…

The Value of Children: A Cross-National Study, Volume Two. Philippines.

Science.gov (United States)

Bulatao, Rodolfo A.

This volume, second in a series of seven reports of the Value of Children Project, discusses results of the survey in the Philippines. The study identifies major values and disvalues that Filipino parents attach to children. It also examines characteristics of parents that are related to values and disvalues. The document is presented in seven…

Computational intelligence for technology enhanced learning

Energy Technology Data Exchange (ETDEWEB)

Xhafa, Fatos [Polytechnic Univ. of Catalonia, Barcelona (Spain). Dept. of Languages and Informatics Systems; Caballe, Santi; Daradoumis, Thanasis [Open Univ. of Catalonia, Barcelona (Spain). Dept. of Computer Sciences Multimedia and Telecommunications; Abraham, Ajith [Machine Intelligence Research Labs (MIR Labs), Auburn, WA (United States). Scientific Network for Innovation and Research Excellence; Juan Perez, Angel Alejandro (eds.) [Open Univ. of Catalonia, Barcelona (Spain). Dept. of Information Sciences

2010-07-01

E-Learning has become one of the most wide spread ways of distance teaching and learning. Technologies such as Web, Grid, and Mobile and Wireless networks are pushing teaching and learning communities to find new and intelligent ways of using these technologies to enhance teaching and learning activities. Indeed, these new technologies can play an important role in increasing the support to teachers and learners, to shorten the time to learning and teaching; yet, it is necessary to use intelligent techniques to take advantage of these new technologies to achieve the desired support to teachers and learners and enhance learners' performance in distributed learning environments. The chapters of this volume bring advances in using intelligent techniques for technology enhanced learning as well as development of e-Learning applications based on such techniques and supported by technology. Such intelligent techniques include clustering and classification for personalization of learning, intelligent context-aware techniques, adaptive learning, data mining techniques and ontologies in e-Learning systems, among others. Academics, scientists, software developers, teachers and tutors and students interested in e-Learning will find this book useful for their academic, research and practice activity. (orig.)

Efficacy of different bone volume expanders for augmenting lumbar fusions.

Science.gov (United States)

Epstein, Nancy E

2008-01-01

A wide variety of bone volume expanders are being used in performing posterolateral lumbar noninstrumented and instrumented lumbar fusions. This article presents a review of their efficacy based on fusion rates, complications, and outcomes. Lumbar noninstrumented and instrumented fusions frequently use laminar autografts and different bone graft expanders. This review presents the utility of multiple forms/ratios of DBMs containing allografts. It also discusses the efficacy of artificial bone graft substitutes, including HA and B-TCP. Dynamic x-ray and/or CT examinations were used to document fusion in most series. Outcomes were variously assessed using Odom's criteria or different outcome questionnaires (Oswestry Questionnaire, SF-36, Dallas Pain Questionnaire, and/or Low Back Pain Rating Scale). Performing noninstrumented and instrumented lumbar posterolateral fusions resulted in comparable fusion rates in many series. Similar outcomes were also documented based on Odom's criteria or the multiple patient-based questionnaires. However, in some studies, the addition of spinal instrumentation increased the reoperation rate, operative time, blood loss, and cost. Various forms of DBMs, applied in different ratios to autografts, effectively supplemented spinal fusions in animal models and patient series. beta-Tricalcium phosphate, which is used to augment autograft fusions addressing idiopathic scoliosis or lumbar disease, also proved to be effective. Different types of bone volume expanders, including various forms of allograft-based DBMs, and artificial bone graft substitutes (HA and B-TCP) effectively promote posterolateral lumbar noninstrumented and instrumented fusions when added to autografts.

Jamming and Learning: Analysing Changing Collective Practice of Changing Participation

Science.gov (United States)

Brinck, Lars

2017-01-01

This article reports a long-term ethnographic study on jamming and learning from an entwined artistic and educational perspective. The study investigates aspects of learning during a professional band's jamming and recording eight groove-jazz frameworks and a series of subsequent concerts with pre-academy students "sitting in." Fieldwork…

Concept learning by direct current design challenges in secondary education

NARCIS (Netherlands)

Van Breukelen, D.H.J.; De Vries, M.J.; Schure, F.A.

2016-01-01

This paper presents a mixed methods study in which 77 students and 3 teachers took part, that investigated the practice of Learning by Design (LBD). The study is part of a series of studies, funded by the Netherlands Organisation for Scientific Research, that aims to improve student learning,

Concept learning by direct current design challenges in secondary education

NARCIS (Netherlands)

MEd Dave van Breukelen; Prof. Dr. Marc de Vries; MEd Frank Schure

2016-01-01

This paper presents a mixed methods study in which 77 students and 3 teachers took part, that investigated the practice of Learning by Design (LBD). The study is part of a series of studies, funded by the Netherlands Organisation for Scientific Research (NWO), that aims to improve student learning,

Concept learning by direct current design challenges in secondary education

NARCIS (Netherlands)

van Breukelen, D.H.J.; de Vries, M.J.; Schure, Frank A.

2016-01-01

This paper presents a mixed methods study in which 77 students and 3 teachers took part, that investigated the practice of Learning by Design (LBD). The study is part of a series of studies, funded by the Netherlands Organisation for Scientific Research, that aims to improve student learning,

Cuadernos de Autoformacion en Participacion Social: Proyectos del INEA. Volumen 3. Primera Edicion (Self-Instructional Notebooks on Social Participation: INEA Projects. Volume 3. First Edition).

Science.gov (United States)

Instituto Nacional para la Educacion de los Adultos, Mexico City (Mexico).

The series "Self-instructional Notes on Social Participation" is a six volume series intended as teaching aids for adult educators. The theoretical, methodological, informative and practical elements of this series will assist professionals in their work and help them achieve greater success. The specific purpose of each notebook is…

What We Muggles Can Learn about Teaching from Hogwarts

Science.gov (United States)

Bixler, Andrea

2011-01-01

The Harry Potter series furnishes many instances of both good and bad teaching. From them, we can learn more about three principles outlined in "How People Learn" (National Research Council 2000a). (1) Teachers should question students about their prior knowledge, as Professor Lupin does before his lessons; (2) we should encourage students to…

Clinical anatomy e-cases: a five-year follow-up of learning analytics.

Science.gov (United States)

Perumal, Vivek; Butson, Russell; Blyth, Phil; Daniel, Ben

2017-01-27

This article explores the development and user experiences of a supplementary e-learning resource (clinical anatomy e-cases) for medical students, across a five-year teaching period. A series of online supplementary e-learning resources (the clinical anatomy e-cases) were developed and introduced to the regional and clinical anatomy module of the medicine course. Usage analytics were collected online from a cohort of third-year medical students and analysed to gain a better understanding of how students utilised these resources. Key results showed that the students used the supplementary learning resource during and outside regular teaching hours that includes a significant access during holidays. Analysis also suggested that the resources were frequently accessed during examination periods and during subsequent clinical study years (fourth or fifth years of medicine course). Increasing interest and positive feedback from students has led to the development of a further series of e-cases. Tailor-made e-learning resources promote clinical anatomy learning outside classroom hours and make supplementary learning a 24/7 task.

Transnational learning in Creative City Challenge

NARCIS (Netherlands)

Romein, A.; Trip, J.J.; Zonneveld, W.A.M.

2012-01-01

Report written in the context of the INTERREG IVB project Creative City Challenge. Based on a series of international expert meetings the report discusses various themes in relation to creative city policy, and analyses the process of transnational learning itself.

Deep Learning and Its Applications in Biomedicine.

Science.gov (United States)

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Technology for Education and Learning

CERN Document Server

2012 international conference on Technology for Education and Learning (ICTEL 2012)

2012-01-01

This volume contains 108 selected papers presented at the 2012 international conference on Technology for Education and Learning (ICTEL 2012), Macau, China, March 1-2, 2012. The conference brought together researchers working in various different areas of Technology for Education and Learning with a main emphasis on technology for business and economy in order to foster international collaborations and exchange of new ideas. This proceedings book has its focus on Technology for Economy, Finance and Education representing some of the major subareas presented at the conference.

Book Review: Sustainable Luxury and Social Entrepreneurship. Volume II: More Stories from the Pioneers

DEFF Research Database (Denmark)

Skjold, Else

2017-01-01

volume Sustainable Luxury and Social Entrepreneurship: Stories of the Pioneers, published in 2014. The book series, as well as the awards, seeks to investigate and promote the motives, context, and practical endeavours of sustainable entrepreneurs within the premium and luxury sector....

Toward automatic time-series forecasting using neural networks.

Science.gov (United States)

Yan, Weizhong

2012-07-01

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

Modeling of plasma chemistry in a corona streamer pulse series in air

International Nuclear Information System (INIS)

Nowakowska, H.; Stanco, J.; Dors, M.; Mizeraczyk, J.

2002-01-01

The aim of this study is to analyse the chemistry in air treated by a series of corona discharge streamers. Attention is focused on the conversion of ozone and nitrogen oxides. In the model it is assumed that the streamer head of relatively small geometrical dimensions propagates from the anode to the cathode, leaving the streamer channel behind. Any elemental gas volume in the streamer path is subjected first to the conditions of the streamer head, and next to those of the streamer channel. The kinetics of plasma-chemical processes occurring in the gas is modeled numerically for a single streamer and a series of streamers. The temporal evolution of 25 chemical compounds initially present or produced in air is calculated. (author)

The Surveillance of Learning: A Critical Analysis of University Attendance Policies