WorldWideScience

Sample records for learning techniques applied

  1. Applying perceptual and adaptive learning techniques for teaching introductory histopathology

    Directory of Open Access Journals (Sweden)

    Sally Krasne

    2013-01-01

    Full Text Available Background: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. Methods: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses that appear during a learning session based on each learner′s accuracy and response time (RT. We developed a perceptual and adaptive learning module (PALM that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. Results: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1 st -year students, but not significantly so for 2 nd -year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1 st and 2 nd year students suggesting good retention of pattern recognition. Student evaluations were very favorable. Conclusion: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students.

  2. Active Learning Techniques Applied to an Interdisciplinary Mineral Resources Course.

    Science.gov (United States)

    Aird, H. M.

    2015-12-01

    An interdisciplinary active learning course was introduced at the University of Puget Sound entitled 'Mineral Resources and the Environment'. Various formative assessment and active learning techniques that have been effective in other courses were adapted and implemented to improve student learning, increase retention and broaden knowledge and understanding of course material. This was an elective course targeted towards upper-level undergraduate geology and environmental majors. The course provided an introduction to the mineral resources industry, discussing geological, environmental, societal and economic aspects, legislation and the processes involved in exploration, extraction, processing, reclamation/remediation and recycling of products. Lectures and associated weekly labs were linked in subject matter; relevant readings from the recent scientific literature were assigned and discussed in the second lecture of the week. Peer-based learning was facilitated through weekly reading assignments with peer-led discussions and through group research projects, in addition to in-class exercises such as debates. Writing and research skills were developed through student groups designing, carrying out and reporting on their own semester-long research projects around the lasting effects of the historical Ruston Smelter on the biology and water systems of Tacoma. The writing of their mini grant proposals and final project reports was carried out in stages to allow for feedback before the deadline. Speakers from industry were invited to share their specialist knowledge as guest lecturers, and students were encouraged to interact with them, with a view to employment opportunities. Formative assessment techniques included jigsaw exercises, gallery walks, placemat surveys, think pair share and take-home point summaries. Summative assessment included discussion leadership, exams, homeworks, group projects, in-class exercises, field trips, and pre-discussion reading exercises

  3. Machine-learning techniques applied to antibacterial drug discovery.

    Science.gov (United States)

    Durrant, Jacob D; Amaro, Rommie E

    2015-01-01

    The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. © 2015 John Wiley & Sons A/S.

  4. Machine learning techniques applied to system characterization and equalization

    DEFF Research Database (Denmark)

    Zibar, Darko; Thrane, Jakob; Wass, Jesper

    2016-01-01

    Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.......Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals....

  5. Applying effective teaching and learning techniques to nephrology education.

    Science.gov (United States)

    Rondon-Berrios, Helbert; Johnston, James R

    2016-10-01

    The interest in nephrology as a career has declined over the last several years. Some of the reasons cited for this decline include the complexity of the specialty, poor mentoring and inadequate teaching of nephrology from medical school through residency. The purpose of this article is to introduce the reader to advances in the science of adult learning, illustrate best teaching practices in medical education that can be extrapolated to nephrology and introduce the basic teaching methods that can be used on the wards, in clinics and in the classroom.

  6. Practising What We Teach: Vocational Teachers Learn to Research through Applying Action Learning Techniques

    Science.gov (United States)

    Lasky, Barbara; Tempone, Irene

    2004-01-01

    Action learning techniques are well suited to the teaching of organisation behaviour students because of their flexibility, inclusiveness, openness, and respect for individuals. They are no less useful as a tool for change for vocational teachers, learning, of necessity, to become researchers. Whereas traditional universities have always had a…

  7. Statistical Techniques Used in Three Applied Linguistics Journals: "Language Learning,""Applied Linguistics" and "TESOL Quarterly," 1980-1986: Implications for Readers and Researchers.

    Science.gov (United States)

    Teleni, Vicki; Baldauf, Richard B., Jr.

    A study investigated the statistical techniques used by applied linguists and reported in three journals, "Language Learning,""Applied Linguistics," and "TESOL Quarterly," between 1980 and 1986. It was found that 47% of the published articles used statistical procedures. In these articles, 63% of the techniques used could be called basic, 28%…

  8. Applied ALARA techniques

    International Nuclear Information System (INIS)

    Waggoner, L.O.

    1998-01-01

    The presentation focuses on some of the time-proven and new technologies being used to accomplish radiological work. These techniques can be applied at nuclear facilities to reduce radiation doses and protect the environment. The last reactor plants and processing facilities were shutdown and Hanford was given a new mission to put the facilities in a safe condition, decontaminate, and prepare them for decommissioning. The skills that were necessary to operate these facilities were different than the skills needed today to clean up Hanford. Workers were not familiar with many of the tools, equipment, and materials needed to accomplish:the new mission, which includes clean up of contaminated areas in and around all the facilities, recovery of reactor fuel from spent fuel pools, and the removal of millions of gallons of highly radioactive waste from 177 underground tanks. In addition, this work has to be done with a reduced number of workers and a smaller budget. At Hanford, facilities contain a myriad of radioactive isotopes that are 2048 located inside plant systems, underground tanks, and the soil. As cleanup work at Hanford began, it became obvious early that in order to get workers to apply ALARA and use hew tools and equipment to accomplish the radiological work it was necessary to plan the work in advance and get radiological control and/or ALARA committee personnel involved early in the planning process. Emphasis was placed on applying,ALARA techniques to reduce dose, limit contamination spread and minimize the amount of radioactive waste generated. Progress on the cleanup has,b6en steady and Hanford workers have learned to use different types of engineered controls and ALARA techniques to perform radiological work. The purpose of this presentation is to share the lessons learned on how Hanford is accomplishing radiological work

  9. Applied ALARA techniques

    Energy Technology Data Exchange (ETDEWEB)

    Waggoner, L.O.

    1998-02-05

    The presentation focuses on some of the time-proven and new technologies being used to accomplish radiological work. These techniques can be applied at nuclear facilities to reduce radiation doses and protect the environment. The last reactor plants and processing facilities were shutdown and Hanford was given a new mission to put the facilities in a safe condition, decontaminate, and prepare them for decommissioning. The skills that were necessary to operate these facilities were different than the skills needed today to clean up Hanford. Workers were not familiar with many of the tools, equipment, and materials needed to accomplish:the new mission, which includes clean up of contaminated areas in and around all the facilities, recovery of reactor fuel from spent fuel pools, and the removal of millions of gallons of highly radioactive waste from 177 underground tanks. In addition, this work has to be done with a reduced number of workers and a smaller budget. At Hanford, facilities contain a myriad of radioactive isotopes that are 2048 located inside plant systems, underground tanks, and the soil. As cleanup work at Hanford began, it became obvious early that in order to get workers to apply ALARA and use hew tools and equipment to accomplish the radiological work it was necessary to plan the work in advance and get radiological control and/or ALARA committee personnel involved early in the planning process. Emphasis was placed on applying,ALARA techniques to reduce dose, limit contamination spread and minimize the amount of radioactive waste generated. Progress on the cleanup has,b6en steady and Hanford workers have learned to use different types of engineered controls and ALARA techniques to perform radiological work. The purpose of this presentation is to share the lessons learned on how Hanford is accomplishing radiological work.

  10. Applying machine-learning techniques to Twitter data for automatic hazard-event classification.

    Science.gov (United States)

    Filgueira, R.; Bee, E. J.; Diaz-Doce, D.; Poole, J., Sr.; Singh, A.

    2017-12-01

    The constant flow of information offered by tweets provides valuable information about all sorts of events at a high temporal and spatial resolution. Over the past year we have been analyzing in real-time geological hazards/phenomenon, such as earthquakes, volcanic eruptions, landslides, floods or the aurora, as part of the GeoSocial project, by geo-locating tweets filtered by keywords in a web-map. However, not all the filtered tweets are related with hazard/phenomenon events. This work explores two classification techniques for automatic hazard-event categorization based on tweets about the "Aurora". First, tweets were filtered using aurora-related keywords, removing stop words and selecting the ones written in English. For classifying the remaining between "aurora-event" or "no-aurora-event" categories, we compared two state-of-art techniques: Support Vector Machine (SVM) and Deep Convolutional Neural Networks (CNN) algorithms. Both approaches belong to the family of supervised learning algorithms, which make predictions based on labelled training dataset. Therefore, we created a training dataset by tagging 1200 tweets between both categories. The general form of SVM is used to separate two classes by a function (kernel). We compared the performance of four different kernels (Linear Regression, Logistic Regression, Multinomial Naïve Bayesian and Stochastic Gradient Descent) provided by Scikit-Learn library using our training dataset to build the SVM classifier. The results shown that the Logistic Regression (LR) gets the best accuracy (87%). So, we selected the SVM-LR classifier to categorise a large collection of tweets using the "dispel4py" framework.Later, we developed a CNN classifier, where the first layer embeds words into low-dimensional vectors. The next layer performs convolutions over the embedded word vectors. Results from the convolutional layer are max-pooled into a long feature vector, which is classified using a softmax layer. The CNN's accuracy

  11. Applying machine learning techniques for forecasting flexibility of virtual power plants

    DEFF Research Database (Denmark)

    MacDougall, Pamela; Kosek, Anna Magdalena; Bindner, Henrik W.

    2016-01-01

    network as well as the multi-variant linear regression. It is found that it is possible to estimate the longevity of flexibility with machine learning. The linear regression algorithm is, on average, able to estimate the longevity with a 15% error. However, there was a significant improvement with the ANN...... approach to investigating the longevity of aggregated response of a virtual power plant using historic bidding and aggregated behaviour with machine learning techniques. The two supervised machine learning techniques investigated and compared in this paper are, multivariate linear regression and single...... algorithm achieving, on average, a 5.3% error. This is lowered 2.4% when learning for the same virtual power plant. With this information it would be possible to accurately offer residential VPP flexibility for market operations to safely avoid causing further imbalances and financial penalties....

  12. Applying contemporary statistical techniques

    CERN Document Server

    Wilcox, Rand R

    2003-01-01

    Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods understandable, practical, and easily accessible.* Assumes no previous training in statistics * Explains how and why modern statistical methods provide more accurate results than conventional methods* Covers the latest developments on multiple comparisons * Includes recent advanc

  13. Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression

    Directory of Open Access Journals (Sweden)

    Land Walker H

    2011-01-01

    Full Text Available Abstract Background When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be difficult to characterize. When the relationship is nonlinear, linear modeling techniques do not capture the nonlinear information content. Statistical learning (SL techniques with kernels are capable of addressing nonlinear problems without making parametric assumptions. However, these techniques do not produce findings relevant for epidemiologic interpretations. A simulated case-control study was used to contrast the information embedding characteristics and separation boundaries produced by a specific SL technique with logistic regression (LR modeling representing a parametric approach. The SL technique was comprised of a kernel mapping in combination with a perceptron neural network. Because the LR model has an important epidemiologic interpretation, the SL method was modified to produce the analogous interpretation and generate odds ratios for comparison. Results The SL approach is capable of generating odds ratios for main effects and risk factor interactions that better capture nonlinear relationships between exposure variables and outcome in comparison with LR. Conclusions The integration of SL methods in epidemiology may improve both the understanding and interpretation of complex exposure/disease relationships.

  14. Learning mediastinoscopy: the need for education, experience and modern techniques--interdependency of the applied technique and surgeon's training level.

    Science.gov (United States)

    Walles, Thorsten; Friedel, Godehard; Stegherr, Tobias; Steger, Volker

    2013-04-01

    Mediastinoscopy represents the gold standard for invasive mediastinal staging. While learning and teaching the surgical technique are challenging due to the limited accessibility of the operation field, both benefited from the implementation of video-assisted techniques. However, it has not been established yet whether video-assisted mediastinoscopy improves the mediastinal staging in itself. Retrospective single-centre cohort analysis of 657 mediastinoscopies performed at a specialized tertiary care thoracic surgery unit from 1994 to 2006. The number of specimens obtained per procedure and per lymph node station (2, 4, 7, 8 for mediastinoscopy and 2-9 for open lymphadenectomy), the number of lymph node stations examined, sensitivity and negative predictive value with a focus on the technique employed (video-assisted vs standard technique) and the surgeon's experience were calculated. Overall sensitivity was 60%, accuracy was 90% and negative predictive value 88%. With the conventional technique, experience alone improved sensitivity from 49 to 57% and it was predominant at the paratracheal right region (from 62 to 82%). But with the video-assisted technique, experienced surgeons rose sensitivity from 57 to 79% in contrast to inexperienced surgeons who lowered sensitivity from 49 to 33%. We found significant differences concerning (i) the total number of specimens taken, (ii) the amount of lymph node stations examined, (iii) the number of specimens taken per lymph node station and (iv) true positive mediastinoscopies. The video-assisted technique can significantly improve the results of mediastinoscopy. A thorough education on the modern video-assisted technique is mandatory for thoracic surgeons until they can fully exhaust its potential.

  15. Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

    Directory of Open Access Journals (Sweden)

    Steren Chabert

    2017-01-01

    Full Text Available Cerebral aneurysm is a cerebrovascular disorder characterized by a bulging in a weak area in the wall of an artery that supplies blood to the brain. It is relevant to understand the mechanisms leading to the apparition of aneurysms, their growth and, more important, leading to their rupture. The purpose of this study is to study the impact on aneurysm rupture of the combination of different parameters, instead of focusing on only one factor at a time as is frequently found in the literature, using machine learning and feature extraction techniques. This discussion takes relevance in the context of the complex decision that the physicians have to take to decide which therapy to apply, as each intervention bares its own risks, and implies to use a complex ensemble of resources (human resources, OR, etc. in hospitals always under very high work load. This project has been raised in our actual working team, composed of interventional neuroradiologist, radiologic technologist, informatics engineers and biomedical engineers, from Valparaiso public Hospital, Hospital Carlos van Buren, and from Universidad de Valparaíso – Facultad de Ingeniería and Facultad de Medicina. This team has been working together in the last few years, and is now participating in the implementation of an “interdisciplinary platform for innovation in health”, as part of a bigger project leaded by Universidad de Valparaiso (PMI UVA1402. It is relevant to emphasize that this project is made feasible by the existence of this network between physicians and engineers, and by the existence of data already registered in an orderly manner, structured and recorded in digital format. The present proposal arises from the description in nowadays literature that the actual indicators, whether based on morphological description of the aneurysm, or based on characterization of biomechanical factor or others, these indicators were shown not to provide sufficient information in order

  16. MACHINE LEARNING TECHNIQUES APPLIED TO LIGNOCELLULOSIC ETHANOL IN SIMULTANEOUS HYDROLYSIS AND FERMENTATION

    Directory of Open Access Journals (Sweden)

    J. Fischer

    Full Text Available Abstract This paper investigates the use of machine learning (ML techniques to study the effect of different process conditions on ethanol production from lignocellulosic sugarcane bagasse biomass using S. cerevisiae in a simultaneous hydrolysis and fermentation (SHF process. The effects of temperature, enzyme concentration, biomass load, inoculum size and time were investigated using artificial neural networks, a C5.0 classification tree and random forest algorithms. The optimization of ethanol production was also evaluated. The results clearly depict that ML techniques can be used to evaluate the SHF (R2 between actual and model predictions higher than 0.90, absolute average deviation lower than 8.1% and RMSE lower than 0.80 and predict optimized conditions which are in close agreement with those found experimentally. Optimal conditions were found to be a temperature of 35 ºC, an SHF time of 36 h, enzymatic load of 99.8%, inoculum size of 29.5 g/L and bagasse concentration of 24.9%. The ethanol concentration and volumetric productivity for these conditions were 12.1 g/L and 0.336 g/L.h, respectively.

  17. Applying squeezing technique to clay-rocks: lessons learned from ten years experiments at Mont Terri

    International Nuclear Information System (INIS)

    Fernandez, A. M.; Melon, A.; Sanchez-Ledesma, D.M.; Tournassat, C.; Gaucher, E.; Astudillo, J.; Vinsot, A.

    2012-01-01

    Document available in extended abstract form only. Argillaceous formations of low permeability are considered in several countries as potential host rocks for the disposal of high level radioactive wastes (HLRW). In order to determine their suitability for waste disposal, evaluations of the hydro-geochemistry and transport mechanisms from such geologic formations to the biosphere must be undertaken. The migration of radionuclides through the geosphere will occur predominantly in the aqueous phase, and hence the pore water chemistry plays an important role in determining ion diffusion characteristics in argillaceous formations. Consequently, a great effort has been made to characterise the pore water chemistry in clay-rocks formations. In the last 10 years various techniques were developed for determining pore water composition of clay-rocks including both direct and indirect methods: 1) In situ pore water sampling (water and gas) from sealed boreholes (Pearson et al., 2003; Vinsot et al. 2008); 2) Laboratory pore water sampling from unaltered core samples by the squeezing technique at high pressures (Fernandez et al., 2009); and 3) Characterization of the water chemistry by geochemical modelling (Gaucher et al. 2009). Pore water chemistry in clay-rocks and extraction techniques were documented and reviewed in different studies (Sacchi et al., 2001). Recovering pristine pore water from low permeable and low water content systems is very difficult and sometimes impossible. Besides, uncertainties are associated to each method used for the pore water characterization. In this paper, a review about the high pressure squeezing technique applied to indurate clay-rocks was performed. For this purpose, the experimental work on Opalinus Clay at the Mont Terri Research Laboratory during the last ten years was evaluated. A complete discussion was made about different issues such as: a) why is necessary to obtain the pore water by squeezing in the context of radioactive waste

  18. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

    Science.gov (United States)

    Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

    2017-06-14

    Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

  19. Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

    Directory of Open Access Journals (Sweden)

    H Kimura

    2009-04-01

    Full Text Available In climate research, pressure patterns are often very important. When a climatologists need to know the days of a specific pressure pattern, for example "low pressure in Western areas of Japan and high pressure in Eastern areas of Japan (Japanese winter-type weather," they have to visually check a huge number of surface weather charts. To overcome this problem, we propose an automatic classification system using a support vector machine (SVM, which is a machine-learning method. We attempted to classify pressure patterns into two classes: "winter type" and "non-winter type". For both training datasets and test datasets, we used the JRA-25 dataset from 1981 to 2000. An experimental evaluation showed that our method obtained a greater than 0.8 F-measure. We noted that variations in results were based on differences in training datasets.

  20. Applying Squeezing Technique to Clayrocks: Lessons Learned from Experiments at Mont Terri Rock Laboratory

    International Nuclear Information System (INIS)

    Fernandez, A. M.; Sanchez-Ledesma, D. M.; Tournassat, C.; Melon, A.; Gaucher, E.; Astudillo, E.; Vinsot, A.

    2013-01-01

    Knowledge of the pore water chemistry in clay rock formations plays an important role in determining radionuclide migration in the context of nuclear waste disposal. Among the different in situ and ex-situ techniques for pore water sampling in clay sediments and soils, squeezing technique dates back 115 years. Although different studies have been performed about the reliability and representativeness of squeezed pore waters, more of them were achieved on high porosity, high water content and unconsolidated clay sediments. A very few of them tackled the analysis of squeezed pore water from low-porosity, low water content and highly consolidated clay rocks. In this work, a specially designed and fabricated one-dimensional compression cell two directional fluid flow was used to extract and analyse the pore water composition of Opalinus Clay core samples from Mont Terri (Switzerland). The reproducibility of the technique is good and no ionic ultrafiltration, chemical fractionation or anion exclusion was found in the range of pressures analysed: 70-200 MPa. Pore waters extracted in this range of pressures do not decrease in concentration, which would indicate a dilution of water by mixing of the free pore water and the outer layers of double layer water (Donnan water). A threshold (safety) squeezing pressure of 175 MPa was established for avoiding membrane effects (ion filtering, anion exclusion, etc.) from clay particles induced by increasing pressures. Besides, the pore waters extracted at these pressures are representative of the Opalinus Clay formation from a direct comparison against in situ collected borehole waters. (Author)

  1. Applying Squeezing Technique to Clayrocks: Lessons Learned from Experiments at Mont Terri Rock Laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Fernandez, A. M.; Sanchez-Ledesma, D. M.; Tournassat, C.; Melon, A.; Gaucher, E.; Astudillo, E.; Vinsot, A.

    2013-07-01

    Knowledge of the pore water chemistry in clay rock formations plays an important role in determining radionuclide migration in the context of nuclear waste disposal. Among the different in situ and ex-situ techniques for pore water sampling in clay sediments and soils, squeezing technique dates back 115 years. Although different studies have been performed about the reliability and representativeness of squeezed pore waters, more of them were achieved on high porosity, high water content and unconsolidated clay sediments. A very few of them tackled the analysis of squeezed pore water from low-porosity, low water content and highly consolidated clay rocks. In this work, a specially designed and fabricated one-dimensional compression cell two directional fluid flow was used to extract and analyse the pore water composition of Opalinus Clay core samples from Mont Terri (Switzerland). The reproducibility of the technique is good and no ionic ultrafiltration, chemical fractionation or anion exclusion was found in the range of pressures analysed: 70-200 MPa. Pore waters extracted in this range of pressures do not decrease in concentration, which would indicate a dilution of water by mixing of the free pore water and the outer layers of double layer water (Donnan water). A threshold (safety) squeezing pressure of 175 MPa was established for avoiding membrane effects (ion filtering, anion exclusion, etc.) from clay particles induced by increasing pressures. Besides, the pore waters extracted at these pressures are representative of the Opalinus Clay formation from a direct comparison against in situ collected borehole waters. (Author)

  2. Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

    Directory of Open Access Journals (Sweden)

    Jonas eKaplan

    2015-03-01

    Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

  3. Teacher Formation in Super Learning Techniques Applied to the Teaching of the Mathematic in the Education Secondary

    Directory of Open Access Journals (Sweden)

    Avilner Rafael Páez Pereira

    2017-11-01

    Full Text Available The purpose of the study was to train LB "José Véliz" teacher for the teaching of mathematics through the application of super-learning techniques, based on the Research Participatory Action modality, proposed by López de Ceballos, (2008, following the model of the Lewin cycles of action (1946, quoted by Latorre (2007, based on the theories of humanism, Martínez (2009; multiple intelligence, Armstrong (2006; the Super learning of Sambrano and Stainer, (2003. Within the framework of the Critical - Social paradigm, in the type Qualitative Research, a plan of approach to the group was made, where through brainstorming and informal interviews the main problems were listed, which were hierarchized and then carried out an awareness - raising process. formulation of an overall plan of action. Among the results were 6 training workshops on techniques of breathing, relaxation, aromatherapy, music therapy, positive programming, color in the classroom, song in mathematical algorithms, in which processes of reflection were established on the benefits or obstacles obtained in the application of these in the transformation of the educational reality, elaborating a didactic strategy product of the experiences reached.

  4. Machine Learning and Applied Linguistics

    OpenAIRE

    Vajjala, Sowmya

    2018-01-01

    This entry introduces the topic of machine learning and provides an overview of its relevance for applied linguistics and language learning. The discussion will focus on giving an introduction to the methods and applications of machine learning in applied linguistics, and will provide references for further study.

  5. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

    Science.gov (United States)

    Hussain, Lal

    2018-06-01

    Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

  6. Lessons learned in applying function analysis

    International Nuclear Information System (INIS)

    Mitchel, G.R.; Davey, E.; Basso, R.

    2001-01-01

    This paper summarizes the lessons learned in undertaking and applying function analysis based on the recent experience of utility, AECL and international design and assessment projects. Function analysis is an analytical technique that can be used to characterize and asses the functions of a system and is widely recognized as an essential component of a 'systematic' approach to design, on that integrated operational and user requirements into the standard design process. (author)

  7. Machine learning techniques for optical communication system optimization

    DEFF Research Database (Denmark)

    Zibar, Darko; Wass, Jesper; Thrane, Jakob

    In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction.......In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction....

  8. Early counterpulse technique applied to vacuum interrupters

    International Nuclear Information System (INIS)

    Warren, R.W.

    1979-11-01

    Interruption of dc currents using counterpulse techniques is investigated with vacuum interrupters and a novel approach in which the counterpulse is applied before contact separation. Important increases have been achieved in this way in the maximum interruptible current as well as large reductions in contact erosion. The factors establishing these new limits are presented and ways are discussed to make further improvements

  9. Early counterpulse technique applied to vacuum interrupters

    International Nuclear Information System (INIS)

    Warren, R.W.

    1979-01-01

    Interruption of dc currents using counterpulse techniques is investigated with vacuum interrupters and a novel approach in which the counterpulse is applied before contact separation. Important increases have been achieved in this way in the maximum interruptible current and large reductions in contact erosion. The factors establishing these new limits are presented and ways are discussed to make further improvements to the maximum interruptible current

  10. Advanced Learning Theories Applied to Leadership Development

    Science.gov (United States)

    2006-11-01

    Center for Army Leadership Technical Report 2006-2 Advanced Learning Theories Applied to Leadership Development Christina Curnow...2006 5a. CONTRACT NUMBER W91QF4-05-F-0026 5b. GRANT NUMBER 4. TITLE AND SUBTITLE Advanced Learning Theories Applied to Leadership Development 5c...ABSTRACT This report describes the development and implementation of an application of advanced learning theories to leadership development. A

  11. Computational optimization techniques applied to microgrids planning

    DEFF Research Database (Denmark)

    Gamarra, Carlos; Guerrero, Josep M.

    2015-01-01

    Microgrids are expected to become part of the next electric power system evolution, not only in rural and remote areas but also in urban communities. Since microgrids are expected to coexist with traditional power grids (such as district heating does with traditional heating systems......), their planning process must be addressed to economic feasibility, as a long-term stability guarantee. Planning a microgrid is a complex process due to existing alternatives, goals, constraints and uncertainties. Usually planning goals conflict each other and, as a consequence, different optimization problems...... appear along the planning process. In this context, technical literature about optimization techniques applied to microgrid planning have been reviewed and the guidelines for innovative planning methodologies focused on economic feasibility can be defined. Finally, some trending techniques and new...

  12. Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample.

    Science.gov (United States)

    Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

    2016-01-01

    Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study. A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009-2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary. Five clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the five key clusters. This

  13. Surface analytical techniques applied to minerals processing

    International Nuclear Information System (INIS)

    Smart, R.St.C.

    1991-01-01

    An understanding of the chemical and physical forms of the chemically altered layers on the surfaces of base metal sulphides, particularly in the form of hydroxides, oxyhydroxides and oxides, and the changes that occur in them during minerals processing lies at the core of a complete description of flotation chemistry. This paper reviews the application of a variety of surface-sensitive techniques and methodologies applied to the study of surface layers on single minerals, mixed minerals, synthetic ores and real ores. Evidence from combined XPS/SAM/SEM studies have provided images and analyses of three forms of oxide, oxyhydroxide and hydroxide products on the surfaces of single sulphide minerals, mineral mixtures and complex sulphide ores. 4 refs., 2 tabs., 4 figs

  14. Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample

    Science.gov (United States)

    Dipnall, Joanna F.

    2016-01-01

    Background Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study. Methods A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009–2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary. Results Five clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the

  15. Applying Brainstorming Techniques to EFL Classroom

    OpenAIRE

    Toshiya, Oishi; 湘北短期大学; aPart-time Lecturer at Shohoku College

    2015-01-01

    This paper focuses on brainstorming techniques for English language learners. From the author's teaching experiences at Shohoku College during the academic year 2014-2015, the importance of brainstorming techniques was made evident. The author explored three elements of brainstorming techniques for writing using literaturereviews: lack of awareness, connecting to prior knowledge, and creativity. The literature reviews showed the advantage of using brainstorming techniques in an English compos...

  16. MACHINE LEARNING TECHNIQUES USED IN BIG DATA

    Directory of Open Access Journals (Sweden)

    STEFANIA LOREDANA NITA

    2016-07-01

    Full Text Available The classical tools used in data analysis are not enough in order to benefit of all advantages of big data. The amount of information is too large for a complete investigation, and the possible connections and relations between data could be missed, because it is difficult or even impossible to verify all assumption over the information. Machine learning is a great solution in order to find concealed correlations or relationships between data, because it runs at scale machine and works very well with large data sets. The more data we have, the more the machine learning algorithm is useful, because it “learns” from the existing data and applies the found rules on new entries. In this paper, we present some machine learning algorithms and techniques used in big data.

  17. Journaling; an active learning technique.

    Science.gov (United States)

    Blake, Tim K

    2005-01-01

    Journaling is a method frequently discussed in nursing literature and educational literature as an active learning technique that is meant to enhance reflective practice. Reflective practice is a means of self-examination that involves looking back over what has happened in practice in an effort to improve, or encourage professional growth. Some of the benefits of reflective practice include discovering meaning, making connections between experiences and the classroom, instilling values of the profession, gaining the perspective of others, reflection on professional roles, and development of critical thinking. A review of theory and research is discussed, as well as suggestions for implementation of journaling into coursework.

  18. Nuclear radioactive techniques applied to materials research

    CERN Document Server

    Correia, João Guilherme; Wahl, Ulrich

    2011-01-01

    In this paper we review materials characterization techniques using radioactive isotopes at the ISOLDE/CERN facility. At ISOLDE intense beams of chemically clean radioactive isotopes are provided by selective ion-sources and high-resolution isotope separators, which are coupled on-line with particle accelerators. There, new experiments are performed by an increasing number of materials researchers, which use nuclear spectroscopic techniques such as Mössbauer, Perturbed Angular Correlations (PAC), beta-NMR and Emission Channeling with short-lived isotopes not available elsewhere. Additionally, diffusion studies and traditionally non-radioactive techniques as Deep Level Transient Spectroscopy, Hall effect and Photoluminescence measurements are performed on radioactive doped samples, providing in this way the element signature upon correlation of the time dependence of the signal with the isotope transmutation half-life. Current developments, applications and perspectives of using radioactive ion beams and tech...

  19. Applying Mixed Methods Techniques in Strategic Planning

    Science.gov (United States)

    Voorhees, Richard A.

    2008-01-01

    In its most basic form, strategic planning is a process of anticipating change, identifying new opportunities, and executing strategy. The use of mixed methods, blending quantitative and qualitative analytical techniques and data, in the process of assembling a strategic plan can help to ensure a successful outcome. In this article, the author…

  20. Applying Nonverbal Techniques to Organizational Diagnosis.

    Science.gov (United States)

    Tubbs, Stewart L.; Koske, W. Cary

    Ongoing research programs conducted at General Motors Institute are motivated by the practical objective of improving the company's organizational effectiveness. Computer technology is being used whenever possible; for example, a technique developed by Herman Chernoff was used to process data from a survey of employee attitudes into 18 different…

  1. Applying Cooperative Techniques in Teaching Problem Solving

    Directory of Open Access Journals (Sweden)

    Krisztina Barczi

    2013-12-01

    Full Text Available Teaching how to solve problems – from solving simple equations to solving difficult competition tasks – has been one of the greatest challenges for mathematics education for many years. Trying to find an effective method is an important educational task. Among others, the question arises as to whether a method in which students help each other might be useful. The present article describes part of an experiment that was designed to determine the effects of cooperative teaching techniques on the development of problem-solving skills.

  2. Basic principles of applied nuclear techniques

    International Nuclear Information System (INIS)

    Basson, J.K.

    1976-01-01

    The technological applications of radioactive isotopes and radiation in South Africa have grown steadily since the first consignment of man-made radioisotopes reached this country in 1948. By the end of 1975 there were 412 authorised non-medical organisations (327 industries) using hundreds of sealed sources as well as their fair share of the thousands of radioisotope consignments, annually either imported or produced locally (mainly for medical purposes). Consequently, it is necessary for South African technologists to understand the principles of radioactivity in order to appreciate the industrial applications of nuclear techniques [af

  3. Dosimetry techniques applied to thermoluminescent age estimation

    International Nuclear Information System (INIS)

    Erramli, H.

    1986-12-01

    The reliability and the ease of the field application of the measuring techniques of natural radioactivity dosimetry are studied. The natural radioactivity in minerals in composed of the internal dose deposited by alpha and beta radiations issued from the sample itself and the external dose deposited by gamma and cosmic radiations issued from the surroundings of the sample. Two technics for external dosimetry are examined in details. TL Dosimetry and field gamma dosimetry. Calibration and experimental conditions are presented. A new integrated dosimetric method for internal and external dose measure is proposed: the TL dosimeter is placed in the soil in exactly the same conditions as the sample ones, during a time long enough for the total dose evaluation [fr

  4. Tracer techniques applied to groundwater studies

    International Nuclear Information System (INIS)

    Sanchez, W.

    1975-01-01

    The determination of several aquifer characteristics, primarily in the satured zone, namely: porosity, permeability, transmissivity, dispersivity, direction and velocity of sub-surface water is presented. These techniques are based on artificial radioisotopes utilization. Only field determination of porosity are considered here and their advantage over laboratory measurements are: better representation of volume average, insensibility to local inhomogenities and no distortion of the structure due to sampling. The radioisotope dilution method is used to obtain an independent and direct measurement of the filtration velocity in a water-bearing formation under natural or induced hydraulic gradient. The velocity of the flow is usually calculated from Darcy's formula through the measurement of gradients and requires a knowledge of the permeability of the formation. The filtration velocity interpreted in conjunction with other parameters can, under favourable conditions, provide valuable information on the permeability, transmissibility and amount of water moving through an aquifer

  5. Nuclear analytical techniques applied to forensic chemistry

    International Nuclear Information System (INIS)

    Nicolau, Veronica; Montoro, Silvia; Pratta, Nora; Giandomenico, Angel Di

    1999-01-01

    Gun shot residues produced by firing guns are mainly composed by visible particles. The individual characterization of these particles allows distinguishing those ones containing heavy metals, from gun shot residues, from those having a different origin or history. In this work, the results obtained from the study of gun shot residues particles collected from hands are presented. The aim of the analysis is to establish whether a person has shot a firing gun has been in contact with one after the shot has been produced. As reference samples, particles collected hands of persons affected to different activities were studied to make comparisons. The complete study was based on the application of nuclear analytical techniques such as Scanning Electron Microscopy, Energy Dispersive X Ray Electron Probe Microanalysis and Graphite Furnace Atomic Absorption Spectrometry. The essays allow to be completed within time compatible with the forensic requirements. (author)

  6. Applying adult learning practices in medical education.

    Science.gov (United States)

    Reed, Suzanne; Shell, Richard; Kassis, Karyn; Tartaglia, Kimberly; Wallihan, Rebecca; Smith, Keely; Hurtubise, Larry; Martin, Bryan; Ledford, Cynthia; Bradbury, Scott; Bernstein, Henry Hank; Mahan, John D

    2014-07-01

    The application of the best practices of teaching adults to the education of adults in medical education settings is important in the process of transforming learners to become and remain effective physicians. Medical education at all levels should be designed to equip physicians with the knowledge, clinical skills, and professionalism that are required to deliver quality patient care. The ultimate outcome is the health of the patient and the health status of the society. In the translational science of medical education, improved patient outcomes linked directly to educational events are the ultimate goal and are best defined by rigorous medical education research efforts. To best develop faculty, the same principles of adult education and teaching adults apply. In a systematic review of faculty development initiatives designed to improve teaching effectiveness in medical education, the use of experiential learning, feedback, effective relationships with peers, and diverse educational methods were found to be most important in the success of these programs. In this article, we present 5 examples of applying the best practices in teaching adults and utilizing the emerging understanding of the neurobiology of learning in teaching students, trainees, and practitioners. These include (1) use of standardized patients to develop communication skills, (2) use of online quizzes to assess knowledge and aid self-directed learning, (3) use of practice sessions and video clips to enhance significant learning of teaching skills, (4) use of case-based discussions to develop professionalism concepts and skills, and (5) use of the American Academy of Pediatrics PediaLink as a model for individualized learner-directed online learning. These examples highlight how experiential leaning, providing valuable feedback, opportunities for practice, and stimulation of self-directed learning can be utilized as medical education continues its dynamic transformation in the years ahead

  7. Motion Capture Technique Applied Research in Sports Technique Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhiwu LIU

    2014-09-01

    Full Text Available The motion capture technology system definition is described in the paper, and its components are researched, the key parameters are obtained from motion technique, the quantitative analysis are made on technical movements, the method of motion capture technology is proposed in sport technical diagnosis. That motion capture step includes calibration system, to attached landmarks to the tester; to capture trajectory, and to analyze the collected data.

  8. Stimulating Deep Learning Using Active Learning Techniques

    Science.gov (United States)

    Yew, Tee Meng; Dawood, Fauziah K. P.; a/p S. Narayansany, Kannaki; a/p Palaniappa Manickam, M. Kamala; Jen, Leong Siok; Hoay, Kuan Chin

    2016-01-01

    When students and teachers behave in ways that reinforce learning as a spectator sport, the result can often be a classroom and overall learning environment that is mostly limited to transmission of information and rote learning rather than deep approaches towards meaningful construction and application of knowledge. A group of college instructors…

  9. Precision Learning Assessment: An Alternative to Traditional Assessment Techniques.

    Science.gov (United States)

    Caltagirone, Paul J.; Glover, Christopher E.

    1985-01-01

    A continuous and curriculum-based assessment method, Precision Learning Assessment (PLA), which integrates precision teaching and norm-referenced techniques, was applied to a math computation curriculum for 214 third graders. The resulting districtwide learning curves defining average annual progress through the computation curriculum provided…

  10. Learning curve estimation techniques for nuclear industry

    International Nuclear Information System (INIS)

    Vaurio, Jussi K.

    1983-01-01

    Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on actuarial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year

  11. CRDM motion analysis using machine learning technique

    International Nuclear Information System (INIS)

    Nishimura, Takuya; Nakayama, Hiroyuki; Saitoh, Mayumi; Yaguchi, Seiji

    2017-01-01

    Magnetic jack type Control Rod Drive Mechanism (CRDM) for pressurized water reactor (PWR) plant operates control rods in response to electrical signals from a reactor control system. CRDM operability is evaluated by quantifying armature's response of closed/opened time which means interval time between coil energizing/de-energizing points and armature closed/opened points. MHI has already developed an automatic CRDM motion analysis and applied it to actual plants so far. However, CRDM operational data has wide variation depending on their characteristics such as plant condition, plant, etc. In the existing motion analysis, there is an issue of analysis accuracy for applying a single analysis technique to all plant conditions, plants, etc. In this study, MHI investigated motion analysis using machine learning (Random Forests) which is flexibly accommodated to CRDM operational data with wide variation, and is improved analysis accuracy. (author)

  12. Application of Machine Learning Techniques in Aquaculture

    OpenAIRE

    Rahman, Akhlaqur; Tasnim, Sumaira

    2014-01-01

    In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications.

  13. Encouraging Interaction by Applying Cooperative Learning

    Directory of Open Access Journals (Sweden)

    González Sonia Helena

    2001-08-01

    Full Text Available A project was conducted in order to improve oral interaction in English by applying cooperative learning to students of seventh grade. These students have lower levels of oral production and attend Marco Fidel Suárez public school. So, I decided to choose topics related to real life and to plan a series of activities of sensitization to create stable work groups and to increase oral interaction. According to the analysis and results, I can say that cooperative work and the oral activities help the students increase oral production, express better and use a foreign language with more security. In spite of the results, I consider that cooperative learning needs more time so that it can be successful. Students must have the will to cooperate. Only when students have that good will and can work together is the potential of acquisition of knowledge maximized.

  14. Data Mining Practical Machine Learning Tools and Techniques

    CERN Document Server

    Witten, Ian H; Hall, Mark A

    2011-01-01

    Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place

  15. Maximizing Reading Narrative Text Ability by Probing Prompting Learning Technique

    Directory of Open Access Journals (Sweden)

    Wiwied Pratiwi

    2017-12-01

    Full Text Available The objective of this research was to know whether Probing Prompting Learning Technique can be used to get the maximum effect of students’ reading narrative ability in teaching and learning process. This research was applied collaborative action reEsearch, this research was done in two cycle. The subject of this research was 23 students at tenth grade of SMA Kartikatama Metro. The result of the research showed that the Probing Prompting Learning Technique is useful and effective to help students get maximum effect of their reading. Based on the results of the questionnaire obtained an average percentage of 95%, it indicated that application of Probing Prompting Learning Technique in teaching l reading was appropriately applied. In short that students’ responses toward Probing Prompting Learning Technique in teaching reading was positive. In conclusion, Probing Prompting Learning Technique can get maximum effect of students’ reading ability. In relation to the result of the reserach, some suggestion are offered to english teacher, that  the use of Probing Prompting learning Technique in teaching reading will get the maximum effect of students’ reading abilty.

  16. Exploring the Earth Using Deep Learning Techniques

    Science.gov (United States)

    Larraondo, P. R.; Evans, B. J. K.; Antony, J.

    2016-12-01

    Research using deep neural networks have significantly matured in recent times, and there is now a surge in interest to apply such methods to Earth systems science and the geosciences. When combined with Big Data, we believe there are opportunities for significantly transforming a number of areas relevant to researchers and policy makers. In particular, by using a combination of data from a range of satellite Earth observations as well as computer simulations from climate models and reanalysis, we can gain new insights into the information that is locked within the data. Global geospatial datasets describe a wide range of physical and chemical parameters, which are mostly available using regular grids covering large spatial and temporal extents. This makes them perfect candidates to apply deep learning methods. So far, these techniques have been successfully applied to image analysis through the use of convolutional neural networks. However, this is only one field of interest, and there is potential for many more use cases to be explored. The deep learning algorithms require fast access to large amounts of data in the form of tensors and make intensive use of CPU in order to train its models. The Australian National Computational Infrastructure (NCI) has recently augmented its Raijin 1.2 PFlop supercomputer with hardware accelerators. Together with NCI's 3000 core high performance OpenStack cloud, these computational systems have direct access to NCI's 10+ PBytes of datasets and associated Big Data software technologies (see http://geonetwork.nci.org.au/ and http://nci.org.au/systems-services/national-facility/nerdip/). To effectively use these computing infrastructures requires that both the data and software are organised in a way that readily supports the deep learning software ecosystem. Deep learning software, such as the open source TensorFlow library, has allowed us to demonstrate the possibility of generating geospatial models by combining information from

  17. Applying BI Techniques To Improve Decision Making And Provide Knowledge Based Management

    Directory of Open Access Journals (Sweden)

    Alexandra Maria Ioana FLOREA

    2015-07-01

    Full Text Available The paper focuses on BI techniques and especially data mining algorithms that can support and improve the decision making process, with applications within the financial sector. We consider the data mining techniques to be more efficient and thus we applied several techniques, supervised and unsupervised learning algorithms The case study in which these algorithms have been implemented regards the activity of a banking institution, with focus on the management of lending activities.

  18. Machine Learning Techniques in Clinical Vision Sciences.

    Science.gov (United States)

    Caixinha, Miguel; Nunes, Sandrina

    2017-01-01

    This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

  19. Chemical vapor deposition: A technique for applying protective coatings

    Energy Technology Data Exchange (ETDEWEB)

    Wallace, T.C. Sr.; Bowman, M.G.

    1979-01-01

    Chemical vapor deposition is discussed as a technique for applying coatings for materials protection in energy systems. The fundamentals of the process are emphasized in order to establish a basis for understanding the relative advantages and limitations of the technique. Several examples of the successful application of CVD coating are described. 31 refs., and 18 figs.

  20. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

    Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

    2016-01-01

    Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter...

  1. Machine learning techniques in optical communication

    DEFF Research Database (Denmark)

    Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

    2015-01-01

    Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework...

  2. Prostate Cancer Probability Prediction By Machine Learning Technique.

    Science.gov (United States)

    Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

    2017-11-26

    The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

  3. Research on Mobile Learning Activities Applying Tablets

    Science.gov (United States)

    Kurilovas, Eugenijus; Juskeviciene, Anita; Bireniene, Virginija

    2015-01-01

    The paper aims to present current research on mobile learning activities in Lithuania while implementing flagship EU-funded CCL project on application of tablet computers in education. In the paper, the quality of modern mobile learning activities based on learning personalisation, problem solving, collaboration, and flipped class methods is…

  4. Learning Theory Applied to the Biology Classroom.

    Science.gov (United States)

    Novak, Joseph D.

    1980-01-01

    The material presented in this article is intended to help students learn how to learn. The seven key concepts of David Ausubel's assimilation theory for cognitive learning are discussed with reference to the classroom. Concept mapping is suggested as a tool for demonstrating how the seven key concepts function. (SA)

  5. Active learning techniques for librarians practical examples

    CERN Document Server

    Walsh, Andrew

    2010-01-01

    A practical work outlining the theory and practice of using active learning techniques in library settings. It explains the theory of active learning and argues for its importance in our teaching and is illustrated using a large number of examples of techniques that can be easily transferred and used in teaching library and information skills to a range of learners within all library sectors. These practical examples recognise that for most of us involved in teaching library and information skills the one off session is the norm, so we need techniques that allow us to quickly grab and hold our

  6. Rethinking Game Based Learning: applying pedagogical standards to educational games

    NARCIS (Netherlands)

    Schmitz, Birgit; Kelle, Sebastian

    2010-01-01

    Schmitz, B., & Kelle, S. (2010, 1-6 February). Rethinking Game Based Learning: applying pedagogical standards to educational games. Presentation at JTEL Winter School 2010 on Advanced Learning Technologies, Innsbruck, Austria.

  7. Storytelling: a teaching-learning technique.

    Science.gov (United States)

    Geanellos, R

    1996-03-01

    Nurses' stories, arising from the practice world, reconstruct the essence of experience as lived and provide vehicles for learning about nursing. The learning process is forwarded by combining storytelling and reflection. Reflection represents an active, purposive, contemplative and deliberative approach to learning through which learners create meaning from the learning experience. The combination of storytelling and reflection allows the creation of links between the materials at hand and prior and future learning. As a teaching-learning technique storytelling engages learners; organizes information; allows exploration of shared lived experiences without the demands, responsibilities and consequences of practice; facilitates remembering; enhances discussion, problem posing and problem solving; and aids understanding of what it is to nurse and to be a nurse.

  8. Applying Economics Using Interactive Learning Modules

    Science.gov (United States)

    Goma, Ophelia D.

    2010-01-01

    This article describes the use of web-based, interactive learning modules in the principles of economics course. The learning modules introduce students to important, historical economic events while providing real-world application of the economic theory presented in class. Each module is designed to supplement and complement the economic theory…

  9. Collaborative DFA learning applied to Grid administration

    NARCIS (Netherlands)

    Mulder, W.; Jacobs, C.J.H.; van Someren, M.; van Erp, M.; Stehouwer, H.; van Zaanen, M.

    2009-01-01

    This paper proposes a distributed learning mechanism that learns patterns from distributed datasets. The complex and dynamic settings of grid environments requires supporting systems to be of a more sophisticated level. Contemporary tools lack the ability to relate and infer events. We developed an

  10. Applying DEA Technique to Library Evaluation in Academic Research Libraries.

    Science.gov (United States)

    Shim, Wonsik

    2003-01-01

    This study applied an analytical technique called Data Envelopment Analysis (DEA) to calculate the relative technical efficiency of 95 academic research libraries, all members of the Association of Research Libraries. DEA, with the proper model of library inputs and outputs, can reveal best practices in the peer groups, as well as the technical…

  11. Applying Technology to Marine Corps Distance Learning

    National Research Council Canada - National Science Library

    Broihier, Michael

    1997-01-01

    The purpose of this thesis is to investigate the application of technology to distance learning with the intention of recommending to the Marine Corps a feasible migration path away from its current...

  12. Machine learning applied to crime prediction

    OpenAIRE

    Vaquero Barnadas, Miquel

    2016-01-01

    Machine Learning is a cornerstone when it comes to artificial intelligence and big data analysis. It provides powerful algorithms that are capable of recognizing patterns, classifying data, and, basically, learn by themselves to perform a specific task. This field has incredibly grown in popularity these days, however, it still remains unknown for the majority of people, and even for most professionals. This project intends to provide an understandable explanation of what is it, what types ar...

  13. Applying Brain-Based Learning Principles to Athletic Training Education

    Science.gov (United States)

    Craig, Debbie I.

    2007-01-01

    Objective: To present different concepts and techniques related to the application of brain-based learning principles to Athletic Training clinical education. Background: The body of knowledge concerning how our brains physically learn continues to grow. Brain-based learning principles, developed by numerous authors, offer advice on how to…

  14. Learning to Apply Models of Materials While Explaining Their Properties

    Science.gov (United States)

    Karpin, Tiia; Juuti, Kalle; Lavonen, Jari

    2014-01-01

    Background: Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose: This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials.…

  15. Multidisciplinary Views on Applying Explicit and Implicit Motor Learning in Practice : an International Survey

    NARCIS (Netherlands)

    Sascha Rasquin; Michel Bleijlevens; Jos Halfens; Mark Wilson; Rich Masters; Anna Beurskens; Melanie Kleynen; Monique Lexis; Susy Braun

    2015-01-01

    Background A variety of options and techniques for causing implicit and explicit motor learning have been described in the literature. The aim of the current paper was to provide clearer guidance for practitioners on how to apply motor learning in practice by exploring experts’ opinions and

  16. [Technique and value of direct MR arthrography applying articular distraction].

    Science.gov (United States)

    Becce, Fabio; Wettstein, Michael; Guntern, Daniel; Mouhsine, Elyazid; Palhais, Nuno; Theumann, Nicolas

    2010-02-24

    Direct MR arthrography has a better diagnostic accuracy than MR imaging alone. However, contrast material is not always homogeneously distributed in the articular space. Lesions of cartilage surfaces or intra-articular soft tissues can thus be misdiagnosed. Concomitant application of axial traction during MR arthrography leads to articular distraction. This enables better distribution of contrast material in the joint and better delineation of intra-articular structures. Therefore, this technique improves detection of cartilage lesions. Moreover, the axial stress applied on articular structures may reveal lesions invisible on MR images without traction. Based on our clinical experience, we believe that this relatively unknown technique is promising and should be further developed.

  17. Determination of palladium in biological samples applying nuclear analytical techniques

    International Nuclear Information System (INIS)

    Cavalcante, Cassio Q.; Sato, Ivone M.; Salvador, Vera L. R.; Saiki, Mitiko

    2008-01-01

    This study presents Pd determinations in bovine tissue samples containing palladium prepared in the laboratory, and CCQM-P63 automotive catalyst materials of the Proficiency Test, using instrumental thermal and epithermal neutron activation analysis and energy dispersive X-ray fluorescence techniques. Solvent extraction and solid phase extraction procedures were also applied to separate Pd from interfering elements before the irradiation in the nuclear reactor. The results obtained by different techniques were compared against each other to examine sensitivity, precision and accuracy. (author)

  18. m-Learning and holography: Compatible techniques?

    Science.gov (United States)

    Calvo, Maria L.

    2014-07-01

    Since the last decades, cell phones have become increasingly popular and are nowadays ubiquitous. New generations of cell phones are now equipped with text messaging, internet, and camera features. They are now making their way into the classroom. This is creating a new teaching and learning technique, the so called m-Learning (or mobile-Learning). Because of the many benefits that cell phones offer, teachers could easily use them as a teaching and learning tool. However, an additional work from the teachers for introducing their students into the m-Learning in the classroom needs to be defined and developed. As an example, optical techniques, based upon interference and diffraction phenomena, such as holography, appear to be convenient topics for m-Learning. They can be approached with simple examples and experiments within the cell phones performances and classroom accessibility. We will present some results carried out at the Faculty of Physical Sciences in UCM to obtain very simple holographic recordings via cell phones. The activities were carried out inside the course on Optical Coherence and Laser, offered to students in the fourth course of the Grade in Physical Sciences. Some open conclusions and proposals will be presented.

  19. Tools and methodologies applied to eLearning

    OpenAIRE

    Seoane Pardo, Antonio M.; García-Peñalvo, Francisco José

    2006-01-01

    The aim of this paper is to show how eLearning technologies and methodologies should be useful for teaching and researching Logic. Firstly, a definition and explanation of eLearning and its main modalities will be given. Then, the most important elements and tools of eLearning activities will be shown. Finally, we will give three suggestions to improve learning experience with eLearning applied to Logic. Se muestran diversas tecnologías y metodologías de e-learning útiles en la enseñanza e...

  20. Applying of USB interface technique in nuclear spectrum acquisition system

    International Nuclear Information System (INIS)

    Zhou Jianbin; Huang Jinhua

    2004-01-01

    This paper introduces applying of USB technique and constructing nuclear spectrum acquisition system via PC's USB interface. The authors choose the USB component USB100 module and the W77E58μc to do the key work. It's easy to apply USB interface technique, when USB100 module is used. USB100 module can be treated as a common I/O component for the μc controller, and can be treated as a communication interface (COM) when connected to PC' USB interface. It's easy to modify the PC's program for the new system with USB100 module. The authors can smoothly change from ISA, RS232 bus to USB bus. (authors)

  1. Three visual techniques to enhance interprofessional learning.

    Science.gov (United States)

    Parsell, G; Gibbs, T; Bligh, J

    1998-07-01

    Many changes in the delivery of healthcare in the UK have highlighted the need for healthcare professionals to learn to work together as teams for the benefit of patients. Whatever the profession or level, whether for postgraduate education and training, continuing professional development, or for undergraduates, learners should have an opportunity to learn about and with, other healthcare practitioners in a stimulating and exciting way. Learning to understand how people think, feel, and react, and the parts they play at work, both as professionals and individuals, can only be achieved through sensitive discussion and exchange of views. Teaching and learning methods must provide opportunities for this to happen. This paper describes three small-group teaching techniques which encourage a high level of learner collaboration and team-working. Learning content is focused on real-life health-care issues and strong visual images are used to stimulate lively discussion and debate. Each description includes the learning objectives of each exercise, basic equipment and resources, and learning outcomes.

  2. Diagonal ordering operation technique applied to Morse oscillator

    Energy Technology Data Exchange (ETDEWEB)

    Popov, Dušan, E-mail: dusan_popov@yahoo.co.uk [Politehnica University Timisoara, Department of Physical Foundations of Engineering, Bd. V. Parvan No. 2, 300223 Timisoara (Romania); Dong, Shi-Hai [CIDETEC, Instituto Politecnico Nacional, Unidad Profesional Adolfo Lopez Mateos, Mexico D.F. 07700 (Mexico); Popov, Miodrag [Politehnica University Timisoara, Department of Steel Structures and Building Mechanics, Traian Lalescu Street, No. 2/A, 300223 Timisoara (Romania)

    2015-11-15

    We generalize the technique called as the integration within a normally ordered product (IWOP) of operators referring to the creation and annihilation operators of the harmonic oscillator coherent states to a new operatorial approach, i.e. the diagonal ordering operation technique (DOOT) about the calculations connected with the normally ordered product of generalized creation and annihilation operators that generate the generalized hypergeometric coherent states. We apply this technique to the coherent states of the Morse oscillator including the mixed (thermal) state case and get the well-known results achieved by other methods in the corresponding coherent state representation. Also, in the last section we construct the coherent states for the continuous dynamics of the Morse oscillator by using two new methods: the discrete–continuous limit, respectively by solving a finite difference equation. Finally, we construct the coherent states corresponding to the whole Morse spectrum (discrete plus continuous) and demonstrate their properties according the Klauder’s prescriptions.

  3. Applying recursive numerical integration techniques for solving high dimensional integrals

    International Nuclear Information System (INIS)

    Ammon, Andreas; Genz, Alan; Hartung, Tobias; Jansen, Karl; Volmer, Julia; Leoevey, Hernan

    2016-11-01

    The error scaling for Markov-Chain Monte Carlo techniques (MCMC) with N samples behaves like 1/√(N). This scaling makes it often very time intensive to reduce the error of computed observables, in particular for applications in lattice QCD. It is therefore highly desirable to have alternative methods at hand which show an improved error scaling. One candidate for such an alternative integration technique is the method of recursive numerical integration (RNI). The basic idea of this method is to use an efficient low-dimensional quadrature rule (usually of Gaussian type) and apply it iteratively to integrate over high-dimensional observables and Boltzmann weights. We present the application of such an algorithm to the topological rotor and the anharmonic oscillator and compare the error scaling to MCMC results. In particular, we demonstrate that the RNI technique shows an error scaling in the number of integration points m that is at least exponential.

  4. Technique applied in electrical power distribution for Satellite Launch Vehicle

    Directory of Open Access Journals (Sweden)

    João Maurício Rosário

    2010-09-01

    Full Text Available The Satellite Launch Vehicle electrical network, which is currently being developed in Brazil, is sub-divided for analysis in the following parts: Service Electrical Network, Controlling Electrical Network, Safety Electrical Network and Telemetry Electrical Network. During the pre-launching and launching phases, these electrical networks are associated electrically and mechanically to the structure of the vehicle. In order to succeed in the integration of these electrical networks it is necessary to employ techniques of electrical power distribution, which are proper to Launch Vehicle systems. This work presents the most important techniques to be considered in the characterization of the electrical power supply applied to Launch Vehicle systems. Such techniques are primarily designed to allow the electrical networks, when submitted to the single-phase fault to ground, to be able of keeping the power supply to the loads.

  5. Applying recursive numerical integration techniques for solving high dimensional integrals

    Energy Technology Data Exchange (ETDEWEB)

    Ammon, Andreas [IVU Traffic Technologies AG, Berlin (Germany); Genz, Alan [Washington State Univ., Pullman, WA (United States). Dept. of Mathematics; Hartung, Tobias [King' s College, London (United Kingdom). Dept. of Mathematics; Jansen, Karl; Volmer, Julia [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Leoevey, Hernan [Humboldt Univ. Berlin (Germany). Inst. fuer Mathematik

    2016-11-15

    The error scaling for Markov-Chain Monte Carlo techniques (MCMC) with N samples behaves like 1/√(N). This scaling makes it often very time intensive to reduce the error of computed observables, in particular for applications in lattice QCD. It is therefore highly desirable to have alternative methods at hand which show an improved error scaling. One candidate for such an alternative integration technique is the method of recursive numerical integration (RNI). The basic idea of this method is to use an efficient low-dimensional quadrature rule (usually of Gaussian type) and apply it iteratively to integrate over high-dimensional observables and Boltzmann weights. We present the application of such an algorithm to the topological rotor and the anharmonic oscillator and compare the error scaling to MCMC results. In particular, we demonstrate that the RNI technique shows an error scaling in the number of integration points m that is at least exponential.

  6. Learning Physics through Project-Based Learning Game Techniques

    Science.gov (United States)

    Baran, Medine; Maskan, Abdulkadir; Yasar, Seyma

    2018-01-01

    The aim of the present study, in which Project and game techniques are used together, is to examine the impact of project-based learning games on students' physics achievement. Participants of the study consist of 34 9th grade students (N = 34). The data were collected using achievement tests and a questionnaire. Throughout the applications, the…

  7. Three-dimensional integrated CAE system applying computer graphic technique

    International Nuclear Information System (INIS)

    Kato, Toshisada; Tanaka, Kazuo; Akitomo, Norio; Obata, Tokayasu.

    1991-01-01

    A three-dimensional CAE system for nuclear power plant design is presented. This system utilizes high-speed computer graphic techniques for the plant design review, and an integrated engineering database for handling the large amount of nuclear power plant engineering data in a unified data format. Applying this system makes it possible to construct a nuclear power plant using only computer data from the basic design phase to the manufacturing phase, and it increases the productivity and reliability of the nuclear power plants. (author)

  8. Machine learning applied to the prediction of citrus production

    OpenAIRE

    Díaz Rodríguez, Susana Irene; Mazza, Silvia M.; Fernández-Combarro Álvarez, Elías; Giménez, Laura I.; Gaiad, José E.

    2017-01-01

    An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analyse...

  9. Six Lessons We Learned Applying Six Sigma

    Science.gov (United States)

    Carroll, Napoleon; Casleton, Christa H.

    2005-01-01

    As Chief Financial Officer of Kennedy Space Center (KSC), I'm not only responsible for financial planning and accounting but also for building strong partnerships with the CFO customers, who include Space Shuttle and International Space Station operations as well all who manage the KSC Spaceport. My never ending goal is to design, manage and continuously improve our core business processes so that they deliver world class products and services to the CFO's customers. I became interested in Six Sigma as Christa Casleton (KSC's first Six Sigma Black belt) applied Six Sigma tools and methods to our Plan and Account for Travel Costs Process. Her analysis was fresh, innovative and thorough but, even more impressive, was her approach to ensure ongoing, continuous process improvement. Encouraged by the results, I launched two more process improvement initiatives aimed at applying Six Sigma principles to CFO processes that not only touch most of my employees but also have direct customer impact. As many of you know, Six Sigma is a measurement scale that compares the output of a process with customer requirements. That's straight forward, but demands that you not only understand your processes but also know your products and the critical customer requirements. The objective is to isolate and eliminate the causes of process variation so that the customer sees consistently high quality.

  10. Action Learning in Virtual Higher Education: Applying Leadership Theory

    Science.gov (United States)

    Curtin, Joseph

    2016-01-01

    This paper reports the historical foundation of Northeastern University's course, LDR 6100: Developing Your Leadership Capability, a partial literature review of action learning (AL) and virtual action learning (VAL), a course methodology of LDR 6100 requiring students to apply leadership perspectives using VAL as instructed by the author,…

  11. Applying field mapping refractive beam shapers to improve holographic techniques

    Science.gov (United States)

    Laskin, Alexander; Williams, Gavin; McWilliam, Richard; Laskin, Vadim

    2012-03-01

    Performance of various holographic techniques can be essentially improved by homogenizing the intensity profile of the laser beam with using beam shaping optics, for example, the achromatic field mapping refractive beam shapers like πShaper. The operational principle of these devices presumes transformation of laser beam intensity from Gaussian to flattop one with high flatness of output wavefront, saving of beam consistency, providing collimated output beam of low divergence, high transmittance, extended depth of field, negligible residual wave aberration, and achromatic design provides capability to work with several laser sources with different wavelengths simultaneously. Applying of these beam shapers brings serious benefits to the Spatial Light Modulator based techniques like Computer Generated Holography or Dot-Matrix mastering of security holograms since uniform illumination of an SLM allows simplifying mathematical calculations and increasing predictability and reliability of the imaging results. Another example is multicolour Denisyuk holography when the achromatic πShaper provides uniform illumination of a field at various wavelengths simultaneously. This paper will describe some design basics of the field mapping refractive beam shapers and optical layouts of their applying in holographic systems. Examples of real implementations and experimental results will be presented as well.

  12. Archaeometry: nuclear and conventional techniques applied to the archaeological research

    International Nuclear Information System (INIS)

    Esparza L, R.; Cardenas G, E.

    2005-01-01

    The book that now is presented is formed by twelve articles that approach from different perspective topics as the archaeological prospecting, the analysis of the pre hispanic and colonial ceramic, the obsidian and the mural painting, besides dating and questions about the data ordaining. Following the chronological order in which the exploration techniques and laboratory studies are required, there are presented in the first place the texts about the systematic and detailed study of the archaeological sites, later we pass to relative topics to the application of diverse nuclear techniques as PIXE, RBS, XRD, NAA, SEM, Moessbauer spectroscopy and other conventional techniques. The multidisciplinary is an aspect that highlights in this work, that which owes to the great specialization of the work that is presented even in the archaeological studies including in the open ground of the topography, mapping, excavation and, of course, in the laboratory tests. Most of the articles are the result of several years of investigation and it has been consigned in the responsibility of each article. The texts here gathered emphasize the technical aspects of each investigation, the modern compute systems applied to the prospecting and the archaeological mapping, the chemical and physical analysis of organic materials, of metal artifacts, of diverse rocks used in the pre hispanic epoch, of mural and ceramic paintings, characteristics that justly underline the potential of the collective works. (Author)

  13. Teaching organization theory for healthcare management: three applied learning methods.

    Science.gov (United States)

    Olden, Peter C

    2006-01-01

    Organization theory (OT) provides a way of seeing, describing, analyzing, understanding, and improving organizations based on patterns of organizational design and behavior (Daft 2004). It gives managers models, principles, and methods with which to diagnose and fix organization structure, design, and process problems. Health care organizations (HCOs) face serious problems such as fatal medical errors, harmful treatment delays, misuse of scarce nurses, costly inefficiency, and service failures. Some of health care managers' most critical work involves designing and structuring their organizations so their missions, visions, and goals can be achieved-and in some cases so their organizations can survive. Thus, it is imperative that graduate healthcare management programs develop effective approaches for teaching OT to students who will manage HCOs. Guided by principles of education, three applied teaching/learning activities/assignments were created to teach OT in a graduate healthcare management program. These educationalmethods develop students' competency with OT applied to HCOs. The teaching techniques in this article may be useful to faculty teaching graduate courses in organization theory and related subjects such as leadership, quality, and operation management.

  14. Circles of Learning: Applying Socratic Pedagogy to Learn Modern Leadership

    Science.gov (United States)

    Friesen, Katherine L.; Stephens, Clinton M.

    2016-01-01

    In response to the National Leadership Education Agenda, this application brief furthers priority one, addressing the teaching, learning, and curriculum development of leadership education. The ability of students to demonstrate leadership outcome mastery in areas of communication, self-awareness, interpersonal interactions, and civic…

  15. Applying Metrological Techniques to Satellite Fundamental Climate Data Records

    Science.gov (United States)

    Woolliams, Emma R.; Mittaz, Jonathan PD; Merchant, Christopher J.; Hunt, Samuel E.; Harris, Peter M.

    2018-02-01

    Quantifying long-term environmental variability, including climatic trends, requires decadal-scale time series of observations. The reliability of such trend analysis depends on the long-term stability of the data record, and understanding the sources of uncertainty in historic, current and future sensors. We give a brief overview on how metrological techniques can be applied to historical satellite data sets. In particular we discuss the implications of error correlation at different spatial and temporal scales and the forms of such correlation and consider how uncertainty is propagated with partial correlation. We give a form of the Law of Propagation of Uncertainties that considers the propagation of uncertainties associated with common errors to give the covariance associated with Earth observations in different spectral channels.

  16. Applying AI techniques to improve alarm display effectiveness

    International Nuclear Information System (INIS)

    Gross, J.M.; Birrer, S.A.; Crosberg, D.R.

    1987-01-01

    The Alarm Filtering System (AFS) addresses the problem of information overload in a control room during abnormal operations. Since operators can miss vital information during these periods, systems which emphasize important messages are beneficial. AFS uses the artificial intelligence (AI) technique of object-oriented programming to filter and dynamically prioritize alarm messages. When an alarm's status changes, AFS determines the relative importance of that change according to the current process state. AFS bases that relative importance on relationships the newly changed alarm has with other activated alarms. Evaluations of a alarm importance take place without regard to the activation sequence of alarm signals. The United States Department of Energy has applied for a patent on the approach used in this software. The approach was originally developed by EG and G Idaho for a nuclear reactor control room

  17. Machine Learning Techniques for Stellar Light Curve Classification

    Science.gov (United States)

    Hinners, Trisha A.; Tat, Kevin; Thorp, Rachel

    2018-07-01

    We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB of Kepler light curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light-curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a long short-term memory recurrent neural network produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (∼2%–4%) and good accuracy (∼75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger data sets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

  18. Airflow measurement techniques applied to radon mitigation problems

    International Nuclear Information System (INIS)

    Harrje, D.T.; Gadsby, K.J.

    1989-01-01

    During the past decade a multitude of diagnostic procedures associated with the evaluation of air infiltration and air leakage sites have been developed. The spirit of international cooperation and exchange of ideas within the AIC-AIVC conferences has greatly facilitated the adoption and use of these measurement techniques in the countries participating in Annex V. But wide application of such diagnostic methods are not limited to air infiltration alone. The subject of this paper concerns the ways to evaluate and improve radon reduction in buildings using diagnostic methods directly related to developments familiar to the AIVC. Radon problems are certainly not unique to the United States, and the methods described here have to a degree been applied by researchers of other countries faced with similar problems. The radon problem involves more than a harmful pollutant of the living spaces of our buildings -- it also involves energy to operate radon removal equipment and the loss of interior conditioned air as a direct result. The techniques used for air infiltration evaluation will be shown to be very useful in dealing with the radon mitigation challenge. 10 refs., 7 figs., 1 tab

  19. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  20. Analytical techniques applied to study cultural heritage objects

    International Nuclear Information System (INIS)

    Rizzutto, M.A.; Curado, J.F.; Bernardes, S.; Campos, P.H.O.V.; Kajiya, E.A.M.; Silva, T.F.; Rodrigues, C.L.; Moro, M.; Tabacniks, M.; Added, N.

    2015-01-01

    The scientific study of artistic and cultural heritage objects have been routinely performed in Europe and the United States for decades. In Brazil this research area is growing, mainly through the use of physical and chemical characterization methods. Since 2003 the Group of Applied Physics with Particle Accelerators of the Physics Institute of the University of Sao Paulo (GFAA-IF) has been working with various methodologies for material characterization and analysis of cultural objects. Initially using ion beam analysis performed with Particle Induced X-Ray Emission (PIXE), Rutherford Backscattering (RBS) and recently Ion Beam Induced Luminescence (IBIL), for the determination of the elements and chemical compounds in the surface layers. These techniques are widely used in the Laboratory of Materials Analysis with Ion Beams (LAMFI-USP). Recently, the GFAA expanded the studies to other possibilities of analysis enabled by imaging techniques that coupled with elemental and compositional characterization provide a better understanding on the materials and techniques used in the creative process in the manufacture of objects. The imaging analysis, mainly used to examine and document artistic and cultural heritage objects, are performed through images with visible light, infrared reflectography (IR), fluorescence with ultraviolet radiation (UV), tangential light and digital radiography. Expanding more the possibilities of analysis, new capabilities were added using portable equipment such as Energy Dispersive X-Ray Fluorescence (ED-XRF) and Raman Spectroscopy that can be used for analysis 'in situ' at the museums. The results of these analyzes are providing valuable information on the manufacturing process and have provided new information on objects of different University of Sao Paulo museums. Improving the arsenal of cultural heritage analysis it was recently constructed an 3D robotic stage for the precise positioning of samples in the external beam setup

  1. Analytical techniques applied to study cultural heritage objects

    Energy Technology Data Exchange (ETDEWEB)

    Rizzutto, M.A.; Curado, J.F.; Bernardes, S.; Campos, P.H.O.V.; Kajiya, E.A.M.; Silva, T.F.; Rodrigues, C.L.; Moro, M.; Tabacniks, M.; Added, N., E-mail: rizzutto@if.usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Instituto de Fisica

    2015-07-01

    The scientific study of artistic and cultural heritage objects have been routinely performed in Europe and the United States for decades. In Brazil this research area is growing, mainly through the use of physical and chemical characterization methods. Since 2003 the Group of Applied Physics with Particle Accelerators of the Physics Institute of the University of Sao Paulo (GFAA-IF) has been working with various methodologies for material characterization and analysis of cultural objects. Initially using ion beam analysis performed with Particle Induced X-Ray Emission (PIXE), Rutherford Backscattering (RBS) and recently Ion Beam Induced Luminescence (IBIL), for the determination of the elements and chemical compounds in the surface layers. These techniques are widely used in the Laboratory of Materials Analysis with Ion Beams (LAMFI-USP). Recently, the GFAA expanded the studies to other possibilities of analysis enabled by imaging techniques that coupled with elemental and compositional characterization provide a better understanding on the materials and techniques used in the creative process in the manufacture of objects. The imaging analysis, mainly used to examine and document artistic and cultural heritage objects, are performed through images with visible light, infrared reflectography (IR), fluorescence with ultraviolet radiation (UV), tangential light and digital radiography. Expanding more the possibilities of analysis, new capabilities were added using portable equipment such as Energy Dispersive X-Ray Fluorescence (ED-XRF) and Raman Spectroscopy that can be used for analysis 'in situ' at the museums. The results of these analyzes are providing valuable information on the manufacturing process and have provided new information on objects of different University of Sao Paulo museums. Improving the arsenal of cultural heritage analysis it was recently constructed an 3D robotic stage for the precise positioning of samples in the external beam setup

  2. Applying advanced digital signal processing techniques in industrial radioisotopes applications

    International Nuclear Information System (INIS)

    Mahmoud, H.K.A.E.

    2012-01-01

    Radioisotopes can be used to obtain signals or images in order to recognize the information inside the industrial systems. The main problems of using these techniques are the difficulty of identification of the obtained signals or images and the requirement of skilled experts for the interpretation process of the output data of these applications. Now, the interpretation of the output data from these applications is performed mainly manually, depending heavily on the skills and the experience of trained operators. This process is time consuming and the results typically suffer from inconsistency and errors. The objective of the thesis is to apply the advanced digital signal processing techniques for improving the treatment and the interpretation of the output data from the different Industrial Radioisotopes Applications (IRA). This thesis focuses on two IRA; the Residence Time Distribution (RTD) measurement and the defect inspection of welded pipes using a gamma source (gamma radiography). In RTD measurement application, this thesis presents methods for signal pre-processing and modeling of the RTD signals. Simulation results have been presented for two case studies. The first case study is a laboratory experiment for measuring the RTD in a water flow rig. The second case study is an experiment for measuring the RTD in a phosphate production unit. The thesis proposes an approach for RTD signal identification in the presence of noise. In this approach, after signal processing, the Mel Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients are extracted from the processed signal or from one of its transforms. The Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) have been tested and compared for efficient feature extraction. Neural networks have been used for matching of the extracted features. Furthermore, the Power Density Spectrum (PDS) of the RTD signal has been also used instead of the discrete

  3. Satellite SAR interferometric techniques applied to emergency mapping

    Science.gov (United States)

    Stefanova Vassileva, Magdalena; Riccardi, Paolo; Lecci, Daniele; Giulio Tonolo, Fabio; Boccardo Boccardo, Piero; Chiesa, Giuliana; Angeluccetti, Irene

    2017-04-01

    This paper aim to investigate the capabilities of the currently available SAR interferometric algorithms in the field of emergency mapping. Several tests have been performed exploiting the Copernicus Sentinel-1 data using the COTS software ENVI/SARscape 5.3. Emergency Mapping can be defined as "creation of maps, geo-information products and spatial analyses dedicated to providing situational awareness emergency management and immediate crisis information for response by means of extraction of reference (pre-event) and crisis (post-event) geographic information/data from satellite or aerial imagery". The conventional differential SAR interferometric technique (DInSAR) and the two currently available multi-temporal SAR interferometric approaches, i.e. Permanent Scatterer Interferometry (PSI) and Small BAseline Subset (SBAS), have been applied to provide crisis information useful for the emergency management activities. Depending on the considered Emergency Management phase, it may be distinguished between rapid mapping, i.e. fast provision of geospatial data regarding the area affected for the immediate emergency response, and monitoring mapping, i.e. detection of phenomena for risk prevention and mitigation activities. In order to evaluate the potential and limitations of the aforementioned SAR interferometric approaches for the specific rapid and monitoring mapping application, five main factors have been taken into account: crisis information extracted, input data required, processing time and expected accuracy. The results highlight that DInSAR has the capacity to delineate areas affected by large and sudden deformations and fulfills most of the immediate response requirements. The main limiting factor of interferometry is the availability of suitable SAR acquisition immediately after the event (e.g. Sentinel-1 mission characterized by 6-day revisiting time may not always satisfy the immediate emergency request). PSI and SBAS techniques are suitable to produce

  4. [Molecular techniques applied in species identification of Toxocara].

    Science.gov (United States)

    Fogt, Renata

    2006-01-01

    Toxocarosis is still an important and actual problem in human medicine. It can manifest as visceral (VLM), ocular (OLM) or covert (CT) larva migrans syndroms. Complicated life cycle of Toxocara, lack of easy and practical methods of species differentiation of the adult nematode and embarrassing in recognition of the infection in definitive hosts create difficulties in fighting with the infection. Although studies on human toxocarosis have been continued for over 50 years there is no conclusive answer, which of species--T. canis or T. cati constitutes a greater risk of transmission of the nematode to man. Neither blood serological examinations nor microscopic observations of the morphological features of the nematode give the satisfied answer on the question. Since the 90-ths molecular methods were developed for species identification and became useful tools being widely applied in parasitological diagnosis. This paper cover the survey of methods of DNA analyses used for identification of Toxocara species. The review may be helpful for researchers focused on Toxocara and toxocarosis as well as on detection of new species. The following techniques are described: PCR (Polymerase Chain Reaction), RFLP (Restriction Fragment Length Polymorphism), RAPD (Random Amplified Polymorphic DNA) and SSCP (Single Strand Conformation Polymorphism).

  5. A Learning Evaluation for an Immersive Virtual Laboratory for Technical Training Applied into a Welding Workshop

    Science.gov (United States)

    Torres, Francisco; Neira Tovar, Leticia A.; del Rio, Marta Sylvia

    2017-01-01

    This study aims to explore the results of welding virtual training performance, designed using a learning model based on cognitive and usability techniques, applying an immersive concept focused on person attention. Moreover, it also intended to demonstrate that exits a moderating effect of performance improvement when the user experience is taken…

  6. FísicActiva: Applying Active Learning Strategies to a Large Engineering Lecture

    Science.gov (United States)

    Auyuanet, Adriana; Modzelewski, Helena; Loureiro, Silvia; Alessandrini, Daniel; Míguez, Marina

    2018-01-01

    This paper presents and analyses the results obtained by applying Active Learning techniques in overcrowded Physics lectures at the University of the Republic, Uruguay. The course referred to is Physics 1, the first Physics course that all students of the Faculty of Engineering take in their first semester for all the Engineering-related careers.…

  7. Multidisciplinary Views on Applying Explicit and Implicit Motor Learning in Practice: An International Survey.

    Directory of Open Access Journals (Sweden)

    Melanie Kleynen

    Full Text Available A variety of options and techniques for causing implicit and explicit motor learning have been described in the literature. The aim of the current paper was to provide clearer guidance for practitioners on how to apply motor learning in practice by exploring experts' opinions and experiences, using the distinction between implicit and explicit motor learning as a conceptual departure point.A survey was designed to collect and aggregate informed opinions and experiences from 40 international respondents who had demonstrable expertise related to motor learning in practice and/or research. The survey was administered through an online survey tool and addressed potential options and learning strategies for applying implicit and explicit motor learning. Responses were analysed in terms of consensus (≥ 70% and trends (≥ 50%. A summary figure was developed to illustrate a taxonomy of the different learning strategies and options indicated by the experts in the survey.Answers of experts were widely distributed. No consensus was found regarding the application of implicit and explicit motor learning. Some trends were identified: Explicit motor learning can be promoted by using instructions and various types of feedback, but when promoting implicit motor learning, instructions and feedback should be restricted. Further, for implicit motor learning, an external focus of attention should be considered, as well as practicing the entire skill. Experts agreed on three factors that influence motor learning choices: the learner's abilities, the type of task, and the stage of motor learning (94.5%; n = 34/36. Most experts agreed with the summary figure (64.7%; n = 22/34.The results provide an overview of possible ways to cause implicit or explicit motor learning, signposting examples from practice and factors that influence day-to-day motor learning decisions.

  8. A New Profile Learning Model for Recommendation System based on Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    Shereen H. Ali

    2016-03-01

    Full Text Available Recommender systems (RSs have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent Adaptive Vertical Recommendation (IAVR system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy.

  9. Opportunities to Create Active Learning Techniques in the Classroom

    Science.gov (United States)

    Camacho, Danielle J.; Legare, Jill M.

    2015-01-01

    The purpose of this article is to contribute to the growing body of research that focuses on active learning techniques. Active learning techniques require students to consider a given set of information, analyze, process, and prepare to restate what has been learned--all strategies are confirmed to improve higher order thinking skills. Active…

  10. The colloquial approach: An active learning technique

    Science.gov (United States)

    Arce, Pedro

    1994-09-01

    This paper addresses the very important problem of the effectiveness of teaching methodologies in fundamental engineering courses such as transport phenomena. An active learning strategy, termed the colloquial approach, is proposed in order to increase student involvement in the learning process. This methodology is a considerable departure from traditional methods that use solo lecturing. It is based on guided discussions, and it promotes student understanding of new concepts by directing the student to construct new ideas by building upon the current knowledge and by focusing on key cases that capture the essential aspects of new concepts. The colloquial approach motivates the student to participate in discussions, to develop detailed notes, and to design (or construct) his or her own explanation for a given problem. This paper discusses the main features of the colloquial approach within the framework of other current and previous techniques. Problem-solving strategies and the need for new textbooks and for future investigations based on the colloquial approach are also outlined.

  11. Machine learning techniques for razor triggers

    CERN Document Server

    Kolosova, Marina

    2015-01-01

    My project was focused on the development of a neural network which can predict if an event passes or not a razor trigger. Using synthetic data containing jets and missing transverse energy we built and trained a razor network by supervised learning. We accomplished a ∼ 91% agreement between the output of the neural network and the target while the other 10% was due to the noise of the neural network. We could apply such networks during the L1 trigger using neuromorhic hardware. Neuromorphic chips are electronic systems that function in a way similar to an actual brain, they are faster than GPUs or CPUs, but they can only be used with spiking neural networks.

  12. Volcanic Monitoring Techniques Applied to Controlled Fragmentation Experiments

    Science.gov (United States)

    Kueppers, U.; Alatorre-Ibarguengoitia, M. A.; Hort, M. K.; Kremers, S.; Meier, K.; Scharff, L.; Scheu, B.; Taddeucci, J.; Dingwell, D. B.

    2010-12-01

    Volcanic eruptions are an inevitable natural threat. The range of eruptive styles is large and short term fluctuations of explosivity or vent position pose a large risk that is not necessarily confined to the immediate vicinity of a volcano. Explosive eruptions rather may also affect aviation, infrastructure and climate, regionally as well as globally. Multiparameter monitoring networks are deployed on many active volcanoes to record signs of magmatic processes and help elucidate the secrets of volcanic phenomena. However, our mechanistic understanding of many processes hiding in recorded signals is still poor. As a direct consequence, a solid interpretation of the state of a volcano is still a challenge. In an attempt to bridge this gap, we combined volcanic monitoring and experimental volcanology. We performed 15 well-monitored, field-based, experiments and fragmented natural rock samples from Colima volcano (Mexico) by rapid decompression. We used cylindrical samples of 60 mm height and 25 mm and 60 mm diameter, respectively, and 25 and 35 vol.% open porosity. The applied pressure range was from 4 to 18 MPa. Using different experimental set-ups, the pressurised volume above the samples ranged from 60 - 170 cm3. The experiments were performed at ambient conditions and at controlled sample porosity and size, confinement geometry, and applied pressure. The experiments have been thoroughly monitored with 1) Doppler Radar (DR), 2) high-speed and high-definition cameras, 3) acoustic and infrasound sensors, 4) pressure transducers, and 5) electrically conducting wires. Our aim was to check for common results achieved by the different approaches and, if so, calibrate state-of-the-art monitoring tools. We present how the velocity of the ejected pyroclasts was measured by and evaluated for the different approaches and how it was affected by the experimental conditions and sample characteristics. We show that all deployed instruments successfully measured the pyroclast

  13. The experience of applying academic service learning within the ...

    African Journals Online (AJOL)

    The experience of applying academic service learning within the discipline of speech pathology and audiology at a South African university. ... The argument put forward is that this type of pedagogy would appear to be applicable across a broad range of disciplines and represents one strategy for assisting higher education ...

  14. Computer Game-based Learning: Applied Game Development Made Simpler

    NARCIS (Netherlands)

    Nyamsuren, Enkhbold

    2018-01-01

    The RAGE project (Realising an Applied Gaming Ecosystem, http://rageproject.eu/) is an ongoing initiative that aims to offer an ecosystem to support serious games’ development and use. Its two main objectives are to provide technologies for computer game-based pedagogy and learning and to establish

  15. Some Consequences of Learning Theory Applied to Division of Fractions

    Science.gov (United States)

    Bidwell, James K.

    1971-01-01

    Reviews the learning theories of Robert Gagne and David Ausubel, and applies these theories to the three most common approaches to teaching division of fractions: common denominator, complex fraction, and inverse operation methods. Such analysis indicates the inverse approach should be most effective for meaningful teaching, as is verified by…

  16. Non destructive assay techniques applied to nuclear materials

    International Nuclear Information System (INIS)

    Gavron, A.

    2001-01-01

    Nondestructive assay is a suite of techniques that has matured and become precise, easily implementable, and remotely usable. These techniques provide elaborate safeguards of nuclear material by providing the necessary information for materials accounting. NDA techniques are ubiquitous, reliable, essentially tamper proof, and simple to use. They make the world a safer place to live in, and they make nuclear energy viable. (author)

  17. Application of machine learning techniques to lepton energy reconstruction in water Cherenkov detectors

    Science.gov (United States)

    Drakopoulou, E.; Cowan, G. A.; Needham, M. D.; Playfer, S.; Taani, M.

    2018-04-01

    The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found that applying these techniques leads to an improvement of more than 50% in the energy resolution for all lepton energies compared to an approach based upon lookup tables. Machine learning techniques can be easily applied to different detector configurations and the results are comparable to likelihood-function based techniques that are currently used.

  18. Photoacoustic technique applied to the study of skin and leather

    International Nuclear Information System (INIS)

    Vargas, M.; Varela, J.; Hernandez, L.; Gonzalez, A.

    1998-01-01

    In this paper the photoacoustic technique is used in bull skin for the determination of thermal and optical properties as a function of the tanning process steps. Our results show that the photoacoustic technique is sensitive to the study of physical changes in this kind of material due to the tanning process

  19. NEW TECHNIQUES APPLIED IN ECONOMICS. ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Constantin Ilie

    2009-05-01

    Full Text Available The present paper has the objective to inform the public regarding the use of new techniques for the modeling, simulate and forecast of system from different field of activity. One of those techniques is Artificial Neural Network, one of the artificial in

  20. Biomechanical study of the funnel technique applied in thoracic ...

    African Journals Online (AJOL)

    of vertebra was made for injury model of anterior and central column ... data were collected to eliminate creep and relaxation of soft tissues in .... 3 Pullout strength curve for Magerl technique (A) and Funnel technique (B). 210x164mm (72 x 72 ...

  1. Improving face image extraction by using deep learning technique

    Science.gov (United States)

    Xue, Zhiyun; Antani, Sameer; Long, L. R.; Demner-Fushman, Dina; Thoma, George R.

    2016-03-01

    The National Library of Medicine (NLM) has made a collection of over a 1.2 million research articles containing 3.2 million figure images searchable using the Open-iSM multimodal (text+image) search engine. Many images are visible light photographs, some of which are images containing faces ("face images"). Some of these face images are acquired in unconstrained settings, while others are studio photos. To extract the face regions in the images, we first applied one of the most widely-used face detectors, a pre-trained Viola-Jones detector implemented in Matlab and OpenCV. The Viola-Jones detector was trained for unconstrained face image detection, but the results for the NLM database included many false positives, which resulted in a very low precision. To improve this performance, we applied a deep learning technique, which reduced the number of false positives and as a result, the detection precision was improved significantly. (For example, the classification accuracy for identifying whether the face regions output by this Viola- Jones detector are true positives or not in a test set is about 96%.) By combining these two techniques (Viola-Jones and deep learning) we were able to increase the system precision considerably, while avoiding the need to manually construct a large training set by manual delineation of the face regions.

  2. Learning-curve estimation techniques for nuclear industry

    Energy Technology Data Exchange (ETDEWEB)

    Vaurio, J.K.

    1983-01-01

    Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on acturial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year.

  3. Learning curve estimation techniques for the nuclear industry

    International Nuclear Information System (INIS)

    Vaurio, J.K.

    1983-01-01

    Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on actuarial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year

  4. Learning-curve estimation techniques for nuclear industry

    International Nuclear Information System (INIS)

    Vaurio, J.K.

    1983-01-01

    Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on acturial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year

  5. APPLYING ARTIFICIAL INTELLIGENCE TECHNIQUES TO HUMAN-COMPUTER INTERFACES

    DEFF Research Database (Denmark)

    Sonnenwald, Diane H.

    1988-01-01

    A description is given of UIMS (User Interface Management System), a system using a variety of artificial intelligence techniques to build knowledge-based user interfaces combining functionality and information from a variety of computer systems that maintain, test, and configure customer telephone...... and data networks. Three artificial intelligence (AI) techniques used in UIMS are discussed, namely, frame representation, object-oriented programming languages, and rule-based systems. The UIMS architecture is presented, and the structure of the UIMS is explained in terms of the AI techniques....

  6. A review of supervised machine learning applied to ageing research.

    Science.gov (United States)

    Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

    2017-04-01

    Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

  7. Techniques to Promote Reflective Practice and Empowered Learning.

    Science.gov (United States)

    Nguyen-Truong, Connie Kim Yen; Davis, Andra; Spencer, Cassius; Rasmor, Melody; Dekker, Lida

    2018-02-01

    Health care environments are fraught with fast-paced critical demands and ethical dilemmas requiring decisive nursing actions. Nurse educators must prepare nursing students to practice skills, behaviors, and attitudes needed to meet the challenges of health care demands. Evidence-based, innovative, multimodal techniques with novice and seasoned nurses were incorporated into a baccalaureate (BSN) completion program (RN to-BSN) to deepen learning, complex skill building, reflective practice, teamwork, and compassion toward the experiences of others. Principles of popular education for engaged teaching-learning were applied. Nursing students experience equitable access to content through co-constructing knowledge with four creative techniques. Four creative techniques include poem reading aloud to facilitate connectedness; mindfulness to cultivate self-awareness; string figure activities to demonstrate indigenous knowledge and teamwork; and cartooning difficult subject matter. Nursing school curricula can promote a milieu for developing organizational skills to manage simultaneous priorities, practice reflectively, and develop empathy and the authenticity that effective nursing requires. [J Nurs Educ. 2018;57(2):115-120.]. Copyright 2018, SLACK Incorporated.

  8. Memorization techniques: Using mnemonics to learn fifth grade science terms

    Science.gov (United States)

    Garcia, Juan O.

    The purpose of this study was to determine whether mnemonic instruction could assist students in learning fifth-grade science terminology more effectively than traditional-study methods of recall currently in practice The task was to examine if fifth-grade students were able to learn a mnemonic and then use it to understand science vocabulary; subsequently, to determine if students were able to remember the science terms after a period of time. The problem is that in general, elementary school students are not being successful in science achievement at the fifth grade level. In view of this problem, if science performance is increased at the elementary level, then it is likely that students will be successful when tested at the 8th and 10th grade in science with the Texas Assessment of Knowledge and Skills (TAKS) in the future. Two research questions were posited: (1) Is there a difference in recall achievement when a mnemonic such as method of loci, pegword method, or keyword method is used in learning fifth-grade science vocabulary as compared to the traditional-study method? (2) If using a mnemonic in learning fifth-grade science vocabulary was effective on recall achievement, would this achievement be maintained over a span of time? The need for this study was to assist students in learning science terms and concepts for state accountability purposes. The first assumption was that memorization techniques are not commonly applied in fifth-grade science classes in elementary schools. A second assumption was that mnemonic devices could be used successfully in learning science terms and increase long term retention. The first limitation was that the study was conducted on one campus in one school district in South Texas which limited the generalization of the study. The second limitation was that it included random assigned intact groups as opposed to random student assignment to fifth-grade classroom groups.

  9. Applying decision-making techniques to Civil Engineering Projects

    Directory of Open Access Journals (Sweden)

    Fam F. Abdel-malak

    2017-12-01

    Full Text Available Multi-Criteria Decision-Making (MCDM techniques are found to be useful tools in project managers’ hands to overcome decision-making (DM problems in Civil Engineering Projects (CEPs. The main contribution of this paper includes selecting and studying the popular MCDM techniques that uses different and wide ranges of data types in CEPs. A detailed study including advantages and pitfalls of using the Analytic Hierarchy Process (AHP and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS is introduced. Those two techniques are selected for the purpose of forming a package that covers most available data types in CEPs. The results indicated that AHP has a structure which simplifies complicated problems, while Fuzzy TOPSIS uses the advantages of linguistic variables to solve the issue of undocumented data and ill-defined problems. Furthermore, AHP is a simple technique that depends on pairwise comparisons of factors and natural attributes, beside it is preferable for widely spread hierarchies. On the other hand, Fuzzy TOPSIS needs more information but works well for the one-tier decision tree as well as it shows more flexibility to work in fuzzy environments. The two techniques have the facility to be integrated and combined in a new module to support most of the decisions required in CEPs. Keywords: Decision-making, AHP, Fuzzy TOPSIS, CBA, Civil Engineering Projects

  10. Action learning in virtual higher education: applying leadership theory.

    Science.gov (United States)

    Curtin, Joseph

    2016-05-03

    This paper reports the historical foundation of Northeastern University's course, LDR 6100: Developing Your Leadership Capability, a partial literature review of action learning (AL) and virtual action learning (VAL), a course methodology of LDR 6100 requiring students to apply leadership perspectives using VAL as instructed by the author, questionnaire and survey results of students who evaluated the effectiveness of their application of leadership theories using VAL and insights believed to have been gained by the author administering VAL. Findings indicate most students thought applying leadership perspectives using AL was better than considering leadership perspectives not using AL. In addition as implemented in LDR 6100, more students evaluated VAL positively than did those who assessed VAL negatively.

  11. Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

    Science.gov (United States)

    Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

    2009-01-01

    In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

  12. APPLYING PBL AND ZUVIO TO ENHANCE ENGLISH LEARNING MOTIVATION

    Directory of Open Access Journals (Sweden)

    BOR-TYNG WANG

    2016-06-01

    Full Text Available To inspire college students’ English learning motivation, this study proposed to combine Project-Based Learning (PBL with ZUVIO online teaching platform. The traditional teaching methods focus on teachers’ direct instruction in class, which mean that students only receive knowledge from teachers instead of formulating the answers on their own. This also decreases interaction in the classroom and prevents students from collaborating with other peers. However, implementing PBL and ZUVIO would allow students to apply knowledge in the social context and work with their classmates. In this study, two freshman English classes in a private university in central Taiwan were chosen as the sample. The students in both classes were low-level students (CEF A2 level. One class (N = 39 was chosen as the experimental group which had to complete the PBL tasks assigned by the teacher and use peer assessment function in ZUVIO for one academic year. The other class (N = 43 was chosen as the control group which was given the traditional teaching instructions. The results showed that the experimental group performed better on the midterm exam compared to the control group during both semesters (p = 0.001. Additionally, the results of the questionnaire showed that students’ motivation to learn English increased when using PBL and ZUVIO as teaching methods. To cite this document: Bor-Tyng Wang, "Applying PBL and ZUVIO to enhance English learning motivation", International Journal of Cyber Society and Education, Vol. 9, No. 1, pp. 1-16, 2016.

  13. Object oriented programming techniques applied to device access and control

    International Nuclear Information System (INIS)

    Goetz, A.; Klotz, W.D.; Meyer, J.

    1992-01-01

    In this paper a model, called the device server model, has been presented for solving the problem of device access and control faced by all control systems. Object Oriented Programming techniques were used to achieve a powerful yet flexible solution. The model provides a solution to the problem which hides device dependancies. It defines a software framework which has to be respected by implementors of device classes - this is very useful for developing groupware. The decision to implement remote access in the root class means that device servers can be easily integrated in a distributed control system. A lot of the advantages and features of the device server model are due to the adoption of OOP techniques. The main conclusion that can be drawn from this paper is that 1. the device access and control problem is adapted to being solved with OOP techniques, 2. OOP techniques offer a distinct advantage over traditional programming techniques for solving the device access problem. (J.P.N.)

  14. Challenges of Using Learning Analytics Techniques to Support Mobile Learning

    Science.gov (United States)

    Arrigo, Marco; Fulantelli, Giovanni; Taibi, Davide

    2015-01-01

    Evaluation of Mobile Learning remains an open research issue, especially as regards the activities that take place outside the classroom. In this context, Learning Analytics can provide answers, and offer the appropriate tools to enhance Mobile Learning experiences. In this poster we introduce a task-interaction framework, using learning analytics…

  15. Rare event techniques applied in the Rasmussen study

    International Nuclear Information System (INIS)

    Vesely, W.E.

    1977-01-01

    The Rasmussen Study estimated public risks from commercial nuclear power plant accidents, and therefore the statistics of rare events had to be treated. Two types of rare events were specifically handled, those rare events which were probabilistically rare events and those which were statistically rare events. Four techniques were used to estimate probabilities of rare events. These techniques were aggregating data samples, discretizing ''continuous'' events, extrapolating from minor to catastrophic severities, and decomposing events using event trees and fault trees. In aggregating or combining data the goal was to enlarge the data sample so that the rare event was no longer rare, i.e., so that the enlarged data sample contained one or more occurrences of the event of interest. This aggregation gave rise to random variable treatments of failure rates, occurrence frequencies, and other characteristics estimated from data. This random variable treatment can be interpreted as being comparable to an empirical Bayes technique or a Bayesian technique. In the discretizing event technique, events of a detailed nature were grouped together into a grosser event for purposes of analysis as well as for data collection. The treatment of data characteristics as random variables helped to account for the uncertainties arising from this discretizing. In the severity extrapolation technique a severity variable was associated with each event occurrence for the purpose of predicting probabilities of catastrophic occurrences. Tail behaviors of distributions therefore needed to be considered. Finally, event trees and fault trees were used to express accident occurrences and system failures in terms of more basic events for which data existed. Common mode failures and general dependencies therefore needed to be treated. 2 figures

  16. Bioremediation techniques applied to aqueous media contaminated with mercury.

    Science.gov (United States)

    Velásquez-Riaño, Möritz; Benavides-Otaya, Holman D

    2016-12-01

    In recent years, the environmental and human health impacts of mercury contamination have driven the search for alternative, eco-efficient techniques different from the traditional physicochemical methods for treating this metal. One of these alternative processes is bioremediation. A comprehensive analysis of the different variables that can affect this process is presented. It focuses on determining the effectiveness of different techniques of bioremediation, with a specific consideration of three variables: the removal percentage, time needed for bioremediation and initial concentration of mercury to be treated in an aqueous medium.

  17. Cross-Cultural Service Learning: American and Russian Students Learn Applied Organizational Communication.

    Science.gov (United States)

    Stevens, Betsy

    2001-01-01

    Describes how American and Russian students engaged in service learning in their own communities as part of an organizational communication class in which they learned communication principles and applied their skills to assist non-profit organizations. Describes both projects, stumbling blocks, and course outcomes. (SR)

  18. X-diffraction technique applied for nano system metrology

    International Nuclear Information System (INIS)

    Kuznetsov, Alexei Yu.; Machado, Rogerio; Robertis, Eveline de; Campos, Andrea P.C.; Archanjo, Braulio S.; Gomes, Lincoln S.; Achete, Carlos A.

    2009-01-01

    The application of nano materials are fast growing in all industrial sectors, with a strong necessity in nano metrology and normalizing in the nano material area. The great potential of the X-ray diffraction technique in this field is illustrated at the example of metals, metal oxides and pharmaceuticals

  19. The ordering operator technique applied to open systems

    International Nuclear Information System (INIS)

    Pedrosa, I.A.; Baseia, B.

    1982-01-01

    A normal ordering technique and the coherent representation are used to discribe the time evolution of an open system of a single oscillator, linearly coupled with an infinite number of reservoir oscillators and it is shown how to include the dissipation and get the exponential decay. (Author) [pt

  20. Ion backscattering techniques applied in materials science research

    International Nuclear Information System (INIS)

    Sood, D.K.

    1978-01-01

    The applications of Ion Backscattering Technique (IBT) to material analysis have expanded rapidly during the last decade. It is now regarded as an analysis tool indispensable for a versatile materials research program. The technique consists of simply shooting a beam of monoenergetic ions (usually 4 He + ions at about 2 MeV) onto a target, and measuring their energy distribution after backscattering at a fixed angle. Simple Rutherford scattering analysis of the backscattered ion spectrum yields information on the mass, the absolute amount and the depth profile of elements present upto a few microns of the target surface. The technique is nondestructive, quick, quantitative and the only known method of analysis which gives quantitative results without recourse to calibration standards. Its major limitations are the inability to separate elements of similar mass and a complete absence of chemical-binding information. A typical experimental set up and spectrum analysis have been described. Examples, some of them based on the work at the Bhabha Atomic Research Centre, Bombay, have been given to illustrate the applications of this technique to semiconductor technology, thin film materials science and nuclear energy materials. Limitations of IBT have been illustrated and a few remedies to partly overcome these limitations are presented. (auth.)

  1. Eddy current technique applied to automated tube profilometry

    International Nuclear Information System (INIS)

    Dobbeni, D.; Melsen, C. van

    1982-01-01

    The use of eddy current methods in the first totally automated pre-service inspection of the internal diameter of PWR steam generator tubes is described. The technique was developed at Laborelec, the Belgian Laboratory of the Electricity Supply Industry. Details are given of the data acquisition system and of the automated manipulator. Representative tube profiles are illustrated. (U.K.)

  2. Flash radiographic technique applied to fuel injector sprays

    International Nuclear Information System (INIS)

    Vantine, H.C.

    1977-01-01

    A flash radiographic technique, using 50 ns exposure times, was used to study the pattern and density distribution of a fuel injector spray. The experimental apparatus and method are described. An 85 kVp flash x-ray generator, designed and fabricated at the Lawrence Livermore Laboratory, is utilized. Radiographic images, recorded on standard x-ray films, are digitized and computer processed

  3. SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

  4. Positron Plasma Control Techniques Applied to Studies of Cold Antihydrogen

    CERN Document Server

    Funakoshi, Ryo

    2003-01-01

    In the year 2002, two experiments at CERN succeeded in producing cold antihydrogen atoms, first ATHENA and subsequently ATRAP. Following on these results, it is now feasible to use antihydrogen to study the properties of antimatter. In the ATHENA experiment, the cold antihydrogen atoms are produced by mixing large amounts of antiprotons and positrons in a nested Penning trap. The complicated behaviors of the charged particles are controlled and monitored by plasma manipulation techniques. The antihydrogen events are studied using position sensitive detectors and the evidence of production of antihydrogen atoms is separated out with the help of analysis software. This thesis covers the first production of cold antihydrogen in the first section as well as the further studies of cold antihydrogen performed by using the plasma control techniques in the second section.

  5. Ion beam analysis techniques applied to large scale pollution studies

    Energy Technology Data Exchange (ETDEWEB)

    Cohen, D D; Bailey, G; Martin, J; Garton, D; Noorman, H; Stelcer, E; Johnson, P [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

    1994-12-31

    Ion Beam Analysis (IBA) techniques are ideally suited to analyse the thousands of filter papers a year that may originate from a large scale aerosol sampling network. They are fast multi-elemental and, for the most part, non-destructive so other analytical methods such as neutron activation and ion chromatography can be performed afterwards. ANSTO in collaboration with the NSW EPA, Pacific Power and the Universities of NSW and Macquarie has established a large area fine aerosol sampling network covering nearly 80,000 square kilometres of NSW with 25 fine particle samplers. This network known as ASP was funded by the Energy Research and Development Corporation (ERDC) and commenced sampling on 1 July 1991. The cyclone sampler at each site has a 2.5 {mu}m particle diameter cut off and runs for 24 hours every Sunday and Wednesday using one Gillman 25mm diameter stretched Teflon filter for each day. These filters are ideal targets for ion beam analysis work. Currently ANSTO receives 300 filters per month from this network for analysis using its accelerator based ion beam techniques on the 3 MV Van de Graaff accelerator. One week a month of accelerator time is dedicated to this analysis. Four simultaneous accelerator based IBA techniques are used at ANSTO, to analyse for the following 24 elements: H, C, N, O, F, Na, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Cu, Ni, Co, Zn, Br and Pb. The IBA techniques were proved invaluable in identifying sources of fine particles and their spatial and seasonal variations accross the large area sampled by the ASP network. 3 figs.

  6. Ion beam analysis techniques applied to large scale pollution studies

    Energy Technology Data Exchange (ETDEWEB)

    Cohen, D.D.; Bailey, G.; Martin, J.; Garton, D.; Noorman, H.; Stelcer, E.; Johnson, P. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

    1993-12-31

    Ion Beam Analysis (IBA) techniques are ideally suited to analyse the thousands of filter papers a year that may originate from a large scale aerosol sampling network. They are fast multi-elemental and, for the most part, non-destructive so other analytical methods such as neutron activation and ion chromatography can be performed afterwards. ANSTO in collaboration with the NSW EPA, Pacific Power and the Universities of NSW and Macquarie has established a large area fine aerosol sampling network covering nearly 80,000 square kilometres of NSW with 25 fine particle samplers. This network known as ASP was funded by the Energy Research and Development Corporation (ERDC) and commenced sampling on 1 July 1991. The cyclone sampler at each site has a 2.5 {mu}m particle diameter cut off and runs for 24 hours every Sunday and Wednesday using one Gillman 25mm diameter stretched Teflon filter for each day. These filters are ideal targets for ion beam analysis work. Currently ANSTO receives 300 filters per month from this network for analysis using its accelerator based ion beam techniques on the 3 MV Van de Graaff accelerator. One week a month of accelerator time is dedicated to this analysis. Four simultaneous accelerator based IBA techniques are used at ANSTO, to analyse for the following 24 elements: H, C, N, O, F, Na, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Cu, Ni, Co, Zn, Br and Pb. The IBA techniques were proved invaluable in identifying sources of fine particles and their spatial and seasonal variations accross the large area sampled by the ASP network. 3 figs.

  7. BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

    OpenAIRE

    Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

    2015-01-01

    Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data...

  8. IoT Security Techniques Based on Machine Learning

    OpenAIRE

    Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di

    2018-01-01

    Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine le...

  9. Applying Authentic Data Analysis in Learning Earth Atmosphere

    Science.gov (United States)

    Johan, H.; Suhandi, A.; Samsudin, A.; Wulan, A. R.

    2017-09-01

    The aim of this research was to develop earth science learning material especially earth atmosphere supported by science research with authentic data analysis to enhance reasoning through. Various earth and space science phenomenon require reasoning. This research used experimental research with one group pre test-post test design. 23 pre-service physics teacher participated in this research. Essay test was conducted to get data about reason ability. Essay test was analyzed quantitatively. Observation sheet was used to capture phenomena during learning process. The results showed that student’s reasoning ability improved from unidentified and no reasoning to evidence based reasoning and inductive/deductive rule-based reasoning. Authentic data was considered using Grid Analysis Display System (GrADS). Visualization from GrADS facilitated students to correlate the concepts and bring out real condition of nature in classroom activity. It also helped student to reason the phenomena related to earth and space science concept. It can be concluded that applying authentic data analysis in learning process can help to enhance students reasoning. This study is expected to help lecture to bring out result of geoscience research in learning process and facilitate student understand concepts.

  10. Enhanced nonlinear iterative techniques applied to a nonequilibrium plasma flow

    International Nuclear Information System (INIS)

    Knoll, D.A.

    1998-01-01

    The authors study the application of enhanced nonlinear iterative methods to the steady-state solution of a system of two-dimensional convection-diffusion-reaction partial differential equations that describe the partially ionized plasma flow in the boundary layer of a tokamak fusion reactor. This system of equations is characterized by multiple time and spatial scales and contains highly anisotropic transport coefficients due to a strong imposed magnetic field. They use Newton's method to linearize the nonlinear system of equations resulting from an implicit, finite volume discretization of the governing partial differential equations, on a staggered Cartesian mesh. The resulting linear systems are neither symmetric nor positive definite, and are poorly conditioned. Preconditioned Krylov iterative techniques are employed to solve these linear systems. They investigate both a modified and a matrix-free Newton-Krylov implementation, with the goal of reducing CPU cost associated with the numerical formation of the Jacobian. A combination of a damped iteration, mesh sequencing, and a pseudotransient continuation technique is used to enhance global nonlinear convergence and CPU efficiency. GMRES is employed as the Krylov method with incomplete lower-upper (ILU) factorization preconditioning. The goal is to construct a combination of nonlinear and linear iterative techniques for this complex physical problem that optimizes trade-offs between robustness, CPU time, memory requirements, and code complexity. It is shown that a mesh sequencing implementation provides significant CPU savings for fine grid calculations. Performance comparisons of modified Newton-Krylov and matrix-free Newton-Krylov algorithms will be presented

  11. Applying NISHIJIN historical textile technique for e-Textile.

    Science.gov (United States)

    Kuroda, Tomohiro; Hirano, Kikuo; Sugimura, Kazushige; Adachi, Satoshi; Igarashi, Hidetsugu; Ueshima, Kazuo; Nakamura, Hideo; Nambu, Masayuki; Doi, Takahiro

    2013-01-01

    The e-Textile is the key technology for continuous ambient health monitoring to increase quality of life of patients with chronic diseases. The authors introduce techniques of Japanese historical textile, NISHIJIN, which illustrate almost any pattern from one continuous yarn within the machine weaving process, which is suitable for mixed flow production. Thus, NISHIJIN is suitable for e-Textile production, which requires rapid prototyping and mass production of very complicated patterns. The authors prototyped and evaluated a few vests to take twelve-lead electrocardiogram. The result tells that the prototypes obtains electrocardiogram, which is good enough for diagnosis.

  12. Applying Supervised Opinion Mining Techniques on Online User Reviews

    Directory of Open Access Journals (Sweden)

    Ion SMEUREANU

    2012-01-01

    Full Text Available In recent years, the spectacular development of web technologies, lead to an enormous quantity of user generated information in online systems. This large amount of information on web platforms make them viable for use as data sources, in applications based on opinion mining and sentiment analysis. The paper proposes an algorithm for detecting sentiments on movie user reviews, based on naive Bayes classifier. We make an analysis of the opinion mining domain, techniques used in sentiment analysis and its applicability. We implemented the proposed algorithm and we tested its performance, and suggested directions of development.

  13. Neutron activation: an invaluable technique for teaching applied radiation

    International Nuclear Information System (INIS)

    Trainer, Matthew

    2002-01-01

    This experiment introduces students to the important method of neutron activation. A sample of aluminium was irradiated with neutrons from an isotropic 241 Am-Be source. Using γ-ray spectroscopy, two radionuclide products were identified as 27 Mg and 28 Al. Applying a cadmium cut-off filter and an optimum irradiation time of 45 min, the half-life of 27 Mg was determined as 9.46±0.50 min. The half-life of the 28 Al radionuclide was determined as 2.28±0.10 min using a polythene moderator and an optimum irradiation time of 10 min. (author)

  14. Compressed Sensing Techniques Applied to Ultrasonic Imaging of Cargo Containers

    Directory of Open Access Journals (Sweden)

    Yuri Álvarez López

    2017-01-01

    Full Text Available One of the key issues in the fight against the smuggling of goods has been the development of scanners for cargo inspection. X-ray-based radiographic system scanners are the most developed sensing modality. However, they are costly and use bulky sources that emit hazardous, ionizing radiation. Aiming to improve the probability of threat detection, an ultrasonic-based technique, capable of detecting the footprint of metallic containers or compartments concealed within the metallic structure of the inspected cargo, has been proposed. The system consists of an array of acoustic transceivers that is attached to the metallic structure-under-inspection, creating a guided acoustic Lamb wave. Reflections due to discontinuities are detected in the images, provided by an imaging algorithm. Taking into consideration that the majority of those images are sparse, this contribution analyzes the application of Compressed Sensing (CS techniques in order to reduce the amount of measurements needed, thus achieving faster scanning, without compromising the detection capabilities of the system. A parametric study of the image quality, as a function of the samples needed in spatial and frequency domains, is presented, as well as the dependence on the sampling pattern. For this purpose, realistic cargo inspection scenarios have been simulated.

  15. Nuclear reactor vessel surface inspecting technique applying electric resistance probe

    International Nuclear Information System (INIS)

    Yamaguchi, T.; Enami, K.; Yoshioka, M.

    1975-01-01

    A new technique for inspecting the inner surface of the PWR type nuclear reactor vessel by use of an electric resistance probe is introduced, centering on a data processing system. This system is composed of a mini-computer, a system typewriter, an interface unit, a D-A converter and controller, and X-Y recorder and others. Its functions are judging flaws and making flaw detection maps. In order to judge flaws by flaw detection signals, three kinds of flaw judging methods have been developed. In case there is a flaw, its position and depth are calculated and listed on the system typewriter. The flaw detection maps are expressed in four kinds of modes and they are displayed on the X-Y recorder. (auth.)

  16. Neoliberal Optimism: Applying Market Techniques to Global Health.

    Science.gov (United States)

    Mei, Yuyang

    2017-01-01

    Global health and neoliberalism are becoming increasingly intertwined as organizations utilize markets and profit motives to solve the traditional problems of poverty and population health. I use field work conducted over 14 months in a global health technology company to explore how the promise of neoliberalism re-envisions humanitarian efforts. In this company's vaccine refrigerator project, staff members expect their investors and their market to allow them to achieve scale and develop accountability to their users in developing countries. However, the translation of neoliberal techniques to the global health sphere falls short of the ideal, as profits are meager and purchasing power remains with donor organizations. The continued optimism in market principles amidst such a non-ideal market reveals the tenacious ideological commitment to neoliberalism in these global health projects.

  17. Nonequilibrium Green function techniques applied to hot electron quantum transport

    International Nuclear Information System (INIS)

    Jauho, A.P.

    1989-01-01

    During the last few years considerable effort has been devoted to deriving quantum transport equations for semiconductors under extreme conditions (high electric fields, spatial quantization in one or two directions). Here we review the results obtained with nonequilibrium Green function techniques as formulated by Baym and Kadanoff, or by Keldysh. In particular, the following topics will be discussed: (i) Systematic approaches to reduce the transport equation governing the correlation function to a transport equation for the Wigner function; (ii) Approximations reducing the nonmarkovian quantum transport equation to a numerically tractable form, and results for model semiconductors; (iii) Recent progress in extending the formalism to inhomogeneous systems; and (iv) Nonequilibrium screening. In all sections we try to direct the reader's attention to points where the present understanding is (at best) incomplete, and indicate possible lines for future work. (orig.)

  18. Quantitative Portfolio Optimization Techniques Applied to the Brazilian Stock Market

    Directory of Open Access Journals (Sweden)

    André Alves Portela Santos

    2012-09-01

    Full Text Available In this paper we assess the out-of-sample performance of two alternative quantitative portfolio optimization techniques - mean-variance and minimum variance optimization – and compare their performance with respect to a naive 1/N (or equally-weighted portfolio and also to the market portfolio given by the Ibovespa. We focus on short selling-constrained portfolios and consider alternative estimators for the covariance matrices: sample covariance matrix, RiskMetrics, and three covariance estimators proposed by Ledoit and Wolf (2003, Ledoit and Wolf (2004a and Ledoit and Wolf (2004b. Taking into account alternative portfolio re-balancing frequencies, we compute out-of-sample performance statistics which indicate that the quantitative approaches delivered improved results in terms of lower portfolio volatility and better risk-adjusted returns. Moreover, the use of more sophisticated estimators for the covariance matrix generated optimal portfolios with lower turnover over time.

  19. Machine Learning Techniques in Optimal Design

    Science.gov (United States)

    Cerbone, Giuseppe

    1992-01-01

    Many important applications can be formalized as constrained optimization tasks. For example, we are studying the engineering domain of two-dimensional (2-D) structural design. In this task, the goal is to design a structure of minimum weight that bears a set of loads. A solution to a design problem in which there is a single load (L) and two stationary support points (S1 and S2) consists of four members, E1, E2, E3, and E4 that connect the load to the support points is discussed. In principle, optimal solutions to problems of this kind can be found by numerical optimization techniques. However, in practice [Vanderplaats, 1984] these methods are slow and they can produce different local solutions whose quality (ratio to the global optimum) varies with the choice of starting points. Hence, their applicability to real-world problems is severely restricted. To overcome these limitations, we propose to augment numerical optimization by first performing a symbolic compilation stage to produce: (a) objective functions that are faster to evaluate and that depend less on the choice of the starting point and (b) selection rules that associate problem instances to a set of recommended solutions. These goals are accomplished by successive specializations of the problem class and of the associated objective functions. In the end, this process reduces the problem to a collection of independent functions that are fast to evaluate, that can be differentiated symbolically, and that represent smaller regions of the overall search space. However, the specialization process can produce a large number of sub-problems. This is overcome by deriving inductively selection rules which associate problems to small sets of specialized independent sub-problems. Each set of candidate solutions is chosen to minimize a cost function which expresses the tradeoff between the quality of the solution that can be obtained from the sub-problem and the time it takes to produce it. The overall solution

  20. Considerations in applying on-line IC techniques to BWR's

    International Nuclear Information System (INIS)

    Kaleda, R.J.

    1992-01-01

    Ion-Chromatography (IC) has moved from its traditional role as a laboratory analytical tool to a real time, dynamic, on-line measurement device to follow ppb and sub-ppb concentrations of deleterious impurities in nuclear power plants. Electric Power Research Institute (EPRI), individual utilities, and industry all have played significant roles in effecting the transition. This paper highlights considerations and the evolution in current on-line Ion Chromatography systems. The first applications of on-line techniques were demonstrated by General Electric (GE) under EPRI sponsorship at Rancho Seco (1980), Calvert Cliffs, and McGuire nuclear units. The primary use was for diagnostic purposes. Today the on-line IC applications have been expanded to include process control and routine plant monitoring. Current on-line IC's are innovative in design, promote operational simplicity, are modular for simplified maintenance and repair, and use field-proven components which enhance reliability. Conductivity detection with electronic or chemical suppression and spectrometric detection techniques are intermixed in applications. Remote multi-point sample systems have addressed memory effects. Early applications measured ionic species in the part per billion range. Today reliable part per trillion measurements are common for on-line systems. Current systems are meeting the challenge of EPRI guideline requirements. Today's on-line IC's, with programmed sampling systems, monitor fluid streams throughout a power plant, supplying data that can be trended, stored and retrieved easily. The on-line IC has come of age. Many technical challenges were overcome to achieve today's IC

  1. Classifying Structures in the ISM with Machine Learning Techniques

    Science.gov (United States)

    Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

    2011-01-01

    The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

  2. Study of CT image texture using deep learning techniques

    Science.gov (United States)

    Dutta, Sandeep; Fan, Jiahua; Chevalier, David

    2018-03-01

    For CT imaging, reduction of radiation dose while improving or maintaining image quality (IQ) is currently a very active research and development topic. Iterative Reconstruction (IR) approaches have been suggested to be able to offer better IQ to dose ratio compared to the conventional Filtered Back Projection (FBP) reconstruction. However, it has been widely reported that often CT image texture from IR is different compared to that from FBP. Researchers have proposed different figure of metrics to quantitate the texture from different reconstruction methods. But there is still a lack of practical and robust method in the field for texture description. This work applied deep learning method for CT image texture study. Multiple dose scans of a 20cm diameter cylindrical water phantom was performed on Revolution CT scanner (GE Healthcare, Waukesha) and the images were reconstructed with FBP and four different IR reconstruction settings. The training images generated were randomly allotted (80:20) to a training and validation set. An independent test set of 256-512 images/class were collected with the same scan and reconstruction settings. Multiple deep learning (DL) networks with Convolution, RELU activation, max-pooling, fully-connected, global average pooling and softmax activation layers were investigated. Impact of different image patch size for training was investigated. Original pixel data as well as normalized image data were evaluated. DL models were reliably able to classify CT image texture with accuracy up to 99%. Results show that the deep learning techniques suggest that CT IR techniques may help lower the radiation dose compared to FBP.

  3. LEARNING SEMANTICS-ENHANCED LANGUAGE MODELS APPLIED TO UNSUEPRVISED WSD

    Energy Technology Data Exchange (ETDEWEB)

    VERSPOOR, KARIN [Los Alamos National Laboratory; LIN, SHOU-DE [Los Alamos National Laboratory

    2007-01-29

    An N-gram language model aims at capturing statistical syntactic word order information from corpora. Although the concept of language models has been applied extensively to handle a variety of NLP problems with reasonable success, the standard model does not incorporate semantic information, and consequently limits its applicability to semantic problems such as word sense disambiguation. We propose a framework that integrates semantic information into the language model schema, allowing a system to exploit both syntactic and semantic information to address NLP problems. Furthermore, acknowledging the limited availability of semantically annotated data, we discuss how the proposed model can be learned without annotated training examples. Finally, we report on a case study showing how the semantics-enhanced language model can be applied to unsupervised word sense disambiguation with promising results.

  4. SPI Trend Analysis of New Zealand Applying the ITA Technique

    Directory of Open Access Journals (Sweden)

    Tommaso Caloiero

    2018-03-01

    Full Text Available A natural temporary imbalance of water availability, consisting of persistent lower-than-average or higher-than-average precipitation, can cause extreme dry and wet conditions that adversely impact agricultural yields, water resources, infrastructure, and human systems. In this study, dry and wet periods in New Zealand were expressed using the Standardized Precipitation Index (SPI. First, both the short term (3 and 6 months and the long term (12 and 24 months SPI were estimated, and then, possible trends in the SPI values were detected by means of a new graphical technique, the Innovative Trend Analysis (ITA, which allows the trend identification of the low, medium, and high values of a series. Results show that, in every area currently subject to drought, an increase in this phenomenon can be expected. Specifically, the results of this paper highlight that agricultural regions on the eastern side of the South Island, as well as the north-eastern regions of the North Island, are the most consistently vulnerable areas. In fact, in these regions, the trend analysis mainly showed a general reduction in all the values of the SPI: that is, a tendency toward heavier droughts and weaker wet periods.

  5. Digital prototyping technique applied for redesigning plastic products

    Science.gov (United States)

    Pop, A.; Andrei, A.

    2015-11-01

    After products are on the market for some time, they often need to be redesigned to meet new market requirements. New products are generally derived from similar but outdated products. Redesigning a product is an important part of the production and development process. The purpose of this paper is to show that using modern technology, like Digital Prototyping in industry is an effective way to produce new products. This paper tries to demonstrate and highlight the effectiveness of the concept of Digital Prototyping, both to reduce the design time of a new product, but also the costs required for implementing this step. The results of this paper show that using Digital Prototyping techniques in designing a new product from an existing one available on the market mould offers a significantly manufacturing time and cost reduction. The ability to simulate and test a new product with modern CAD-CAM programs in all aspects of production (designing of the 3D model, simulation of the structural resistance, analysis of the injection process and beautification) offers a helpful tool for engineers. The whole process can be realised by one skilled engineer very fast and effective.

  6. Acoustic Emission Technique Applied in Textiles Mechanical Characterization

    Directory of Open Access Journals (Sweden)

    Rios-Soberanis Carlos Rolando

    2017-01-01

    Full Text Available The common textile architecture/geometry are woven, braided, knitted, stitch boded, and Z-pinned. Fibres in textile form exhibit good out-of-plane properties and good fatigue and impact resistance, additionally, they have better dimensional stability and conformability. Besides the nature of the textile, the architecture has a great role in the mechanical behaviour and mechanisms of damage in textiles, therefore damage mechanisms and mechanical performance in structural applications textiles have been a major concern. Mechanical damage occurs to a large extent during the service lifetime consequently it is vital to understand the material mechanical behaviour by identifying its mechanisms of failure such as onset of damage, crack generation and propagation. In this work, textiles of different architecture were used to manufacture epoxy based composites in order to study failure events under tensile load by using acoustic emission technique which is a powerful characterization tool due to its link between AE data and fracture mechanics, which makes this relation a very useful from the engineering point of view.

  7. A Kalman filter technique applied for medical image reconstruction

    International Nuclear Information System (INIS)

    Goliaei, S.; Ghorshi, S.; Manzuri, M. T.; Mortazavi, M.

    2011-01-01

    Medical images contain information about vital organic tissues inside of human body and are widely used for diagnoses of disease or for surgical purposes. Image reconstruction is essential for medical images for some applications such as suppression of noise or de-blurring the image in order to provide images with better quality and contrast. Due to vital rule of image reconstruction in medical sciences the corresponding algorithms with better efficiency and higher speed is desirable. Most algorithms in image reconstruction are operated on frequency domain such as the most popular one known as filtered back projection. In this paper we introduce a Kalman filter technique which is operated in time domain for medical image reconstruction. Results indicated that as the number of projection increases in both normal collected ray sum and the collected ray sum corrupted by noise the quality of reconstructed image becomes better in terms of contract and transparency. It is also seen that as the number of projection increases the error index decreases.

  8. Advanced Gradient Based Optimization Techniques Applied on Sheet Metal Forming

    International Nuclear Information System (INIS)

    Endelt, Benny; Nielsen, Karl Brian

    2005-01-01

    The computational-costs for finite element simulations of general sheet metal forming processes are considerable, especially measured in time. In combination with optimization, the performance of the optimization algorithm is crucial for the overall performance of the system, i.e. the optimization algorithm should gain as much information about the system in each iteration as possible. Least-square formulation of the object function is widely applied for solution of inverse problems, due to the superior performance of this formulation.In this work focus will be on small problems which are defined as problems with less than 1000 design parameters; as the majority of real life optimization and inverse problems, represented in literature, can be characterized as small problems, typically with less than 20 design parameters.We will show that the least square formulation is well suited for two classes of inverse problems; identification of constitutive parameters and process optimization.The scalability and robustness of the approach are illustrated through a number of process optimizations and inverse material characterization problems; tube hydro forming, two step hydro forming, flexible aluminum tubes, inverse identification of material parameters

  9. Applying data mining techniques to improve diagnosis in neonatal jaundice

    Directory of Open Access Journals (Sweden)

    Ferreira Duarte

    2012-12-01

    Full Text Available Abstract Background Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies. Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques. Methods This study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology. This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa – EPE, from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer. Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48 and neural networks (multilayer perceptron. The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia. Results The application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic. Conclusions The findings of our study sustain that, new approaches, such as data mining, may support

  10. Semantic Data And Visualization Techniques Applied To Geologic Field Mapping

    Science.gov (United States)

    Houser, P. I. Q.; Royo-Leon, M.; Munoz, R.; Estrada, E.; Villanueva-Rosales, N.; Pennington, D. D.

    2015-12-01

    Geologic field mapping involves the use of technology before, during, and after visiting a site. Geologists utilize hardware such as Global Positioning Systems (GPS) connected to mobile computing platforms such as tablets that include software such as ESRI's ArcPad and other software to produce maps and figures for a final analysis and report. Hand written field notes contain important information and drawings or sketches of specific areas within the field study. Our goal is to collect and geo-tag final and raw field data into a cyber-infrastructure environment with an ontology that allows for large data processing, visualization, sharing, and searching, aiding in connecting field research with prior research in the same area and/or aid with experiment replication. Online searches of a specific field area return results such as weather data from NOAA and QuakeML seismic data from USGS. These results that can then be saved to a field mobile device and searched while in the field where there is no Internet connection. To accomplish this we created the GeoField ontology service using the Web Ontology Language (OWL) and Protégé software. Advanced queries on the dataset can be made using reasoning capabilities can be supported that go beyond a standard database service. These improvements include the automated discovery of data relevant to a specific field site and visualization techniques aimed at enhancing analysis and collaboration while in the field by draping data over mobile views of the site using augmented reality. A case study is being performed at University of Texas at El Paso's Indio Mountains Research Station located near Van Horn, Texas, an active multi-disciplinary field study site. The user can interactively move the camera around the study site and view their data digitally. Geologist's can check their data against the site in real-time and improve collaboration with another person as both parties have the same interactive view of the data.

  11. Supervised Learning Applied to Air Traffic Trajectory Classification

    Science.gov (United States)

    Bosson, Christabelle; Nikoleris, Tasos

    2018-01-01

    Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

  12. Machine learning applied to the prediction of citrus production

    Energy Technology Data Exchange (ETDEWEB)

    Díaz, I.; Mazza, S.M.; Combarro, E.F.; Giménez, L.I.; Gaiad, J.E.

    2017-07-01

    An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

  13. Machine learning applied to the prediction of citrus production

    International Nuclear Information System (INIS)

    Díaz, I.; Mazza, S.M.; Combarro, E.F.; Giménez, L.I.; Gaiad, J.E.

    2017-01-01

    An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

  14. Machine learning applied to the prediction of citrus production

    Directory of Open Access Journals (Sweden)

    Irene Díaz

    2017-07-01

    Full Text Available An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i studies the effectiveness of machine learning techniques for predicting orchards production; and (ii variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees’ age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8 and relative mean absolute error (~0.1. These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

  15. Applying the principles of augmented learning to photonics laboratory work

    Science.gov (United States)

    Fischer, U. H. P.; Haupt, Matthias; Reinboth, Christian; Just, Jens-Uwe

    2007-06-01

    Most modern communication systems are based on opto-electrical methods, wavelength division multiplex (WDM) being the most widespread. Likewise, the use of polymeric fibres (POF) as an optical transmission medium is expanding rapidly. Therefore, enabling students to understand how WDM and/or POF systems are designed and maintained is an important task of universities and vocational schools that offer education in photonics. In the current academic setting, theory is mostly being taught in the classroom, while students gain practical knowledge by performing lab experiments utilizing specialized teaching systems. In an ideal setting, students should perform such experiments with a high degree of autonomy. By applying the principles of augmented learning to photonics training, contemporary lab work can be brought closer to these ideal conditions. This paper introduces "OPTOTEACH", a new teaching system for photonics lab work, designed by Harz University and successfully released on the German market by HarzOptics. OPTOTEACH is the first POF-WDM teaching system, specifically designed to cover a multitude of lab experiments in the field of optical communication technology. It is illustrated, how this lab system is supplemented by a newly developed optical teaching software - "OPTOSOFT" - and how the combination of system and software creates a unique augmented learning environment. The paper details, how the didactic concept for the software was conceptualised and introduces the latest beta version. OPTOSOFT is specifically designed not only as an attachment to OPTOTEACH, it also allows students to rehearse various aspects of theoretical optics and experience a fully interactive and feature-rich self-learning environment. The paper further details the first experiences educators at Harz University have made working with the lab system as well as the teaching software. So far, the augmented learning concept was received mostly positive, although there is some potential

  16. Applying multimedia design principles enhances learning in medical education.

    Science.gov (United States)

    Issa, Nabil; Schuller, Mary; Santacaterina, Susan; Shapiro, Michael; Wang, Edward; Mayer, Richard E; DaRosa, Debra A

    2011-08-01

    The Association of American Medical Colleges' Institute for Improving Medical Education's report entitled 'Effective Use of Educational Technology' called on researchers to study the effectiveness of multimedia design principles. These principles were empirically shown to result in superior learning when used with college students in laboratory studies, but have not been studied with undergraduate medical students as participants. A pre-test/post-test control group design was used, in which the traditional-learning group received a lecture on shock using traditionally designed slides and the modified-design group received the same lecture using slides modified in accord with Mayer's principles of multimedia design. Participants included Year 3 medical students at a private, midwestern medical school progressing through their surgery clerkship during the academic year 2009-2010. The medical school divides students into four groups; each group attends the surgery clerkship during one of the four quarters of the academic year. Students in the second and third quarters served as the modified-design group (n=91) and students in the fourth-quarter clerkship served as the traditional-design group (n=39). Both student cohorts had similar levels of pre-lecture knowledge. Both groups showed significant improvements in retention (paffect transfer of learning. Further research on applying the principles of multimedia design to medical education is needed to verify the impact it has on the long-term learning of medical students, as well as its impact on other forms of multimedia instructional programmes used in the education of medical students. © Blackwell Publishing Ltd 2011.

  17. E-learning systems intelligent techniques for personalization

    CERN Document Server

    Klašnja-Milićević, Aleksandra; Ivanović, Mirjana; Budimac, Zoran; Jain, Lakhmi C

    2017-01-01

    This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques. .

  18. A Comparative Analysis of Machine Learning Techniques for Credit Scoring

    OpenAIRE

    Nwulu, Nnamdi; Oroja, Shola; İlkan, Mustafa

    2012-01-01

    Abstract Credit Scoring has become an oft researched topic in light of the increasing volatility of the global economy and the recent world financial crisis. Amidst the many methods used for credit scoring, machine learning techniques are becoming increasingly popular due to their efficient and accurate nature and relative simplicity. Furthermore machine learning techniques minimize the risk of human bias and error and maximize speed as they are able to perform computation...

  19. AGE GROUP CLASSIFICATION USING MACHINE LEARNING TECHNIQUES

    OpenAIRE

    Arshdeep Singh Syal*1 & Abhinav Gupta2

    2017-01-01

    A human face provides a lot of information that allows another person to identify characteristics such as age, sex, etc. Therefore, the challenge is to develop an age group prediction system using the automatic learning method. The task of estimating the age group of the human from their frontal facial images is very captivating, but also challenging because of the pattern of personalized and non-linear aging that differs from one person to another. This paper examines the problem of predicti...

  20. Strategies Applied by Teachers as Classroom Managers to Strengthen Learning

    Directory of Open Access Journals (Sweden)

    Franco Javier Jáuregui Contreras

    2017-05-01

    Full Text Available The present research was executed within the quantitative paradigm, under the feasible project modality, with the objective of designing strategies aimed at teachers to strengthen learning in the students of the Arnoldo Gabaldon educational unit, located in Delicias, Rafael Urdaneta municipality State Táchira. The methodology used responds to the characteristics of field descriptive research, not experimental. The study population consisted of 72 teachers. To obtain the information, a 20-item contentive instrument was applied, which measured the factors that influence the application of managerial strategies to strengthen student learning. This instrument was validated and a reliability index of 0.81 was obtained. In the analysis of results, the single and absolute frequencies of each reagent were determined. This resulted in a series of constantly occurring situations that led to the following conclusions: managerial strategies are employed by teachers on average, as well as in organization and planning, there is no commitment in relation to it, although there It was possible to detect that teachers comply with the planned activities on a regular basis. In addition, in the case of management and control, it is denoted how forcefully both processes are fulfilled, even in the majority, and both are assumed as managerial strategies. Therefore, the implementation of strategies is recommended.

  1. Distance learning in the Applied Sciences of Oncology

    Energy Technology Data Exchange (ETDEWEB)

    Barton, Michael B., E-mail: Michael.Barton@swsahs.nsw.gov.a [CCORE and the South Western Clinical School, Liverpool Hospital, University of NSW (Australia); Thode, Richard J [CCORE and the South Western Clinical School, Liverpool Hospital, University of NSW (Australia)

    2010-04-15

    Background: The major impediment to the expansion of oncology services is a shortage of personnel. Purpose: To develop a distance learning course for radiation oncology trainees. Materials: Under the sponsorship of the Asia Pacific Regional Cooperative Agreement administered by the International Atomic Energy Agency (IAEA), a CD ROM-based Applied Sciences of Oncology (ASOC) distance learning course of 71 modules was created. The course covers communications, critical appraisal, functional anatomy, molecular biology, pathology. The materials include interactive text and illustrations that require students to answer questions before they can progress. The course aims to supplement existing oncology curricula and does not provide a qualification. It aims to assist students in acquiring their own profession's qualification. The course was piloted in seven countries in Asia, Africa and Latin America during 2004. After feedback from the pilot course, a further nine modules were added to cover imaging physics (three modules), informed consent, burnout and coping with death and dying, Economic analysis and cancer care, Nutrition, cachexia and fatigue, radiation-induced second cancers and mathematical tools and background for radiation oncology. The course was widely distributed and can be downloaded from (http://www.iaea.org/Publications/Training/Aso/register.html). ASOC has been downloaded over 1100 times in the first year after it was posted. There is a huge demand for educational materials but the interactive approach is labour-intensive and expensive to compile. The course must be maintained to remain relevant.

  2. Distance learning in the Applied Sciences of Oncology

    International Nuclear Information System (INIS)

    Barton, Michael B.; Thode, Richard J.

    2010-01-01

    Background: The major impediment to the expansion of oncology services is a shortage of personnel. Purpose: To develop a distance learning course for radiation oncology trainees. Materials: Under the sponsorship of the Asia Pacific Regional Cooperative Agreement administered by the International Atomic Energy Agency (IAEA), a CD ROM-based Applied Sciences of Oncology (ASOC) distance learning course of 71 modules was created. The course covers communications, critical appraisal, functional anatomy, molecular biology, pathology. The materials include interactive text and illustrations that require students to answer questions before they can progress. The course aims to supplement existing oncology curricula and does not provide a qualification. It aims to assist students in acquiring their own profession's qualification. The course was piloted in seven countries in Asia, Africa and Latin America during 2004. After feedback from the pilot course, a further nine modules were added to cover imaging physics (three modules), informed consent, burnout and coping with death and dying, Economic analysis and cancer care, Nutrition, cachexia and fatigue, radiation-induced second cancers and mathematical tools and background for radiation oncology. The course was widely distributed and can be downloaded from (http://www.iaea.org/Publications/Training/Aso/register.html). ASOC has been downloaded over 1100 times in the first year after it was posted. There is a huge demand for educational materials but the interactive approach is labour-intensive and expensive to compile. The course must be maintained to remain relevant.

  3. Modern machine learning techniques and their applications in cartoon animation research

    CERN Document Server

    Yu, Jun

    2013-01-01

    The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations

  4. Modelling tick abundance using machine learning techniques and satellite imagery

    DEFF Research Database (Denmark)

    Kjær, Lene Jung; Korslund, L.; Kjelland, V.

    satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

  5. Machine learning in Python essential techniques for predictive analysis

    CERN Document Server

    Bowles, Michael

    2015-01-01

    Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, d

  6. Enhancing E-Learning with VRML Techniques

    OpenAIRE

    Sangeetha Senthilkumar; E. Kirubakaran

    2011-01-01

    Virtual Reality (VR) is a computer-generated three-dimensional space that is multi-sensorial, interactive and engaging. Virtual reality is an artificial environment that is created with software and presented to the user in such a way that the user suspends belief and accepts it as a real environment. On a computer, virtual reality is primarily experienced through two of the five senses: sight and sound. This research paper is focused on enhancing E-Learning using the three dimensional Web Te...

  7. Effect of active learning techniques on students' choice of approach ...

    African Journals Online (AJOL)

    The purpose of this article is to report on empirical work, related to a techniques module, undertaken with the dental students of the University of the Western Cape, South Africa. I will relate how a range of different active learning techniques (tutorials; question papers and mock tests) assisted students to adopt a deep ...

  8. Machine learning techniques to examine large patient databases.

    Science.gov (United States)

    Meyfroidt, Geert; Güiza, Fabian; Ramon, Jan; Bruynooghe, Maurice

    2009-03-01

    Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

  9. Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

    Directory of Open Access Journals (Sweden)

    Wei-Chien Wang

    2018-05-01

    Full Text Available Deep learning has become the most popular research subject in the fields of artificial intelligence (AI and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA, support vector machines (SVM, k-nearest neighbors algorithm (K-NN, and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW, with machine-learning classifiers, and histogram of oriented gradients (HOG/pyramid histogram of oriented gradients (PHOG with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10−4, and this accuracy is far higher than those achieved by BOW with SVM (80.50% and PHOG with SVM (53.0%.

  10. Learning Programming Technique through Visual Programming Application as Learning Media with Fuzzy Rating

    Science.gov (United States)

    Buditjahjanto, I. G. P. Asto; Nurlaela, Luthfiyah; Ekohariadi; Riduwan, Mochamad

    2017-01-01

    Programming technique is one of the subjects at Vocational High School in Indonesia. This subject contains theory and application of programming utilizing Visual Programming. Students experience some difficulties to learn textual learning. Therefore, it is necessary to develop media as a tool to transfer learning materials. The objectives of this…

  11. APPLICABILITY OF COOPERATIVE LEARNING TECHNIQUES IN DIFFERENT CLASSROOM CONTEXTS

    Directory of Open Access Journals (Sweden)

    Dr. Issy Yuliasri

    2017-04-01

    Full Text Available This paper is based on the results of pre-test post-test, feedback questionnaire and observation during a community service program entitled ―Training on English Teaching using Cooperative Learning Techniques for Elementary and Junior High School Teachers of Sekolah Alam Arridho Semarang‖. It was an English teaching training program intended to equip the teachers with the knowledge and skills of using the different cooperative learning techniques such as jigsaw, think-pair-share, three-step interview, roundrobin braistorming, three-minute review, numbered heads together, team-pair-solo, circle the sage, dan partners. This program was participated by 8 teachers of different subjects (not only English, but most of them had good mastery of English. The objectives of this program was to improve teachers‘ skills in using the different cooperative learning techniques to vary their teaching, so that students would be more motivated to learn and improve their English skill. Besides, the training also gave the teachers the knowledge and skills to adjust their techniques with the basic competence and learning objectives to be achieved as well as with the teaching materials to be used. This was also done through workshops using cooperative learning techniques, so that the participants had real experiences of using cooperative learning techniques (learning by doing. The participants were also encouraged to explore the applicability of the techniques in their classroom contexts, in different areas of their teaching. This community service program showed very positive results. The pre-test and post-test results showed that before the training program all the participants did not know the nine cooperative techniques to be trained, but after the program they mastered the techniques as shown from the teaching-learning scenarios they developed following the test instructions. In addition, the anonymous questionnaires showed that all the participants

  12. Predicting radiotherapy outcomes using statistical learning techniques

    International Nuclear Information System (INIS)

    El Naqa, Issam; Bradley, Jeffrey D; Deasy, Joseph O; Lindsay, Patricia E; Hope, Andrew J

    2009-01-01

    Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model

  13. The Effect of Learning Based on Technology Model and Assessment Technique toward Thermodynamic Learning Achievement

    Science.gov (United States)

    Makahinda, T.

    2018-02-01

    The purpose of this research is to find out the effect of learning model based on technology and assessment technique toward thermodynamic achievement by controlling students intelligence. This research is an experimental research. The sample is taken through cluster random sampling with the total respondent of 80 students. The result of the research shows that the result of learning of thermodynamics of students who taught the learning model of environmental utilization is higher than the learning result of student thermodynamics taught by simulation animation, after controlling student intelligence. There is influence of student interaction, and the subject between models of technology-based learning with assessment technique to student learning result of Thermodynamics, after controlling student intelligence. Based on the finding in the lecture then should be used a thermodynamic model of the learning environment with the use of project assessment technique.

  14. Adaptive Landmark-Based Navigation System Using Learning Techniques

    DEFF Research Database (Denmark)

    Zeidan, Bassel; Dasgupta, Sakyasingha; Wörgötter, Florentin

    2014-01-01

    The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. In...... hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.......The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal....... Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex...

  15. Applied learning-based color tone mapping for face recognition in video surveillance system

    Science.gov (United States)

    Yew, Chuu Tian; Suandi, Shahrel Azmin

    2012-04-01

    In this paper, we present an applied learning-based color tone mapping technique for video surveillance system. This technique can be applied onto both color and grayscale surveillance images. The basic idea is to learn the color or intensity statistics from a training dataset of photorealistic images of the candidates appeared in the surveillance images, and remap the color or intensity of the input image so that the color or intensity statistics match those in the training dataset. It is well known that the difference in commercial surveillance cameras models, and signal processing chipsets used by different manufacturers will cause the color and intensity of the images to differ from one another, thus creating additional challenges for face recognition in video surveillance system. Using Multi-Class Support Vector Machines as the classifier on a publicly available video surveillance camera database, namely SCface database, this approach is validated and compared to the results of using holistic approach on grayscale images. The results show that this technique is suitable to improve the color or intensity quality of video surveillance system for face recognition.

  16. Analysing CMS transfers using Machine Learning techniques

    CERN Document Server

    Diotalevi, Tommaso

    2016-01-01

    LHC experiments transfer more than 10 PB/week between all grid sites using the FTS transfer service. In particular, CMS manages almost 5 PB/week of FTS transfers with PhEDEx (Physics Experiment Data Export). FTS sends metrics about each transfer (e.g. transfer rate, duration, size) to a central HDFS storage at CERN. The work done during these three months, here as a Summer Student, involved the usage of ML techniques, using a CMS framework called DCAFPilot, to process this new data and generate predictions of transfer latencies on all links between Grid sites. This analysis will provide, as a future service, the necessary information in order to proactively identify and maybe fix latency issued transfer over the WLCG.

  17. The application of machine learning techniques in the clinical drug therapy.

    Science.gov (United States)

    Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

    2018-05-25

    The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  18. Applying a Hybrid Model: Can It Enhance Student Learning Outcomes?

    Science.gov (United States)

    Potter, Jodi

    2015-01-01

    There has been a marked increase in the use of online learning over the past decade. There remains conflict in the current body of research on the efficacy of online versus face to face learning in these environments. One resolution of these issues is the hybrid learning option which is a combination of face-to-face classroom instruction with…

  19. Contemporary machine learning: techniques for practitioners in the physical sciences

    Science.gov (United States)

    Spears, Brian

    2017-10-01

    Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

  20. Study on the effect of smart learning applied at a radiationtherapy subject on self directed learning, self learning efficacy, learning satisfaction of college students

    International Nuclear Information System (INIS)

    Shin, Jae Goo; Park, Soo Jin; Kim, Yon Min

    2016-01-01

    The purpose of this was to study and analyze smart learning the self directed learning, self efficacy, learning satisfaction about department of radiology in a college. For this study total students 102 in 3 classes were surveyed at the end of semester. The research data was analyzed using SPSS also self directed learning ,self learning efficacy, learning satisfaction analyzed t-test, ANOVA and Pearson's correlation coefficient results were followings. First, Men is more higher than women in a self learning efficacy, self directed learning, learning satisfaction. Second, in a learning satisfaction smart learning ever heard in a first time group more satisfaction. Third, during the smart learning classes a students appeared a positive response. As a results, learning satisfaction will increase a learning when learners need a ability of self control planning and learning motivation by themselves in voluntarily and actively. Suggest to change a paradigm in a radiology classes so we have to improve a teaching skills this solution recommend is two way communication. In conclusion, smart learning applied for classes of college is meaningful as a new teaching, which can be change gradually learning satisfaction by teaching methods

  1. Study on the effect of smart learning applied at a radiationtherapy subject on self directed learning, self learning efficacy, learning satisfaction of college students

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Jae Goo; Park, Soo Jin [Daegu Health College, Daegu (Korea, Republic of); Kim, Yon Min [Dept. of Radiotechnology, Wonkwang Health Science University, Iksan (Korea, Republic of)

    2016-12-15

    The purpose of this was to study and analyze smart learning the self directed learning, self efficacy, learning satisfaction about department of radiology in a college. For this study total students 102 in 3 classes were surveyed at the end of semester. The research data was analyzed using SPSS also self directed learning ,self learning efficacy, learning satisfaction analyzed t-test, ANOVA and Pearson's correlation coefficient results were followings. First, Men is more higher than women in a self learning efficacy, self directed learning, learning satisfaction. Second, in a learning satisfaction smart learning ever heard in a first time group more satisfaction. Third, during the smart learning classes a students appeared a positive response. As a results, learning satisfaction will increase a learning when learners need a ability of self control planning and learning motivation by themselves in voluntarily and actively. Suggest to change a paradigm in a radiology classes so we have to improve a teaching skills this solution recommend is two way communication. In conclusion, smart learning applied for classes of college is meaningful as a new teaching, which can be change gradually learning satisfaction by teaching methods.

  2. Event Streams Clustering Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Hanen Bouali

    2015-10-01

    Full Text Available Data streams are usually of unbounded lengths which push users to consider only recent observations by focusing on a time window, and ignore past data. However, in many real world applications, past data must be taken in consideration to guarantee the efficiency, the performance of decision making and to handle data streams evolution over time. In order to build a selectively history to track the underlying event streams changes, we opt for the continuously data of the sliding window which increases the time window based on changes over historical data. In this paper, to have the ability to access to historical data without requiring any significant storage or multiple passes over the data. In this paper, we propose a new algorithm for clustering multiple data streams using incremental support vector machine and data representative points’ technique. The algorithm uses a sliding window model for the most recent clustering results and data representative points to model the old data clustering results. Our experimental results on electromyography signal show a better clustering than other present in the literature

  3. Applying Social Cognitive Theory to Academic Advising to Assess Student Learning Outcomes

    Science.gov (United States)

    Erlich, Richard J.; Russ-Eft, Darlene

    2011-01-01

    Review of social cognitive theory constructs of self-efficacy and self-regulated learning is applied to academic advising for the purposes of assessing student learning. A brief overview of the history of student learning outcomes in higher education is followed by an explanation of self-efficacy and self-regulated learning constructs and how they…

  4. A Comprehensive Review and meta-analysis on Applications of Machine Learning Techniques in Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Manojit Chattopadhyay

    2018-05-01

    Full Text Available Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for changing usage pattern by comparing different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.

  5. Designing the Electronic Classroom: Applying Learning Theory and Ergonomic Design Principles.

    Science.gov (United States)

    Emmons, Mark; Wilkinson, Frances C.

    2001-01-01

    Applies learning theory and ergonomic principles to the design of effective learning environments for library instruction. Discusses features of electronic classroom ergonomics, including the ergonomics of physical space, environmental factors, and workstations; and includes classroom layouts. (Author/LRW)

  6. Figure analysis: A teaching technique to promote visual literacy and active Learning.

    Science.gov (United States)

    Wiles, Amy M

    2016-07-08

    Learning often improves when active learning techniques are used in place of traditional lectures. For many of these techniques, however, students are expected to apply concepts that they have already grasped. A challenge, therefore, is how to incorporate active learning into the classroom of courses with heavy content, such as molecular-based biology courses. An additional challenge is that visual literacy is often overlooked in undergraduate science education. To address both of these challenges, a technique called figure analysis was developed and implemented in three different levels of undergraduate biology courses. Here, students learn content while gaining practice in interpreting visual information by discussing figures with their peers. Student groups also make connections between new and previously learned concepts on their own while in class. The instructor summarizes the material for the class only after students grapple with it in small groups. Students reported a preference for learning by figure analysis over traditional lecture, and female students in particular reported increased confidence in their analytical abilities. There is not a technology requirement for this technique; therefore, it may be utilized both in classrooms and in nontraditional spaces. Additionally, the amount of preparation required is comparable to that of a traditional lecture. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(4):336-344, 2016. © 2016 The International Union of Biochemistry and Molecular Biology.

  7. An Effective Performance Analysis of Machine Learning Techniques for Cardiovascular Disease

    Directory of Open Access Journals (Sweden)

    Vinitha DOMINIC

    2015-03-01

    Full Text Available Machine learning techniques will help in deriving hidden knowledge from clinical data which can be of great benefit for society, such as reduce the number of clinical trials required for precise diagnosis of a disease of a person etc. Various areas of study are available in healthcare domain like cancer, diabetes, drugs etc. This paper focuses on heart disease dataset and how machine learning techniques can help in understanding the level of risk associated with heart diseases. Initially, data is preprocessed then analysis is done in two stages, in first stage feature selection techniques are applied on 13 commonly used attributes and in second stage feature selection techniques are applied on 75 attributes which are related to anatomic structure of the heart like blood vessels of the heart, arteries etc. Finally, validation of the reduced set of features using an exhaustive list of classifiers is done.In parallel study of the anatomy of the heart is done using the identified features and the characteristics of each class is understood. It is observed that these reduced set of features are anatomically relevant. Thus, it can be concluded that, applying machine learning techniques on clinical data is beneficial and necessary.

  8. Machine learning techniques for persuasion dectection in conversation

    OpenAIRE

    Ortiz, Pedro.

    2010-01-01

    Approved for public release; distribution is unlimited We determined that it is possible to automatically detect persuasion in conversations using three traditional machine learning techniques, naive bayes, maximum entropy, and support vector machine. These results are the first of their kind and serve as a baseline for all future work in this field. The three techniques consistently outperformed the baseline F-score, but not at a level that would be useful for real world applications. The...

  9. I WHATSAPP AN IGUANA: AN ATTEMPT TO APPLY UBIQUITOUS LEARNING

    OpenAIRE

    Dwi Haryanti

    2017-01-01

    This paper aims at presenting how Whatsapp provides alternative method to enhance writing skills on personal letter for senior high school student. This method Whatsapps inspired by the concept called ubiquitous learning (U-Learning), which means ―learning supported by ICT resources held anytime, anywhere and fits the context of the learner‖ (de Sousa Monteiro et al., 2014). Participants were 40 11-th grade students in one class in Cirebon High School. A short story in the form of personal le...

  10. THE GAME TECHNIQUE NTCHNIQUE STIMULATING LEARNING ACTIVITY OF JUNIOR STUDENTS SPECIALIZING IN ECONOMICS

    Directory of Open Access Journals (Sweden)

    Juri. S. Ezrokh

    2014-01-01

    Full Text Available The research is aimed at specifying and developing the modern control system of current academic achievements of junior university students; and the main task is to find the adequate ways for stimulating the junior students’ learning activities, and estimating their individual achievements.Methods: The author applies his own assessment method for estimating and stimulating students’ learning outcomes, based on the rating-point system of gradually obtained points building up a student’s integrated learning outcomes.Results: The research findings prove that implementation of the given method can increase the motivational, multiplicative and controlling components of the learning process.Scientific novelty: The method in question is based on the new original game approach to controlling procedures and stimulation of learning motivation of the economic profile students.Practical significance: The recommended technique can intensify the incentivebased training activities both in and outside a classroom, developing thereby students’ professional and personal qualities.

  11. Applying reinforcement learning to the weapon assignment problem ...

    African Journals Online (AJOL)

    A combination of the techniques investigated and tested in this work, together with other techniques in Artificial Intelligence (AI) and modern computational techniques, may hold the key to relieving the burden of the decision-maker and aiding in better decision-making under pressure. The techniques investigated in this ...

  12. Instructional Television: Visual Production Techniques and Learning Comprehension.

    Science.gov (United States)

    Silbergleid, Michael Ian

    The purpose of this study was to determine if increasing levels of complexity in visual production techniques would increase the viewer's learning comprehension and the degree of likeness expressed for a college level instructional television program. A total of 119 mass communications students at the University of Alabama participated in the…

  13. Generating a Spanish Affective Dictionary with Supervised Learning Techniques

    Science.gov (United States)

    Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

    2016-01-01

    Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

  14. Applying Deep Learning in Medical Images: The Case of Bone Age Estimation.

    Science.gov (United States)

    Lee, Jang Hyung; Kim, Kwang Gi

    2018-01-01

    A diagnostic need often arises to estimate bone age from X-ray images of the hand of a subject during the growth period. Together with measured physical height, such information may be used as indicators for the height growth prognosis of the subject. We present a way to apply the deep learning technique to medical image analysis using hand bone age estimation as an example. Age estimation was formulated as a regression problem with hand X-ray images as input and estimated age as output. A set of hand X-ray images was used to form a training set with which a regression model was trained. An image preprocessing procedure is described which reduces image variations across data instances that are unrelated to age-wise variation. The use of Caffe, a deep learning tool is demonstrated. A rather simple deep learning network was adopted and trained for tutorial purpose. A test set distinct from the training set was formed to assess the validity of the approach. The measured mean absolute difference value was 18.9 months, and the concordance correlation coefficient was 0.78. It is shown that the proposed deep learning-based neural network can be used to estimate a subject's age from hand X-ray images, which eliminates the need for tedious atlas look-ups in clinical environments and should improve the time and cost efficiency of the estimation process.

  15. Applying active learning to supervised word sense disambiguation in MEDLINE

    Science.gov (United States)

    Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

    2013-01-01

    Objectives This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. Methods We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Results Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. Conclusions This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models. PMID:23364851

  16. Optimizing Computer Assisted Instruction By Applying Principles of Learning Theory.

    Science.gov (United States)

    Edwards, Thomas O.

    The development of learning theory and its application to computer-assisted instruction (CAI) are described. Among the early theoretical constructs thought to be important are E. L. Thorndike's concept of learning connectisms, Neal Miller's theory of motivation, and B. F. Skinner's theory of operant conditioning. Early devices incorporating those…

  17. Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises

    Science.gov (United States)

    Bone, Daniel; Goodwin, Matthew S.; Black, Matthew P.; Lee, Chi-Chun; Audhkhasi, Kartik; Narayanan, Shrikanth

    2015-01-01

    Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead…

  18. Applying Andragogical Concepts in Creating a Sustainable Lifelong Learning Society

    Science.gov (United States)

    Charungkaittikul, Suwithida; Henschke, John A.

    2017-01-01

    Today, the world is changing, re-establishing the role of education to have a developed society. This article aims to explore the practical application of Andragogy as a key element for creating a sustainable lifelong learning society, to propose strategies for developing a lifelong learning society using andragogical concepts, to enhance…

  19. Applying Distributed Learning Theory in Online Business Communication Courses.

    Science.gov (United States)

    Walker, Kristin

    2003-01-01

    Focuses on the critical use of technology in online formats that entail relatively new teaching media. Argues that distributed learning theory is valuable for teachers of online business communication courses for several reasons. Discusses the application of distributed learning theory to the teaching of business communication online. (SG)

  20. Applying active learning to supervised word sense disambiguation in MEDLINE.

    Science.gov (United States)

    Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

    2013-01-01

    This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models.

  1. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Science.gov (United States)

    Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

    2016-01-01

    Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  2. Computer-aided auscultation learning system for nursing technique instruction.

    Science.gov (United States)

    Hou, Chun-Ju; Chen, Yen-Ting; Hu, Ling-Chen; Chuang, Chih-Chieh; Chiu, Yu-Hsien; Tsai, Ming-Shih

    2008-01-01

    Pulmonary auscultation is a physical assessment skill learned by nursing students for examining the respiratory system. Generally, a sound simulator equipped mannequin is used to group teach auscultation techniques via classroom demonstration. However, nursing students cannot readily duplicate this learning environment for self-study. The advancement of electronic and digital signal processing technologies facilitates simulating this learning environment. This study aims to develop a computer-aided auscultation learning system for assisting teachers and nursing students in auscultation teaching and learning. This system provides teachers with signal recording and processing of lung sounds and immediate playback of lung sounds for students. A graphical user interface allows teachers to control the measuring device, draw lung sound waveforms, highlight lung sound segments of interest, and include descriptive text. Effects on learning lung sound auscultation were evaluated for verifying the feasibility of the system. Fifteen nursing students voluntarily participated in the repeated experiment. The results of a paired t test showed that auscultative abilities of the students were significantly improved by using the computer-aided auscultation learning system.

  3. I WHATSAPP AN IGUANA: AN ATTEMPT TO APPLY UBIQUITOUS LEARNING

    Directory of Open Access Journals (Sweden)

    Dwi Haryanti

    2017-12-01

    Full Text Available This paper aims at presenting how Whatsapp provides alternative method to enhance writing skills on personal letter for senior high school student. This method Whatsapps inspired by the concept called ubiquitous learning (U-Learning, which means ―learning supported by ICT resources held anytime, anywhere and fits the context of the learner‖ (de Sousa Monteiro et al., 2014. Participants were 40 11-th grade students in one class in Cirebon High School. A short story in the form of personal letter entitled I Wanna Iguana by Karen Kaufman Orloff was used as main learning source and the mobile version of the story was sent to the class Whatsapp group along with the reading comprehension questions and the personal letter template. The study was conducted for three weeks in the middle of the second semester year 2017. Contrary to the basic face-to-face teaching and learning, the using of Whatsapp group demonstrates how mobile technology can be fully integrated in an educational context to support students‘ learning beyond the classroom. Another advantages of using Whatsapp group is the fact that learning become the responsibility of learners and may take place through observation, trial and error, asking for help, conversing with others, reading to stories, reflecting on a one‘s personal event, or stimulated by general interests

  4. Simulation-based optimization parametric optimization techniques and reinforcement learning

    CERN Document Server

    Gosavi, Abhijit

    2003-01-01

    Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

  5. Physical Activity and Wellness: Applied Learning through Collaboration

    Science.gov (United States)

    Long, Lynn Hunt; Franzidis, Alexia

    2015-01-01

    This article describes how two university professors teamed up to initiate a university-sponsored physical activity and wellness expo in an effort to promote an authentic and transformative learning experience for preservice students.

  6. Learning Theories Applied to the Teaching of Business Communication.

    Science.gov (United States)

    Hart, Maxine Barton

    1980-01-01

    Reviews major learning theories that can be followed by business communication instructors, including those by David Ausubel, Albert Bandura, Kurt Lewin, Edward Thorndike, B.F. Skinner, and Robert Gagne. (LRA)

  7. applying reinforcement learning to the weapon assignment problem

    African Journals Online (AJOL)

    ismith

    Carlo (MC) control algorithm with exploring starts (MCES), and an off-policy ..... closest to the threat should fire (that weapon also had the highest probability to ... Monte Carlo ..... “Reinforcement learning: Theory, methods and application to.

  8. Toward accelerating landslide mapping with interactive machine learning techniques

    Science.gov (United States)

    Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

    2013-04-01

    Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also

  9. Action Research to Improve the Learning Space for Diagnostic Techniques

    Directory of Open Access Journals (Sweden)

    Ellen Ariel

    2015-08-01

    Full Text Available The module described and evaluated here was created in response to perceived learning difficulties in diagnostic test design and interpretation for students in third-year Clinical Microbiology. Previously, the activities in lectures and laboratory classes in the module fell into the lower cognitive operations of “knowledge” and “understanding.” The new approach was to exchange part of the traditional activities with elements of interactive learning, where students had the opportunity to engage in deep learning using a variety of learning styles. The effectiveness of the new curriculum was assessed by means of on-course student assessment throughout the module, a final exam, an anonymous questionnaire on student evaluation of the different activities and a focus group of volunteers. Although the new curriculum enabled a major part of the student cohort to achieve higher pass grades (p < 0.001, it did not meet the requirements of the weaker students, and the proportion of the students failing the module remained at 34%. The action research applied here provided a number of valuable suggestions from students on how to improve future curricula from their perspective. Most importantly, an interactive online program that facilitated flexibility in the learning space for the different reagents and their interaction in diagnostic tests was proposed. The methods applied to improve and assess a curriculum refresh by involving students as partners in the process, as well as the outcomes, are discussed.

  10. Action Research to Improve the Learning Space for Diagnostic Techniques.

    Science.gov (United States)

    Ariel, Ellen; Owens, Leigh

    2015-12-01

    The module described and evaluated here was created in response to perceived learning difficulties in diagnostic test design and interpretation for students in third-year Clinical Microbiology. Previously, the activities in lectures and laboratory classes in the module fell into the lower cognitive operations of "knowledge" and "understanding." The new approach was to exchange part of the traditional activities with elements of interactive learning, where students had the opportunity to engage in deep learning using a variety of learning styles. The effectiveness of the new curriculum was assessed by means of on-course student assessment throughout the module, a final exam, an anonymous questionnaire on student evaluation of the different activities and a focus group of volunteers. Although the new curriculum enabled a major part of the student cohort to achieve higher pass grades (p < 0.001), it did not meet the requirements of the weaker students, and the proportion of the students failing the module remained at 34%. The action research applied here provided a number of valuable suggestions from students on how to improve future curricula from their perspective. Most importantly, an interactive online program that facilitated flexibility in the learning space for the different reagents and their interaction in diagnostic tests was proposed. The methods applied to improve and assess a curriculum refresh by involving students as partners in the process, as well as the outcomes, are discussed. Journal of Microbiology & Biology Education.

  11. Applying reinforcement learning to the weapon assignment problem in air defence

    CSIR Research Space (South Africa)

    Mouton, H

    2011-12-01

    Full Text Available . The techniques investigated in this article were two methods from the machine-learning subfield of reinforcement learning (RL), namely a Monte Carlo (MC) control algorithm with exploring starts (MCES), and an off-policy temporal-difference (TD) learning...

  12. The Effect of Group Investigation Learning Model with Brainstroming Technique on Students Learning Outcomes

    Directory of Open Access Journals (Sweden)

    Astiti Kade kAyu

    2018-01-01

    Full Text Available This study aims to determine the effect of group investigation (GI learning model with brainstorming technique on student physics learning outcomes (PLO compared to jigsaw learning model with brainstroming technique. The learning outcome in this research are the results of learning in the cognitive domain. The method used in this research is experiment with Randomised Postest Only Control Group Design. Population in this research is all students of class XI IPA SMA Negeri 9 Kupang year lesson 2015/2016. The selected sample are 40 students of class XI IPA 1 as the experimental class and 38 students of class XI IPA 2 as the control class using simple random sampling technique. The instrument used is 13 items description test. The first hypothesis was tested by using two tailed t-test. From that, it is obtained that H0 rejected which means there are differences of students physics learning outcome. The second hypothesis was tested using one tailed t-test. It is obtained that H0 rejected which means the students PLO in experiment class were higher than control class. Based on the results of this study, researchers recommend the use of GI learning models with brainstorming techniques to improve PLO, especially in the cognitive domain.

  13. An Examination of Digital Game-Based Situated Learning Applied to Chinese Language Poetry Education

    Science.gov (United States)

    Chen, Hong-Ren; Lin, You-Shiuan

    2016-01-01

    By gradually placing more importance on game-based education and changing learning motivation by applying game-playing characteristics, students' learning experiences can be enhanced and a better learning effect can be achieved. When teaching the content of Chinese poetry in Taiwanese junior high schools, most teachers only explain the meaning of…

  14. Applying the Science of Learning: Evidence-Based Principles for the Design of Multimedia Instruction

    Science.gov (United States)

    Mayer, Richard E.

    2008-01-01

    During the last 100 years, a major accomplishment of psychology has been the development of a science of learning aimed at understanding how people learn. In attempting to apply the science of learning, a central challenge of psychology and education is the development of a science of instruction aimed at understanding how to present material in…

  15. Cooperative learning as applied to resident instruction in radiology reporting.

    Science.gov (United States)

    Mueller, Donald; Georges, Alexandra; Vaslow, Dale

    2007-12-01

    The study is designed to evaluate the effectiveness of an active form of resident instruction, cooperative learning, and the residents' response to that form of instruction. The residents dictated three sets of reports both before and after instruction in radiology reporting using the cooperative learning method. The reports were evaluated for word count, Flesch-Kincaid grade level, advancement on clinical spectrum, clarity, and comparison to prior reports. The reports were evaluated for changes in performance characteristics between the pre- and postinstruction dictations. The residents' response to this form of instruction was evaluated by means of a questionnaire. The instruction was effective in changing the resident dictations. The results became shorter (Pcooperative learning activities. The least positive responses related to the amount of time devoted to the project. Sixty-three percent of respondents stated that the time devoted to the project was appropriate. Cooperative learning can be an effective tool in the setting of the radiology residency. Instructional time requirements must be strongly considered in designing a cooperative learning program.

  16. Mobile Robot Navigation Based on Q-Learning Technique

    Directory of Open Access Journals (Sweden)

    Lazhar Khriji

    2011-03-01

    Full Text Available This paper shows how Q-learning approach can be used in a successful way to deal with the problem of mobile robot navigation. In real situations where a large number of obstacles are involved, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. In this paper, we propose a navigation approach for mobile robot, in which the prior knowledge is used within Q-learning. We address the issue of individual behavior design using fuzzy logic. The strategy of behaviors based navigation reduces the complexity of the navigation problem by dividing them in small actions easier for design and implementation. The Q-Learning algorithm is applied to coordinate between these behaviors, which make a great reduction in learning convergence times. Simulation and experimental results confirm the convergence to the desired results in terms of saved time and computational resources.

  17. Teachers and Learners’ Perceptions of Applying Translation as a Method, Strategy, or Technique in an Iranian EFL Setting

    Directory of Open Access Journals (Sweden)

    Fatemeh Mollaei

    2017-04-01

    Full Text Available It has been found that translation is an efficient means to teach/learn grammar, syntax, and lexis of a foreign language. Meanwhile, translation is good for beginners who do not still enjoy the critical level of proficiency in their target language for expression.  This study was conducted to examine the teachers and learners’ perceptions of employing translation in the foreign language classroom; i.e., the effects, merits, demerits, limitations, as well as its use as a method, strategy or technique. Both quantitative and qualitative methods were used to collect and analyze the data from graduate and undergraduate learners (n=56 and teachers (n=44, male and female, who responded to two questionnaires. Additionally, only the teachers were interviewed to gain richer insight into their perceptions and attitudes. According to the results of independent samples t-test, there was no significant difference between teachers and learners’ attitude to applying translation as a method, strategy, or technique in learning a foreign language.  Based on the interview results, some teachers believed that employing translation in the foreign language context was helpful but not constantly. They claimed that translation was only effective in teaching vocabulary and grammar apart from leaners’ proficiency level as it can clarify meaning. But some other teachers noted that mother tongue would interfere with learning foreign language; they considered translation as a time-consuming activity through which students cannot capture the exact meaning.

  18. Finding the Right Fit: Helping Students Apply Theory to Service-Learning Contexts

    Science.gov (United States)

    Ricke, Audrey

    2018-01-01

    Background: Although past studies of service-learning focus on assessing student growth, few studies address how to support students in applying theory to their service-learning experiences. Yet, the task of applying theory is a central component of critical reflections within the social sciences in higher education and often causes anxiety among…

  19. Use of the Learning together technique associated to the theory of significative learning

    Directory of Open Access Journals (Sweden)

    Ester López Donoso

    2008-09-01

    Full Text Available This article deals with an experimental research, regarding a qualitative and quantitative design, applied to a group of students of General Physics course during the first semester of the university career of Engineering. Historically, students of this course present learning difficulties that directly affect their performance, conceptualization and permanence in the university. The present methodology integrates the collaborative learning, denominated Learning Together", with the theory of significant learning to avoid the above-written difficulties. Results of this research show that the proposed methodology works properly, especially to improve the conceptualization.

  20. Beyond Astro 101: A First Report on Applying Interactive Education Techniques to an Astronphysics Class for Majors

    Science.gov (United States)

    Perrin, Marshall D.; Ghez, A. M.

    2009-05-01

    Learner-centered interactive instruction methods now have a proven track record in improving learning in "Astro 101" courses for non-majors, but have rarely been applied to higher-level astronomy courses. Can we hope for similar gains in classes aimed at astrophysics majors, or is the subject matter too fundamentally different for those techniques to apply? We present here an initial report on an updated calculus-based Introduction to Astrophysics class at UCLA that suggests such techniques can indeed result in increased learning for major students. We augmented the traditional blackboard-derivation lectures and challenging weekly problem sets by adding online questions on pre-reading assignments (''just-in-time teaching'') and frequent multiple-choice questions in class ("Think-Pair-Share''). We describe our approach, and present examples of the new Think-Pair-Share questions developed for this more sophisticated material. Our informal observations after one term are that with this approach, students are more engaged and alert, and score higher on exams than typical in previous years. This is anecdotal evidence, not hard data yet, and there is clearly a vast amount of work to be done in this area. But our first impressions strongly encourage us that interactive methods should be able improve the astrophysics major just as they have improved Astro 101.

  1. Dielectric spectroscopy technique applied to study the behaviour of irradiated polymer

    International Nuclear Information System (INIS)

    Saoud, R.; Soualmia, A.; Guerbi, C.A.; Benrekaa, N.

    2006-01-01

    Relaxation spectroscopy provides an excellent method for the study of motional processes in materials and has been widely applied to macromolecules and polymers. The technique is potentially of most interest when applied to irradiated systems. Application to the study of the structure beam-irradiated Teflon is thus an outstanding opportunity for the dielectric relaxation technique, particularly as this material exhibits clamping problems when subjected to dynamic mechanical relaxation studies. A very wide frequency range is necessary to resolve dipolar effects. In this paper, we discuss some significant results about the behavior and the modification of the structure of Teflon submitted to weak energy radiations

  2. MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques

    Directory of Open Access Journals (Sweden)

    Cerqueira Fabio R

    2012-10-01

    Full Text Available Abstract Background The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. Results Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. Conclusion Our approach not only enhances the computational performance, and

  3. Database 'catalogue of techniques applied to materials and products of nuclear engineering'

    International Nuclear Information System (INIS)

    Lebedeva, E.E.; Golovanov, V.N.; Podkopayeva, I.A.; Temnoyeva, T.A.

    2002-01-01

    The database 'Catalogue of techniques applied to materials and products of nuclear engineering' (IS MERI) was developed to provide informational support for SSC RF RIAR and other enterprises in scientific investigations. This database contains information on the techniques used at RF Minatom enterprises for reactor material properties investigation. The main purpose of this system consists in the assessment of the current status of the reactor material science experimental base for the further planning of experimental activities and methodical support improvement. (author)

  4. Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

    Science.gov (United States)

    Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

    Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

  5. The Degree of Applying E-Learning in English Departments at Al-Balqa Applied University from Instructors' Perspectives

    Science.gov (United States)

    Alzu'bi, Mohammad Akram Mohammad

    2018-01-01

    The study aimed at identifying the degree of applying e-learning in Al-Balqa Applied University from instructors' perspectives so the researcher designed a questionnaire of 20 items which is applied on a sample of 48 lecturers. The study showed that the percentage of (64.0%) out of 48 participants apply e-learning in English departments at…

  6. Applied predictive analytics principles and techniques for the professional data analyst

    CERN Document Server

    Abbott, Dean

    2014-01-01

    Learn the art and science of predictive analytics - techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive mode

  7. Data mining practical machine learning tools and techniques

    CERN Document Server

    Witten, Ian H

    2005-01-01

    As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same

  8. Using Machine Learning Techniques in the Analysis of Oceanographic Data

    Science.gov (United States)

    Falcinelli, K. E.; Abuomar, S.

    2017-12-01

    Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

  9. Efficient Software Assets for Fostering Learning in Applied Games

    NARCIS (Netherlands)

    Maurer, Matthias; Nussbaumer, Alexander; Steiner, Christina; Van der Vegt, Wim; Nadolski, Rob; Nyamsuren, Enkhbold; Albert, Dietrich

    2018-01-01

    Digital game technologies are a promising way to enable training providers to reach other target groups, namely those who are not interested in traditional learning technologies. Theoretically, through using digital game technologies we are able to foster the acquisition of any competence by

  10. Strategies and techniques of communication and public relations applied to non-profit sector

    Directory of Open Access Journals (Sweden)

    Ioana – Julieta Josan

    2010-05-01

    Full Text Available The aim of this paper is to summarize the strategies and techniques of communication and public relations applied to non-profit sector.The approach of the paper is to identify the most appropriate strategies and techniques that non-profit sector can use to accomplish its objectives, to highlight specific differences between the strategies and techniques of the profit and non-profit sectors and to identify potential communication and public relations actions in order to increase visibility among target audience, create brand awareness and to change into positive brand sentiment the target perception about the non-profit sector.

  11. Improving skill development: an exploratory study comparing a philosophical and an applied ethical analysis technique

    Science.gov (United States)

    Al-Saggaf, Yeslam; Burmeister, Oliver K.

    2012-09-01

    This exploratory study compares and contrasts two types of critical thinking techniques; one is a philosophical and the other an applied ethical analysis technique. The two techniques analyse an ethically challenging situation involving ICT that a recent media article raised to demonstrate their ability to develop the ethical analysis skills of ICT students and professionals. In particular the skill development focused on includes: being able to recognise ethical challenges and formulate coherent responses; distancing oneself from subjective judgements; developing ethical literacy; identifying stakeholders; and communicating ethical decisions made, to name a few.

  12. English Language Teachers' Perceptions on Knowing and Applying Contemporary Language Teaching Techniques

    Science.gov (United States)

    Sucuoglu, Esen

    2017-01-01

    The aim of this study is to determine the perceptions of English language teachers teaching at a preparatory school in relation to their knowing and applying contemporary language teaching techniques in their lessons. An investigation was conducted of 21 English language teachers at a preparatory school in North Cyprus. The SPSS statistical…

  13. Integrative Teaching Techniques and Improvement of German Speaking Learning Skills

    Science.gov (United States)

    Litualy, Samuel Jusuf

    2016-01-01

    This research ist a Quasi-Experimental research which only applied to one group without comparison group. It aims to prove whether the implementation of integrative teaching technique has influenced the speaking skill of the students in German Education Study Program of FKIP, Pattimura University. The research was held in the German Education…

  14. Applying CIPP Model for Learning-Object Management

    Science.gov (United States)

    Morgado, Erla M. Morales; Peñalvo, Francisco J. García; Martín, Carlos Muñoz; Gonzalez, Miguel Ángel Conde

    Although knowledge management process needs to receive some evaluation in order to determine their suitable functionality. There is not a clear definition about the stages where LOs need to be evaluated and the specific metrics to continuously promote their quality. This paper presents a proposal for LOs evaluation during their management for e-learning systems. To achieve this, we suggest specific steps for LOs design, implementation and evaluation into the four stages proposed by CIPP model (Context, Input, Process, Product).

  15. FísicActiva: applying active learning strategies to a large engineering lecture

    Science.gov (United States)

    Auyuanet, Adriana; Modzelewski, Helena; Loureiro, Silvia; Alessandrini, Daniel; Míguez, Marina

    2018-01-01

    This paper presents and analyses the results obtained by applying Active Learning techniques in overcrowded Physics lectures at the University of the Republic, Uruguay. The course referred to is Physics 1, the first Physics course that all students of the Faculty of Engineering take in their first semester for all the Engineering-related careers. Qualitative and quantitative data corresponding to three semesters are shown and discussed, indicating that the students that attended these lectures outperformed the students that followed the course in the traditional way: the pass rates increased, whereas the failure rates decreased. The students highly valued this methodology, in particular, the interactive and relaxed dynamics, highlighting the concern of professors to answer questions by means of new questions so as to promote reasoning. The results obtained point to a work path that deserves to be deepened and extended to other Engineering courses.

  16. [Digital learning object for diagnostic reasoning in nursing applied to the integumentary system].

    Science.gov (United States)

    da Costa, Cecília Passos Vaz; Luz, Maria Helena Barros Araújo

    2015-12-01

    To describe the creation of a digital learning object for diagnostic reasoning in nursing applied to the integumentary system at a public university of Piaui. A methodological study applied to technological production based on the pedagogical framework of problem-based learning. The methodology for creating the learning object observed the stages of analysis, design, development, implementation and evaluation recommended for contextualized instructional design. The revised taxonomy of Bloom was used to list the educational goals. The four modules of the developed learning object were inserted into the educational platform Moodle. The theoretical assumptions allowed the design of an important online resource that promotes effective learning in the scope of nursing education. This study should add value to nursing teaching practices through the use of digital learning objects for teaching diagnostic reasoning applied to skin and skin appendages.

  17. Digital learning object for diagnostic reasoning in nursing applied to the integumentary system

    Directory of Open Access Journals (Sweden)

    Cecília Passos Vaz da Costa

    Full Text Available Objective: To describe the creation of a digital learning object for diagnostic reasoning in nursing applied to the integumentary system at a public university of Piaui. Method: A methodological study applied to technological production based on the pedagogical framework of problem-based learning. The methodology for creating the learning object observed the stages of analysis, design, development, implementation and evaluation recommended for contextualized instructional design. The revised taxonomy of Bloom was used to list the educational goals. Results: The four modules of the developed learning object were inserted into the educational platform Moodle. The theoretical assumptions allowed the design of an important online resource that promotes effective learning in the scope of nursing education. Conclusion: This study should add value to nursing teaching practices through the use of digital learning objects for teaching diagnostic reasoning applied to skin and skin appendages.

  18. Improvement technique of sensitized HAZ by GTAW cladding applied to a BWR power plant

    International Nuclear Information System (INIS)

    Tujimura, Hiroshi; Tamai, Yasumasa; Furukawa, Hideyasu; Kurosawa, Kouichi; Chiba, Isao; Nomura, Keiichi.

    1995-01-01

    A SCC(Stress Corrosion Cracking)-resistant technique, in which the sleeve installed by expansion is melted by GTAW process without filler metal with outside water cooling, was developed. The technique was applied to ICM (In-Core Monitor) housings of a BWR power plant in 1993. The ICM housings of which materials are type 304 Stainless Steels are sensitized with high tensile residual stresses by welding to the RPV (Reactor Pressure Vessel). As the result, ICM housings have potential of SCC initiation. Therefore, the improvement technique resistant to SCC was needed. The technique can improve chemical composition of the housing inside and residual stresses of the housing outside at the same time. Sensitization of the housing inner surface area is eliminated by replacing low-carbon with proper-ferrite microstructure clad. High tensile residual stresses of housing outside surface area is improved into compressive side. Compressive stresses of outside surface are induced by thermal stresses which are caused by inside cladding with outside water cooling. The clad is required to be low-carbon metal with proper ferrite and not to have the new sensitized HAZ (Heat Affected Zone) on the surface by cladding. The effect of the technique was qualified by SCC test, chemical composition check, ferrite content measurement and residual stresses measurement etc. All equipment for remote application were developed and qualified, too. The technique was successfully applied to a BWR plant after sufficient training

  19. Applied Drama and the Higher Education Learning Spaces: A Reflective Analysis

    Science.gov (United States)

    Moyo, Cletus

    2015-01-01

    This paper explores Applied Drama as a teaching approach in Higher Education learning spaces. The exploration takes a reflective analysis approach by first examining the impact that Applied Drama has had on my career as a Lecturer/Educator/Teacher working in Higher Education environments. My engagement with Applied Drama practice and theory is…

  20. The impact of applying product-modelling techniques in configurator projects

    DEFF Research Database (Denmark)

    Hvam, Lars; Kristjansdottir, Katrin; Shafiee, Sara

    2018-01-01

    This paper aims to increase understanding of the impact of using product-modelling techniques to structure and formalise knowledge in configurator projects. Companies that provide customised products increasingly apply configurators in support of sales and design activities, reaping benefits...... that include shorter lead times, improved quality of specifications and products, and lower overall product costs. The design and implementation of configurators are a challenging task that calls for scientifically based modelling techniques to support the formal representation of configurator knowledge. Even...... the phenomenon model and information model are considered visually, (2) non-UML-based modelling techniques, in which only the phenomenon model is considered and (3) non-formal modelling techniques. This study analyses the impact to companies from increased availability of product knowledge and improved control...

  1. Applied potential tomography. A new noninvasive technique for measuring gastric emptying

    International Nuclear Information System (INIS)

    Avill, R.; Mangnall, Y.F.; Bird, N.C.; Brown, B.H.; Barber, D.C.; Seagar, A.D.; Johnson, A.G.; Read, N.W.

    1987-01-01

    Applied potential tomography is a new, noninvasive technique that yields sequential images of the resistivity of gastric contents after subjects have ingested a liquid or semisolid meal. This study validates the technique as a means of measuring gastric emptying. Experiments in vitro showed an excellent correlation between measurements of resistivity and either the square of the radius of a glass rod or the volume of water in a spherical balloon when both were placed in an oval tank containing saline. Altering the lateral position of the rod in the tank did not alter the values obtained. Images of abdominal resistivity were also directly correlated with the volume of air in a gastric balloon. Profiles of gastric emptying of liquid meals obtained using applied potential tomography were very similar to those obtained using scintigraphy or dye dilution techniques, provided that acid secretion was inhibited by cimetidine. Profiles of emptying of a mashed potato meal using applied potential tomography were also very similar to those obtained by scintigraphy. Measurements of the emptying of a liquid meal from the stomach were reproducible if acid secretion was inhibited by cimetidine. Thus, applied potential tomography is an accurate and reproducible method of measuring gastric emptying of liquids and particulate food. It is inexpensive, well tolerated, easy to use, and ideally suited for multiple studies in patients, even those who are pregnant

  2. Applied potential tomography. A new noninvasive technique for measuring gastric emptying

    Energy Technology Data Exchange (ETDEWEB)

    Avill, R.; Mangnall, Y.F.; Bird, N.C.; Brown, B.H.; Barber, D.C.; Seagar, A.D.; Johnson, A.G.; Read, N.W.

    1987-04-01

    Applied potential tomography is a new, noninvasive technique that yields sequential images of the resistivity of gastric contents after subjects have ingested a liquid or semisolid meal. This study validates the technique as a means of measuring gastric emptying. Experiments in vitro showed an excellent correlation between measurements of resistivity and either the square of the radius of a glass rod or the volume of water in a spherical balloon when both were placed in an oval tank containing saline. Altering the lateral position of the rod in the tank did not alter the values obtained. Images of abdominal resistivity were also directly correlated with the volume of air in a gastric balloon. Profiles of gastric emptying of liquid meals obtained using applied potential tomography were very similar to those obtained using scintigraphy or dye dilution techniques, provided that acid secretion was inhibited by cimetidine. Profiles of emptying of a mashed potato meal using applied potential tomography were also very similar to those obtained by scintigraphy. Measurements of the emptying of a liquid meal from the stomach were reproducible if acid secretion was inhibited by cimetidine. Thus, applied potential tomography is an accurate and reproducible method of measuring gastric emptying of liquids and particulate food. It is inexpensive, well tolerated, easy to use, and ideally suited for multiple studies in patients, even those who are pregnant.

  3. Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning

    Science.gov (United States)

    Lu, Owen H. T.; Huang, Anna Y. Q.; Huang, Jeff C. H.; Lin, Albert J. Q.; Ogata, Hiroaki; Yang, Stephen J. H.

    2018-01-01

    Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and is a part of our Precision education used to analyze…

  4. [Learning from errors: applying aviation safety concepts to medicine].

    Science.gov (United States)

    Sommer, K-J

    2012-11-01

    Health care safety levels range below other complex industries. Civil aviation has throughout its history developed methods and concepts that have made the airplane into one of the safest means of mass transport. Key elements are accident investigations that focus on cause instead of blame, human-centered design of machinery and processes, continuous training of all personnel and a shared safety culture. These methods and concepts can basically be applied to medicine which has successfully been achieved in certain areas, however, a comprehensive implementation remains to be completed. This applies particularly to including the topic of safety into relevant curricula. Physicians are obliged by the oath"primum nil nocere" to act, but economic as well as political pressure will eventually confine professional freedom if initiative is not taken soon.

  5. Optimization technique applied to interpretation of experimental data and research of constitutive laws

    International Nuclear Information System (INIS)

    Grossette, J.C.

    1982-01-01

    The feasibility of identification technique applied to one dimensional numerical analysis of the split-Hopkinson pressure bar experiment is proven. A general 1-D elastic-plastic-viscoplastic computer program was written down so as to give an adequate solution for elastic-plastic-viscoplastic response of a pressure bar subjected to a general Heaviside step loading function in time which is applied over one end of the bar. Special emphasis is placed on the response of the specimen during the first microseconds where no equilibrium conditions can be stated. During this transient phase discontinuity conditions related to wave propagation are encountered and must be carefully taken into account. Having derived an adequate numerical model, then Pontryagin identification technique has been applied in such a way that the unknowns are physical parameters. The solutions depend mainly on the selection of a class of proper eigen objective functionals (cost functions) which may be combined so as to obtain a convenient numerical objective function. A number of significant questions arising in the choice of parameter adjustment algorithms are discussed. In particular, this technique leads to a two point boundary value problem which has been solved using an iterative gradient like technique usually referred to as a double operator gradient method. This method combines the classical Fletcher-Powell technique and a partial quadratic technique with an automatic parameter step size selection. This method is much more efficient than usual ones. Numerical experimentation with simulated data was performed to test the accuracy and stability of the identification algorithm and to determine the most adequate type and quantity of data for estimation purposes

  6. Investigation of the shear bond strength to dentin of universal adhesives applied with two different techniques

    Directory of Open Access Journals (Sweden)

    Elif Yaşa

    2017-09-01

    Full Text Available Objective: The aim of this study was to evaluate the shear bond strength of universal adhesives applied with self-etch and etch&rinse techniques to dentin. Materials and Method: Fourty-eight sound extracted human third molars were used in this study. Occlusal enamel was removed in order to expose the dentinal surface, and the surface was flattened. Specimens were randomly divided into four groups and were sectioned vestibulo-lingually using a diamond disc. The universal adhesives: All Bond Universal (Group 1a and 1b, Gluma Bond Universal (Group 2a and 2b and Single Bond Universal (Group 3a and 3b were applied onto the tooth specimens either with self-etch technique (a or with etch&rinse technique (b according to the manufacturers’ instructions. Clearfil SE Bond (Group 4a; self-etch and Optibond FL (Group 4b; etch&rinse were used as control groups. Then the specimens were restored with a nanohybrid composite resin (Filtek Z550. After thermocycling, shear bond strength test was performed with a universal test machine at a crosshead speed of 0.5 mm/min. Fracture analysis was done under a stereomicroscope (×40 magnification. Data were analyzed using two-way ANOVA and post-hoc Tukey tests. Results: Statistical analysis showed significant differences in shear bond strength values between the universal adhesives (p<0.05. Significantly higher bond strength values were observed in self-etch groups (a in comparison to etch&rinse groups (b (p<0.05. Among all groups, Single Bond Universal showed the greatest shear bond strength values, whereas All Bond Universal showed the lowest shear bond strength values with both application techniques. Conclusion: Dentin bonding strengths of universal adhesives applied with different techniques may vary depending on the adhesive material. For the universal bonding agents tested in this study, the etch&rinse technique negatively affected the bond strength to dentin.

  7. Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

    Directory of Open Access Journals (Sweden)

    Shuibo Hu

    2018-03-01

    Full Text Available The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs, in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD high performance liquid chromatography (HPLC database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS, artificial neural networks (ANN, support vector machine (SVM and random forests (RF, and feature selection techniques, including genetic algorithm (GA, successive projection algorithm (SPA and recursive feature elimination based on support vector machine (SVM-RFE, for inferring PSCs from remote sensing data. Results showed that: (1 SVM-RFE worked better in selecting sensitive features; (2 RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3 machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4 sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5 the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing.

  8. Modelling the effects of the sterile insect technique applied to Eldana saccharina Walker in sugarcane

    Directory of Open Access Journals (Sweden)

    L Potgieter

    2012-12-01

    Full Text Available A mathematical model is formulated for the population dynamics of an Eldana saccharina Walker infestation of sugarcane under the influence of partially sterile released insects. The model describes the population growth of and interaction between normal and sterile E.saccharina moths in a temporally variable, but spatially homogeneous environment. The model consists of a deterministic system of difference equations subject to strictly positive initial data. The primary objective of this model is to determine suitable parameters in terms of which the above population growth and interaction may be quantified and according to which E.saccharina infestation levels and the associated sugarcane damage may be measured. Although many models have been formulated in the past describing the sterile insect technique, few of these models describe the technique for Lepidopteran species with more than one life stage and where F1-sterility is relevant. In addition, none of these models consider the technique when fully sterile females and partially sterile males are being released. The model formulated is also the first to describe the technique applied specifically to E.saccharina, and to consider the economic viability of applying the technique to this species. Pertinent decision support is provided to farm managers in terms of the best timing for releases, release ratios and release frequencies.

  9. Microscale and nanoscale strain mapping techniques applied to creep of rocks

    Science.gov (United States)

    Quintanilla-Terminel, Alejandra; Zimmerman, Mark E.; Evans, Brian; Kohlstedt, David L.

    2017-07-01

    Usually several deformation mechanisms interact to accommodate plastic deformation. Quantifying the contribution of each to the total strain is necessary to bridge the gaps from observations of microstructures, to geomechanical descriptions, to extrapolating from laboratory data to field observations. Here, we describe the experimental and computational techniques involved in microscale strain mapping (MSSM), which allows strain produced during high-pressure, high-temperature deformation experiments to be tracked with high resolution. MSSM relies on the analysis of the relative displacement of initially regularly spaced markers after deformation. We present two lithography techniques used to pattern rock substrates at different scales: photolithography and electron-beam lithography. Further, we discuss the challenges of applying the MSSM technique to samples used in high-temperature and high-pressure experiments. We applied the MSSM technique to a study of strain partitioning during creep of Carrara marble and grain boundary sliding in San Carlos olivine, synthetic forsterite, and Solnhofen limestone at a confining pressure, Pc, of 300 MPa and homologous temperatures, T/Tm, of 0.3 to 0.6. The MSSM technique works very well up to temperatures of 700 °C. The experimental developments described here show promising results for higher-temperature applications.

  10. Predicting breast screening attendance using machine learning techniques.

    Science.gov (United States)

    Baskaran, Vikraman; Guergachi, Aziz; Bali, Rajeev K; Naguib, Raouf N G

    2011-03-01

    Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale.

  11. Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

    OpenAIRE

    Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

    2015-01-01

    Machine learning techniques can be used to analyse data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful in terms of software bug prediction. In this paper, a comparative performance analysis of different machine learning techniques is explored f or software bug prediction on public available data sets. Results showed most of the mac ...

  12. Renormalization techniques applied to the study of density of states in disordered systems

    International Nuclear Information System (INIS)

    Ramirez Ibanez, J.

    1985-01-01

    A general scheme for real space renormalization of formal scattering theory is presented and applied to the calculation of density of states (DOS) in some finite width systems. This technique is extended in a self-consistent way, to the treatment of disordered and partially ordered chains. Numerical results of moments and DOS are presented in comparison with previous calculations. In addition, a self-consistent theory for the magnetic order problem in a Hubbard chain is derived and a parametric transition is observed. Properties of localization of the electronic states in disordered chains are studied through various decimation averaging techniques and using numerical simulations. (author) [pt

  13. Applying Data Mining Techniques to Improve Information Security in the Cloud: A Single Cache System Approach

    OpenAIRE

    Amany AlShawi

    2016-01-01

    Presently, the popularity of cloud computing is gradually increasing day by day. The purpose of this research was to enhance the security of the cloud using techniques such as data mining with specific reference to the single cache system. From the findings of the research, it was observed that the security in the cloud could be enhanced with the single cache system. For future purposes, an Apriori algorithm can be applied to the single cache system. This can be applied by all cloud providers...

  14. Revitalizing pathology laboratories in a gastrointestinal pathophysiology course using multimedia and team-based learning techniques.

    Science.gov (United States)

    Carbo, Alexander R; Blanco, Paola G; Graeme-Cooke, Fiona; Misdraji, Joseph; Kappler, Steven; Shaffer, Kitt; Goldsmith, Jeffrey D; Berzin, Tyler; Leffler, Daniel; Najarian, Robert; Sepe, Paul; Kaplan, Jennifer; Pitman, Martha; Goldman, Harvey; Pelletier, Stephen; Hayward, Jane N; Shields, Helen M

    2012-05-15

    In 2008, we changed the gastrointestinal pathology laboratories in a gastrointestinal pathophysiology course to a more interactive format using modified team-based learning techniques and multimedia presentations. The results were remarkably positive and can be used as a model for pathology laboratory improvement in any organ system. Over a two-year period, engaging and interactive pathology laboratories were designed. The initial restructuring of the laboratories included new case material, Digital Atlas of Video Education Project videos, animations and overlays. Subsequent changes included USMLE board-style quizzes at the beginning of each laboratory, with individual readiness assessment testing and group readiness assessment testing, incorporation of a clinician as a co-teacher and role playing for the student groups. Student responses for pathology laboratory contribution to learning improved significantly compared to baseline. Increased voluntary attendance at pathology laboratories was observed. Spontaneous student comments noted the positive impact of the laboratories on their learning. Pathology laboratory innovations, including modified team-based learning techniques with individual and group self-assessment quizzes, multimedia presentations, and paired teaching by a pathologist and clinical gastroenterologist led to improvement in student perceptions of pathology laboratory contributions to their learning and better pathology faculty evaluations. These changes can be universally applied to other pathology laboratories to improve student satisfaction. Copyright © 2012 Elsevier GmbH. All rights reserved.

  15. Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

    Science.gov (United States)

    Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

    2015-08-01

    Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Applying machine learning to predict patient-specific current CD4 ...

    African Journals Online (AJOL)

    Apple apple

    This work shows the application of machine learning to predict current CD4 cell count of an HIV- .... Pre-processing ... remaining data elements of the PR and RT datasets. ... technique based on the structure of the human brain's neuron.

  17. Position Paper: Applying Machine Learning to Software Analysis to Achieve Trusted, Repeatable Scientific Computing

    Energy Technology Data Exchange (ETDEWEB)

    Prowell, Stacy J [ORNL; Symons, Christopher T [ORNL

    2015-01-01

    Producing trusted results from high-performance codes is essential for policy and has significant economic impact. We propose combining rigorous analytical methods with machine learning techniques to achieve the goal of repeatable, trustworthy scientific computing.

  18. Deep Learning Techniques for Top-Quark Reconstruction

    CERN Document Server

    Naderi, Kiarash

    2017-01-01

    Top quarks are unique probes of the standard model (SM) predictions and have the potential to be a window for physics beyond the SM (BSM). Top quarks decay to a $Wb$ pair, and the $W$ can decay in leptons or jets. In a top pair event, assigning jets to their correct source is a challenge. In this study, I studied different methods for improving top reconstruction. The main motivation was to use Deep Learning Techniques in order to enhance the precision of top reconstruction.

  19. Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation

    Science.gov (United States)

    Qiu, Yuchen; Lu, Xianglan; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Li, Shibo; Liu, Hong; Zheng, Bin

    2016-03-01

    Automated high throughput scanning microscopy is a fast developing screening technology used in cytogenetic laboratories for the diagnosis of leukemia or other genetic diseases. However, one of the major challenges of using this new technology is how to efficiently detect the analyzable metaphase chromosomes during the scanning process. The purpose of this investigation is to develop a computer aided detection (CAD) scheme based on deep learning technology, which can identify the metaphase chromosomes with high accuracy. The CAD scheme includes an eight layer neural network. The first six layers compose of an automatic feature extraction module, which has an architecture of three convolution-max-pooling layer pairs. The 1st, 2nd and 3rd pair contains 30, 20, 20 feature maps, respectively. The seventh and eighth layers compose of a multiple layer perception (MLP) based classifier, which is used to identify the analyzable metaphase chromosomes. The performance of new CAD scheme was assessed by receiver operation characteristic (ROC) method. A number of 150 regions of interest (ROIs) were selected to test the performance of our new CAD scheme. Each ROI contains either interphase cell or metaphase chromosomes. The results indicate that new scheme is able to achieve an area under the ROC curve (AUC) of 0.886+/-0.043. This investigation demonstrates that applying a deep learning technique may enable to significantly improve the accuracy of the metaphase chromosome detection using a scanning microscopic imaging technology in the future.

  20. Development of technique to apply induction heating stress improvement to recirculation inlet nozzle

    International Nuclear Information System (INIS)

    Chiba, Kunihiko; Nihei, Kenichi; Ootaka, Minoru

    2009-01-01

    Stress corrosion cracking (SCC) have been found in the primary loop recirculation (PLR) systems of boiling water reactors (BWR). Residual stress in welding heat-affected zone is one of the factors of SCC, and the residual stress improvement is one of the most effective methods to prevent SCC. Induction heating stress improvement (IHSI) is one of the techniques to improve reduce residual stress. However, it is difficult to apply IHSI to the place such as the recirculation inlet nozzle where the flow stagnates. In this present study, the technique to apply IHSI to the recirculation inlet nozzle was developed using water jet which blowed into the crevice between the nozzle safe end and the thermal sleeve. (author)

  1. Evaluation of Economic Merger Control Techniques Applied to the European Electricity Sector

    International Nuclear Information System (INIS)

    Vandezande, Leen; Meeus, Leonardo; Delvaux, Bram; Van Calster, Geert; Belmans, Ronnie

    2006-01-01

    With European electricity markets not yet functioning on a competitive basis and consolidation increasing, the European Commission has said it intends to more intensively apply competition law in the electricity sector. Yet economic techniques and theories used in EC merger control fail to take sufficiently into account some specific features of electricity markets. The authors offer suggestions to enhance their reliability and applicability in the electricity sector. (author)

  2. Just-in-Time techniques as applied to hazardous materials management

    OpenAIRE

    Spicer, John S.

    1996-01-01

    Approved for public release; distribution is unlimited This study investigates the feasibility of integrating JIT techniques in the context of hazardous materials management. This study provides a description of JIT, a description of environmental compliance issues and the outgrowth of related HAZMAT policies, and a broad perspective on strategies for applying JIT to HAZMAT management. http://archive.org/details/justintimetechn00spic Lieutenant Commander, United States Navy

  3. A mixed methods evaluation of team-based learning for applied pathophysiology in undergraduate nursing education.

    Science.gov (United States)

    Branney, Jonathan; Priego-Hernández, Jacqueline

    2018-02-01

    It is important for nurses to have a thorough understanding of the biosciences such as pathophysiology that underpin nursing care. These courses include content that can be difficult to learn. Team-based learning is emerging as a strategy for enhancing learning in nurse education due to the promotion of individual learning as well as learning in teams. In this study we sought to evaluate the use of team-based learning in the teaching of applied pathophysiology to undergraduate student nurses. A mixed methods observational study. In a year two, undergraduate nursing applied pathophysiology module circulatory shock was taught using Team-based Learning while all remaining topics were taught using traditional lectures. After the Team-based Learning intervention the students were invited to complete the Team-based Learning Student Assessment Instrument, which measures accountability, preference and satisfaction with Team-based Learning. Students were also invited to focus group discussions to gain a more thorough understanding of their experience with Team-based Learning. Exam scores for answers to questions based on Team-based Learning-taught material were compared with those from lecture-taught material. Of the 197 students enrolled on the module, 167 (85% response rate) returned the instrument, the results from which indicated a favourable experience with Team-based Learning. Most students reported higher accountability (93%) and satisfaction (92%) with Team-based Learning. Lectures that promoted active learning were viewed as an important feature of the university experience which may explain the 76% exhibiting a preference for Team-based Learning. Most students wanted to make a meaningful contribution so as not to let down their team and they saw a clear relevance between the Team-based Learning activities and their own experiences of teamwork in clinical practice. Exam scores on the question related to Team-based Learning-taught material were comparable to those

  4. [Learning experience of acupuncture technique from professor ZHANG Jin].

    Science.gov (United States)

    Xue, Hongsheng; Zhang, Jin

    2017-08-12

    As a famous acupuncturist in the world, professor ZHANG Jin believes the key of acupuncture technique is the use of force, and the understanding of the "concentrating the force into needle body" is essential to understand the essence of acupuncture technique. With deep study of Huangdi Neijing ( The Inner Canon of Huangdi ) and Zhenjiu Dacheng ( Compendium of Acupuncture and Moxibustion ), the author further learned professor ZHANG Jin 's theory and operation specification of "concentrating force into needle body, so the force arriving before and together with needle". The whole-body force should be subtly focused on the tip of needle, and gentle force at tip of needle could get significant reinforcing and reducing effect. In addition, proper timing at tip of needle could start reinforcing and reducing effect, lead qi to disease location, and achieve superior clinical efficacy.

  5. Applying Cultural Project Based Learning to Develop Students’ Academic Writing

    Directory of Open Access Journals (Sweden)

    Lulus Irawati

    2015-06-01

    Full Text Available Writing is considered to be the most demanding and difficult skill for many college students, since there are some steps to be followed such as prewriting, drafting, editing, revising and publishing. The interesting topic like culture including lifestyle, costume, and custom is necessary to be offered in Academic Writing class. Accordingly, this article aims to elaborate the application of a cultural project based learning to develop students’ ability in academic writing. This descriptive qualitative research was conducted in Academic Writing class consisting of 20 students of the fourth semester. The students were divided into some groups, each consisting of 4-5 people assigned to make a cultural project within 6 weeks, in the form of essay. Each member of the groups has to create his/ her own essay and then compile the essays to be a mini-journal. Therefore, one group has one mini-journal consisting of 4-5 essays. To check the content of mini-journal, the lecturer also asked the groups to present in front of the class to get some suggestions, feedback, or comments.

  6. Learning outcomes and effective communication techniques for hazard recognition learning programmes in the transportation thrust area.

    CSIR Research Space (South Africa)

    Krige, PD

    2001-12-01

    Full Text Available on South African mines ............................................ 32 4.3 People development and training techniques associated with confidence, attitudes and leadership............................................ 34 Page 4 4.4 Recommended learning... to rules and procedures, safety commitment of management, supervision style, organising for safety, equipment design and maintenance. Only the last two are engineering issues. The trend is clear. Improvements in engineering design have significantly...

  7. E-Learning System Using Segmentation-Based MR Technique for Learning Circuit Construction

    Science.gov (United States)

    Takemura, Atsushi

    2016-01-01

    This paper proposes a novel e-Learning system using the mixed reality (MR) technique for technical experiments involving the construction of electronic circuits. The proposed system comprises experimenters' mobile computers and a remote analysis system. When constructing circuits, each learner uses a mobile computer to transmit image data from the…

  8. A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets

    Science.gov (United States)

    Voß, Lydia; Schatten, Carlotta; Mazziotti, Claudia; Schmidt-Thieme, Lars

    2015-01-01

    Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL),…

  9. An Investigation of Employees' Use of E-Learning Systems: Applying the Technology Acceptance Model

    Science.gov (United States)

    Lee, Yi-Hsuan; Hsieh, Yi-Chuan; Chen, Yen-Hsun

    2013-01-01

    The purpose of this study is to apply the technology acceptance model to examine the employees' attitudes and acceptance of electronic learning (e-learning) systems in organisations. This study examines four factors (organisational support, computer self-efficacy, prior experience and task equivocality) that are believed to influence employees'…

  10. A Delphi Study on Technology Enhanced Learning (TEL) Applied on Computer Science (CS) Skills

    Science.gov (United States)

    Porta, Marcela; Mas-Machuca, Marta; Martinez-Costa, Carme; Maillet, Katherine

    2012-01-01

    Technology Enhanced Learning (TEL) is a new pedagogical domain aiming to study the usage of information and communication technologies to support teaching and learning. The following study investigated how this domain is used to increase technical skills in Computer Science (CS). A Delphi method was applied, using three-rounds of online survey…

  11. Problems and advantages of applying the e-learning model to the teaching of English

    OpenAIRE

    Shaparenko, А.; Golikova, А.

    2013-01-01

    In this article we mention some potential and noted problems and advantages of applying the e-learning model to the teaching of English. In the area of foreign language teaching a lot has been done, but there are constant attempts for new solutions. Another option for e-learning is a hybrid course.

  12. The digital geometric phase technique applied to the deformation evaluation of MEMS devices

    International Nuclear Information System (INIS)

    Liu, Z W; Xie, H M; Gu, C Z; Meng, Y G

    2009-01-01

    Quantitative evaluation of the structure deformation of microfabricated electromechanical systems is of importance for the design and functional control of microsystems. In this investigation, a novel digital geometric phase technique was developed to meet the deformation evaluation requirement of microelectromechanical systems (MEMS). The technique is performed on the basis of regular artificial lattices, instead of a natural atom lattice. The regular artificial lattices with a pitch ranging from micrometer to nanometer will be directly fabricated on the measured surface of MEMS devices by using a focused ion beam (FIB). Phase information can be obtained from the Bragg filtered images after fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) of the scanning electronic microscope (SEM) images. Then the in-plane displacement field and the local strain field related to the phase information will be evaluated. The obtained results show that the technique can be well applied to deformation measurement with nanometer sensitivity and stiction force estimation of a MEMS device

  13. Applying a Problem Based Learning Approach to Land Management Education

    DEFF Research Database (Denmark)

    Enemark, Stig

    Land management covers a wide range activities associated with the management of land and natural resources that are required to fulfil political objectives and achieve sustainable development. This paper presents an overall understanding of the land management paradigm and the benefits of good...... land governance to society. A land administration system provides a country with the infrastructure to implement land-related policies and land management strategies. By applying this land management profile to surveying education, this paper suggests that there is a need to move away from an exclusive...... engineering focus toward adopting an interdisciplinary and problem-based approach to ensure that academic programmes can cope with the wide range of land administration functions and challenges. An interdisciplinary approach to surveying education calls for the need to address issues and problems in a real...

  14. Meta-learning framework applied in bioinformatics inference system design.

    Science.gov (United States)

    Arredondo, Tomás; Ormazábal, Wladimir

    2015-01-01

    This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

  15. Evaluation of irradiation damage effect by applying electric properties based techniques

    International Nuclear Information System (INIS)

    Acosta, B.; Sevini, F.

    2004-01-01

    The most important effect of the degradation by radiation is the decrease in the ductility of the pressure vessel of the reactor (RPV) ferritic steels. The main way to determine the mechanical behaviour of the RPV steels is tensile and impact tests, from which the ductile to brittle transition temperature (DBTT) and its increase due to neutron irradiation can be calculated. These tests are destructive and regularly applied to surveillance specimens to assess the integrity of RPV. The possibility of applying validated non-destructive ageing monitoring techniques would however facilitate the surveillance of the materials that form the reactor vessel. The JRC-IE has developed two devices, focused on the measurement of the electrical properties to assess non-destructively the embrittlement state of materials. The first technique, called Seebeck and Thomson Effects on Aged Material (STEAM), is based on the measurement of the Seebeck coefficient, characteristic of the material and related to the microstructural changes induced by irradiation embrittlement. With the same aim the second technique, named Resistivity Effects on Aged Material (REAM), measures instead the resistivity of the material. The purpose of this research is to correlate the results of the impact tests, STEAM and REAM measurements with the change in the mechanical properties due to neutron irradiation. These results will make possible the improvement of such techniques based on the measurement of material electrical properties for their application to the irradiation embrittlement assessment

  16. Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

    OpenAIRE

    Sugiarto Indar; Pasila Felix

    2018-01-01

    Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still scept...

  17. Neuroscience applied to learning alphabetic principles: new proposals

    Directory of Open Access Journals (Sweden)

    Leonor Scliar-Cabral

    2012-12-01

    Full Text Available http://dx.doi.org/10.5007/2175-8026.2012n63p187   This study reviews recent data on functional illiteracy and advances on neuroscience about the reading process. alarming figures on functional illiteracy will be presented with examples of UK and brazil. Empirical evidences brought by neuroscience prove the neuropsychological basis constructs, namely invariance already claimed by modern linguistics. however, emphasis will be given to the psychological reality of letters’ feature invariance, demonstrated by various experiments which had been recently run by neuroscientists. two types of invariance are shown, the spatial and the font invariance, exemplified by a description of invariant features of the roman alphabet. We then cite the major difficulties faced at by beginning readers, namely, how to dismember the chain speech into words (separated in the written space by blanks and the syllable into its units, in order to link them to their correspondent graphemes (composed by one or more letters.  in addition, one of the major difficulties is how to teach neurons to dissymmetrize the letter features. neuroscience conclusions from experiments about the readingprocess demonstrate that neurons of the  occipito-temporalventral region of the left hemisphere must be recycled in orderto learn how to recognize the written word.  altogether withthe results obtained on a well succeeded experience run by theprogram Early intervention initiative (Eii and by an experimentrun in a Florianopolis school, in 2012, they give support to thestrategies to prevent functional illiteracy.

  18. New Techniques for Deep Learning with Geospatial Data using TensorFlow, Earth Engine, and Google Cloud Platform

    Science.gov (United States)

    Hancher, M.

    2017-12-01

    Recent years have seen promising results from many research teams applying deep learning techniques to geospatial data processing. In that same timeframe, TensorFlow has emerged as the most popular framework for deep learning in general, and Google has assembled petabytes of Earth observation data from a wide variety of sources and made them available in analysis-ready form in the cloud through Google Earth Engine. Nevertheless, developing and applying deep learning to geospatial data at scale has been somewhat cumbersome to date. We present a new set of tools and techniques that simplify this process. Our approach combines the strengths of several underlying tools: TensorFlow for its expressive deep learning framework; Earth Engine for data management, preprocessing, postprocessing, and visualization; and other tools in Google Cloud Platform to train TensorFlow models at scale, perform additional custom parallel data processing, and drive the entire process from a single familiar Python development environment. These tools can be used to easily apply standard deep neural networks, convolutional neural networks, and other custom model architectures to a variety of geospatial data structures. We discuss our experiences applying these and related tools to a range of machine learning problems, including classic problems like cloud detection, building detection, land cover classification, as well as more novel problems like illegal fishing detection. Our improved tools will make it easier for geospatial data scientists to apply modern deep learning techniques to their own problems, and will also make it easier for machine learning researchers to advance the state of the art of those techniques.

  19. Distance Learning

    National Research Council Canada - National Science Library

    Braddock, Joseph

    1997-01-01

    A study reviewing the existing Army Distance Learning Plan (ADLP) and current Distance Learning practices, with a focus on the Army's training and educational challenges and the benefits of applying Distance Learning techniques...

  20. eLearning techniques supporting problem based learning in clinical simulation.

    Science.gov (United States)

    Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

    2005-08-01

    This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

  1. Personal Learning Environments (PLEs) in a Distance Learning Course on Mathematics Applied to Business

    Science.gov (United States)

    Bidarra, Jose; Araujo, Joao

    2013-01-01

    This paper argues that the dominant form of distance learning that is common in most e-learning systems rests on a set of learning devices and environments that may be outdated from the student's perspective, namely because it is not supportive of learner empowerment and does not facilitate the efforts of self-directed learners. For this study we…

  2. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Directory of Open Access Journals (Sweden)

    Nadia Said

    Full Text Available Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  3. Super Resolution and Interference Suppression Technique applied to SHARAD Radar Data

    Science.gov (United States)

    Raguso, M. C.; Mastrogiuseppe, M.; Seu, R.; Piazzo, L.

    2017-12-01

    We will present a super resolution and interference suppression technique applied to the data acquired by the SHAllow RADar (SHARAD) on board the NASA's 2005 Mars Reconnaissance Orbiter (MRO) mission, currently operating around Mars [1]. The algorithms allow to improve the range resolution roughly by a factor of 3 and the Signal to Noise Ratio (SNR) by a several decibels. Range compression algorithms usually adopt conventional Fourier transform techniques, which are limited in the resolution by the transmitted signal bandwidth, analogous to the Rayleigh's criterion in optics. In this work, we investigate a super resolution method based on autoregressive models and linear prediction techniques [2]. Starting from the estimation of the linear prediction coefficients from the spectral data, the algorithm performs the radar bandwidth extrapolation (BWE), thereby improving the range resolution of the pulse-compressed coherent radar data. Moreover, the EMIs (ElectroMagnetic Interferences) are detected and the spectra is interpolated in order to reconstruct an interference free spectrum, thereby improving the SNR. The algorithm can be applied to the single complex look image after synthetic aperture processing (SAR). We apply the proposed algorithm to simulated as well as to real radar data. We will demonstrate the effective enhancement on vertical resolution with respect to the classical spectral estimator. We will show that the imaging of the subsurface layered structures observed in radargrams is improved, allowing additional insights for the scientific community in the interpretation of the SHARAD radar data, which will help to further our understanding of the formation and evolution of known geological features on Mars. References: [1] Seu et al. 2007, Science, 2007, 317, 1715-1718 [2] K.M. Cuomo, "A Bandwidth Extrapolation Technique for Improved Range Resolution of Coherent Radar Data", Project Report CJP-60, Revision 1, MIT Lincoln Laboratory (4 Dec. 1992).

  4. Wire-mesh and ultrasound techniques applied for the characterization of gas-liquid slug flow

    Energy Technology Data Exchange (ETDEWEB)

    Ofuchi, Cesar Y.; Sieczkowski, Wytila Chagas; Neves Junior, Flavio; Arruda, Lucia V.R.; Morales, Rigoberto E.M.; Amaral, Carlos E.F.; Silva, Marco J. da [Federal University of Technology of Parana, Curitiba, PR (Brazil)], e-mails: ofuchi@utfpr.edu.br, wytila@utfpr.edu.br, neves@utfpr.edu.br, lvrarruda@utfpr.edu.br, rmorales@utfpr.edu.br, camaral@utfpr.edu.br, mdasilva@utfpr.edu.br

    2010-07-01

    Gas-liquid two-phase flows are found in a broad range of industrial applications, such as chemical, petrochemical and nuclear industries and quite often determine the efficiency and safety of process and plants. Several experimental techniques have been proposed and applied to measure and quantify two-phase flows so far. In this experimental study the wire-mesh sensor and an ultrasound technique are used and comparatively evaluated to study two-phase slug flows in horizontal pipes. The wire-mesh is an imaging technique and thus appropriated for scientific studies while ultrasound-based technique is robust and non-intrusive and hence well suited for industrial applications. Based on the measured raw data it is possible to extract some specific slug flow parameters of interest such as mean void fraction and characteristic frequency. The experiments were performed in the Thermal Sciences Laboratory (LACIT) at UTFPR, Brazil, in which an experimental two-phase flow loop is available. The experimental flow loop comprises a horizontal acrylic pipe of 26 mm diameter and 9 m length. Water and air were used to produce the two phase flow under controlled conditions. The results show good agreement between the techniques. (author)

  5. The Effects of Group Investigation and Cooperative Learning Techniques Applied in Teaching Force and Motion Subjects on Students’ Academic Achievements / Kuvvet ve Hareket Konularının Grup Araştırması ve Birlikte Öğrenme Teknikleri ile Uygulanmasının Öğrencilerin Akademik Başarılarına Etkisi

    Directory of Open Access Journals (Sweden)

    Nilüfer OKUR AKÇAY

    2012-06-01

    Full Text Available The aim of this study is to determine the effect of group investigation and cooperative learning techniques on the academic achievements of first year university students attending classes in which the units of force and motion are taught within the general physics course. The sample of this study consists of 96 first year pre-service science teachers during the 2010-2011 academic year. As data collection instruments, the Academic Achievement Test (AAT, Graphic Test (GT, Module Tests (Module A, B, C, D and E were used. This study was carried out in three different groups. One of these groups was Group Investigation Group (GIG, the second group was the Learning Together Group (LTG and the other was the Control Group (CG, in which teacher-centered instruction was applied. The data obtained on instruments were evaluated using ANOVA and descriptive statistics. The results of this study indicated no significant difference between GIG and LTG, but a significant difference between LTG and CG.

  6. Who is that masked educator? Deconstructing the teaching and learning processes of an innovative humanistic simulation technique.

    Science.gov (United States)

    McAllister, Margaret; Searl, Kerry Reid; Davis, Susan

    2013-12-01

    Simulation learning in nursing has long made use of mannequins, standardized actors and role play to allow students opportunity to practice technical body-care skills and interventions. Even though numerous strategies have been developed to mimic or amplify clinical situations, a common problem that is difficult to overcome in even the most well-executed simulation experiences, is that students may realize the setting is artificial and fail to fully engage, remember or apply the learning. Another problem is that students may learn technical competence but remain uncertain about communicating with the person. Since communication capabilities are imperative in human service work, simulation learning that only achieves technical competence in students is not fully effective for the needs of nursing education. Furthermore, while simulation learning is a burgeoning space for innovative practices, it has been criticized for the absence of a basis in theory. It is within this context that an innovative simulation learning experience named "Mask-Ed (KRS simulation)", has been deconstructed and the active learning components examined. Establishing a theoretical basis for creative teaching and learning practices provides an understanding of how, why and when simulation learning has been effective and it may help to distinguish aspects of the experience that could be improved. Three conceptual theoretical fields help explain the power of this simulation technique: Vygotskian sociocultural learning theory, applied theatre and embodiment. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. An Aural Learning Project: Assimilating Jazz Education Methods for Traditional Applied Pedagogy

    Science.gov (United States)

    Gamso, Nancy M.

    2011-01-01

    The Aural Learning Project (ALP) was developed to incorporate jazz method components into the author's classical practice and her applied woodwind lesson curriculum. The primary objective was to place a more focused pedagogical emphasis on listening and hearing than is traditionally used in the classical applied curriculum. The components of the…

  8. The correlated k-distribution technique as applied to the AVHRR channels

    Science.gov (United States)

    Kratz, David P.

    1995-01-01

    Correlated k-distributions have been created to account for the molecular absorption found in the spectral ranges of the five Advanced Very High Resolution Radiometer (AVHRR) satellite channels. The production of the k-distributions was based upon an exponential-sum fitting of transmissions (ESFT) technique which was applied to reference line-by-line absorptance calculations. To account for the overlap of spectral features from different molecular species, the present routines made use of the multiplication transmissivity property which allows for considerable flexibility, especially when altering relative mixing ratios of the various molecular species. To determine the accuracy of the correlated k-distribution technique as compared to the line-by-line procedure, atmospheric flux and heating rate calculations were run for a wide variety of atmospheric conditions. For the atmospheric conditions taken into consideration, the correlated k-distribution technique has yielded results within about 0.5% for both the cases where the satellite spectral response functions were applied and where they were not. The correlated k-distribution's principal advantages is that it can be incorporated directly into multiple scattering routines that consider scattering as well as absorption by clouds and aerosol particles.

  9. The Effect of using Teams Games Tournaments (TGT Technique for Learning Mathematics in Bangladesh

    Directory of Open Access Journals (Sweden)

    Abdus Salam

    2015-07-01

    Full Text Available Games-based learning has captured the interest of educationalists and industrialists who seek to reveal the characteristics of computer games as they are perceived by some to be a potentially effective approach for teaching and learning. Despite this interest in using games-based learning, there is a dearth of studies context of gaming and education in third world countries. This study investigated the effects of game playing on performance and attitudes of students towards mathematics of Grade VIII. The study was undergone by implementing TGT technique for the experimental group and typical lecture-based approach for the control group. A same achievement test was employed as in both pretest and post test, an inventory of attitudes towards mathematics were applied for the pretest and post test on TGT experimental and control group, an attitude scale on computer games was employed for the TGT experimental group, a semi-structured interview for teacher and an FGD guideline for students were applied to serving the purpose of research objectives. After three-weeks of intervention, it had been found out that TGT experimental group students had achieved a significant learning outcome than lecture based control group students. Attitude towards mathematics were differed to a certain positive extent on TGT experimental group. On the basis of findings of this study, some recommendations were made to overcome the barriers of integrating web-based game playing in a classroom.

  10. Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

    Directory of Open Access Journals (Sweden)

    Laura Cornejo-Bueno

    2017-11-01

    Full Text Available Wind Power Ramp Events (WPREs are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains. Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

  11. Locomotion training of legged robots using hybrid machine learning techniques

    Science.gov (United States)

    Simon, William E.; Doerschuk, Peggy I.; Zhang, Wen-Ran; Li, Andrew L.

    1995-01-01

    In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible

  12. Machine learning and statistical techniques : an application to the prediction of insolvency in Spanish non-life insurance companies

    OpenAIRE

    Díaz, Zuleyka; Segovia, María Jesús; Fernández, José

    2005-01-01

    Prediction of insurance companies insolvency has arisen as an important problem in the field of financial research. Most methods applied in the past to tackle this issue are traditional statistical techniques which use financial ratios as explicative variables. However, these variables often do not satisfy statistical assumptions, which complicates the application of the mentioned methods. In this paper, a comparative study of the performance of two non-parametric machine learning techniques ...

  13. Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images.

    Science.gov (United States)

    Boix, Macarena; Cantó, Begoña

    2013-04-01

    Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells.

  14. Mathematical Model and Artificial Intelligent Techniques Applied to a Milk Industry through DSM

    Science.gov (United States)

    Babu, P. Ravi; Divya, V. P. Sree

    2011-08-01

    The resources for electrical energy are depleting and hence the gap between the supply and the demand is continuously increasing. Under such circumstances, the option left is optimal utilization of available energy resources. The main objective of this chapter is to discuss about the Peak load management and overcome the problems associated with it in processing industries such as Milk industry with the help of DSM techniques. The chapter presents a generalized mathematical model for minimizing the total operating cost of the industry subject to the constraints. The work presented in this chapter also deals with the results of application of Neural Network, Fuzzy Logic and Demand Side Management (DSM) techniques applied to a medium scale milk industrial consumer in India to achieve the improvement in load factor, reduction in Maximum Demand (MD) and also the consumer gets saving in the energy bill.

  15. Applied methods and techniques for mechatronic systems modelling, identification and control

    CERN Document Server

    Zhu, Quanmin; Cheng, Lei; Wang, Yongji; Zhao, Dongya

    2014-01-01

    Applied Methods and Techniques for Mechatronic Systems brings together the relevant studies in mechatronic systems with the latest research from interdisciplinary theoretical studies, computational algorithm development and exemplary applications. Readers can easily tailor the techniques in this book to accommodate their ad hoc applications. The clear structure of each paper, background - motivation - quantitative development (equations) - case studies/illustration/tutorial (curve, table, etc.) is also helpful. It is mainly aimed at graduate students, professors and academic researchers in related fields, but it will also be helpful to engineers and scientists from industry. Lei Liu is a lecturer at Huazhong University of Science and Technology (HUST), China; Quanmin Zhu is a professor at University of the West of England, UK; Lei Cheng is an associate professor at Wuhan University of Science and Technology, China; Yongji Wang is a professor at HUST; Dongya Zhao is an associate professor at China University o...

  16. Advanced gamma spectrum processing technique applied to the analysis of scattering spectra for determining material thickness

    International Nuclear Information System (INIS)

    Hoang Duc Tam; VNUHCM-University of Science, Ho Chi Minh City; Huynh Dinh Chuong; Tran Thien Thanh; Vo Hoang Nguyen; Hoang Thi Kieu Trang; Chau Van Tao

    2015-01-01

    In this work, an advanced gamma spectrum processing technique is applied to analyze experimental scattering spectra for determining the thickness of C45 heat-resistant steel plates. The single scattering peak of scattering spectra is taken as an advantage to measure the intensity of single scattering photons. Based on these results, the thickness of steel plates is determined with a maximum deviation of real thickness and measured thickness of about 4 %. Monte Carlo simulation using MCNP5 code is also performed to cross check the results, which yields a maximum deviation of 2 %. These results strongly confirm the capability of this technique in analyzing gamma scattering spectra, which is a simple, effective and convenient method for determining material thickness. (author)

  17. Software engineering techniques applied to agricultural systems an object-oriented and UML approach

    CERN Document Server

    Papajorgji, Petraq J

    2014-01-01

    Software Engineering Techniques Applied to Agricultural Systems presents cutting-edge software engineering techniques for designing and implementing better agricultural software systems based on the object-oriented paradigm and the Unified Modeling Language (UML). The focus is on the presentation of  rigorous step-by-step approaches for modeling flexible agricultural and environmental systems, starting with a conceptual diagram representing elements of the system and their relationships. Furthermore, diagrams such as sequential and collaboration diagrams are used to explain the dynamic and static aspects of the software system.    This second edition includes: a new chapter on Object Constraint Language (OCL), a new section dedicated to the Model-VIEW-Controller (MVC) design pattern, new chapters presenting details of two MDA-based tools – the Virtual Enterprise and Olivia Nova, and a new chapter with exercises on conceptual modeling.  It may be highly useful to undergraduate and graduate students as t...

  18. Applying traditional signal processing techniques to social media exploitation for situational understanding

    Science.gov (United States)

    Abdelzaher, Tarek; Roy, Heather; Wang, Shiguang; Giridhar, Prasanna; Al Amin, Md. Tanvir; Bowman, Elizabeth K.; Kolodny, Michael A.

    2016-05-01

    Signal processing techniques such as filtering, detection, estimation and frequency domain analysis have long been applied to extract information from noisy sensor data. This paper describes the exploitation of these signal processing techniques to extract information from social networks, such as Twitter and Instagram. Specifically, we view social networks as noisy sensors that report events in the physical world. We then present a data processing stack for detection, localization, tracking, and veracity analysis of reported events using social network data. We show using a controlled experiment that the behavior of social sources as information relays varies dramatically depending on context. In benign contexts, there is general agreement on events, whereas in conflict scenarios, a significant amount of collective filtering is introduced by conflicted groups, creating a large data distortion. We describe signal processing techniques that mitigate such distortion, resulting in meaningful approximations of actual ground truth, given noisy reported observations. Finally, we briefly present an implementation of the aforementioned social network data processing stack in a sensor network analysis toolkit, called Apollo. Experiences with Apollo show that our techniques are successful at identifying and tracking credible events in the physical world.

  19. Applying Data Mining Techniques to Improve Information Security in the Cloud: A Single Cache System Approach

    Directory of Open Access Journals (Sweden)

    Amany AlShawi

    2016-01-01

    Full Text Available Presently, the popularity of cloud computing is gradually increasing day by day. The purpose of this research was to enhance the security of the cloud using techniques such as data mining with specific reference to the single cache system. From the findings of the research, it was observed that the security in the cloud could be enhanced with the single cache system. For future purposes, an Apriori algorithm can be applied to the single cache system. This can be applied by all cloud providers, vendors, data distributors, and others. Further, data objects entered into the single cache system can be extended into 12 components. Database and SPSS modelers can be used to implement the same.

  20. Markov chain Monte Carlo techniques applied to parton distribution functions determination: Proof of concept

    Science.gov (United States)

    Gbedo, Yémalin Gabin; Mangin-Brinet, Mariane

    2017-07-01

    We present a new procedure to determine parton distribution functions (PDFs), based on Markov chain Monte Carlo (MCMC) methods. The aim of this paper is to show that we can replace the standard χ2 minimization by procedures grounded on statistical methods, and on Bayesian inference in particular, thus offering additional insight into the rich field of PDFs determination. After a basic introduction to these techniques, we introduce the algorithm we have chosen to implement—namely Hybrid (or Hamiltonian) Monte Carlo. This algorithm, initially developed for Lattice QCD, turns out to be very interesting when applied to PDFs determination by global analyses; we show that it allows us to circumvent the difficulties due to the high dimensionality of the problem, in particular concerning the acceptance. A first feasibility study is performed and presented, which indicates that Markov chain Monte Carlo can successfully be applied to the extraction of PDFs and of their uncertainties.

  1. Removal of benzaldehyde from a water/ethanol mixture by applying scavenging techniques

    DEFF Research Database (Denmark)

    Mitic, Aleksandar; Skov, Thomas; Gernaey, Krist V.

    2017-01-01

    A presence of carbonyl compounds is very common in the food industry. The nature of such compounds is to be reactive and thus many products involve aldehydes/ketones in their synthetic routes. By contrast, the high reactivity of carbonyl compounds could also lead to formation of undesired compounds......, such as genotoxic impurities. It can therefore be important to remove carbonyl compounds by implementing suitable removal techniques, with the aim of protecting final product quality. This work is focused on benzaldehyde as a model component, studying its removal from a water/ethanol mixture by applying different...

  2. Prediction of lung cancer patient survival via supervised machine learning classification techniques.

    Science.gov (United States)

    Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

    2017-12-01

    Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time

  3. Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning

    Science.gov (United States)

    Prabakaran, S.; Mitra, Shilpa

    2018-04-01

    Data mining is the field containing procedures for finding designs or patterns in a huge dataset, it includes strategies at the convergence of machine learning and database framework. It can be applied to various fields like future healthcare, market basket analysis, education, manufacturing engineering, crime investigation etc. Among these, crime investigation is an interesting application to process crime characteristics to help the society for a better living. This paper survey various data mining techniques used in this domain. This study may be helpful in designing new strategies for crime prediction and analysis.

  4. THE PUZZLE TECHNIQUE, COOPERATIVE LEARNING STRATEGY TO IMPROVE ACADEMIC PERFORMANCE

    Directory of Open Access Journals (Sweden)

    M.ª José Mayorga Fernández

    2012-04-01

    Full Text Available This  article  presents  an  innovative  experience  carried  out  in  the  subject Pedagogical Bases of Special Education, a 4.5 credit core subject taught at the second year of the Degree in Physical Education Teacher Training (to be extinguish, based on the use of a methodological strategic in accordance with the new demands of the EEES. With the experience we pursue a double purpose: firstly, to present the technique of jigsaw or puzzle as a useful methodological strategy for university learning and, on the other hand, to show whether this strategy improves students results. Comparing the results with students previous year results shows that the performance of students who participated in the innovative experience has improved considerably, increasing their motivation and involvement towards the task.

  5. Using machine learning techniques to differentiate acute coronary syndrome

    Directory of Open Access Journals (Sweden)

    Sougand Setareh

    2015-02-01

    Full Text Available Backgroud: Acute coronary syndrome (ACS is an unstable and dynamic process that includes unstable angina, ST elevation myocardial infarction, and non-ST elevation myocardial infarction. Despite recent technological advances in early diognosis of ACS, differentiating between different types of coronary diseases in the early hours of admission is controversial. The present study was aimed to accurately differentiate between various coronary events, using machine learning techniques. Such methods, as a subset of artificial intelligence, include algorithms that allow computers to learn and play a major role in treatment decisions. Methods: 1902 patients diagnosed with ACS and admitted to hospital were selected according to Euro Heart Survey on ACS. Patients were classified based on decision tree J48. Bagging aggregation algorithms was implemented to increase the efficiency of algorithm. Results: The performance of classifiers was estimated and compared based on their accuracy computed from confusion matrix. The accuracy rates of decision tree and bagging algorithm were calculated to be 91.74% and 92.53%, respectively. Conclusion: The proposed methods used in this study proved to have the ability to identify various ACS. In addition, using matrix of confusion, an acceptable number of subjects with acute coronary syndrome were identified in each class.

  6. Reinforcement learning techniques for controlling resources in power networks

    Science.gov (United States)

    Kowli, Anupama Sunil

    As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

  7. Classification of Phishing Email Using Random Forest Machine Learning Technique

    Directory of Open Access Journals (Sweden)

    Andronicus A. Akinyelu

    2014-01-01

    Full Text Available Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN and false positive (FP rates.

  8. Estimation of Alpine Skier Posture Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Bojan Nemec

    2014-10-01

    Full Text Available High precision Global Navigation Satellite System (GNSS measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier’s neck. A key issue is how to estimate other more relevant parameters of the skier’s body, like the center of mass (COM and ski trajectories. Previously, these parameters were estimated by modeling the skier’s body with an inverted-pendulum model that oversimplified the skier’s body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier’s body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing.

  9. Integrating Experiential Learning and Applied Sociology to Promote Student Learning and Faculty Research

    Science.gov (United States)

    Holtzman, Mellisa; Menning, Chadwick

    2015-01-01

    Although the benefits of experiential learning for students are well documented, such courses are sometimes seen as a professional burden for faculty because they are very labor- and time-intensive endeavors. This paper suggests, however, that the time investment in experiential learning courses can be made more efficient if faculty members treat…

  10. Developing the master learner: applying learning theory to the learner, the teacher, and the learning environment.

    Science.gov (United States)

    Schumacher, Daniel J; Englander, Robert; Carraccio, Carol

    2013-11-01

    As a result of the paradigm shift to a competency-based framework, both self-directed lifelong learning and learner-centeredness have become essential tenets of medical education. In the competency-based framework, learners drive their own educational process, and both learners and teachers share the responsibility for the path and content of learning. This learner-centered emphasis requires each physician to develop and maintain lifelong learning skills, which the authors propose culminate in becoming a "master leaner." To better understand the development of these skills and the attainment of that goal, the authors explore how learning theories inform the development of master learners and how to translate these theories into practical strategies for the learner, the teacher, and the learning environment so as to optimize this development.The authors begin by exploring self-determination theory, which lays the groundwork for understanding the motivation to learn. They next consider the theories of cognitive load and situated cognition, which inform the optimal context and environment for learning. Building from this foundation, the authors consider key educational theories that affect learners' abilities to serve as primary drivers of their learning, including self-directed learning (SDL); the self-assessment skills necessary for SDL; factors affecting self-assessment (self-concept, self-efficacy, illusory superiority, gap filling); and ways to mitigate the inaccuracies of self-assessment (reflection, self-monitoring, external information seeking, and self-directed assessment seeking).For each theory, they suggest practical action steps for the learner, the teacher, and the learning environment in an effort to provide a road map for developing master learners.

  11. Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques

    CSIR Research Space (South Africa)

    Ngxande, Mkhuseli

    2017-11-01

    Full Text Available This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features...

  12. Personal recommender systems for learners in lifelong learning: requirements, techniques and model

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Koper, Rob

    2007-01-01

    Drachsler, H., Hummel, H. G. K., & Koper, R. (2008). Personal recommender systems for learners in lifelong learning: requirements, techniques and model. International Journal of Learning Technology, 3(4), 404-423.

  13. Applying the ISO 9126 Model to the Evaluation of an E-learning System in Iran

    OpenAIRE

    Hossein Pedram; Davood Karimzadegan Moghaddam; Zhaleh Asheghi

    2012-01-01

    One of the models presented in e-learning quality system field is ISO 9126 model, which applied in this research to evaluate e-learning system of Amirkabir University. This model system for evaluation, the six main variables provided that each of these variables by several other indicators was measured. Thus, the model parameters as ISO 9126 and turned the questionnaire survey among samples (120 experts and students of Amirkabir University) and the distribution were completed. Based on the re...

  14. Perspective for applying traditional and inovative teaching and learning methods to nurses continuing education

    OpenAIRE

    Bendinskaitė, Irmina

    2015-01-01

    Bendinskaitė I. Perspective for applying traditional and innovative teaching and learning methods to nurse’s continuing education, magister thesis / supervisor Assoc. Prof. O. Riklikienė; Departament of Nursing and Care, Faculty of Nursing, Lithuanian University of Health Sciences. – Kaunas, 2015, – p. 92 The purpose of this study was to investigate traditional and innovative teaching and learning methods perspective to nurse’s continuing education. Material and methods. In a period fro...

  15. Applied research on air pollution using nuclear-related analytical techniques

    International Nuclear Information System (INIS)

    1994-01-01

    A co-ordinated research programme (CRP) on applied research on air pollution using nuclear-related techniques is a global CRP which will run from 1992-1996, and will build upon the experience gained by the Agency from the laboratory support that it has been providing for several years to BAPMoN - the Background Air Pollution Monitoring Network programme organized under the auspices of the World Meterological Organization. The purpose of this CRP is to promote the use of nuclear analytical techniques in air pollution studies, e.g. NAA, XFR, and PIXE for the analysis of toxic and other trace elements in suspended particulate matter (including air filter samples), rainwater and fog-water samples, and in biological indicators of air pollution (e.g. lichens and mosses). The main purposes of the core programme are i) to support the use of nuclear and nuclear-related analytical techniques for practically-oriented research and monitoring studies on air pollution ii) to identify major sources of air pollution affecting each of the participating countries with particular reference to toxic heavy metals, and iii) to obtain comparative data on pollution levels in areas of high pollution (e.g. a city centre or a populated area downwind of a large pollution source) and low pollution (e.g. rural areas). This document reports the discussions held during the first Research Co-ordination Meeting (RCM) for the CRP which took place at the IAEA Headquarters in Vienna. Refs, figs and tabs

  16. Case study: how to apply data mining techniques in a healthcare data warehouse.

    Science.gov (United States)

    Silver, M; Sakata, T; Su, H C; Herman, C; Dolins, S B; O'Shea, M J

    2001-01-01

    Healthcare provider organizations are faced with a rising number of financial pressures. Both administrators and physicians need help analyzing large numbers of clinical and financial data when making decisions. To assist them, Rush-Presbyterian-St. Luke's Medical Center and Hitachi America, Ltd. (HAL), Inc., have partnered to build an enterprise data warehouse and perform a series of case study analyses. This article focuses on one analysis, which was performed by a team of physicians and computer science researchers, using a commercially available on-line analytical processing (OLAP) tool in conjunction with proprietary data mining techniques developed by HAL researchers. The initial objective of the analysis was to discover how to use data mining techniques to make business decisions that can influence cost, revenue, and operational efficiency while maintaining a high level of care. Another objective was to understand how to apply these techniques appropriately and to find a repeatable method for analyzing data and finding business insights. The process used to identify opportunities and effect changes is described.

  17. Improving Student Learning Outcomes Marketing Strategy Lesson By Applying SFAE Learning Model

    Directory of Open Access Journals (Sweden)

    Winda Nur Rohmawati

    2017-11-01

    Full Text Available Research objectives for improving student learning outcomes on the subjects of marketing strategy through the implementation of model learning SFAE. This type of research this is a class action research using a qualitative approach which consists of two cycles with the subject Marketing X grade SMK YPI Darussalam 2 Cerme Gresik Regency. This research consists of four stages: (1 the Planning Act, (2 the implementation of the action, (3 observations (observation, and (4 Reflection. The result of the research shows that cognitive and affective learning outcomes of students have increased significantly.

  18. Applying Web-Based Co-Regulated Learning to Develop Students' Learning and Involvement in a Blended Computing Course

    Science.gov (United States)

    Tsai, Chia-Wen

    2015-01-01

    This research investigated, via quasi-experiments, the effects of web-based co-regulated learning (CRL) on developing students' computing skills. Two classes of 68 undergraduates in a one-semester course titled "Applied Information Technology: Data Processing" were chosen for this research. The first class (CRL group, n = 38) received…

  19. Machine Learning Method Applied in Readout System of Superheated Droplet Detector

    Science.gov (United States)

    Liu, Yi; Sullivan, Clair Julia; d'Errico, Francesco

    2017-07-01

    Direct readability is one advantage of superheated droplet detectors in neutron dosimetry. Utilizing such a distinct characteristic, an imaging readout system analyzes image of the detector for neutron dose readout. To improve the accuracy and precision of algorithms in the imaging readout system, machine learning algorithms were developed. Deep learning neural network and support vector machine algorithms are applied and compared with generally used Hough transform and curvature analysis methods. The machine learning methods showed a much higher accuracy and better precision in recognizing circular gas bubbles.

  20. Machine Learning Techniques for Modelling Short Term Land-Use Change

    Directory of Open Access Journals (Sweden)

    Mileva Samardžić-Petrović

    2017-11-01

    Full Text Available The representation of land use change (LUC is often achieved by using data-driven methods that include machine learning (ML techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT, Neural Networks (NN, and Support Vector Machines (SVM for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.

  1. [Applying Game-Based Learning in Nursing Education: Empathy Board Game Learning].

    Science.gov (United States)

    Lu, Chueh-Fen; Wu, Shu-Mei; Shu, Ying-Mei; Yeh, Mei-Yu

    2018-02-01

    Attending lectures and reading are two common approaches to acquiring knowledge, while repetitive practice is a common approach to acquiring skills. Nurturing proper attitudes in students is one of the greatest challenges for educators. Health professionals must incorporate empathy into their practice. Creative teaching strategies may offer a feasible approach to enhancing empathy-related competence. The present article focuses on analyzing current, empathy-related curriculums in nursing education in Taiwan, exploring the concepts of empathy and game-based learning, presenting the development of an empathy board game as a teaching aid, and, finally, evaluating the developed education application. Based on the learner-centered principle, this aid was designed with peer learning, allowing learners to influence the learning process, to simulate the various roles of clients, and to develop diverse interpersonal dialogues. The continuous learning loops were formed using the gamification mechanism and transformation, enabling students to connect and practice the three elements of empathy ability: emotion, cognition and expression. Via the game elements of competition, interaction, storytelling, real-time responses, concretizing feedback, integrated peer learning, and equality between teachers and students, students who play patient roles are able to perceive different levels of comfort, which encourages the development of insight into the meaning of empathy. Thereby, the goals of the empathy lesson is achievable within a creative game-based learning environment.

  2. Recent developments and evaluation of selected geochemical techniques applied to uranium exploration

    International Nuclear Information System (INIS)

    Wenrich-Verbeek, K.J.; Cadigan, R.A.; Felmlee, J.K.; Reimer, G.M.; Spirakis, C.S.

    1976-01-01

    Various geochemical techniques for uranium exploration are currently under study by the geochemical techniques team of the Branch of Uranium and Thorium Resources, US Geological Survey. Radium-226 and its parent uranium-238 occur in mineral spring water largely independently of the geochemistry of the solutions and thus are potential indicators of uranium in source rocks. Many radioactive springs, hot or cold, are believed to be related to hydrothermal systems which contain uranium at depth. Radium, when present in the water, is co-precipitated in iron and/or manganese oxides and hydroxides or in barium sulphate associated with calcium carbonate spring deposits. Studies of surface water samples have resulted in improved standardized sample treatment and collection procedures. Stream discharge has been shown to have a significant effect on uranium concentration, while conductivity shows promise as a ''pathfinder'' for uranium. Turbid samples behave differently and consequently must be treated with more caution than samples from clear streams. Both water and stream sediments should be sampled concurrently, as anomalous uranium concentrations may occur in only one of these media and would be overlooked if only one, the wrong one, were analysed. The fission-track technique has been applied to uranium determinations in the above water studies. The advantages of the designed sample collecting system are that only a small quantity, typically one drop, of water is required and sample manipulation is minimized, thereby reducing contamination risks. The fission-track analytical technique is effective at the uranium concentration levels commonly found in natural waters (5.0-0.01 μg/litre). Landsat data were used to detect alteration associated with uranium deposits. Altered areas were detected but were not uniquely defined. Nevertheless, computer processing of Landsat data did suggest a smaller size target for further evaluation and thus is useful as an exploration tool

  3. Semi-supervised learning of hyperspectral image segmentation applied to vine tomatoes and table grapes

    Directory of Open Access Journals (Sweden)

    Jeroen van Roy

    2018-03-01

    Full Text Available Nowadays, quality inspection of fruit and vegetables is typically accomplished through visual inspection. Automation of this inspection is desirable to make it more objective. For this, hyperspectral imaging has been identified as a promising technique. When the field of view includes multiple objects, hypercubes should be segmented to assign individual pixels to different objects. Unsupervised and supervised methods have been proposed. While the latter are labour intensive as they require masking of the training images, the former are too computationally intensive for in-line use and may provide different results for different hypercubes. Therefore, a semi-supervised method is proposed to train a computationally efficient segmentation algorithm with minimal human interaction. As a first step, an unsupervised classification model is used to cluster spectra in similar groups. In the second step, a pixel selection algorithm applied to the output of the unsupervised classification is used to build a supervised model which is fast enough for in-line use. To evaluate this approach, it is applied to hypercubes of vine tomatoes and table grapes. After first derivative spectral preprocessing to remove intensity variation due to curvature and gloss effects, the unsupervised models segmented 86.11% of the vine tomato images correctly. Considering overall accuracy, sensitivity, specificity and time needed to segment one hypercube, partial least squares discriminant analysis (PLS-DA was found to be the best choice for in-line use, when using one training image. By adding a second image, the segmentation results improved considerably, yielding an overall accuracy of 96.95% for segmentation of vine tomatoes and 98.52% for segmentation of table grapes, demonstrating the added value of the learning phase in the algorithm.

  4. Machine Learning Techniques for Arterial Pressure Waveform Analysis

    Directory of Open Access Journals (Sweden)

    João Cardoso

    2013-05-01

    Full Text Available The Arterial Pressure Waveform (APW can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1 a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2 the acquired position and amplitude of onset, Systolic Peak (SP, Point of Inflection (Pi and Dicrotic Wave (DW were used for the computation of some morphological attributes; (3 pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4 classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic, J48 (decision tree and RIPPER (rule-based induction; and (5 we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx. Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95% and high area under the curve (AUC of a Receiver Operating Characteristic (ROC curve (0.961. Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.

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

    Science.gov (United States)

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

    2016-01-01

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

  6. Determination of hydrogen diffusivity and permeability in W near room temperature applying a tritium tracer technique

    International Nuclear Information System (INIS)

    Ikeda, T.; Otsuka, T.; Tanabe, T.

    2011-01-01

    Tungsten is a primary candidate of plasma facing material in ITER and beyond, owing to its good thermal property and low erosion. But hydrogen solubility and diffusivity near ITER operation temperatures (below 500 K) have scarcely studied. Mainly because its low hydrogen solubility and diffusivity at lower temperatures make the detection of hydrogen quite difficult. We have tried to observe hydrogen plasma driven permeation (PDP) through nickel and tungsten near room temperatures applying a tritium tracer technique, which is extremely sensible to detect tritium diluted in hydrogen. The apparent diffusion coefficients for PDP were determined by permeation lag times at first time, and those for nickel and tungsten were similar or a few times larger than those for gas driven permeation (GDP). The permeation rates for PDP in nickel and tungsten were larger than those for GDP normalized to the same gas pressure about 20 and 5 times larger, respectively.

  7. Laser--Doppler anemometry technique applied to two-phase dispersed flows in a rectangular channel

    International Nuclear Information System (INIS)

    Lee, S.L.; Srinivasan, J.

    1979-01-01

    A new optical technique using Laser--Doppler anemometry has been applied to the local measurement of turbulent upward flow of a dilute water droplet--air two-phase dispersion in a vertical rectangular channel. Individually examined were over 20,000 droplet signals coming from each of a total of ten transversely placed measuring points, the closest of which to the channel wall was 250 μ away from the wall. Two flows of different patterns due to different imposed flow conditions were investigated, one with and the other without a liquid film formed on the channel wall. Reported are the size and number density distribution and the axial and lateral velocity distributions for the droplets as well as the axial and lateral velocity distributions for the air

  8. Technique of uranium exploration in tropical rain forests as applied in Sumatra and other tropical areas

    International Nuclear Information System (INIS)

    Hahn, L.

    1983-01-01

    The technique of uranium prospecting in areas covered by tropical rain forest is discussed using a uranium exploration campaign conducted from 1976 to 1978 in Western Sumatra as an example. A regional reconnaissance survey using stream sediment samples combined with radiometric field measurements proved ideal for covering very large areas. A mobile field laboratory was used for the geochemical survey. Helicopter support in diffult terrain was found to be very efficient and economical. A field procedure for detecting low uranium concentrations in stream water samples is described. This method has been successfully applied in Sarawak. To distinguish meaningful uranium anomalies in water from those with no meaning for prospecting, the correlations between U content and conductivity of the water and between U content and Ca and HCO 3 content must be considered. This method has been used successfully in a geochemical survey in Thailand. (author)

  9. A systematic review of applying modern software engineering techniques to developing robotic systems

    Directory of Open Access Journals (Sweden)

    Claudia Pons

    2012-01-01

    Full Text Available Robots have become collaborators in our daily life. While robotic systems become more and more complex, the need to engineer their software development grows as well. The traditional approaches used in developing these software systems are reaching their limits; currently used methodologies and tools fall short of addressing the needs of such complex software development. Separating robotics’ knowledge from short-cycled implementation technologies is essential to foster reuse and maintenance. This paper presents a systematic review (SLR of the current use of modern software engineering techniques for developing robotic software systems and their actual automation level. The survey was aimed at summarizing existing evidence concerning applying such technologies to the field of robotic systems to identify any gaps in current research to suggest areas for further investigation and provide a background for positioning new research activities.

  10. Vibration monitoring/diagnostic techniques, as applied to reactor coolant pumps

    International Nuclear Information System (INIS)

    Sculthorpe, B.R.; Johnson, K.M.

    1986-01-01

    With the increased awareness of reactor coolant pump (RCP) cracked shafts, brought about by the catastrophic shaft failure at Crystal River number3, Florida Power and Light Company, in conjunction with Bently Nevada Corporation, undertook a test program at St. Lucie Nuclear Unit number2, to confirm the integrity of all four RCP pump shafts. Reactor coolant pumps play a major roll in the operation of nuclear-powered generation facilities. The time required to disassemble and physically inspect a single RCP shaft would be lengthy, monetarily costly to the utility and its customers, and cause possible unnecessary man-rem exposure to plant personnel. When properly applied, vibration instrumentation can increase unit availability/reliability, as well as provide enhanced diagnostic capability. This paper reviews monitoring benefits and diagnostic techniques applicable to RCPs/motor drives

  11. Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

    Science.gov (United States)

    Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

  12. Predicting the Failure of Dental Implants Using Supervised Learning Techniques

    Directory of Open Access Journals (Sweden)

    Chia-Hui Liu

    2018-05-01

    Full Text Available Prosthodontic treatment has been a crucial part of dental treatment for patients with full mouth rehabilitation. Dental implant surgeries that replace conventional dentures using titanium fixtures have become the top choice. However, because of the wide-ranging scope of implant surgeries, patients’ body conditions, surgeons’ experience, and the choice of implant system should be considered during treatment. The higher price charged by dental implant treatments compared to conventional dentures has led to a rush among medical staff; therefore, the future impact of surgeries has not been analyzed in detail, resulting in medial disputes. Previous literature on the success factors of dental implants is mainly focused on single factors such as patients’ systemic diseases, operation methods, or prosthesis types for statistical correlation significance analysis. This study developed a prediction model for providing an early warning mechanism to reduce the chances of dental implant failure. We collected the clinical data of patients who received artificial dental implants at the case hospital for a total of 8 categories and 20 variables. Supervised learning techniques such as decision tree (DT, support vector machines, logistic regressions, and classifier ensembles (i.e., Bagging and AdaBoost were used to analyze the prediction of the failure of dental implants. The results show that DT with both Bagging and Adaboost techniques possesses the highest prediction performance for the failure of dental implant (area under the receiver operating characteristic curve, AUC: 0.741; the analysis also revealed that the implant systems affect dental implant failure. The model can help clinical surgeons to reduce medical failures by choosing the optimal implant system and prosthodontics treatments for their patients.

  13. Creep lifing methodologies applied to a single crystal superalloy by use of small scale test techniques

    Energy Technology Data Exchange (ETDEWEB)

    Jeffs, S.P., E-mail: s.p.jeffs@swansea.ac.uk [Institute of Structural Materials, Swansea University, Singleton Park SA2 8PP (United Kingdom); Lancaster, R.J. [Institute of Structural Materials, Swansea University, Singleton Park SA2 8PP (United Kingdom); Garcia, T.E. [IUTA (University Institute of Industrial Technology of Asturias), University of Oviedo, Edificio Departamental Oeste 7.1.17, Campus Universitario, 33203 Gijón (Spain)

    2015-06-11

    In recent years, advances in creep data interpretation have been achieved either by modified Monkman–Grant relationships or through the more contemporary Wilshire equations, which offer the opportunity of predicting long term behaviour extrapolated from short term results. Long term lifing techniques prove extremely useful in creep dominated applications, such as in the power generation industry and in particular nuclear where large static loads are applied, equally a reduction in lead time for new alloy implementation within the industry is critical. The latter requirement brings about the utilisation of the small punch (SP) creep test, a widely recognised approach for obtaining useful mechanical property information from limited material volumes, as is typically the case with novel alloy development and for any in-situ mechanical testing that may be required. The ability to correlate SP creep results with uniaxial data is vital when considering the benefits of the technique. As such an equation has been developed, known as the k{sub SP} method, which has been proven to be an effective tool across several material systems. The current work now explores the application of the aforementioned empirical approaches to correlate small punch creep data obtained on a single crystal superalloy over a range of elevated temperatures. Finite element modelling through ABAQUS software based on the uniaxial creep data has also been implemented to characterise the SP deformation and help corroborate the experimental results.

  14. Creep lifing methodologies applied to a single crystal superalloy by use of small scale test techniques

    International Nuclear Information System (INIS)

    Jeffs, S.P.; Lancaster, R.J.; Garcia, T.E.

    2015-01-01

    In recent years, advances in creep data interpretation have been achieved either by modified Monkman–Grant relationships or through the more contemporary Wilshire equations, which offer the opportunity of predicting long term behaviour extrapolated from short term results. Long term lifing techniques prove extremely useful in creep dominated applications, such as in the power generation industry and in particular nuclear where large static loads are applied, equally a reduction in lead time for new alloy implementation within the industry is critical. The latter requirement brings about the utilisation of the small punch (SP) creep test, a widely recognised approach for obtaining useful mechanical property information from limited material volumes, as is typically the case with novel alloy development and for any in-situ mechanical testing that may be required. The ability to correlate SP creep results with uniaxial data is vital when considering the benefits of the technique. As such an equation has been developed, known as the k SP method, which has been proven to be an effective tool across several material systems. The current work now explores the application of the aforementioned empirical approaches to correlate small punch creep data obtained on a single crystal superalloy over a range of elevated temperatures. Finite element modelling through ABAQUS software based on the uniaxial creep data has also been implemented to characterise the SP deformation and help corroborate the experimental results

  15. VIDEOGRAMMETRIC RECONSTRUCTION APPLIED TO VOLCANOLOGY: PERSPECTIVES FOR A NEW MEASUREMENT TECHNIQUE IN VOLCANO MONITORING

    Directory of Open Access Journals (Sweden)

    Emmanuelle Cecchi

    2011-05-01

    Full Text Available This article deals with videogrammetric reconstruction of volcanic structures. As a first step, the method is tested in laboratory. The objective is to reconstruct small sand and plaster cones, analogous to volcanoes, that deform with time. The initial stage consists in modelling the sensor (internal parameters and calculating its orientation and position in space, using a multi-view calibration method. In practice two sets of views are taken: a first one around a calibration target and a second one around the studied object. Both sets are combined in the calibration software to simultaneously compute the internal parameters modelling the sensor, and the external parameters giving the spatial location of each view around the cone. Following this first stage, a N-view reconstruction process is carried out. The principle is as follows: an initial 3D model of the cone is created and then iteratively deformed to fit the real object. The deformation of the meshed model is based on a texture coherence criterion. At present, this reconstruction method and its precision are being validated at laboratory scale. The objective will be then to follow analogue model deformation with time using successive reconstructions. In the future, the method will be applied to real volcanic structures. Modifications of the initial code will certainly be required, however excellent reconstruction accuracy, valuable simplicity and flexibility of the technique are expected, compared to classic stereophotogrammetric techniques used in volcanology.

  16. How Can Synchrotron Radiation Techniques Be Applied for Detecting Microstructures in Amorphous Alloys?

    Directory of Open Access Journals (Sweden)

    Gu-Qing Guo

    2015-11-01

    Full Text Available In this work, how synchrotron radiation techniques can be applied for detecting the microstructure in metallic glass (MG is studied. The unit cells are the basic structural units in crystals, though it has been suggested that the co-existence of various clusters may be the universal structural feature in MG. Therefore, it is a challenge to detect microstructures of MG even at the short-range scale by directly using synchrotron radiation techniques, such as X-ray diffraction and X-ray absorption methods. Here, a feasible scheme is developed where some state-of-the-art synchrotron radiation-based experiments can be combined with simulations to investigate the microstructure in MG. By studying a typical MG composition (Zr70Pd30, it is found that various clusters do co-exist in its microstructure, and icosahedral-like clusters are the popular structural units. This is the structural origin where there is precipitation of an icosahedral quasicrystalline phase prior to phase transformation from glass to crystal when heating Zr70Pd30 MG.

  17. An acceleration technique for the Gauss-Seidel method applied to symmetric linear systems

    Directory of Open Access Journals (Sweden)

    Jesús Cajigas

    2014-06-01

    Full Text Available A preconditioning technique to improve the convergence of the Gauss-Seidel method applied to symmetric linear systems while preserving symmetry is proposed. The preconditioner is of the form I + K and can be applied an arbitrary number of times. It is shown that under certain conditions the application of the preconditioner a finite number of steps reduces the matrix to a diagonal. A series of numerical experiments using matrices from spatial discretizations of partial differential equations demonstrates that both versions of the preconditioner, point and block version, exhibit lower iteration counts than its non-symmetric version. Resumen. Se propone una técnica de precondicionamiento para mejorar la convergencia del método Gauss-Seidel aplicado a sistemas lineales simétricos pero preservando simetría. El precondicionador es de la forma I + K y puede ser aplicado un número arbitrario de veces. Se demuestra que bajo ciertas condiciones la aplicación del precondicionador un número finito de pasos reduce la matriz del sistema precondicionado a una diagonal. Una serie de experimentos con matrices que provienen de la discretización de ecuaciones en derivadas parciales muestra que ambas versiones del precondicionador, por punto y por bloque, muestran un menor número de iteraciones en comparación con la versión que no preserva simetría.

  18. Personnel contamination protection techniques applied during the TMI-2 [Three Mile Island Unit 2] cleanup

    International Nuclear Information System (INIS)

    Hildebrand, J.E.

    1988-01-01

    The severe damage to the Three Mile Island Unit 2 (TMI-2) core and the subsequent discharge of reactor coolant to the reactor and auxiliary buildings resulted in extremely hostile radiological environments in the TMI-2 plant. High fission product surface contamination and radiation levels necessitated the implementation of innovative techniques and methods in performing cleanup operations while assuring effective as low as reasonably achievable (ALARA) practices. The approach utilized by GPU Nuclear throughout the cleanup in applying protective clothing requirements was to consider the overall health risk to the worker including factors such as cardiopulmonary stress, visual and hearing acuity, and heat stress. In applying protective clothing requirements, trade-off considerations had to be made between preventing skin contaminations and possibly overprotecting the worker, thus impacting his ability to perform his intended task at maximum efficiency and in accordance with ALARA principles. The paper discusses the following topics: protective clothing-general use, beta protection, skin contamination, training, personnel access facility, and heat stress

  19. MULTIVARIATE TECHNIQUES APPLIED TO EVALUATION OF LIGNOCELLULOSIC RESIDUES FOR BIOENERGY PRODUCTION

    Directory of Open Access Journals (Sweden)

    Thiago de Paula Protásio

    2013-12-01

    Full Text Available http://dx.doi.org/10.5902/1980509812361The evaluation of lignocellulosic wastes for bioenergy production demands to consider several characteristicsand properties that may be correlated. This fact demands the use of various multivariate analysis techniquesthat allow the evaluation of relevant energetic factors. This work aimed to apply cluster analysis and principalcomponents analyses for the selection and evaluation of lignocellulosic wastes for bioenergy production.8 types of residual biomass were used, whose the elemental components (C, H, O, N, S content, lignin, totalextractives and ashes contents, basic density and higher and lower heating values were determined. Bothmultivariate techniques applied for evaluation and selection of lignocellulosic wastes were efficient andsimilarities were observed between the biomass groups formed by them. Through the interpretation of thefirst principal component obtained, it was possible to create a global development index for the evaluationof the viability of energetic uses of biomass. The interpretation of the second principal component alloweda contrast between nitrogen and sulfur contents with oxygen content.

  20. A Case Study of Universal Design for Learning Applied in the College Classroom

    Science.gov (United States)

    Leichliter, Marie E.

    2010-01-01

    As the landscape of education and the demographics of the postsecondary classroom continue to evolve, so too must the teaching practices at our nation's institutions of higher education. This study follows an instructor who has evolved to incorporate Universal Design for Learning (UDL) techniques into her classroom, even though prior to…

  1. Applying Cognitive Psychology Based Instructional Design Principles in Mathematics Teaching and Learning: Introduction

    Science.gov (United States)

    Verschaffel, Lieven; Van Dooren, W.; Star, J.

    2017-01-01

    This special issue comprises contributions that address the breadth of current lines of recent research from cognitive psychology that appear promising for positively impacting students' learning of mathematics. More specifically, we included contributions (a) that refer to cognitive psychology based principles and techniques, such as explanatory…

  2. Applying the GNSS Volcanic Ash Plume Detection Technique to Consumer Navigation Receivers

    Science.gov (United States)

    Rainville, N.; Palo, S.; Larson, K. M.

    2017-12-01

    Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS) rely on predictably structured and constant power RF signals to fulfill their primary use for navigation and timing. When the received strength of GNSS signals deviates from the expected baseline, it is typically due to a change in the local environment. This can occur when signal reflections from the ground are modified by changes in snow or soil moisture content, as well as by attenuation of the signal from volcanic ash. This effect allows GNSS signals to be used as a source for passive remote sensing. Larson et al. (2017) have developed a detection technique for volcanic ash plumes based on the attenuation seen at existing geodetic GNSS sites. Since these existing networks are relatively sparse, this technique has been extended to use lower cost consumer GNSS receiver chips to enable higher density measurements of volcanic ash. These low-cost receiver chips have been integrated into a fully stand-alone sensor, with independent power, communications, and logging capabilities as part of a Volcanic Ash Plume Receiver (VAPR) network. A mesh network of these sensors transmits data to a local base-station which then streams the data real-time to a web accessible server. Initial testing of this sensor network has uncovered that a different detection approach is necessary when using consumer GNSS receivers and antennas. The techniques to filter and process the lower quality data from consumer receivers will be discussed and will be applied to initial results from a functioning VAPR network installation.

  3. Does the modality principle for multimedia learning apply to science classrooms?

    NARCIS (Netherlands)

    Harskamp, Egbert G.; Mayer, Richard E.; Suhre, Cor

    2007-01-01

    This study demonstrated that the modality principle applies to multimedia learning of regular science lessons in school settings. In the first field experiment, 27 Dutch secondary school students (age 16-17) received a self-paced, web-based multimedia lesson in biology. Students who received lessons

  4. Applying Cognitive Linguistics to Instructed L2 Learning: The English Modals

    Science.gov (United States)

    Tyler, Andrea; Mueller, Charles M.; Ho, Vu

    2010-01-01

    This paper reports the results of a quasi-experimental effects-of-instruction study examining the efficacy of applying a Cognitive Linguistic (CL) approach to L2 learning of the semantics of English modals. In spite of their frequency in typical input, modal verbs present L2 learners with difficulties, party due to their inherent…

  5. Sociocultural Theory Applied to Second Language Learning: Collaborative Learning with Reference to the Chinese Context

    Science.gov (United States)

    Dongyu, Zhang; Fanyu, B.; Wanyi, Du

    2013-01-01

    This paper discusses the sociocultural theory (SCT). In particular, three significant concepts of Vyogtsky's theory: self-regulation, the Zone of Proximal Development (ZPD), and scaffolding all of which have been discussed in numerous second language acquisition (SLA) and second language learning (SLL) research papers. These concepts lay the…

  6. Using the 5E Learning Cycle with Metacognitive Technique to Enhance Students’ Mathematical Critical Thinking Skills

    Directory of Open Access Journals (Sweden)

    Runisah Runisah

    2017-02-01

    Full Text Available This study aims to describe enhancement and achievement of mathematical critical thinking skills of students who received the 5E Learning Cycle with Metacognitive technique, the 5E Learning Cycle, and conventional learning. This study use experimental method with pretest-posttest control group design. Population are junior high school students in Indramayu city, Indonesia. Sample are three classes of eighth grade students from high level school and three classes from medium level school. The study reveal that in terms of overall, mathematical critical thinking skills enhancement and achievement of students who received the 5E Learning Cycle with Metacognitive technique is better than students who received the 5E Learning Cycle and conventional learning. Mathematical critical thinking skills of students who received the 5E Learning Cycle is better than students who received conventional learning. There is no interaction effect between learning model and school level toward enhancement and achievement of students’ mathematical critical thinking skills.

  7. Self-Regulated Learning Strategies Applied to Undergraduate, Graduate and Specialization Students from Civil Engineering

    Directory of Open Access Journals (Sweden)

    Jose Carlos Redaelli

    2013-03-01

    Full Text Available The current demand for civil engineering work requires new skills and knowledge and calls for new and effective learning methods. This paper shows self-regulated learning strategies applied to undergraduate, graduate and specialization students from Civil Engineering in a Brazilian University. A Scale of Evaluation of Learning Strategies was administered with a view to identifying students´ cognitive, metacognitive and dysfunctional learning strategies.

  8. Optimizing physicians' instruction of PACS through e-learning: cognitive load theory applied.

    Science.gov (United States)

    Devolder, P; Pynoo, B; Voet, T; Adang, L; Vercruysse, J; Duyck, P

    2009-03-01

    This article outlines the strategy used by our hospital to maximize the knowledge transfer to referring physicians on using a picture archiving and communication system (PACS). We developed an e-learning platform underpinned by the cognitive load theory (CLT) so that in depth knowledge of PACS' abilities becomes attainable regardless of the user's prior experience with computers. The application of the techniques proposed by CLT optimizes the learning of the new actions necessary to obtain and manipulate radiological images. The application of cognitive load reducing techniques is explained with several examples. We discuss the need to safeguard the physicians' main mental processes to keep the patient's interests in focus. A holistic adoption of CLT techniques both in teaching and in configuration of information systems could be adopted to attain this goal. An overview of the advantages of this instruction method is given both on the individual and organizational level.

  9. Does Applied STEM Course Taking Link to STEM Outcomes for High School Students With Learning Disabilities?

    Science.gov (United States)

    Gottfried, Michael A; Sublett, Cameron

    Over the most recent two decades, federal policy has urged high schools to embed applied science, technology, engineering, and mathematics (STEM) courses into the curriculum to reinforce concepts learned in traditional math and science classes as well as to motivate students' interests and long-term pursuits in STEM areas. While prior research has examined whether these courses link to STEM persistence for the general student population, no work has examined the role of these courses for students with learning disabilities (LDs). This is a critical lapse, as these courses have been supported as being one path by which STEM material can become more accessible for students with diverse learning needs. Hence, this descriptive study examines the landscape of applied STEM course taking for students with LDs. The findings suggest students with LDs are less likely to take applied STEM courses in high school compared to the general population. Additionally, while the general population does benefit from taking these courses, there is a unique association between applied STEM course taking and advanced math and science course taking or math achievement for students with LDs. Hence, there is no evidence that applied STEM course taking is related to any closure of the STEM achievement gap for students with LDs.

  10. Comparative exploration of learning styles and teaching techniques between Thai and Vietnamese EFL students and instructors

    Directory of Open Access Journals (Sweden)

    Supalak Nakhornsri

    2016-09-01

    Full Text Available Learning styles have been a particular focus of a number of researchers over the past decades. Findings from various studies researching into how students learn highlight significant relationships between learners’ styles of learning and their language learning processes and achievement. This research focuses on a comparative analysis of the preferences of English learning styles and teaching techniques perceived by students from Thailand and Vietnam, and the teaching styles and techniques practiced by their instructors. The purposes were 1 to investigate the learning styles and teaching techniques students from both countries preferred, 2 to investigate the compatibility of the teaching styles and techniques practiced by instructors and those preferred by the students, 3 to specify the learning styles and teaching techniques students with high level of English proficiency preferred, and 4 to investigate the similarities of Thai and Vietnamese students’ preferences for learning styles and teaching techniques. The sample consisted of two main groups: 1 undergraduate students from King Mongkut’s University of Technology North Bangkok (KMUTNB, Thailand and Thai Nguyen University (TNU, Vietnam and 2 English instructors from both institutions. The instruments employed comprised the Students’ Preferred English Learning Style and Teaching Technique Questionnaire and the Teachers’ Practiced English Teaching Style and Technique Questionnaire. The collected data were analyzed using arithmetic means and standard deviation. The findings can contribute to the curriculum development and assist teachers to teach outside their comfort level to match the students’ preferred learning styles. In addition, the findings could better promote the courses provided for students. By understanding the learning style make-up of the students enrolled in the courses, faculty can adjust their modes of content delivery to match student preferences and maximize

  11. Competitive debate classroom as a cooperative learning technique for the human resources subject

    Directory of Open Access Journals (Sweden)

    Guillermo A. SANCHEZ PRIETO

    2018-01-01

    Full Text Available The paper shows an academic debate model as a cooperative learning technique for teaching human resources at University. The general objective of this paper is to conclude if academic debate can be included in the category of cooperative learning. The Specific objective it is presenting a model to implement this technique. Thus the first part of the paper shows the concept of cooperative learning and its main characteristics. The second part presents the debate model believed to be labelled as cooperative learning. Last part concludes with the characteristics of the model that match different aspects or not of the cooperative learning.

  12. A simple pulse shape discrimination technique applied to a silicon strip detector

    International Nuclear Information System (INIS)

    Figuera, P.; Lu, J.; Amorini, F.; Cardella, G.; DiPietro, A.; Papa, M.; Musumarra, A.; Pappalardo, G.; Rizzo, F.; Tudisco, S.

    2001-01-01

    Full text: Since the early sixties, it has been known that the shape of signals from solid state detectors can be used for particle identification. Recently, this idea has been revised in a group of papers where it has been shown that the shape of current signals from solid state detectors is mainly governed by the combination of plasma erosion time and charge carrier collection time effects. We will present the results of a systematic study on a pulse shape identification method which, contrary to the techniques proposed, is based on the use of the same electronic chain normally used in the conventional time of flight technique. The method is based on the use of charge preamplifiers, low polarization voltages (i.e. just above full depletion ones), rear side injection of the incident particles, and on a proper setting of the constant fraction discriminators which enhances the dependence of the timing output on the rise time of the input signals (which depends on the charge and energy of the incident ions). The method has been applied to an annular Si strip detector with an inner radius of about 16 mm and an outer radius of about 88 mm. The detector, manufactured by Eurisys Measures (Type Ips.73.74.300.N9), is 300 microns thick and consists of 8 independent sectors each divided into 9 circular strips. On beam tests have been performed at the cyclotron of the Laboratori Nazionali del Sud in Catania using a 25.7 MeV/nucleon 58 Ni beam impinging on a 51 V and 45 Sc composite target. Excellent charge identification from H up to the Ni projectile has been observed and typical charge identification thresholds are: ∼ 1.7 MeV/nucleon for Z ≅ 6, ∼ 3.0 MeV/nucleon for Z ≅ 11, and ∼ 5.5 MeV/nucleon for Z ≅ 20. Isotope identification up to A ≅ 13 has been observed with an energy threshold of about 6 MeV/nucleon. The identification quality has been studied as a function of the constant fraction settings. The method has been applied to all the 72 independent strips

  13. Advanced nondestructive techniques applied for the detection of discontinuities in aluminum foams

    Science.gov (United States)

    Katchadjian, Pablo; García, Alejandro; Brizuela, Jose; Camacho, Jorge; Chiné, Bruno; Mussi, Valerio; Britto, Ivan

    2018-04-01

    Metal foams are finding an increasing range of applications by their lightweight structure and physical, chemical and mechanical properties. Foams can be used to fill closed moulds for manufacturing structural foam parts of complex shape [1]; foam filled structures are expected to provide good mechanical properties and energy absorption capabilities. The complexity of the foaming process and the number of parameters to simultaneously control, demand a preliminary and hugely wide experimental activity to manufacture foamed components with a good quality. That is why there are many efforts to improve the structure of foams, in order to obtain a product with good properties. The problem is that even for seemingly identical foaming conditions, the effective foaming can vary significantly from one foaming trial to another. The variation of the foams often is related by structural imperfections, joining region (foam-foam or foam-wall mold) or difficulties in achieving a complete filling of the mould. That is, in a closed mold, the result of the mold filling and its structure or defects are not known a priori and can eventually vary significantly. These defects can cause a drastic deterioration of the mechanical properties [2] and lead to a low performance in its application. This work proposes the use of advanced nondestructive techniques for evaluating the foam distribution after filling the mold to improve the manufacturing process. To achieved this purpose ultrasonic technique (UT) and cone beam computed tomography (CT) were applied on plate and structures of different thicknesses filled with foam of different porosity. UT was carried out on transmission mode with low frequency air-coupled transducers [3], in focused and unfocused configurations.

  14. Situational Awareness Applied to Geology Field Mapping using Integration of Semantic Data and Visualization Techniques

    Science.gov (United States)

    Houser, P. I. Q.

    2017-12-01

    21st century earth science is data-intensive, characterized by heterogeneous, sometimes voluminous collections representing phenomena at different scales collected for different purposes and managed in disparate ways. However, much of the earth's surface still requires boots-on-the-ground, in-person fieldwork in order to detect the subtle variations from which humans can infer complex structures and patterns. Nevertheless, field experiences can and should be enabled and enhanced by a variety of emerging technologies. The goal of the proposed research project is to pilot test emerging data integration, semantic and visualization technologies for evaluation of their potential usefulness in the field sciences, particularly in the context of field geology. The proposed project will investigate new techniques for data management and integration enabled by semantic web technologies, along with new techniques for augmented reality that can operate on such integrated data to enable in situ visualization in the field. The research objectives include: Develop new technical infrastructure that applies target technologies to field geology; Test, evaluate, and assess the technical infrastructure in a pilot field site; Evaluate the capabilities of the systems for supporting and augmenting field science; and Assess the generality of the system for implementation in new and different types of field sites. Our hypothesis is that these technologies will enable what we call "field science situational awareness" - a cognitive state formerly attained only through long experience in the field - that is highly desirable but difficult to achieve in time- and resource-limited settings. Expected outcomes include elucidation of how, and in what ways, these technologies are beneficial in the field; enumeration of the steps and requirements to implement these systems; and cost/benefit analyses that evaluate under what conditions the investments of time and resources are advisable to construct

  15. TEACHING AND LEARNING METHODOLOGIES SUPPORTED BY ICT APPLIED IN COMPUTER SCIENCE

    Directory of Open Access Journals (Sweden)

    Jose CAPACHO

    2016-04-01

    Full Text Available The main objective of this paper is to show a set of new methodologies applied in the teaching of Computer Science using ICT. The methodologies are framed in the conceptual basis of the following sciences: Psychology, Education and Computer Science. The theoretical framework of the research is supported by Behavioral Theory, Gestalt Theory. Genetic-Cognitive Psychology Theory and Dialectics Psychology. Based on the theoretical framework the following methodologies were developed: Game Theory, Constructivist Approach, Personalized Teaching, Problem Solving, Cooperative Collaborative learning, Learning projects using ICT. These methodologies were applied to the teaching learning process during the Algorithms and Complexity – A&C course, which belongs to the area of ​​Computer Science. The course develops the concepts of Computers, Complexity and Intractability, Recurrence Equations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Shortest Path Problem and Graph Theory. The main value of the research is the theoretical support of the methodologies and their application supported by ICT using learning objects. The course aforementioned was built on the Blackboard platform evaluating the operation of methodologies. The results of the evaluation are presented for each of them, showing the learning outcomes achieved by students, which verifies that methodologies are functional.

  16. Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.

    Science.gov (United States)

    Vallmuur, Kirsten; Marucci-Wellman, Helen R; Taylor, Jennifer A; Lehto, Mark; Corns, Helen L; Smith, Gordon S

    2016-04-01

    Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

  17. The simulation of Typhoon-induced coastal inundation in Busan, South Korea applying the downscaling technique

    Science.gov (United States)

    Jang, Dongmin; Park, Junghyun; Yuk, Jin-Hee; Joh, MinSu

    2017-04-01

    Due to typhoons, the south coastal cities including Busan in South Korea coastal are very vulnerable to a surge, wave and corresponding coastal inundation, and are affected every year. In 2016, South Korea suffered tremendous damage by typhoon 'Chaba', which was developed near east-north of Guam on Sep. 28 and had maximum 10-minute sustained wind speed of about 50 m/s, 1-minute sustained wind speed of 75 m/s and a minimum central pressure of 905 hpa. As 'Chaba', which is the strongest since typhoon 'Maemi' in 2003, hit South Korea on Oct. 5, it caused a massive economic and casualty damage to Ulsan, Gyeongju and Busan in South Korea. In particular, the damage of typhoon-induced coastal inundation in Busan, where many high-rise buildings and residential areas are concentrated near coast, was serious. The coastal inundation could be more affected by strong wind-induced wave than surge. In fact, it was observed that the surge height was about 1 m averagely and a significant wave height was about 8 m at coastal sea nearby Busan on Oct. 5 due to 'Chaba'. Even though the typhoon-induced surge elevated the sea level, the typhoon-induced long period wave with wave period of more than 15s could play more important role in the inundation. The present work simulated the coastal inundation induced by 'Chaba' in Busan, South Korea considering the effects of typhoon-induced surge and wave. For 'Chaba' hindcast, high resolution Weather Research and Forecasting model (WRF) was applied using a reanalysis data produced by NCEP (FNL 0.25 degree) on the boundary and initial conditions, and was validated by the observation of wind speed, direction and pressure. The typhoon-induced coastal inundation was simulated by an unstructured gird model, Finite Volume Community Ocean Model (FVCOM), which is fully current-wave coupled model. To simulate the wave-induced inundation, 1-way downscaling technique of multi domain was applied. Firstly, a mother's domain including Korean peninsula was

  18. A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning

    Directory of Open Access Journals (Sweden)

    Bruno A. Angélico

    2013-12-01

    Full Text Available This work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO, Genetic Algorithm (GA and an Ant Colony Optimization (ACO are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.

  19. SOCAP: Lessons learned in applying SIPE-2 to the military operations crisis action planning domain

    Science.gov (United States)

    Desimone, Roberto

    1992-01-01

    This report describes work funded under the DARPA Planning and Scheduling Initiative that led to the development of SOCAP (System for Operations Crisis Action Planning). In particular, it describes lessons learned in applying SIPE-2, the underlying AI planning technology within SOCAP, to the domain of military operations deliberate and crisis action planning. SOCAP was demonstrated at the U.S. Central Command and at the Pentagon in early 1992. A more detailed report about the lessons learned is currently being prepared. This report was presented during one of the panel discussions on 'The Relevance of Scheduling to AI Planning Systems.'

  20. Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction

    OpenAIRE

    Ruijian Zhang; Deren Li

    2017-01-01

    Water quality assessment and prediction is a more and more important issue. Traditional ways either take lots of time or they can only do assessments. In this research, by applying machine learning algorithm to a long period time of water attributes’ data; we can generate a decision tree so that it can predict the future day’s water quality in an easy and efficient way. The idea is to combine the traditional ways and the computer algorithms together. Using machine learning algorithms, the ass...

  1. Advanced examination techniques applied to the qualification of critical welds for the ITER correction coils

    CERN Document Server

    Sgobba, Stefano; Libeyre, Paul; Marcinek, Dawid Jaroslaw; Piguiet, Aline; Cécillon, Alexandre

    2015-01-01

    The ITER correction coils (CCs) consist of three sets of six coils located in between the toroidal (TF) and poloidal field (PF) magnets. The CCs rely on a Cable-in-Conduit Conductor (CICC), whose supercritical cooling at 4.5 K is provided by helium inlets and outlets. The assembly of the nozzles to the stainless steel conductor conduit includes fillet welds requiring full penetration through the thickness of the nozzle. Static and cyclic stresses have to be sustained by the inlet welds during operation. The entire volume of helium inlet and outlet welds, that are submitted to the most stringent quality levels of imperfections according to standards in force, is virtually uninspectable with sufficient resolution by conventional or computed radiography or by Ultrasonic Testing. On the other hand, X-ray computed tomography (CT) was successfully applied to inspect the full weld volume of several dozens of helium inlet qualification samples. The extensive use of CT techniques allowed a significant progress in the ...

  2. Spatial analysis techniques applied to uranium prospecting in Chihuahua State, Mexico

    Science.gov (United States)

    Hinojosa de la Garza, Octavio R.; Montero Cabrera, María Elena; Sanín, Luz H.; Reyes Cortés, Manuel; Martínez Meyer, Enrique

    2014-07-01

    To estimate the distribution of uranium minerals in Chihuahua, the advanced statistical model "Maximun Entropy Method" (MaxEnt) was applied. A distinguishing feature of this method is that it can fit more complex models in case of small datasets (x and y data), as is the location of uranium ores in the State of Chihuahua. For georeferencing uranium ores, a database from the United States Geological Survey and workgroup of experts in Mexico was used. The main contribution of this paper is the proposal of maximum entropy techniques to obtain the mineral's potential distribution. For this model were used 24 environmental layers like topography, gravimetry, climate (worldclim), soil properties and others that were useful to project the uranium's distribution across the study area. For the validation of the places predicted by the model, comparisons were done with other research of the Mexican Service of Geological Survey, with direct exploration of specific areas and by talks with former exploration workers of the enterprise "Uranio de Mexico". Results. New uranium areas predicted by the model were validated, finding some relationship between the model predictions and geological faults. Conclusions. Modeling by spatial analysis provides additional information to the energy and mineral resources sectors.

  3. Applying Toyota production system techniques for medication delivery: improving hospital safety and efficiency.

    Science.gov (United States)

    Newell, Terry L; Steinmetz-Malato, Laura L; Van Dyke, Deborah L

    2011-01-01

    The inpatient medication delivery system used at a large regional acute care hospital in the Midwest had become antiquated and inefficient. The existing 24-hr medication cart-fill exchange process with delivery to the patients' bedside did not always provide ordered medications to the nursing units when they were needed. In 2007 the principles of the Toyota Production System (TPS) were applied to the system. Project objectives were to improve medication safety and reduce the time needed for nurses to retrieve patient medications. A multidisciplinary team was formed that included representatives from nursing, pharmacy, informatics, quality, and various operational support departments. Team members were educated and trained in the tools and techniques of TPS, and then designed and implemented a new pull system benchmarking the TPS Ideal State model. The newly installed process, providing just-in-time medication availability, has measurably improved delivery processes as well as patient safety and satisfaction. Other positive outcomes have included improved nursing satisfaction, reduced nursing wait time for delivered medications, and improved efficiency in the pharmacy. After a successful pilot on two nursing units, the system is being extended to the rest of the hospital. © 2010 National Association for Healthcare Quality.

  4. Statistical Techniques Applied to Aerial Radiometric Surveys (STAARS): cluster analysis. National Uranium Resource Evaluation

    International Nuclear Information System (INIS)

    Pirkle, F.L.; Stablein, N.K.; Howell, J.A.; Wecksung, G.W.; Duran, B.S.

    1982-11-01

    One objective of the aerial radiometric surveys flown as part of the US Department of Energy's National Uranium Resource Evaluation (NURE) program was to ascertain the regional distribution of near-surface radioelement abundances. Some method for identifying groups of observations with similar radioelement values was therefore required. It is shown in this report that cluster analysis can identify such groups even when no a priori knowledge of the geology of an area exists. A method of convergent k-means cluster analysis coupled with a hierarchical cluster analysis is used to classify 6991 observations (three radiometric variables at each observation location) from the Precambrian rocks of the Copper Mountain, Wyoming, area. Another method, one that combines a principal components analysis with a convergent k-means analysis, is applied to the same data. These two methods are compared with a convergent k-means analysis that utilizes available geologic knowledge. All three methods identify four clusters. Three of the clusters represent background values for the Precambrian rocks of the area, and one represents outliers (anomalously high 214 Bi). A segmentation of the data corresponding to geologic reality as discovered by other methods has been achieved based solely on analysis of aerial radiometric data. The techniques employed are composites of classical clustering methods designed to handle the special problems presented by large data sets. 20 figures, 7 tables

  5. The Study of Mining Activities and their Influences in the Almaden Region Applying Remote Sensing Techniques

    International Nuclear Information System (INIS)

    Rico, C.; Schmid, T.; Millan, R.; Gumuzzio, J.

    2010-01-01

    This scientific-technical report is a part of an ongoing research work carried out by Celia Rico Fraile in order to obtain the Diploma of Advanced Studies as part of her PhD studies. This work has been developed in collaboration with the Faculty of Science at The Universidad Autonoma de Madrid and the Department of Environment at CIEMAT. The main objective of this work was the characterization and classification of land use in Almaden (Ciudad Real) during cinnabar mineral exploitation and after mining activities ceased in 2002, developing a methodology focused on the integration of remote sensing techniques applying multispectral and hyper spectral satellite data. By means of preprocessing and processing of data from the satellite images as well as data obtained from field campaigns, a spectral library was compiled in order to obtain representative land surfaces within the study area. Monitoring results show that the distribution of areas affected by mining activities is rapidly diminishing in recent years. (Author) 130 refs

  6. Using spreadsheets to develop applied skills in a business math course: Student feedback and perceived learning

    Directory of Open Access Journals (Sweden)

    Thomas Mays

    2015-10-01

    Full Text Available This paper describes the redesign of a business math course and its delivery in both face-to-face and online formats. Central to the redesigned course was the addition of applied spreadsheet exercises that served as both learning and summative assessment tools. Several other learning activities and assignments were integrated in the course to address diverse student learning styles and levels of math anxiety. Students were invited to complete a survey that asked them to rank course activities and assignments based on how well they helped the student learn course material. Open-ended items were also included in the survey. In the online course sections, students reported higher perceived learning from the use the spreadsheet-based application assignments, while face-to-face students preferred demonstrations. Qualitative remarks from the online students included numerous comments about the positive learning impact of the business application spreadsheet-based assignments, as well as the link between these assignments and what students considered the “real world.”

  7. Application and evaluation of a combination of socratice and learning through discussion techniques

    Directory of Open Access Journals (Sweden)

    EJ van Aswegen

    2001-09-01

    Full Text Available This article has its genesis in the inquirer’s interest in the need for internalizing critical thinking, creative thinking and reflective skills in adult learners. As part of a broader study the inquirer used a combination of two techniques over a period of nine months, namely: Socratic discussion/questioning and Learning Through Discussion Technique. The inquirer within this inquiry elected mainly qualitative methods, because they were seen as more adaptable to dealing with multiple realities and more sensitive and adaptable to the many shaping influences and value patterns that may be encountered (Lincoln & Guba, 1989. Purposive sampling was used and sample size (n =10 was determined by the willingness of potential participants to enlist in the chosen techniques. Feedback from participants was obtained: (1 verbally after each discussion session, and (2 in written format after completion of the course content. The final/ summative evaluation was obtained through a semi-structured questionnaire. This was deemed necessary, in that the participants were already studying for the end of the year examination. For the purpose of this condensed report the inquirer reflected only on the feedback obtained with the help of the questionnaire. The empirical study showed that in spite of various adaptation problems experienced, eight (8 of the ten (10 participants felt positive toward the applied techniques.

  8. Application and evaluation of a combination of socratice and learning through discussion techniques.

    Science.gov (United States)

    van Aswegen, E J; Brink, H I; Steyn, P J

    2001-11-01

    This article has its genesis in the inquirer's interest in the need for internalizing critical thinking, creative thinking and reflective skills in adult learners. As part of a broader study the inquirer used a combination of two techniques over a period of nine months, namely: Socratic discussion/questioning and Learning Through Discussion Technique. The inquirer within this inquiry elected mainly qualitative methods, because they were seen as more adaptable to dealing with multiple realities and more sensitive and adaptable to the many shaping influences and value patterns that may be encountered (Lincoln & Guba, 1989). Purposive sampling was used and sample size (n = 10) was determined by the willingness of potential participants to enlist in the chosen techniques. Feedback from participants was obtained: (1) verbally after each discussion session, and (2) in written format after completion of the course content. The final/summative evaluation was obtained through a semi-structured questionnaire. This was deemed necessary, in that the participants were already studying for the end of the year examination. For the purpose of this condensed report the inquirer reflected only on the feedback obtained with the help of the questionnaire. The empirical study showed that in spite of various adaptation problems experienced, eight (8) of the ten (10) participants felt positive toward the applied techniques.

  9. Coldness applied to plastic engineering techniques and rooms; Le froid applique aux techniques de la plasturgie et a ses locaux

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2001-07-01

    This technical dossier is the result of a collaboration between the CFE, EdF Industrie and the French federation of plastic engineering. It aims at answering all questions relative to plastic materials processing: 1 - general study on the economical aspects of plastic engineering, plastic materials, and manufacturing processes; 2 - the different cold processing techniques (air cooling and refrigerating systems); 3 - the main transformation processes for thermo-plastic materials and the advantage of cooling techniques; 4 - the environmental conditioning of rooms (clean rooms); 5 - examples of realizations. (J.S.)

  10. Changing the Learning Environment in the College of Engineering and Applied Science Using Challenge Based Learning

    Directory of Open Access Journals (Sweden)

    Whitney Brooke Gaskins

    2015-02-01

    Full Text Available Over the past 20 years there have been many changes to the primary and secondary educational system that have impacted students, teachers, and post-secondary institutions across the United States of America. One of the most important is the large number of standardized tests students are required to take to show adequate performance in school. Students think differently because they are taught differently due to this focus on standardized testing, thus changing the skill sets students acquire in secondary school. This presents a critical problem for colleges and universities, as they now are using practices for and have expectations of these students that are unrealistic for the changing times. High dropout rates in the colleges of engineering have been attributed to the cultural atmosphere of the institution. Students have reported a low sense of belonging and low relatability to course material. To reduce negative experiences and increase motivation, Challenge Based Learning (CBL was introduced in an undergraduate Basic Electric Circuits (BEC course. CBL is a structured model for course content with a foundation in problem-based learning. CBL offers general concepts from which students derive the challenges they will address. Results show an improved classroom experience for students who were taught with CBL.

  11. Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

    Science.gov (United States)

    Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

    2017-07-01

    Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

  12. Approaching Assessment from a Learning Perspective: Elevating Assessment beyond Technique

    Science.gov (United States)

    Simms, Michele; George, Beena

    2014-01-01

    Assessment is a key process in assuring quality education but how is it linked to the scholarship of teaching and learning (SoTL)? How can we join teaching and learning to the assessment process rather than view it as a stand-alone component in course and/or program development? This paper explores the relationship between assessment and the SoTL…

  13. Learning Faults Detection by AIS Techniques in CSCL Environments

    Science.gov (United States)

    Zedadra, Amina; Lafifi, Yacine

    2015-01-01

    By the increase of e-learning platforms, huge data sets are made from different kinds of the collected traces. These traces differ from one learner to another according to their characteristics (learning styles, preferences, performed actions, etc.). Learners' traces are very heterogeneous and voluminous, so their treatments and exploitations are…

  14. Enhanced Quality Control in Pharmaceutical Applications by Combining Raman Spectroscopy and Machine Learning Techniques

    Science.gov (United States)

    Martinez, J. C.; Guzmán-Sepúlveda, J. R.; Bolañoz Evia, G. R.; Córdova, T.; Guzmán-Cabrera, R.

    2018-06-01

    In this work, we applied machine learning techniques to Raman spectra for the characterization and classification of manufactured pharmaceutical products. Our measurements were taken with commercial equipment, for accurate assessment of variations with respect to one calibrated control sample. Unlike the typical use of Raman spectroscopy in pharmaceutical applications, in our approach the principal components of the Raman spectrum are used concurrently as attributes in machine learning algorithms. This permits an efficient comparison and classification of the spectra measured from the samples under study. This also allows for accurate quality control as all relevant spectral components are considered simultaneously. We demonstrate our approach with respect to the specific case of acetaminophen, which is one of the most widely used analgesics in the market. In the experiments, commercial samples from thirteen different laboratories were analyzed and compared against a control sample. The raw data were analyzed based on an arithmetic difference between the nominal active substance and the measured values in each commercial sample. The principal component analysis was applied to the data for quantitative verification (i.e., without considering the actual concentration of the active substance) of the difference in the calibrated sample. Our results show that by following this approach adulterations in pharmaceutical compositions can be clearly identified and accurately quantified.

  15. Lessons learned applying CASE methods/tools to Ada software development projects

    Science.gov (United States)

    Blumberg, Maurice H.; Randall, Richard L.

    1993-01-01

    This paper describes the lessons learned from introducing CASE methods/tools into organizations and applying them to actual Ada software development projects. This paper will be useful to any organization planning to introduce a software engineering environment (SEE) or evolving an existing one. It contains management level lessons learned, as well as lessons learned in using specific SEE tools/methods. The experiences presented are from Alpha Test projects established under the STARS (Software Technology for Adaptable and Reliable Systems) project. They reflect the front end efforts by those projects to understand the tools/methods, initial experiences in their introduction and use, and later experiences in the use of specific tools/methods and the introduction of new ones.

  16. Investigation about the efficiency of the bioaugmentation technique when applied to diesel oil contaminated soils

    Directory of Open Access Journals (Sweden)

    Adriano Pinto Mariano

    2009-10-01

    Full Text Available This work investigated the efficiency of the bioaugmentation technique when applied to diesel oil contaminated soils collected at three service stations. Batch biodegradation experiments were carried out in Bartha biometer flasks (250 mL used to measure the microbial CO2 production. Biodegradation efficiency was also measured by quantifying the concentration of hydrocarbons. In addition to the biodegradation experiments, the capability of the studied cultures and the native microorganisms to biodegrade the diesel oil purchased from a local service station, was verified using a technique based on the redox indicator 2,6 -dichlorophenol indophenol (DCPIP. Results obtained with this test showed that the inocula used in the biodegradation experiments were able to degrade the diesel oil and the tests carried out with the native microorganisms indicated that these soils had a microbiota adapted to degrade the hydrocarbons. In general, no gain was obtained with the addition of microorganisms or even negative effects were observed in the biodegradation experiments.Este trabalho investigou a eficiência da técnica do bioaumento quando aplicada a solos contaminados com óleo diesel coletados em três postos de combustíveis. Experimentos de biodegradação foram realizados em frascos de Bartha (250 mL, usados para medir a produção microbiana de CO2. A eficiência de biodegradação também foi quantificada pela concentração de hidrocarbonetos. Conjuntamente aos experimentos de biodegradação, a capacidade das culturas estudadas e dos microrganismos nativos em biodegradar óleo diesel comprado de um posto de combustíveis local, foi verificada utilizando-se a técnica baseada no indicador redox 2,6 - diclorofenol indofenol (DCPIP. Resultados obtidos com esse teste mostraram que os inóculos empregados nos experimentos de biodegradação foram capazes de biodegradar óleo diesel e os testes com os microrganismos nativos indicaram que estes solos

  17. The learning theories’ knowledge applied in the performance of distance tutor

    Directory of Open Access Journals (Sweden)

    Fernanda Abreu de Moraes Figueiredo

    2016-07-01

    Full Text Available Abstract: This study aimed to identify the most influential theory of learning related to the practice of mentoring from behaviorism, cognitivism, humanism, the sociocultural theory and connectivism, and apply the most appropriate theories to solve common problems in distance education. For this purpose, we used the literature method. It was noted that each of the theories end up being influential to the role of tutor. Therefore, the learning tends to be richer in the ratio and effective to apply different theories together. However, that support better substantiating tutor's role is humanism, the sociocultural theory and connectivism. It was noticed that the problems often experienced by students in distance education are due to failures tutor interaction and affection, implying to resolve them closer tutor with the student to have more responsibility in the exchange of information, meeting deadlines and clarity in the disclosure notes assessments. Knowledge are mainly from humanism and sociocultural theory that end up not only reasons for existence of the tutor as serving to improve the development of the quality of tutor-student interaction. Keywords: learning theories; distance learning (DL; tutor distance.

  18. Evaluation and Implementation of Distance Learning: Technologies, Tools and Techniques

    Directory of Open Access Journals (Sweden)

    Figen UNAL

    2004-04-01

    Full Text Available This book is published by Idea Group Publishing. The book consistsof seven chapters, a bibliography and references section, fourappendices, an index, and author biography. In appendix A, thereare three data forms those can used by distance learning coursedesigners. In appendix B, under the title of ‘definitions’, there is a dictionary consists of Internet and e-learning terms. In appendixC, there is a table relevant to infrastructure survey and upgraderequirements. Finally appendix D contains a list of web sites thatoffer discussions of distance learning issues and concepts.

  19. Applying Augmented Reality to a Mobile-Assisted Learning System for Martial Arts Using Kinect Motion Capture

    Science.gov (United States)

    Hsu, Wen-Chun; Shih, Ju-Ling

    2016-01-01

    In this study, to learn the routine of Tantui, a branch of martial arts was taken as an object of research. Fitts' stages of motor learning and augmented reality (AR) were applied to a 3D mobile-assisted learning system for martial arts, which was characterized by free viewing angles. With the new system, learners could rotate the viewing angle of…

  20. Applying a learning design methodology in the flipped classroom approach – empowering teachers to reflect and design for learning

    Directory of Open Access Journals (Sweden)

    Evangelia Triantafyllou

    2016-05-01

    Full Text Available One of the recent developments in teaching that heavily relies on current technology is the “flipped classroom” approach. In a flipped classroom the traditional lecture and homework sessions are inverted. Students are provided with online material in order to gain necessary knowledge before class, while class time is devoted to clarifications and application of this knowledge. The hypothesis is that there could be deep and creative discussions when teacher and students physically meet. This paper discusses how the learning design methodology can be applied to represent, share and guide educators through flipped classroom designs. In order to discuss the opportunities arising by this approach, the different components of the Learning Design – Conceptual Map (LD-CM are presented and examined in the context of the flipped classroom. It is shown that viewing the flipped classroom through the lens of learning design can promote the use of theories and methods to evaluate its effect on the achievement of learning objectives, and that it may draw attention to the employment of methods to gather learner responses. Moreover, a learning design approach can enforce the detailed description of activities, tools and resources used in specific flipped classroom models, and it can make educators more aware of the decisions that have to be taken and people who have to be involved when designing a flipped classroom. By using the LD-CM, this paper also draws attention to the importance of characteristics and values of different stakeholders (i.e. institutions, educators, learners, and external agents, which influence the design and success of flipped classrooms. Moreover, it looks at the teaching cycle from a flipped instruction model perspective and adjusts it to cater for the reflection loops educators are involved when designing, implementing and re-designing a flipped classroom. Finally, it highlights the effect of learning design on the guidance

  1. Multidisciplinary Design Techniques Applied to Conceptual Aerospace Vehicle Design. Ph.D. Thesis Final Technical Report

    Science.gov (United States)

    Olds, John Robert; Walberg, Gerald D.

    1993-01-01

    Multidisciplinary design optimization (MDO) is an emerging discipline within aerospace engineering. Its goal is to bring structure and efficiency to the complex design process associated with advanced aerospace launch vehicles. Aerospace vehicles generally require input from a variety of traditional aerospace disciplines - aerodynamics, structures, performance, etc. As such, traditional optimization methods cannot always be applied. Several multidisciplinary techniques and methods were proposed as potentially applicable to this class of design problem. Among the candidate options are calculus-based (or gradient-based) optimization schemes and parametric schemes based on design of experiments theory. A brief overview of several applicable multidisciplinary design optimization methods is included. Methods from the calculus-based class and the parametric class are reviewed, but the research application reported focuses on methods from the parametric class. A vehicle of current interest was chosen as a test application for this research. The rocket-based combined-cycle (RBCC) single-stage-to-orbit (SSTO) launch vehicle combines elements of rocket and airbreathing propulsion in an attempt to produce an attractive option for launching medium sized payloads into low earth orbit. The RBCC SSTO presents a particularly difficult problem for traditional one-variable-at-a-time optimization methods because of the lack of an adequate experience base and the highly coupled nature of the design variables. MDO, however, with it's structured approach to design, is well suited to this problem. The result of the application of Taguchi methods, central composite designs, and response surface methods to the design optimization of the RBCC SSTO are presented. Attention is given to the aspect of Taguchi methods that attempts to locate a 'robust' design - that is, a design that is least sensitive to uncontrollable influences on the design. Near-optimum minimum dry weight solutions are

  2. Study for applying microwave power saturation technique on fingernail/EPR dosimetry

    Energy Technology Data Exchange (ETDEWEB)

    Park, Byeong Ryong; Choi, Hoon; Nam, Hyun Ill; Lee, Byung Ill [Radiation Health Research Institute, Seoul (Korea, Republic of)

    2012-10-15

    There is growing recognition worldwide of the need to develop effective uses of dosimetry methods to assess unexpected exposure to radiation in the event of a large scale event. One of physically based dosimetry methods electron paramagnetic resonance (EPR) spectroscopy has been applied to perform retrospective radiation dosimetry using extracted samples of tooth enamel and nail(fingernail and toenail), following radiation accidents and exposures resulting from weapon use, testing, and production. Human fingernails are composed largely of a keratin, which consists of {alpha} helical peptide chains that are twisted into a left handed coil and strengthened by disulphide cross links. Ionizing radiation generates free radicals in the keratin matrix, and these radicals are stable over a relatively long period (days to weeks). Most importantly, the number of radicals is proportional to the magnitude of the dose over a wide dose range (0{approx}30 Gy). Also, dose can be estimated at four different locations on the human body, providing information on the homogeneity of the radiation exposure. And The results from EPR nail dosimetry are immediately available However, relatively large background signal (BKS) converted from mechanically induced signal (MIS) after cutting process of fingernail, normally overlaps with the radiation induced signal (RIS), make it difficult to estimate accurate dose accidental exposure. Therefore, estimation method using dose response curve was difficult to ensure reliability below 5 Gy. In this study, In order to overcome these disadvantages, we measured the reactions of RIS and BKS (MIS) according to the change of Microwave power level, and researched about the applicability of the Power saturation technique at low dose.

  3. Student’s Perceptions on Simulation as Part of Experiential Learning in Approaches, Methods, and Techniques (AMT Course

    Directory of Open Access Journals (Sweden)

    Marselina Karina Purnomo

    2017-03-01

    Full Text Available Simulation is a part of Experiential Learning which represents certain real-life events. In this study, simulation is used as a learning activity in Approaches, Methods, and Techniques (AMT course which is one of the courses in English Language Education Study Program (ELESP of Sanata Dharma University. Since simulation represents the real-life events, it encourages students to apply the approaches, methods, and techniques being studied based on the real-life classroom. Several experts state that students are able to involve their personal experiences through simulation which additionally is believed to create a meaningful learning in the class. This study aimed to discover ELESP students’ perceptions toward simulation as a part of Experiential Learning in AMT course. From the findings, it could be inferred that students agreed that simulation in class was important for students’ learning for it formed a meaningful learning in class.  DOI: https://doi.org/10.24071/llt.2017.200104

  4. Plasma-based techniques applied to the determination of metals and metalloids in atmospheric aerosols

    International Nuclear Information System (INIS)

    Smichowski, Patricia

    2011-01-01

    Full text: This lecture presents an overview of the research carried out by our group during the last decade on the determination of metals, metalloids, ions and species in atmospheric aerosols and related matrices using plasma-based techniques. In our first studies we explored the application of a size fractionation procedure and the subsequent determination of minor, major and trace elements in samples of deposited particles collected one day after the eruption of the Copahue Volcano, located in the Chile-Argentina border to assess the content of relevant elements with respect of the environment and the local population health. We employed a multi-technique approach (ICP-MS, XRD and NAA) to gain complete information of the characteristics of the sample. In addition to the study of ashes emitted for natural sources we also studied ashes of anthropogenic origin such as those arising from coal combustion in thermal power plants. For estimating the behavior and fate of elements in atmospheric particles and ashes we applied in this case a chemical fractionation procedure in order to establish the distribution of many elements amongst soluble, bound to carbonates, bound to oxides and bound to organic matter and environmental immobile fraction. Studies on the air quality of the mega-city of Buenos Aires were scarce and fragmentary and our objective was, and still is, to contribute to clarify key issues related to levels of crustal, toxic and potentially toxic elements in this air basin. Our findings were compared with average concentrations of metals and metalloids with results reported for other Latin American cities such as Sao Paulo, Mexico and Santiago de Chile. In this context, a series of studies were carried out since 2004 considering different sampling strategies to reflect local aspects of air pollution sources. In the last years, our interest was focused on the levels of traffic-related elements in the urban atmosphere. We have contributed with the first data

  5. Plasma-based techniques applied to the determination of metals and metalloids in atmospheric aerosols

    Energy Technology Data Exchange (ETDEWEB)

    Smichowski, Patricia, E-mail: smichows@cnea.gov.ar [Comision Nacional de Energia Atomica, Gerencia Quimica, Pcia de Buenos Aires (Argentina)

    2011-07-01

    Full text: This lecture presents an overview of the research carried out by our group during the last decade on the determination of metals, metalloids, ions and species in atmospheric aerosols and related matrices using plasma-based techniques. In our first studies we explored the application of a size fractionation procedure and the subsequent determination of minor, major and trace elements in samples of deposited particles collected one day after the eruption of the Copahue Volcano, located in the Chile-Argentina border to assess the content of relevant elements with respect of the environment and the local population health. We employed a multi-technique approach (ICP-MS, XRD and NAA) to gain complete information of the characteristics of the sample. In addition to the study of ashes emitted for natural sources we also studied ashes of anthropogenic origin such as those arising from coal combustion in thermal power plants. For estimating the behavior and fate of elements in atmospheric particles and ashes we applied in this case a chemical fractionation procedure in order to establish the distribution of many elements amongst soluble, bound to carbonates, bound to oxides and bound to organic matter and environmental immobile fraction. Studies on the air quality of the mega-city of Buenos Aires were scarce and fragmentary and our objective was, and still is, to contribute to clarify key issues related to levels of crustal, toxic and potentially toxic elements in this air basin. Our findings were compared with average concentrations of metals and metalloids with results reported for other Latin American cities such as Sao Paulo, Mexico and Santiago de Chile. In this context, a series of studies were carried out since 2004 considering different sampling strategies to reflect local aspects of air pollution sources. In the last years, our interest was focused on the levels of traffic-related elements in the urban atmosphere. We have contributed with the first data

  6. Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

    Science.gov (United States)

    Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

    2017-05-01

    In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

  7. Educating patients: understanding barriers, learning styles, and teaching techniques.

    Science.gov (United States)

    Beagley, Linda

    2011-10-01

    Health care delivery and education has become a challenge for providers. Nurses and other professionals are challenged daily to assure that the patient has the necessary information to make informed decisions. Patients and their families are given a multitude of information about their health and commonly must make important decisions from these facts. Obstacles that prevent easy delivery of health care information include literacy, culture, language, and physiological barriers. It is up to the nurse to assess and evaluate the patient's learning needs and readiness to learn because everyone learns differently. This article will examine how each of these barriers impact care delivery along with teaching and learning strategies will be examined. Copyright © 2011 American Society of PeriAnesthesia Nurses. Published by Elsevier Inc. All rights reserved.

  8. Interactive Multimedia Instruction for Training Self-Directed Learning Techniques

    Science.gov (United States)

    2016-06-01

    feedback and input on the content, format, and pedagogical approach of the lesson. This survey could be e-mailed to the principal ARI researcher for...peers in self-directed learning. Some examples of the metaphorical relationships and common examples woven into this IMI are identified in Table 1...20 Table 1 Metaphorical Relationships and Illustrations Used in Self-Directed Learning Training Military or Common Example Self-Directed

  9. An empirical study on the performance of spectral manifold learning techniques

    DEFF Research Database (Denmark)

    Mysling, Peter; Hauberg, Søren; Pedersen, Kim Steenstrup

    2011-01-01

    In recent years, there has been a surge of interest in spectral manifold learning techniques. Despite the interest, only little work has focused on the empirical behavior of these techniques. We construct synthetic data of variable complexity and observe the performance of the techniques as they ...

  10. E-learning Materials Development: Applying and Implementing Software Reuse Principles and Granularity Levels in the Small

    OpenAIRE

    Nabil Arman

    2010-01-01

    E-learning materials development is typically acknowledged as an expensive, complicated, and lengthy process, often producing materials that are of low quality and difficult to adaptand maintain. It has always been a challenge to identify proper e-learning materials that can be reused at a reasonable cost and effort. In this paper, software engineering reuse principlesare applied to e-learning materials development process. These principles are then applied and implemented in a prototype that...

  11. Trends in analytical techniques applied to particulate matter characterization: A critical review of fundaments and applications.

    Science.gov (United States)

    Galvão, Elson Silva; Santos, Jane Meri; Lima, Ana Teresa; Reis, Neyval Costa; Orlando, Marcos Tadeu D'Azeredo; Stuetz, Richard Michael

    2018-05-01

    Epidemiological studies have shown the association of airborne particulate matter (PM) size and chemical composition with health problems affecting the cardiorespiratory and central nervous systems. PM also act as cloud condensation nuclei (CNN) or ice nuclei (IN), taking part in the clouds formation process, and therefore can impact the climate. There are several works using different analytical techniques in PM chemical and physical characterization to supply information to source apportionment models that help environmental agencies to assess damages accountability. Despite the numerous analytical techniques described in the literature available for PM characterization, laboratories are normally limited to the in-house available techniques, which raises the question if a given technique is suitable for the purpose of a specific experimental work. The aim of this work consists of summarizing the main available technologies for PM characterization, serving as a guide for readers to find the most appropriate technique(s) for their investigation. Elemental analysis techniques like atomic spectrometry based and X-ray based techniques, organic and carbonaceous techniques and surface analysis techniques are discussed, illustrating their main features as well as their advantages and drawbacks. We also discuss the trends in analytical techniques used over the last two decades. The choice among all techniques is a function of a number of parameters such as: the relevant particles physical properties, sampling and measuring time, access to available facilities and the costs associated to equipment acquisition, among other considerations. An analytical guide map is presented as a guideline for choosing the most appropriated technique for a given analytical information required. Copyright © 2018 Elsevier Ltd. All rights reserved.

  12. Time-series-analysis techniques applied to nuclear-material accounting

    International Nuclear Information System (INIS)

    Pike, D.H.; Morrison, G.W.; Downing, D.J.

    1982-05-01

    This document is designed to introduce the reader to the applications of Time Series Analysis techniques to Nuclear Material Accountability data. Time series analysis techniques are designed to extract information from a collection of random variables ordered by time by seeking to identify any trends, patterns, or other structure in the series. Since nuclear material accountability data is a time series, one can extract more information using time series analysis techniques than by using other statistical techniques. Specifically, the objective of this document is to examine the applicability of time series analysis techniques to enhance loss detection of special nuclear materials. An introductory section examines the current industry approach which utilizes inventory differences. The error structure of inventory differences is presented. Time series analysis techniques discussed include the Shewhart Control Chart, the Cumulative Summation of Inventory Differences Statistics (CUSUM) and the Kalman Filter and Linear Smoother

  13. Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry

    Directory of Open Access Journals (Sweden)

    A. Anguera

    2016-01-01

    This paper illustrates the application of different knowledge discovery techniques for the purposes of classification within the above domains. The accuracy of this application for the two classes considered in each case is 99.86% and 98.11% for epilepsy diagnosis in the electroencephalography (EEG domain and 99.4% and 99.1% for early-age sports talent classification in the stabilometry domain. The KDD techniques achieve better results than other traditional neural network-based classification techniques.

  14. Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea

    International Nuclear Information System (INIS)

    Palit, Mousumi; Tudu, Bipan; Bhattacharyya, Nabarun; Dutta, Ankur; Dutta, Pallab Kumar; Jana, Arun; Bandyopadhyay, Rajib; Chatterjee, Anutosh

    2010-01-01

    In an electronic tongue, preprocessing on raw data precedes pattern analysis and choice of the appropriate preprocessing technique is crucial for the performance of the pattern classifier. While attempting to classify different grades of black tea using a voltammetric electronic tongue, different preprocessing techniques have been explored and a comparison of their performances is presented in this paper. The preprocessing techniques are compared first by a quantitative measurement of separability followed by principle component analysis; and then two different supervised pattern recognition models based on neural networks are used to evaluate the performance of the preprocessing techniques.

  15. Final Aperture Superposition Technique applied to fast calculation of electron output factors and depth dose curves

    International Nuclear Information System (INIS)

    Faddegon, B.A.; Villarreal-Barajas, J.E.

    2005-01-01

    The Final Aperture Superposition Technique (FAST) is described and applied to accurate, near instantaneous calculation of the relative output factor (ROF) and central axis percentage depth dose curve (PDD) for clinical electron beams used in radiotherapy. FAST is based on precalculation of dose at select points for the two extreme situations of a fully open final aperture and a final aperture with no opening (fully shielded). This technique is different than conventional superposition of dose deposition kernels: The precalculated dose is differential in position of the electron or photon at the downstream surface of the insert. The calculation for a particular aperture (x-ray jaws or MLC, insert in electron applicator) is done with superposition of the precalculated dose data, using the open field data over the open part of the aperture and the fully shielded data over the remainder. The calculation takes explicit account of all interactions in the shielded region of the aperture except the collimator effect: Particles that pass from the open part into the shielded part, or visa versa. For the clinical demonstration, FAST was compared to full Monte Carlo simulation of 10x10,2.5x2.5, and 2x8 cm 2 inserts. Dose was calculated to 0.5% precision in 0.4x0.4x0.2 cm 3 voxels, spaced at 0.2 cm depth intervals along the central axis, using detailed Monte Carlo simulation of the treatment head of a commercial linear accelerator for six different electron beams with energies of 6-21 MeV. Each simulation took several hours on a personal computer with a 1.7 Mhz processor. The calculation for the individual inserts, done with superposition, was completed in under a second on the same PC. Since simulations for the pre calculation are only performed once, higher precision and resolution can be obtained without increasing the calculation time for individual inserts. Fully shielded contributions were largest for small fields and high beam energy, at the surface, reaching a maximum

  16. Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

    Directory of Open Access Journals (Sweden)

    Sugiarto Indar

    2018-01-01

    Full Text Available Deep learning (DL has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still sceptical about its true capability: can the intelligence demonstrated by deep learning technique be applied for general tasks? This question motivates the emergence of another research discipline: neuromorphic computing (NC. In NC, researchers try to identify the most fundamental ingredients that construct intelligence behaviour produced by the brain itself. To achieve this, neuromorphic systems are developed to mimic the brain functionality down to cellular level. In this paper, a neuromorphic platform called SpiNNaker is described and evaluated in order to understand its potential use as a platform for a deep learning approach. This paper is a literature review that contains comparative study on algorithms that have been implemented in SpiNNaker.

  17. Element selective detection of molecular species applying chromatographic techniques and diode laser atomic absorption spectrometry.

    Science.gov (United States)

    Kunze, K; Zybin, A; Koch, J; Franzke, J; Miclea, M; Niemax, K

    2004-12-01

    Tunable diode laser atomic absorption spectroscopy (DLAAS) combined with separation techniques and atomization in plasmas and flames is presented as a powerful method for analysis of molecular species. The analytical figures of merit of the technique are demonstrated by the measurement of Cr(VI) and Mn compounds, as well as molecular species including halogen atoms, hydrogen, carbon and sulfur.

  18. Impacts of Vocabulary Acquisition Techniques Instruction on Students' Learning

    Science.gov (United States)

    Orawiwatnakul, Wiwat

    2011-01-01

    The objectives of this study were to determine how the selected vocabulary acquisition techniques affected the vocabulary ability of 35 students who took EN 111 and investigate their attitudes towards the techniques instruction. The research study was one-group pretest and post-test design. The instruments employed were in-class exercises…

  19. Applying the Flipped Learning Model to an English-Medium Nursing Course.

    Science.gov (United States)

    Choi, Heeseung; Kim, Jeongeun; Bang, Kyung Sook; Park, Yeon Hwan; Lee, Nam Ju; Kim, Chanhee

    2015-12-01

    An emerging trend in Asian higher education is English-medium instruction (EMI), which uses English as the primary instructional language. EMI prepares domestic students for international leadership; however, students report difficulty in learning, and educators have raised questions concerning the effectiveness of EMI. The flipped learning model (FLM), in which lecture and homework activities for a course are reversed, was applied to an English-medium course offered by a college of nursing in Korea. The aims of this study were to: 1) revise an existing English-medium nursing course using the FLM; 2) explore students' learning experiences and their acceptance of the FLM; and 3) identify key factors in the success of FLM. We used a descriptive, cross-sectional, mixed-methods design and the participants were students at one nursing school in Korea. A series of course development meetings with faculties from the nursing school and the center for teaching and learning were used to develop the course format and content. We conducted course evaluations using the Flipped Course Evaluation Questionnaire with open-ended questions and focus group interviews. Students (N=75) in a 15-week nursing course responded to a survey after completing the course. Among them, seven students participated in one of two focus groups. Overall, students accepted and favored the flipped learning strategy, and indicated that the method enhanced lecture content and their understanding of it. Factors associated with effective instruction included structured monitoring systems and motivational environments. The FLM requires sufficient preparation to facilitate student motivation and maximize learning outcomes.

  20. Comparing visualization techniques for learning second language prosody

    DEFF Research Database (Denmark)

    Niebuhr, Oliver; Alm, Maria Helena; Schümchen, Nathalie

    2017-01-01

    We tested the usability of prosody visualization techniques for second language (L2) learners. Eighteen Danish learners realized target sentences in German based on different visualization techniques. The sentence realizations were annotated by means of the phonological Kiel Intonation Model...... and then analyzed in terms of (a) prosodic-pattern consistency and (b) correctness of the prosodic patterns. In addition, the participants rated the usability of the visualization techniques. The results from the phonological analysis converged with the usability ratings in showing that iconic techniques......, in particular the stylized “hat pattern” visualization, performed better than symbolic techniques, and that marking prosodic information beyond intonation can be more confusing than instructive. In discussing our findings, we also provide a description of the new Danish-German learner corpus we created: DANGER...

  1. Departing from PowerPoint default mode: Applying Mayer's multimedia principles for enhanced learning of parasitology.

    Science.gov (United States)

    Nagmoti, Jyoti Mahantesh

    2017-01-01

    PowerPoint (PPT™) presentation has become an integral part of day-to-day teaching in medicine. Most often, PPT™ is used in its default mode which in fact, is known to cause boredom and ineffective learning. Research has shown improved short-term memory by applying multimedia principles for designing and delivering lectures. However, such evidence in medical education is scarce. Therefore, we attempted to evaluate the effect of multimedia principles on enhanced learning of parasitology. Second-year medical students received a series of lectures, half of the lectures used traditionally designed PPT™ and the rest used slides designed by Mayer's multimedia principles. Students answered pre and post-tests at the end of each lecture (test-I) and an essay test after six months (test-II) which assessed their short and long term knowledge retention respectively. Students' feedback on quality and content of lectures were collected. Statistically significant difference was found between post test scores of traditional and modified lectures (P = 0.019) indicating, improved short-term memory after modified lectures. Similarly, students scored better in test II on the contents learnt through modified lectures indicating, enhanced comprehension and improved long-term memory (P learning through multimedia designed PPT™ and suggested for their continued use. It is time to depart from default PPT™ and adopt multimedia principles to enhance comprehension and improve short and long term knowledge retention. Further, medical educators may be trained and encouraged to apply multimedia principles for designing and delivering effective lectures.

  2. Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction

    Directory of Open Access Journals (Sweden)

    Ruijian Zhang

    2017-12-01

    Full Text Available Water quality assessment and prediction is a more and more important issue. Traditional ways either take lots of time or they can only do assessments. In this research, by applying machine learning algorithm to a long period time of water attributes’ data; we can generate a decision tree so that it can predict the future day’s water quality in an easy and efficient way. The idea is to combine the traditional ways and the computer algorithms together. Using machine learning algorithms, the assessment of water quality will be far more efficient, and by generating the decision tree, the prediction will be quite accurate. The drawback of the machine learning modeling is that the execution takes quite long time, especially when we employ a better accuracy but more time-consuming algorithm in clustering. Therefore, we applied the high performance computing (HPC System to deal with this problem. Up to now, the pilot experiments have achieved very promising preliminary results. The visualized water quality assessment and prediction obtained from this project would be published in an interactive website so that the public and the environmental managers could use the information for their decision making.

  3. Students' perceptions of effective learning experiences in dental school: a qualitative study using a critical incident technique.

    Science.gov (United States)

    Victoroff, Kristin Zakariasen; Hogan, Sarah

    2006-02-01

    Students' views of their educational experience can be an important source of information for curriculum assessment. Although quantitative methods, particularly surveys, are frequently used to gather such data, fewer studies have employed qualitative methods to examine students' dental education experiences. The purpose of this study is to explore characteristics of effective learning experiences in dental school using a qualitative method. All third-year (seventy) and fourth-year (seventy) dental students enrolled in one midwestern dental school were invited to participate. Fifty-three dental students (thirty-five male and eighteen female; thirty-two third-year and twenty-one fourth-year) were interviewed using a critical incident interview technique. Each student was asked to describe a specific, particularly effective learning incident that he or she had experienced in dental school and a specific, particularly ineffective learning incident, for comparison. Each interview was audiotaped. Students were assured that only the interviewer and one additional researcher would have access to the tapes. Data analysis resulted in identification of key themes in the data describing characteristics of effective learning experiences. The following characteristics of effective learning experiences were identified: 1) instructor characteristics (personal qualities, "checking-in" with students, and an interactive style); 2) characteristics of the learning process (focus on the "big picture," modeling and demonstrations, opportunities to apply new knowledge, high-quality feedback, focus, specificity and relevance, and peer interactions); and 3) learning environment (culture of the learning environment, technology). Common themes emerged across a wide variety of learning incidents. Although additional research is needed, the characteristics of effective learning experiences identified in this study may have implications for individual course design and for the dental school

  4. Arrangement and Applying of Movement Patterns in the Cerebellum Based on Semi-supervised Learning.

    Science.gov (United States)

    Solouki, Saeed; Pooyan, Mohammad

    2016-06-01

    Biological control systems have long been studied as a possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. Therefore, highly regular structure of the cerebellum has been in the core of attention in theoretical and computational modeling. However, most of these models reflect some special features of the cerebellum without regarding the whole motor command computational process. In this paper, we try to make a logical relation between the most significant models of the cerebellum and introduce a new learning strategy to arrange the movement patterns: cerebellar modular arrangement and applying of movement patterns based on semi-supervised learning (CMAPS). We assume here the cerebellum like a big archive of patterns that has an efficient organization to classify and recall them. The main idea is to achieve an optimal use of memory locations by more than just a supervised learning and classification algorithm. Surely, more experimental and physiological researches are needed to confirm our hypothesis.

  5. New supervised learning theory applied to cerebellar modeling for suppression of variability of saccade end points.

    Science.gov (United States)

    Fujita, Masahiko

    2013-06-01

    A new supervised learning theory is proposed for a hierarchical neural network with a single hidden layer of threshold units, which can approximate any continuous transformation, and applied to a cerebellar function to suppress the end-point variability of saccades. In motor systems, feedback control can reduce noise effects if the noise is added in a pathway from a motor center to a peripheral effector; however, it cannot reduce noise effects if the noise is generated in the motor center itself: a new control scheme is necessary for such noise. The cerebellar cortex is well known as a supervised learning system, and a novel theory of cerebellar cortical function developed in this study can explain the capability of the cerebellum to feedforwardly reduce noise effects, such as end-point variability of saccades. This theory assumes that a Golgi-granule cell system can encode the strength of a mossy fiber input as the state of neuronal activity of parallel fibers. By combining these parallel fiber signals with appropriate connection weights to produce a Purkinje cell output, an arbitrary continuous input-output relationship can be obtained. By incorporating such flexible computation and learning ability in a process of saccadic gain adaptation, a new control scheme in which the cerebellar cortex feedforwardly suppresses the end-point variability when it detects a variation in saccadic commands can be devised. Computer simulation confirmed the efficiency of such learning and showed a reduction in the variability of saccadic end points, similar to results obtained from experimental data.

  6. Climbing up the Leaderboard: An Empirical Study of Applying Gamification Techniques to a Computer Programming Class

    Science.gov (United States)

    Fotaris, Panagiotis; Mastoras, Theodoros; Leinfellner, Richard; Rosunally, Yasmine

    2016-01-01

    Conventional taught learning practices often experience difficulties in keeping students motivated and engaged. Video games, however, are very successful at sustaining high levels of motivation and engagement through a set of tasks for hours without apparent loss of focus. In addition, gamers solve complex problems within a gaming environment…

  7. An Exploration of Prospective Teachers' Learning of Clinical Interview Techniques

    Science.gov (United States)

    Groth, Randall E.; Bergner, Jennifer A.; Burgess, Claudia R.

    2016-01-01

    The present study followed four prospective teachers through the process of learning to interview during an undergraduate research project experience. Participants conducted and video recorded a series of interviews with children. They also carried out guided analyses of the videos and written artefacts from the interviews to formulate conjectures…

  8. Cooperative Learning Technique through Internet Based Education: A Model Proposal

    Science.gov (United States)

    Ozkan, Hasan Huseyin

    2010-01-01

    Internet is gradually becoming the most valuable learning environment for the people which form the information society. That the internet provides written, oral and visual communication between the participants who are at different places, that it enables the students' interaction with other students and teachers, and that it does these so fast…

  9. Software Engineering Techniques for Computer-Aided Learning.

    Science.gov (United States)

    Ibrahim, Bertrand

    1989-01-01

    Describes the process for developing tutorials for computer-aided learning (CAL) using a programing language rather than an authoring system. The workstation used is described, the use of graphics is discussed, the role of a local area network (LAN) is explained, and future plans are discussed. (five references) (LRW)

  10. Using Deep Learning Techniques to Forecast Environmental Consumption Level

    Directory of Open Access Journals (Sweden)

    Donghyun Lee

    2017-10-01

    Full Text Available Artificial intelligence is a promising futuristic concept in the field of science and technology, and is widely used in new industries. The deep-learning technology leads to performance enhancement and generalization of artificial intelligence technology. The global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems such as climate change, but few environmental applications have so far been developed. This study uses deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN model. To verify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial neural network models. The RNN model predicts the pro-environmental consumption index better than any other model. We expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly sophisticated as the volume of data grows. Moreover, the framework of this study could be useful in environmental forecasting to prevent damage caused by climate change.

  11. Using the Technique of Journal Writing to Learn Emergency Psychiatry

    Science.gov (United States)

    Bhuvaneswar, Chaya; Stern, Theodore; Beresin, Eugene

    2009-01-01

    Objective: The authors discuss journal writing in learning emergency psychiatry. Methods: The journal of a psychiatry intern rotating through an emergency department is used as sample material for analysis that could take place in supervision or a resident support group. A range of articles are reviewed that illuminate the relevance of journal…

  12. A comparison of machine learning techniques for predicting downstream acid mine drainage

    CSIR Research Space (South Africa)

    van Zyl, TL

    2014-07-01

    Full Text Available windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear...

  13. Quantitative thoracic CT techniques in adults: can they be applied in the pediatric population?

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, Soon Ho [Seoul National University Medical Research Center, Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul (Korea, Republic of); Goo, Jin Mo [Seoul National University Medical Research Center, Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul (Korea, Republic of); Seoul National University College of Medicine, Cancer Research Institute, Jongno-gu, Seoul (Korea, Republic of); Goo, Hyun Woo [University of Ulsan College of Medicine, Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul (Korea, Republic of)

    2013-03-15

    With the rapid evolution of the multidetector row CT technique, quantitative CT has started to be used in clinical studies for revealing a heterogeneous entity of airflow limitation in chronic obstructive pulmonary disease that is caused by a combination of lung parenchymal destruction and remodeling of the small airways in adults. There is growing evidence of a good correlation between quantitative CT findings and pathological findings, pulmonary function test results and other clinical parameters. This article provides an overview of current quantitative thoracic CT techniques used in adults, and how to translate these CT techniques to the pediatric population. (orig.)

  14. Applied measuring techniques for the investigation of time-dependent flow phenomena in centrifugal compressors

    International Nuclear Information System (INIS)

    Hass, U.; Haupt, U.; Jansen, M.; Kassens, K.; Knapp, P.; Rautenberg, M.

    1978-01-01

    During the past 10 years new measuring techniques have been developed for the experimental investigation of highly loaded centrifugal compressors. These measuring techniques take into account the time dependency of the fluctuating physical quantities such as pressure, temperature, and velocity. Some key points of these experimental techniques are shown and explained in this paper. An important basis for such measurements is the accurate dynamic calibration of the measuring apparatus. In addition, some problems involved analyzing measured signals are dealt with and pressure measurements and their interpretation are shown. Finally optical, acoustical and vibrational measuring procedures are described which are additionally used for the investigation of non-stationary flow phenomena. (orig.) [de

  15. Neutron Filter Technique and its use for Fundamental and applied Investigations

    International Nuclear Information System (INIS)

    Gritzay, V.; Kolotyi, V.

    2008-01-01

    At Kyiv Research Reactor (KRR) the neutron filtered beam technique is used for more than 30 years and its development continues, the new and updated facilities for neutron cross section measurements provide the receipt of neutron cross sections with rather high accuracy: total neutron cross sections with accuracy 1% and better, neutron scattering cross sections with 3-6% accuracy. The main purpose of this paper is presentation of the neutron measurement techniques, developed at KRR, and demonstration some experimental results, obtained using these techniques

  16. Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Evanthia E. Tripoliti

    Full Text Available Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years. About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented. Keywords: Heart failure, Diagnosis, Prediction, Severity estimation, Classification, Data mining

  17. Swarm Intelligence: New Techniques for Adaptive Systems to Provide Learning Support

    Science.gov (United States)

    Wong, Lung-Hsiang; Looi, Chee-Kit

    2012-01-01

    The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…

  18. Applying of Reliability Techniques and Expert Systems in Management of Radioactive Accidents

    International Nuclear Information System (INIS)

    Aldaihan, S.; Alhbaib, A.; Alrushudi, S.; Karazaitri, C.

    1998-01-01

    Accidents including radioactive exposure have variety of nature and size. This makes such accidents complex situations to be handled by radiation protection agencies or any responsible authority. The situations becomes worse with introducing advanced technology with high complexity that provide operator huge information about system working on. This paper discusses the application of reliability techniques in radioactive risk management. Event tree technique from nuclear field is described as well as two other techniques from nonnuclear fields, Hazard and Operability and Quality Function Deployment. The objective is to show the importance and the applicability of these techniques in radiation risk management. Finally, Expert Systems in the field of accidents management are explored and classified upon their applications

  19. Clustering: An Interactive Technique to Enhance Learning in Biology.

    Science.gov (United States)

    Ambron, Joanna

    1988-01-01

    Explains an interdisciplinary approach to biology and writing which increases students' mastery of vocabulary, scientific concepts, creativity, and expression. Describes modifications of the clustering technique used to summarize lectures, integrate reading and understand textbook material. (RT)

  20. Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images

    OpenAIRE

    Boix García, Macarena; Cantó Colomina, Begoña

    2013-01-01

    Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet...

  1. Error analysis of the phase-shifting technique when applied to shadow moire

    International Nuclear Information System (INIS)

    Han, Changwoon; Han Bongtae

    2006-01-01

    An exact solution for the intensity distribution of shadow moire fringes produced by a broad spectrum light is presented. A mathematical study quantifies errors in fractional fringe orders determined by the phase-shifting technique, and its validity is corroborated experimentally. The errors vary cyclically as the distance between the reference grating and the specimen increases. The amplitude of the maximum error is approximately 0.017 fringe, which defines the theoretical limit of resolution enhancement offered by the phase-shifting technique

  2. Use of machine learning techniques for modeling of snow depth

    Directory of Open Access Journals (Sweden)

    G. V. Ayzel

    2017-01-01

    Full Text Available Snow exerts significant regulating effect on the land hydrological cycle since it controls intensity of heat and water exchange between the soil-vegetative cover and the atmosphere. Estimating of a spring flood runoff or a rain-flood on mountainous rivers requires understanding of the snow cover dynamics on a watershed. In our work, solving a problem of the snow cover depth modeling is based on both available databases of hydro-meteorological observations and easily accessible scientific software that allows complete reproduction of investigation results and further development of this theme by scientific community. In this research we used the daily observational data on the snow cover and surface meteorological parameters, obtained at three stations situated in different geographical regions: Col de Porte (France, Sodankyla (Finland, and Snoquamie Pass (USA.Statistical modeling of the snow cover depth is based on a complex of freely distributed the present-day machine learning models: Decision Trees, Adaptive Boosting, Gradient Boosting. It is demonstrated that use of combination of modern machine learning methods with available meteorological data provides the good accuracy of the snow cover modeling. The best results of snow cover depth modeling for every investigated site were obtained by the ensemble method of gradient boosting above decision trees – this model reproduces well both, the periods of snow cover accumulation and its melting. The purposeful character of learning process for models of the gradient boosting type, their ensemble character, and use of combined redundancy of a test sample in learning procedure makes this type of models a good and sustainable research tool. The results obtained can be used for estimating the snow cover characteristics for river basins where hydro-meteorological information is absent or insufficient.

  3. Machine learning and evolutionary techniques in interplanetary trajectory design

    OpenAIRE

    Izzo, Dario; Sprague, Christopher; Tailor, Dharmesh

    2018-01-01

    After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth-Mars orbital transfer, extend the findings made previously for landing ...

  4. Machine learning techniques for gait biometric recognition using the ground reaction force

    CERN Document Server

    Mason, James Eric; Woungang, Isaac

    2016-01-01

    This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of ...

  5. A Learning Method for Neural Networks Based on a Pseudoinverse Technique

    Directory of Open Access Journals (Sweden)

    Chinmoy Pal

    1996-01-01

    Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.

  6. Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

    Science.gov (United States)

    Mohammed, Husam Jasim; Kasim, Maznah Mat; Shaharanee, Izwan Nizal Mohd

    2017-11-01

    This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.

  7. Water spray cooling technique applied on a photovoltaic panel: The performance response

    International Nuclear Information System (INIS)

    Nižetić, S.; Čoko, D.; Yadav, A.; Grubišić-Čabo, F.

    2016-01-01

    Highlights: • An experimental study was conducted on a monocrystalline photovoltaic panel (PV). • A water spray cooling technique was implemented to determine PV panel response. • The experimental results showed favorable cooling effect on the panel performance. • A feasibility aspect of the water spray cooling technique was also proven. - Abstract: This paper presents an alternative cooling technique for photovoltaic (PV) panels that includes a water spray application over panel surfaces. An alternative cooling technique in the sense that both sides of the PV panel were cooled simultaneously, to investigate the total water spray cooling effect on the PV panel performance in circumstances of peak solar irradiation levels. A specific experimental setup was elaborated in detail and the developed cooling system for the PV panel was tested in a geographical location with a typical Mediterranean climate. The experimental result shows that it is possible to achieve a maximal total increase of 16.3% (effective 7.7%) in electric power output and a total increase of 14.1% (effective 5.9%) in PV panel electrical efficiency by using the proposed cooling technique in circumstances of peak solar irradiation. Furthermore, it was also possible to decrease panel temperature from an average 54 °C (non-cooled PV panel) to 24 °C in the case of simultaneous front and backside PV panel cooling. Economic feasibility was also determined for of the proposed water spray cooling technique, where the main advantage of the analyzed cooling technique is regarding the PV panel’s surface and its self-cleaning effect, which additionally acts as a booster to the average delivered electricity.

  8. Performance values for non destructive assay (NDA) techniques applied to safeguards: the 2002 evaluation by the ESARDA NDA Working Group

    International Nuclear Information System (INIS)

    Guardini, S.

    2003-01-01

    The first evaluation of NDA performance values undertaken by the ESARDA Working Group for Standards and Non Destructive Assay Techniques (WGNDA) was published in 1993. Almost 10 years later the Working Group decided to review those values, to report about improvements and to issue new performance values for techniques which were not applied in the early nineties, or were at that time only emerging. Non-Destructive Assay techniques have become more and more important in recent years, and they are used to a large extent in nuclear material accountancy and control both by operators and control authorities. As a consequence, the performance evaluation for NDA techniques is of particular relevance to safeguards authorities in optimising Safeguards operations and reducing costs. Performance values are important also for NMAC regulators, to define detection levels, limits for anomalies, goal quantities and to negotiate basic audit rules. This paper presents the latest evaluation of ESARDA Performance Values (EPVs) for the most common NDA techniques currently used for the assay of nuclear materials for Safeguards purposes. The main topics covered by the document are: techniques for plutonium bearing materials: PuO 2 and MOX; techniques for U-bearing materials; techniques for U and Pu in liquid form; techniques for spent fuel assay. This issue of the performance values is the result of specific international round robin exercises, field measurements and ad hoc experiments, evaluated and discussed in the ESARDA NDA Working Group. (author)

  9. Synchrotron and Simulations Techniques Applied to Problems in Materials Science: Catalysts and Azul Maya Pigments

    International Nuclear Information System (INIS)

    Chianelli, R.

    2005-01-01

    Development of synchrotron techniques for the determination of the structure of disordered, amorphous and surface materials has exploded over the past twenty years due to the increasing availability of high flux synchrotron radiation and the continuing development of increasingly powerful synchrotron techniques. These techniques are available to materials scientists who are not necessarily synchrotron scientists through interaction with effective user communities that exist at synchrotrons such as the Stanford Synchrotron Radiation Laboratory (SSRL). In this article we review the application of multiple synchrotron characterization techniques to two classes of materials defined as ''surface compounds.'' One class of surface compounds are materials like MoS 2-x C x that are widely used petroleum catalysts used to improve the environmental properties of transportation fuels. These compounds may be viewed as ''sulfide supported carbides'' in their catalytically active states. The second class of ''surface compounds'' is the ''Maya Blue'' pigments that are based on technology created by the ancient Maya. These compounds are organic/inorganic ''surface complexes'' consisting of the dye indigo and palygorskite, a common clay. The identification of both surface compounds relies on the application of synchrotron techniques as described in this report

  10. Schlieren technique applied to the arc temperature measurement in a high energy density cutting torch

    International Nuclear Information System (INIS)

    Prevosto, L.; Mancinelli, B.; Artana, G.; Kelly, H.

    2010-01-01

    Plasma temperature and radial density profiles of the plasma species in a high energy density cutting arc have been obtained by using a quantitative schlieren technique. A Z-type two-mirror schlieren system was used in this research. Due to its great sensibility such technique allows measuring plasma composition and temperature from the arc axis to the surrounding medium by processing the gray-level contrast values of digital schlieren images recorded at the observation plane for a given position of a transverse knife located at the exit focal plane of the system. The technique has provided a good visualization of the plasma flow emerging from the nozzle and its interactions with the surrounding medium and the anode. The obtained temperature values are in good agreement with those values previously obtained by the authors on the same torch using Langmuir probes.

  11. Applied techniques for high bandwidth data transfers across wide area networks

    International Nuclear Information System (INIS)

    Lee, Jason; Gunter, Dan; Tierney, Brian; Allcock, Bill; Bester, Joe; Bresnahan, John; Tuecke, Steve

    2001-01-01

    Large distributed systems such as Computational/Data Grids require large amounts of data to be co-located with the computing facilities for processing. Ensuring that the data is there in time for the computation in today's Internet is a massive problem. From our work developing a scalable distributed network cache, we have gained experience with techniques necessary to achieve high data throughput over high bandwidth Wide Area Networks (WAN). In this paper, we discuss several hardware and software design techniques and issues, and then describe their application to an implementation of an enhanced FTP protocol called GridFTP. We also describe results from two applications using these techniques, which were obtained at the Supercomputing 2000 conference

  12. DOES MULTIMEDIA THEORY APPLY TO ALL STUDENTS? THE IMPACT OF MULTIMEDIA PRESENTATIONS ON SCIENCE LEARNING

    Directory of Open Access Journals (Sweden)

    Peter G. Schrader

    2016-01-01

    Full Text Available User You are logged in as... mocak My Profile Log Out Log Out as User Journal Content Search Search Scope Browse By Issue By Author By Title Indexing/Abstracting -Doaj -Google Scholar -J Gate/Informatics -Ulrich's Under review by: -Ebsco -Journal Seek -info BASE INDEX -ERIC -Ulakbim/tr index Article Tools Abstract Print this article Indexing metadata How to cite item Finding References Review policy Email this article Email the author Related Items Show all The fourth issue of Journal of Learning and Teaching in Digital Age(JOLTIDA has been published. Editorial Board Open Journal Systems Journal Help Notifications View (564 new Manage Information For Readers For Authors For Librarians Creative Commons License Font Size Make font size smaller Make font size default Make font size larger Home About User Home Search Current Archives Announcements Home > Vol 1, No 1 (2016 > Schrader  DOES MULTIMEDIA THEORY APPLY TO ALL STUDENTS? THE IMPACT OF MULTIMEDIA PRESENTATIONS ON SCIENCE LEARNING Peter G. Schrader University of Nevada Las Vegas, USA pg.schrader@unlv.edu Eric E. Rapp ericrapp@icloud.com ABSTRACT In K-12 school settings in the United States, there is a preponderance of information delivered via multimedia to students everyday (e.g., visual aids found in science textbooks, electronic tablets, streamed video content, web pages, animations, and PowerPoint presentations. The cognitive theory of multimedia learning (CTML outlines numerous principles associated with learning from and with multimedia (Mayer, Hegarty, Mayer, & Cambell, 2005. However, the bulk of the research like the CTML has been conducted using college age students (Jones, 2010; McTigue, 2009. There is ample evidence that college age students and younger students exhibit numerous and important differences when learning from multimedia content (Hannus & Hyona, 1999; McTique, 2009; Moreno, 2007; Van Parreren, 1983. As a result, the objective of the current study is to examine the

  13. Applied techniques for high bandwidth data transfers across wide area networks

    International Nuclear Information System (INIS)

    Lee, J.; Gunter, D.; Tierney, B.; Allcock, B.; Bester, J.; Bresnahan, J.; Tuecke, S.

    2001-01-01

    Large distributed systems such as Computational/Data Grids require large amounts of data to be co-located with the computing facilities for processing. From their work developing a scalable distributed network cache, the authors have gained experience with techniques necessary to achieve high data throughput over high bandwidth Wide Area Networks (WAN). The authors discuss several hardware and software design techniques, and then describe their application to an implementation of an enhanced FTP protocol called GridFTP. The authors describe results from the Supercomputing 2000 conference

  14. People Recognition for Loja ECU911 applying artificial vision techniques

    Directory of Open Access Journals (Sweden)

    Diego Cale

    2016-05-01

    Full Text Available This article presents a technological proposal based on artificial vision which aims to search people in an intelligent way by using IP video cameras. Currently, manual searching process is time and resource demanding in contrast to automated searching one, which means that it could be replaced. In order to obtain optimal results, three different techniques of artificial vision were analyzed (Eigenfaces, Fisherfaces, Local Binary Patterns Histograms. The selection process considered factors like lighting changes, image quality and changes in the angle of focus of the camera. Besides, a literature review was conducted to evaluate several points of view regarding artificial vision techniques.

  15. U P1, an example for advanced techniques applied to high level activity dismantling

    International Nuclear Information System (INIS)

    Michel-Noel, M.; Calixte, O.; Blanchard, S.; Bani, J.; Girones, P.; Moitrier, C.; Terry, G.; Bourdy, R.

    2014-01-01

    The U P1 plant on the CEA Marcoule site was dedicated to the processing of spend fuels from the G1, G2 and G3 plutonium-producing reactors. This plant represents 20.000 m 2 of workshops housing about 1000 hot cells. In 1998, a huge program for the dismantling and cleaning-up of the UP1 plant was launched. CEA has developed new techniques to face the complexity of the dismantling operations. These techniques include immersive virtual reality, laser cutting, a specific manipulator arm called MAESTRO and remote handling. (A.C.)

  16. Effect of the reinforcement bar arrangement on the efficiency of electrochemical chloride removal technique applied to reinforced concrete structures

    International Nuclear Information System (INIS)

    Garces, P.; Sanchez de Rojas, M.J.; Climent, M.A.

    2006-01-01

    This paper reports on the research done to find out the effect that different bar arrangements may have on the efficiency of the electrochemical chloride removal (ECR) technique when applied to a reinforced concrete structural member. Five different types of bar arrangements were considered, corresponding to typical structural members such as columns (with single and double bar reinforcing), slabs, beams and footings. ECR was applied in several steps. We observe that the extraction efficiency depends on the reinforcing bar arrangement. A uniform layer set-up favours chloride extraction. Electrochemical techniques were also used to estimate the reinforcing bar corrosion states, as well as measure the corrosion potential, and instant corrosion rate based on the polarization resistance technique. After ECR treatment, a reduction in the corrosion levels is observed falling short of the depassivation threshold

  17. Validation and qualification of surface-applied fibre optic strain sensors using application-independent optical techniques

    International Nuclear Information System (INIS)

    Schukar, Vivien G; Kadoke, Daniel; Kusche, Nadine; Münzenberger, Sven; Gründer, Klaus-Peter; Habel, Wolfgang R

    2012-01-01

    Surface-applied fibre optic strain sensors were investigated using a unique validation facility equipped with application-independent optical reference systems. First, different adhesives for the sensor's application were analysed regarding their material properties. Measurements resulting from conventional measurement techniques, such as thermo-mechanical analysis and dynamic mechanical analysis, were compared with measurements resulting from digital image correlation, which has the advantage of being a non-contact technique. Second, fibre optic strain sensors were applied to test specimens with the selected adhesives. Their strain-transfer mechanism was analysed in comparison with conventional strain gauges. Relative movements between the applied sensor and the test specimen were visualized easily using optical reference methods, digital image correlation and electronic speckle pattern interferometry. Conventional strain gauges showed limited opportunities for an objective strain-transfer analysis because they are also affected by application conditions. (paper)

  18. Comparative assessment of PIV-based pressure evaluation techniques applied to a transonic base flow

    NARCIS (Netherlands)

    Blinde, P; Michaelis, D; van Oudheusden, B.W.; Weiss, P.E.; de Kat, R.; Laskari, A.; Jeon, Y.J.; David, L; Schanz, D; Huhn, F.; Gesemann, S; Novara, M.; McPhaden, C.; Neeteson, N.; Rival, D.; Schneiders, J.F.G.; Schrijer, F.F.J.

    2016-01-01

    A test case for PIV-based pressure evaluation techniques has been developed by constructing a simulated experiment from a ZDES simulation for an axisymmetric base flow at Mach 0.7. The test case comprises sequences of four subsequent particle images (representing multi-pulse data) as well as

  19. Nuclear and conventional techniques applied to the analysis of Purhepecha metals of the Pareyon collection

    International Nuclear Information System (INIS)

    Mendez, U.; Tenorio C, D.; Ruvalcaba, J.L.; Lopez, J.A.

    2005-01-01

    The main objective of this investigation was to determine the composition and microstructure of 13 metallic devices by means of the nuclear techniques of PIXE, RBS and conventional; which were elaborated starting from copper and gold, and they were in the offering of a tarasc personage located in the 'Matamoros' porch in Uruapan, Michoacan, Mexico. (Author)

  20. Urban field guide: applying social forestry observation techniques to the east coast megalopolis

    Science.gov (United States)

    E. Svendsen; V. Marshall; M.F. Ufer

    2006-01-01

    A changing economy and different lifestyles have altered the meaning of the forest in the northeastern United States, prompting scientists to reconsider the spatial form, stewardship and function of the urban forest. The Authors describe how social observation techniques and the employment of a novel, locally based, participatory hand-held monitoring system could aid...

  1. Space-mapping techniques applied to the optimization of a safety isolating transformer

    NARCIS (Netherlands)

    T.V. Tran; S. Brisset; D. Echeverria (David); D.J.P. Lahaye (Domenico); P. Brochet

    2007-01-01

    textabstractSpace-mapping optimization techniques allow to allign low-fidelity and high-fidelity models in order to reduce the computational time and increase the accuracy of the solution. The main idea is to build an approximate model from the difference of response between both models. Therefore

  2. MSC/NASTRAN ''expert'' techniques developed and applied to the TFTR poloidal field coils

    International Nuclear Information System (INIS)

    O'Toole, J.A.

    1986-01-01

    The TFTR poloidal field (PF) coils are being analyzed by PPPL and Grumman using MSC/NASTRAN as a part of an overall effort to establish the absolute limiting conditions of operation for TFTR. Each of the PF coils will be analyzed in depth, using a detailed set of finite element models. Several of the models developed are quite large because each copper turn, as well as its surrounding insulation, was modeled using solid elements. Several of the finite element models proved large enough to tax the capabilities of the National Magnetic Fusion Energy Computer Center (NMFECC), specifically disk storage space. To allow the use of substructuring techniques with their associated data bases for the larger models, it became necessary to employ certain infrequently used MSC/NASTRAN ''expert'' techniques. The techniques developed used multiple data bases and data base sets to divide each problem into a series of computer runs. For each run, only the data required was kept on active disk space, the remainder being placed in inactive ''FILEM'' storage, thus, minimizing active disk space required at any time and permitting problem solution using the NMFECC. A representative problem using the TFTR OH-1 coil global model provides an example of the techniques developed. The special considerations necessary to obtain proper results are discussed

  3. "PowerPoint[R] Engagement" Techniques to Foster Deep Learning

    Science.gov (United States)

    Berk, Ronald A.

    2011-01-01

    The purpose of this article is to describe a bunch of strategies with which teachers may already be familiar and, perhaps, use regularly, but not always in the context of a formal PowerPoint[R] presentation. Here are the author's top 10 engagement techniques that fit neatly within any version of PowerPoint[R]. Some of these may also be used with…

  4. Promoting Cooperative Learning in the Classroom: Comparing Explicit and Implicit Training Techniques

    Directory of Open Access Journals (Sweden)

    Anne Elliott

    2003-07-01

    Full Text Available In this study, we investigated whether providing 4th and 5th-grade students with explicit instruction in prerequisite cooperative-learning skills and techniques would enhance their academic performance and promote in them positive attitudes towards cooperative learning. Overall, students who received explicit training outperformed their peers on both the unit project and test and presented more favourable attitudes towards cooperative learning. The findings of this study support the use of explicitly instructing students about the components of cooperative learning prior to engaging in collaborative activities. Implications for teacher-education are discussed.

  5. The learning technique. Theoretical considerations for planning lessons wit h a strategic learning approach

    Directory of Open Access Journals (Sweden)

    Dania Regueira Martínez

    2014-03-01

    Full Text Available This article presents the learning task considered as the unit of smaller organization level in the teaching-learning process that conditions in its systemic structuring, the learning actions, for the students acquisition of the content, by means of the development of the reflection and the metacognitiv e regulation when they conscious ly or partially plan different types of learning strategies in the ir realization, with the objective to solv e the pedagogic professional problems that are p resented in the disciplines they receive and in its research task during the direction o f the teaching-learning process.

  6. The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

    Science.gov (United States)

    Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

    2017-09-01

    Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Applying Agnotology-Based Learning in a Mooc to Counter Climate Misconceptions

    Science.gov (United States)

    Cook, J.

    2014-12-01

    A key challenge facing educators and climate communicators is the wide array of misconceptions about climate science, often fostered by misinformation. A number of myths interfere with a sound understanding of the science, with key myths moderating public support for mitigation policies. An effective way to reduce the influence of misinformation is through agnotology-based learning. Agnotology is the study of ignorance while agnotology-based learning teaches students through the direct addressing of myths and misconceptions. This approach of "refutational teaching" is being applied in a MOOC (Massive Online Open Course) currently being developed by Skeptical Science and The University of Queensland, in collaboration with universities in Canada, USA and the UK. The MOOC will examine the science of climate change denial. Why is the issue so controversial given there is an overwhelming consensus on human-caused global warming? How do climate myths distort the science? What can scientists and laypeople do in response to misinformation? The MOOC will be released on the EdX platform in early 2015. I will summarise the research underpinning agnotology-based learning and present the approach taken in the MOOC to be released in early 2015

  8. Spectral deformation techniques applied to the study of quantum statistical irreversible processes

    International Nuclear Information System (INIS)

    Courbage, M.

    1978-01-01

    A procedure of analytic continuation of the resolvent of Liouville operators for quantum statistical systems is discussed. When applied to the theory of irreversible processes of the Brussels School, this method supports the idea that the restriction to a class of initial conditions is necessary to obtain an irreversible behaviour. The general results are tested on the Friedrichs model. (Auth.)

  9. Do trained practice nurses apply motivational interviewing techniques in primary care consultations?

    NARCIS (Netherlands)

    Noordman, J.; Lee, I. van der; Nielen, M.; Vlek, H.; Weijden, T. van der; Dulmen, S. van

    2012-01-01

    Background: Reducing the prevalence of unhealthy lifestyle behaviour could positively influence health. Motivational interviewing (MI) is used to promote change in unhealthy lifestyle behaviour as part of primary or secondary prevention. Whether MI is actually applied as taught is unknown. Practice

  10. New enhanced sensitivity infrared laser spectroscopy techniques applied to reactive plasmas and trace gas detection

    NARCIS (Netherlands)

    Welzel, S.

    2009-01-01

    Infrared laser absorption spectroscopy (IRLAS) employing both tuneable diode and quantum cascade lasers (TDLs, QCLs) has been applied with both high sensitivity and high time resolution to plasma diagnostics and trace gas measurements. TDLAS combined with a conventional White type multiple pass cell

  11. Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

    Science.gov (United States)

    Warner, Rebecca M.

    2007-01-01

    This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

  12. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

    Science.gov (United States)

    Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Li, Yuanyao; Catani, Filippo; Pourghasemi, Hamid Reza

    2018-03-01

    Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was taken as a case study for landslide susceptibility assessment in order to develop effective risk prevention and mitigation strategies. To begin, 202 landslides were identified, including 95 colluvial landslides and 107 rockfalls. Twelve landslide causal factor maps were prepared initially, and the relationship between these factors and each landslide type was analyzed using the information value model. Later, the unimportant factors were selected and eliminated using the information gain ratio technique. The landslide locations were randomly divided into two groups: 70% for training and 30% for verifying. Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment results of the two landslide types, were classified into five levels. The performance of the LSMs was evaluated using the receiver operating characteristics curve and Friedman test. Results show that the elimination of noise-generating factors and the separated modeling of each landslide type have significantly increased the prediction accuracy. The machine learning models outperformed the multivariate statistical model and SVM model was found ideal for the case study area.

  13. Machine Learning Techniques for Prediction of Early Childhood Obesity.

    Science.gov (United States)

    Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

    2015-01-01

    This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

  14. Scoping literature review on the Learning Organisation concept as applied to the health system.

    Science.gov (United States)

    Akhnif, E; Macq, J; Idrissi Fakhreddine, M O; Meessen, B

    2017-03-01

    ᅟ: There is growing interest in the use of the management concept of a 'learning organisation'. The objective of this review is to explore work undertaken towards the application of this concept to the health sector in general and to reach the goal of universal health coverage in particular. Of interest are the exploration of evaluation frameworks and their application in health. We used a scoping literature review based on the York methodology. We conducted an online search using selected keywords on some of the main databases on health science, selected websites and main reference books on learning organisations. We restricted the focus of our search on sources in the English language only. Inclusive and exclusive criteria were applied to arrive at a final list of articles, from which information was extracted and then selected and inserted in a chart. We identified 263 articles and other documents from our search. From these, 50 articles were selected for a full analysis and 27 articles were used for the summary. The majority of the articles concerned hospital settings (15 articles, 55%). Seven articles (25%) were related to the application of the concept to the health centre setting. Four articles discussed the application of the concept to the health system (14%). Most of the applications involved high-income countries (21 articles, 78%), with only one article being related to a low-income country. We found 13 different frameworks that were applied to different health organisations. The scoping review allowed us to assess applications of the learning organisation concept to the health sector to date. Such applications are still rare, but are increasingly being used. There is no uniform framework thus far, but convergence as for the dimensions that matter is increasing. Many methodological questions remain unanswered. We also identified a gap in terms of the use of this concept in low- and middle-income countries and to the health system as a whole.

  15. Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

    Science.gov (United States)

    Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

    2017-12-01

    In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

  16. Applying Consumer and Homemaking Skills to Jobs and Careers. Secondary Learning Guide 13. Project Connect. Linking Self-Family-Work.

    Science.gov (United States)

    Emily Hall Tremaine Foundation, Inc., Hartford, CT.

    This competency-based secondary learning guide on applying consumer and homemaking skills to jobs and careers is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative…

  17. Temporal difference learning for the game Tic-Tac-Toe 3D : applying structure to neural networks

    NARCIS (Netherlands)

    van de Steeg, M.; Drugan, M.M.; Wiering, M.

    2015-01-01

    When reinforcement learning is applied to large state spaces, such as those occurring in playing board games, the use of a good function approximator to learn to approximate the value function is very important. In previous research, multi-layer perceptrons have often been quite successfully used as

  18. Parameters and definitions in applied technique quality test for nuclear magnetic resonance imaging system (NMRI)

    International Nuclear Information System (INIS)

    Lin Zhikai; Zhao Lancai

    1999-08-01

    During the past two decades, medical diagnostic imaging technique has achieved dramatic development such as CT, MRI, PET, DSA and so on. The most striking examples of them are the application of X ray computerized tomography (CT) and magnetic resonance imaging in the field of medical diagnosis. It can be predicted that magnetic resonance imaging (MRI) will definitely have more widespread prospects of applications and play more and more important role in clinical diagnosis looking forward to the development of image diagnostic technique for 21 st century. The authors also present the measuring methods for some parameters. The parameters described can be used for reference by clinical diagnosticians, operators on MRI and medical physicists who engages in image quality assurance (QA) and control (QC) in performing MRI acceptance test and routine test

  19. Geologic-radiometric techniques applied for uranium prospection at the Hierro-Cayo Largo area

    International Nuclear Information System (INIS)

    Gongora, L.E.; Olivera, J.

    1995-01-01

    Using geologic-radiometric techniques uraniferous anomalies were evaluated at the Hierro-Cayo Largo area located in Pinar del Rio province. During the uranium prospection works at most promising areas, geologic itineraries and gamma ray, radon emanation spectrometric survey were done. Trenches were made and some boreholes were drilled (up to 20-30 m depth). In addition a lot of samples were taken in order to determine the amount of U, Ra, Th, y K by spectrometric techniques. As result of this investigation, a geological placing, a mineralogical and geochemical characteristic of uraniferous mineralization was possible to find out. The appropriate prospection indications for uranium exploration at Esperanza geologic zone were defined

  20. Magnetic Resonance Techniques Applied to the Diagnosis and Treatment of Parkinson’s Disease

    Science.gov (United States)

    de Celis Alonso, Benito; Hidalgo-Tobón, Silvia S.; Menéndez-González, Manuel; Salas-Pacheco, José; Arias-Carrión, Oscar

    2015-01-01

    Parkinson’s disease (PD) affects at least 10 million people worldwide. It is a neurodegenerative disease, which is currently diagnosed by neurological examination. No neuroimaging investigation or blood biomarker is available to aid diagnosis and prognosis. Most effort toward diagnosis using magnetic resonance (MR) has been focused on the use of structural/anatomical neuroimaging and diffusion tensor imaging (DTI). However, deep brain stimulation, a current strategy for treating PD, is guided by MR imaging (MRI). For clinical prognosis, diagnosis, and follow-up investigations, blood oxygen level-dependent MRI, DTI, spectroscopy, and transcranial magnetic stimulation have been used. These techniques represent the state of the art in the last 5 years. Here, we focus on MR techniques for the diagnosis and treatment of Parkinson’s disease. PMID:26191037

  1. The Ecological Profiles Technique applied to data from Lichtenburg, South Africa

    Directory of Open Access Journals (Sweden)

    J. W. Morris

    1974-12-01

    Full Text Available The method of ecological profiles and information shared between species and ecological variables, developed in France, is described for the first time in English. Preliminary results, using the technique on Bankenveld quadrat data from Lichtenburg, Western Transvaal, are given. It is concluded that the method has great potential value for the understanding of the autecology of South African species provided that the sampling method is appropriate.

  2. Computer vision techniques applied to the quality control of ceramic plates

    OpenAIRE

    Silveira, Joaquim; Ferreira, Manuel João Oliveira; Santos, Cristina; Martins, Teresa

    2009-01-01

    This paper presents a system, based on computer vision techniques, that detects and quantifies different types of defects in ceramic plates. It was developed in collaboration with the industrial ceramic sector and consequently it was focused on the defects that are considered more quality depreciating by the Portuguese industry. They are of three main types: cracks; granules and relief surface. For each type the development was specific as far as image processing techn...

  3. Applying Multi-Criteria Decision-Making Techniques to Prioritize Agility Drivers

    OpenAIRE

    Ahmad Jafarnejad; Sayyed Mohammad Reza Davoodi; Abolfazl Sherafat

    2013-01-01

    It seems that to recognize and classify the factors affecting organizational agility and need to specify the amount of their importance for the organization is essential to preserve survival and success in today's environment. This paper reviews the concept of agility and its division in the following indicators included the factors of motivations organizational agility that have been ranked in terms of level of importance and their influence by the techniques of MCDM. The inner complexity, s...

  4. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index

    Energy Technology Data Exchange (ETDEWEB)

    Zarzalejo, L.F.; Ramirez, L.; Polo, J. [DER-CIEMAT, Madrid (Spain). Renewable Energy Dept.

    2005-07-01

    Artificial intelligence techniques, such as fuzzy logic and neural networks, have been used for estimating hourly global radiation from satellite images. The models have been fitted to measured global irradiance data from 15 Spanish terrestrial stations. Both satellite imaging data and terrestrial information from the years 1994, 1995 and 1996 were used. The results of these artificial intelligence models were compared to a multivariate regression based upon Heliosat I model. A general better behaviour was observed for the artificial intelligence models. (author)

  5. 3D-Laser-Scanning Technique Applied to Bulk Density Measurements of Apollo Lunar Samples

    Science.gov (United States)

    Macke, R. J.; Kent, J. J.; Kiefer, W. S.; Britt, D. T.

    2015-01-01

    In order to better interpret gravimetric data from orbiters such as GRAIL and LRO to understand the subsurface composition and structure of the lunar crust, it is import to have a reliable database of the density and porosity of lunar materials. To this end, we have been surveying these physical properties in both lunar meteorites and Apollo lunar samples. To measure porosity, both grain density and bulk density are required. For bulk density, our group has historically utilized sub-mm bead immersion techniques extensively, though several factors have made this technique problematic for our work with Apollo samples. Samples allocated for measurement are often smaller than optimal for the technique, leading to large error bars. Also, for some samples we were required to use pure alumina beads instead of our usual glass beads. The alumina beads were subject to undesirable static effects, producing unreliable results. Other investigators have tested the use of 3d laser scanners on meteorites for measuring bulk volumes. Early work, though promising, was plagued with difficulties including poor response on dark or reflective surfaces, difficulty reproducing sharp edges, and large processing time for producing shape models. Due to progress in technology, however, laser scanners have improved considerably in recent years. We tested this technique on 27 lunar samples in the Apollo collection using a scanner at NASA Johnson Space Center. We found it to be reliable and more precise than beads, with the added benefit that it involves no direct contact with the sample, enabling the study of particularly friable samples for which bead immersion is not possible

  6. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index

    International Nuclear Information System (INIS)

    Zarzalejo, Luis F.; Ramirez, Lourdes; Polo, Jesus

    2005-01-01

    Artificial intelligence techniques, such as fuzzy logic and neural networks, have been used for estimating hourly global radiation from satellite images. The models have been fitted to measured global irradiance data from 15 Spanish terrestrial stations. Both satellite imaging data and terrestrial information from the years 1994, 1995 and 1996 were used. The results of these artificial intelligence models were compared to a multivariate regression based upon Heliosat I model. A general better behaviour was observed for the artificial intelligence models

  7. Development of Innovation by Constructivist Theory with using Cooperative Learning Technique STAD of Mathayomsuksa 3 Students at Anuban Mahasarakham School

    Directory of Open Access Journals (Sweden)

    Apinya Phonpinyo

    2017-03-01

    Full Text Available The purposes of the this research: 1. were study the problems and needed science activities learning 2. to improve students activities 3. study the activities; 3.1 to improve the learning of course to pass the standard in 70 percentage 3.2 to improve basic science process skills to pass in 70 percentage 3.3 to study on attitude in science. the Target group was mathayomsuksa 3 students in the class 1 of Anuban Mahasarakham school by using purposive sampling technique that totally were 32 persons. The research instruments were an interview of teacher, the questionnaires of students who were managed in science learning activities and learning management based, the evaluation of learning achievement that had 4 choices were totally 30 items are have discrimination levels from 0.20 - 0.64 and all reliability levels were 0.74, the test of science process skills on basic level that had 4 choices with 30 items had discrimination levels from 0.28 - 0.83 and all reliability levels were 0.73. The evaluation of attitude to science course had 5-scale levels scale 5 levels, 20-item and difficulty levels from 0.20 - 0.71. The reliability levels were 0.69. The statistics used was percentage, mean and standard division. The research found as follows; 1. Study of the problems and needed science activities learning was found that concerning learning activities management focused on description, note by student non-action with learning activities, it non-evaluating science process skills and attitude in science. The knowledge of most student on science was lower. The motivated students students in learning activities in science were at high level ( = 3.81 2. Learning activities management was developed by 5 stages as follow; 1 introduction stage, 2 review old idea stage, 3 improvement and change concept stage, 4 applying a new idea stage, 5 conclusion stage and appropriately learning activities plan was at high level ( = 4.30 3. the Effects of learning activities

  8. Applying the sterile insect technique to the control of insect pests

    International Nuclear Information System (INIS)

    LaChance, L.E.; Klassen, W.

    1991-01-01

    The sterile insect technique involves the mass-rearing of insects, which are sterilized by gamma rays from a 60 Co source before being released in a controlled fashion into nature. Matings between the sterile insects released and native insects produce no progeny, and so if enough of these matings occur the pest population can be controlled or even eradicated. A modification of the technique, especially suitable for the suppression of the moths and butterflies, is called the F, or inherited sterility method. In this, lower radiation doses are used such that the released males are only partially sterile (30-60%) and the females are fully sterile. When released males mate with native females some progeny are produced, but they are completely sterile. Thus, full expression of the sterility is delayed by one generation. This article describes the use of the sterile insect technique in controlling the screwworm fly, the tsetse fly, the medfly, the pink bollworm and the melon fly, and of the F 1 sterility method in the eradication of local gypsy moth infestations. 18 refs, 5 figs, 1 tab

  9. Acoustic Emission and Echo Signal Compensation Techniques Applied to an Ultrasonic Logging-While-Drilling Caliper.

    Science.gov (United States)

    Yao, Yongchao; Ju, Xiaodong; Lu, Junqiang; Men, Baiyong

    2017-06-10

    A logging-while-drilling (LWD) caliper is a tool used for the real-time measurement of a borehole diameter in oil drilling engineering. This study introduces the mechanical structure and working principle of a new LWD caliper based on ultrasonic distance measurement (UDM). The detection range is a major performance index of a UDM system. This index is determined by the blind zone length and remote reflecting interface detection capability of the system. To reduce the blind zone length and detect near the reflecting interface, a full bridge acoustic emission technique based on bootstrap gate driver (BGD) and metal-oxide-semiconductor field effect transistor (MOSFET) is designed by analyzing the working principle and impedance characteristics of a given piezoelectric transducer. To detect the remote reflecting interface and reduce the dynamic range of the received echo signals, the relationships between the echo amplitude and propagation distance of ultrasonic waves are determined. A signal compensation technique based on time-varying amplification theory, which can automatically change the gain according to the echo arrival time is designed. Lastly, the aforementioned techniques and corresponding circuits are experimentally verified. Results show that the blind zone length in the UDM system of the LWD caliper is significantly reduced and the capability to detect the remote reflecting interface is considerably improved.

  10. Acoustic Emission and Echo Signal Compensation Techniques Applied to an Ultrasonic Logging-While-Drilling Caliper

    Directory of Open Access Journals (Sweden)

    Yongchao Yao

    2017-06-01

    Full Text Available A logging-while-drilling (LWD caliper is a tool used for the real-time measurement of a borehole diameter in oil drilling engineering. This study introduces the mechanical structure and working principle of a new LWD caliper based on ultrasonic distance measurement (UDM. The detection range is a major performance index of a UDM system. This index is determined by the blind zone length and remote reflecting interface detection capability of the system. To reduce the blind zone length and detect near the reflecting interface, a full bridge acoustic emission technique based on bootstrap gate driver (BGD and metal-oxide-semiconductor field effect transistor (MOSFET is designed by analyzing the working principle and impedance characteristics of a given piezoelectric transducer. To detect the remote reflecting interface and reduce the dynamic range of the received echo signals, the relationships between the echo amplitude and propagation distance of ultrasonic waves are determined. A signal compensation technique based on time-varying amplification theory, which can automatically change the gain according to the echo arrival time is designed. Lastly, the aforementioned techniques and corresponding circuits are experimentally verified. Results show that the blind zone length in the UDM system of the LWD caliper is significantly reduced and the capability to detect the remote reflecting interface is considerably improved.

  11. Applied nuclear γ-resonance as fingerprint technique in geochemistry and mineralogy

    International Nuclear Information System (INIS)

    Constantinescu, S.

    2003-01-01

    The aim of the present paper is to evidence the new developments of one of the most refined technique, the nuclear γ resonance or the well-known Moessbauer effect, in the field of mineralogical and geo-chemical investigation. There are many Moessbauer studies on minerals, but the development, the new performance of the Moessbauer equipment and of the computers impose to review more profoundly and more thoroughly the information, which this non-destructive technique offers. This task became more and more pressingly because a lot of minerals contain in high proportion, the Moessbauer isotopes. Generally, the mineralogists, physicists and chemists hope to obtain more refined and complete information about the physics and chemistry synthesis aspects in solid state transformation of some natural and synthetic materials and also about the structural aspects, by these kind of techniques. On this line, the authors very shortly review the principal aspects of the Moessbauer spectroscopy and underline the most important information one can obtain from spectra. The recent results, which have been obtained on minerals extracted from Romanian geological deposits by the authors, will be discussed in detail in the second part of this article. (authors)

  12. An efficient permeability scaling-up technique applied to the discretized flow equations

    Energy Technology Data Exchange (ETDEWEB)

    Urgelli, D.; Ding, Yu [Institut Francais du Petrole, Rueil Malmaison (France)

    1997-08-01

    Grid-block permeability scaling-up for numerical reservoir simulations has been discussed for a long time in the literature. It is now recognized that a full permeability tensor is needed to get an accurate reservoir description at large scale. However, two major difficulties are encountered: (1) grid-block permeability cannot be properly defined because it depends on boundary conditions; (2) discretization of flow equations with a full permeability tensor is not straightforward and little work has been done on this subject. In this paper, we propose a new method, which allows us to get around both difficulties. As the two major problems are closely related, a global approach will preserve the accuracy. So, in the proposed method, the permeability up-scaling technique is integrated in the discretized numerical scheme for flow simulation. The permeability is scaled-up via the transmissibility term, in accordance with the fluid flow calculation in the numerical scheme. A finite-volume scheme is particularly studied, and the transmissibility scaling-up technique for this scheme is presented. Some numerical examples are tested for flow simulation. This new method is compared with some published numerical schemes for full permeability tensor discretization where the full permeability tensor is scaled-up through various techniques. Comparing the results with fine grid simulations shows that the new method is more accurate and more efficient.

  13. Metal oxide collectors for storing matter technique applied in secondary ion mass spectrometry

    Energy Technology Data Exchange (ETDEWEB)

    Miśnik, Maciej [Institute of Tele and Radio Technology, ul. Ratuszowa 11, 03-450 Warszawa (Poland); Gdańsk University of Technology (Poland); Konarski, Piotr [Institute of Tele and Radio Technology, ul. Ratuszowa 11, 03-450 Warszawa (Poland); Zawada, Aleksander [Institute of Tele and Radio Technology, ul. Ratuszowa 11, 03-450 Warszawa (Poland); Military University of Technology, Warszawa (Poland)

    2016-03-15

    We present results of the use of metal and metal oxide substrates that serve as collectors in ‘storing matter’, the quantitative technique of secondary ion mass spectrometry (SIMS). This technique allows separating the two base processes of secondary ion formation in SIMS. Namely, the process of ion sputtering is separated from the process of ionisation. The technique allows sputtering of the analysed sample and storing the sputtered material, with sub-monolayer coverage, onto a collector surface. Such deposits can be then analysed by SIMS, and as a result, the so called ‘matrix effects’ are significantly reduced. We perform deposition of the sputtered material onto Ti and Cu substrates and also onto metal oxide substrates as molybdenum, titanium, tin and indium oxides. The process of sputtering is carried within the same vacuum chamber where the SIMS analysis of the collected material is performed. For sputtering and SIMS analysis of the deposited material we use 5 keV Ar{sup +} beam of 500 nA. The presented results are obtained with the use of stationary collectors. Here we present a case study of chromium. The obtained results show that the molybdenum and titanium oxide substrates used as collectors increase useful yield by two orders, with respect to such pure elemental collectors as Cu and Ti. Here we define useful yield as a ratio of the number of detected secondary ions during SIMS analysis and the number of atoms sputtered during the deposition process.

  14. Domain Immersion Technique And Free Surface Computations Applied To Extrusion And Mixing Processes

    Science.gov (United States)

    Valette, Rudy; Vergnes, Bruno; Basset, Olivier; Coupez, Thierry

    2007-04-01

    This work focuses on the development of numerical techniques devoted to the simulation of mixing processes of complex fluids such as twin-screw extrusion or batch mixing. In mixing process simulation, the absence of symmetry of the moving boundaries (the screws or the rotors) implies that their rigid body motion has to be taken into account by using a special treatment. We therefore use a mesh immersion technique (MIT), which consists in using a P1+/P1-based (MINI-element) mixed finite element method for solving the velocity-pressure problem and then solving the problem in the whole barrel cavity by imposing a rigid motion (rotation) to nodes found located inside the so called immersed domain, each subdomain (screw, rotor) being represented by a surface CAD mesh (or its mathematical equation in simple cases). The independent meshes are immersed into a unique backgound computational mesh by computing the distance function to their boundaries. Intersections of meshes are accounted for, allowing to compute a fill factor usable as for the VOF methodology. This technique, combined with the use of parallel computing, allows to compute the time-dependent flow of generalized Newtonian fluids including yield stress fluids in a complex system such as a twin screw extruder, including moving free surfaces, which are treated by a "level set" and Hamilton-Jacobi method.

  15. Applying Data-mining techniques to study drought periods in Spain

    Science.gov (United States)

    Belda, F.; Penades, M. C.

    2010-09-01

    Data-mining is a technique that it can be used to interact with large databases and to help in the discovery relations between parameters by extracting information from massive and multiple data archives. Drought affects many economic and social sectors, from agricultural to transportation, going through urban water deficit and the development of modern industries. With these problems and drought geographical and temporal distribution it's difficult to find a single definition of drought. Improving the understanding of the knowledge of climatic index is necessary to reduce the impacts of drought and to facilitate quick decisions regarding this problem. The main objective is to analyze drought periods from 1950 to 2009 in Spain. We use several kinds of information, different formats, sources and transmission mode. We use satellite-based Vegetation Index, dryness index for several temporal periods. We use daily and monthly precipitation and temperature data and soil moisture data from numerical weather model. We calculate mainly Standardized Precipitation Index (SPI) that it has been used amply in the bibliography. We use OLAP-Mining techniques to discovery of association rules between remote-sensing, numerical weather model and climatic index. Time series Data- Mining techniques organize data as a sequence of events, with each event having a time of recurrence, to cluster the data into groups of records or cluster with similar characteristics. Prior climatological classification is necessary if we want to study drought periods over all Spain.

  16. Air flow measurement techniques applied to noise reduction of a centrifugal blower

    Science.gov (United States)

    Laage, John W.; Armstrong, Ashli J.; Eilers, Daniel J.; Olsen, Michael G.; Mann, J. Adin

    2005-09-01

    The air flow in a centrifugal blower was studied using a variety of flow and sound measurement techniques. The flow measurement techniques employed included Particle Image Velocimetry (PIV), pitot tubes, and a five hole spherical probe. PIV was used to measure instantaneous and ensemble-averaged velocity fields over large area of the outlet duct as a function of fan position, allowing for the visualization of the flow as it leave the fan blades and progressed downstream. The results from the flow measurements were reviewed along side the results of the sound measurements with the goal of identifying sources of noise and inefficiencies in flow performance. The radiated sound power was divided into broadband and tone noise and measures of the flow. The changes in the tone and broadband sound were compared to changes in flow quantities such as the turbulent kinetic energy and Reynolds stress. Results for each method will be presented to demonstrate the strengths of each flow measurement technique as well as their limitations. Finally, the role that each played in identifying noise sources is described.

  17. Sentiment Analysis in Geo Social Streams by using Machine Learning Techniques

    OpenAIRE

    Twanabasu, Bikesh

    2018-01-01

    Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2017-2018 Massive amounts of sentiment rich data are generated on social media in the form of Tweets, status updates, blog post, reviews, etc. Different people and organizations are using these user generated content for decision making. Symbolic techniques or Knowledge base approaches and Machine learning techniques are two main techniques used for analysis sentiment...

  18. A photoacoustic technique applied to detection of ethylene emissions in edible coated passion fruit

    International Nuclear Information System (INIS)

    Alves, G V L; Santos, W C dos; Vargas, H; Silva, M G da; Waldman, W R; Oliveira, J G

    2010-01-01

    Photoacoustic spectroscopy was applied to study the physiological behavior of passion fruit when coated with edible films. The results have shown a reduction of the ethylene emission rate. Weight loss monitoring has not shown any significant differences between the coated and uncoated passion fruit. On the other hand, slower color changes of coated samples suggest a slowdown of the ripening process in coated passion fruit.

  19. Online Learning Techniques for Improving Robot Navigation in Unfamiliar Domains

    Science.gov (United States)

    2010-12-01

    Thesis Committee: Tony Stentz, co-chair J. Andrew Bagnell, co-chair Christopher Urmson Lawrence Jackel, AT&T Labs Division Manager (Emeritus) Copyright...fellowship has given me. v vi Acknowledgments There are many to thank for this dissertation, but I must start with my advisors, Tony and Drew. They...of Statistics, pages 1091–1114, 1987. 6.2.1 [69] TL Lai and H. Robbins . Asymptotically efficient adaptive allocation rules. Advances in applied

  20. Hybrid multicore/vectorisation technique applied to the elastic wave equation on a staggered grid

    Science.gov (United States)

    Titarenko, Sofya; Hildyard, Mark

    2017-07-01

    In modern physics it has become common to find the solution of a problem by solving numerically a set of PDEs. Whether solving them on a finite difference grid or by a finite element approach, the main calculations are often applied to a stencil structure. In the last decade it has become usual to work with so called big data problems where calculations are very heavy and accelerators and modern architectures are widely used. Although CPU and GPU clusters are often used to solve such problems, parallelisation of any calculation ideally starts from a single processor optimisation. Unfortunately, it is impossible to vectorise a stencil structured loop with high level instructions. In this paper we suggest a new approach to rearranging the data structure which makes it possible to apply high level vectorisation instructions to a stencil loop and which results in significant acceleration. The suggested method allows further acceleration if shared memory APIs are used. We show the effectiveness of the method by applying it to an elastic wave propagation problem on a finite difference grid. We have chosen Intel architecture for the test problem and OpenMP (Open Multi-Processing) since they are extensively used in many applications.