WorldWideScience

Sample records for learning techniques specifically

  1. Specific radiography technique

    International Nuclear Information System (INIS)

    Abdul Nassir Ibrahim; Azali Muhammad; Ab. Razak Hamzah; Abd. Aziz Mohamed; Mohamad Pauzi Ismail

    2008-01-01

    Beside radiography testing using x-ray machine and gamma source, there are several technique that developed specifically to complete the testing that cannot be done with the two earlier. This technique was specific based on several factor, for the example, the advantages of neutron and electron using to show the image was unique compare to x-ray and gamma. Besides that, these special radiography techniques maybe differ in how to detect the radiation get through the object. These technique can used to inspect thin or specimen that contained radioactive material. There are several technique will discussed in this chapter such as neutron radiography, electron radiography, fluoroscopy and also autoradiography.

  2. 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

  3. 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.

  4. Cognitive Clusters in Specific Learning Disorder.

    Science.gov (United States)

    Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo

    The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. The introduction of the single overarching diagnostic category of specific learning disorder (SLD) could underemphasize interindividual clinical differences regarding intracategory cognitive functioning and learning proficiency, according to current models of multiple cognitive deficits at the basis of neurodevelopmental disorders. The characterization of specific cognitive profiles associated with an already manifest SLD could help identify possible early cognitive markers of SLD risk and distinct trajectories of atypical cognitive development leading to SLD. In this perspective, we applied a cluster analysis to identify groups of children with a Diagnostic and Statistical Manual-based diagnosis of SLD with similar cognitive profiles and to describe the association between clusters and SLD subtypes. A sample of 205 children with a diagnosis of SLD were enrolled. Cluster analyses (agglomerative hierarchical and nonhierarchical iterative clustering technique) were used successively on 10 core subtests of the Wechsler Intelligence Scale for Children-Fourth Edition. The 4-cluster solution was adopted, and external validation found differences in terms of SLD subtype frequencies and learning proficiency among clusters. Clinical implications of these findings are discussed, tracing directions for further studies.

  5. 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…

  6. 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...

  7. 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...

  8. 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. .

  9. 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

  10. 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.

  11. 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.

  12. Greek Young Adults with Specific Learning Disabilities Seeking Learning Assessments

    Science.gov (United States)

    Bonti, Eleni; Bampalou, Christina E.; Kouimtzi, Eleni M.; Kyritsis, Zacharias

    2018-01-01

    The purpose of this study is to investigate the reasons why Greek young adults with Specific Learning Disabilities (SLD) seek learning assessments. The study sample consisted of 106 adults meeting Diagnostic and Statistical Manual of Mental Disorders criteria for SLD. Data were collected through self-report records (clinical interview) of adults…

  13. Adolescents with specific learning disabilities - perceptions of specific learning disabilities in the environment of secondary schools

    OpenAIRE

    Pospíšilová, Zuzana

    2012-01-01

    The thesis focuses on adolescents with specific learning disabilities in the milieu of secondary schools. It is divided into a theoretical part and an empirical part. The first part introduces a topic of specific learning disabilities in the developmental stage of adolescence. It first describes the most relevant aspects of adolescent development. The attention is then paid to typical manifestations of specific learning disabilities in adolescence, and also to secondary symptoms usually conne...

  14. Learning LM Specificity for Ganglion Cells

    Science.gov (United States)

    Ahumada, Albert J.

    2015-01-01

    Unsupervised learning models have been proposed based on experience (Ahumada and Mulligan, 1990;Wachtler, Doi, Lee and Sejnowski, 2007) that allow the cortex to develop units with LM specific color opponent receptive fields like the blob cells reported by Hubel and Wiesel on the basis of visual experience. These models used ganglion cells with LM indiscriminate wiring as inputs to the learning mechanism, which was presumed to occur at the cortical level.

  15. 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.

  16. 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.

  17. Risk based technique for improving technical specifications

    International Nuclear Information System (INIS)

    Kim, I. S.; Jae, M. S.; Kim, B. S.; Hwang, S. W.; Kang, K. M.; Park, S. S.; Yu, Y. S.

    2001-03-01

    The objective of this study is to develop the systematic guidance for reviewing the documents associated with the changes of technical specifications. The work done in this fiscal year is the following : surveys in TS requirements, TS improvements and TS regulations in foreign countries as well as Korea, surveys on the state-of-the-art of RITSs and their use in Korea, development of a decision-making framework for both the licensee and the regulation agency, description of risk measures, assessment methodology on STI/AOT, and adverse effects caused by periodic maintenance, which are explained in appendix. The results of this study might contribute to enhancing the quality of the current technical specifications and contribute to preparing the risk informed regulation program using the decision-making framework developed in this study

  18. 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…

  19. 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.

  20. Specific binding assay technique; standardization of reagent

    International Nuclear Information System (INIS)

    Huggins, K.G.; Roitt, I.M.

    1979-01-01

    The standardization of a labelled constituent, such as anti-IgE, for use in a specific binding assay method is disclosed. A labelled ligand, such as IgE, is standardized against a ligand reference substance, such as WHO standard IgE, to determine the weight of IgE protein represented by the labelled ligand. Anti-light chain antibodies are contacted with varying concentrations of the labelled ligand. The ligand is then contacted with the labelled constituent which is then quantitated in relation to the amount of ligand protein present. The preparation of 131 I-labelled IgE is described. Also disclosed is an improved specific binding assay test method for determining the potency of an allergen extract in serum from an allergic individual. The improvement involved using a parallel model system of a second complex which consisted of anti-light chain antibodies, labelled ligand and the standardized labelled constituent (anti-IgE). The amount of standardized labelled constituent bound to the ligand in the first complex was determined, as described above, and the weight of ligand inhibited by addition of soluble allergen was then used as a measure of the potency of the allergen extract. (author)

  1. 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

  2. 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....

  3. ANALYSIS OF RELATIONS BETWEEN JUDO TECHNIQUES AND SPECIFIC MOTOR ABILITIES

    Directory of Open Access Journals (Sweden)

    Patrik Drid

    2006-06-01

    Full Text Available Specific physical preparation affects the development of motor abilities required for execution of specific movements in judo. When selecting proper specific exercises for judo for a target motor ability, it is necessary to precede it with the study of the structure of specific judo techniques and activities of individual muscle groups engaged for execution of the technique. On the basis of this, one can understand which muscles are most engaged during realization of individual techniques, which serves as a standpoint for selection of a particular complex of specific exercises to produce the highest effects. In addition to the development of particular muscle groups, the means of specific preparation will take effect on the development of those motor abilities which are evaluated as the indispensable for the development of particular qualities which are characteristic for judo. This paper analyses the relationship between judo techniques field and specific motor abilities.

  4. 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…

  5. Teaching Foreign Languages to Pupils with Specific Learning Disability

    OpenAIRE

    VOLDÁNOVÁ, Veronika

    2015-01-01

    This diploma thesis deals with the topic of specific learning disability. In the theoretical part I define the term specific learning disability and I mention the related terms. I deal with the history, types and causes of specific learning disability, further I describe the possibilities of diagnostics and re-education concerning specific learning disability. I also attend to the situation of a pupil in the family and school background. The main attention is especially paid to teaching forei...

  6. 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

  7. 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…

  8. 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)

  9. 34 CFR 300.307 - Specific learning disabilities.

    Science.gov (United States)

    2010-07-01

    ... 34 Education 2 2010-07-01 2010-07-01 false Specific learning disabilities. 300.307 Section 300.307... Educational Placements Additional Procedures for Identifying Children with Specific Learning Disabilities § 300.307 Specific learning disabilities. (a) General. A State must adopt, consistent with § 300.309...

  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. Information Requirements Specification II: Brainstorming Collective Decision-Making Technique.

    Science.gov (United States)

    Telem, Moshe

    1988-01-01

    Information requirements specification (IRS) constitutes an Achilles heel in the system life cycle of management information systems. This article establishes a systematic overall IRS technique applicable to organizations of all types and sizes. The technique's integration of brainstorming and theory Z principles creates an effective, stimulating,…

  12. Statistical and Economic Techniques for Site-specific Nematode Management.

    Science.gov (United States)

    Liu, Zheng; Griffin, Terry; Kirkpatrick, Terrence L

    2014-03-01

    Recent advances in precision agriculture technologies and spatial statistics allow realistic, site-specific estimation of nematode damage to field crops and provide a platform for the site-specific delivery of nematicides within individual fields. This paper reviews the spatial statistical techniques that model correlations among neighboring observations and develop a spatial economic analysis to determine the potential of site-specific nematicide application. The spatial econometric methodology applied in the context of site-specific crop yield response contributes to closing the gap between data analysis and realistic site-specific nematicide recommendations and helps to provide a practical method of site-specifically controlling nematodes.

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

    Science.gov (United States)

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

    2009-01-01

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

  14. 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.

  15. 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.

  16. Are Students' Learning Styles Discipline Specific?

    Science.gov (United States)

    Jones, Cheryl; Reichard, Carla; Mokhtari, Kouider

    2003-01-01

    This study examines the extent to which community college students' learning style preferences vary as a function of discipline. Reports significant differences in students' learning style preferences across disciplines, but not by gender. Adds that student learning style preferences varied by academic performance as measured by gender. Discusses…

  17. 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…

  18. 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…

  19. [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.

  20. 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.

  1. Specific learning disorder: prevalence and gender differences.

    Directory of Open Access Journals (Sweden)

    Kristina Moll

    Full Text Available Comprehensive models of learning disorders have to consider both isolated learning disorders that affect one learning domain only, as well as comorbidity between learning disorders. However, empirical evidence on comorbidity rates including all three learning disorders as defined by DSM-5 (deficits in reading, writing, and mathematics is scarce. The current study assessed prevalence rates and gender ratios for isolated as well as comorbid learning disorders in a representative sample of 1633 German speaking children in 3rd and 4th Grade. Prevalence rates were analysed for isolated as well as combined learning disorders and for different deficit criteria, including a criterion for normal performance. Comorbid learning disorders occurred as frequently as isolated learning disorders, even when stricter cutoff criteria were applied. The relative proportion of isolated and combined disorders did not change when including a criterion for normal performance. Reading and spelling deficits differed with respect to their association with arithmetic problems: Deficits in arithmetic co-occurred more often with deficits in spelling than with deficits in reading. In addition, comorbidity rates for arithmetic and reading decreased when applying stricter deficit criteria, but stayed high for arithmetic and spelling irrespective of the chosen deficit criterion. These findings suggest that the processes underlying the relationship between arithmetic and reading might differ from those underlying the relationship between arithmetic and spelling. With respect to gender ratios, more boys than girls showed spelling deficits, while more girls were impaired in arithmetic. No gender differences were observed for isolated reading problems and for the combination of all three learning disorders. Implications of these findings for assessment and intervention of learning disorders are discussed.

  2. 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...

  3. 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...

  4. An introduction to Kundalini yoga meditation techniques that are specific for the treatment of psychiatric disorders.

    Science.gov (United States)

    Shannahoff-Khalsa, David S

    2004-02-01

    The ancient system of Kundalini yoga includes a vast array of meditation techniques and many were discovered to be specific for treating the psychiatric disorders as we know them today. One such technique was found to be specific for treating obsessive-compulsive disorder (OCD), the fourth most common psychiatric disorder, and the tenth most disabling disorder worldwide. Two published clinical trials are described here for treating OCD using a specific Kundalini yoga protocol. This OCD protocol also includes techniques that are useful for a wide range of anxiety disorders, as well as a technique specific for learning to manage fear, one for tranquilizing an angry mind, one for meeting mental challenges, and one for turning negative thoughts into positive thoughts. Part of that protocol is included here and published in detail elsewhere. In addition, a number of other disorder-specific meditation techniques are included here to help bring these tools to the attention of the medical and scientific community. These techniques are specific for phobias, addictive and substance abuse disorders, major depressive disorders, dyslexia, grief, insomnia and other sleep disorders.

  5. Specificity and sensitivity assessment of selected nasal provocation testing techniques

    Directory of Open Access Journals (Sweden)

    Edyta Krzych-Fałta

    2016-12-01

    Full Text Available Introduction: Nasal provocation testing involves an allergen-specific local reaction of the nasal mucosa to the administered allergen. Aim: To determine the most objective nasal occlusion assessment technique that could be used in nasal provocation testing. Material and methods : A total of 60 subjects, including 30 patients diagnosed with allergy to common environmental allergens and 30 healthy subjects were enrolled into the study. The method used in the study was a nasal provocation test with an allergen, with a standard dose of a control solution and an allergen (5,000 SBU/ml administered using a calibrated atomizer into both nostrils at room temperature. Early-phase nasal mucosa response in the early phase of the allergic reaction was assessed via acoustic rhinometry, optical rhinometry, nitric oxide in nasal air, and tryptase levels in the nasal lavage fluid. Results : In estimating the homogeneity of the average values, the Levene’s test was used and receiver operating characteristic curves were plotted for all the methods used for assessing the nasal provocation test with an allergen. Statistically significant results were defined for p < 0.05. Of all the objective assessment techniques, the most sensitive and characteristic ones were the optical rhinometry techniques (specificity = 1, sensitivity = 1, AUC = 1, PPV = 1, NPV = 1. Conclusions : The techniques used showed significant differences between the group of patients with allergic rhinitis and the control group. Of all the objective assessment techniques, those most sensitive and characteristic were the optical rhinometry.

  6. 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

  7. 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

  8. 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.

  9. Cognitive Clusters in Specific Learning Disorder

    Science.gov (United States)

    Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo

    2018-01-01

    The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the "Diagnostic and Statistical Manual of Mental Disorders." The…

  10. 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.

  11. 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...

  12. 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…

  13. 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...

  14. 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.

  15. 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

  16. 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....

  17. Gas chromatographic isolation technique for compound-specific radiocarbon analysis

    International Nuclear Information System (INIS)

    Uchida, M.; Kumamoto, Y.; Shibata, Y.; Yoneda, M.; Morita, M.; Kawamura, K.

    2002-01-01

    Full text: We present here a gas chromatographic isolation technique for the compound-specific radiocarbon analysis of biomarkers from the marine sediments. The biomarkers of fatty acids, hydrocarbon and sterols were isolated with enough amount for radiocarbon analysis using a preparative capillary gas chromatograph (PCGC) system. The PCGC systems used here is composed of an HP 6890 GC with FID, a cooled injection system (CIS, Gerstel, Germany), a zero-dead-volume effluent splitter, and a cryogenic preparative collection device (PFC, Gerstel). For AMS analysis, we need to separate and recover sufficient quantity of target individual compounds (>50 μgC). Yields of target compounds from C 14 n-alkanes to C 40 to C 30 n-alkanes and approximately that of 80% for higher molecular weights compounds more than C 30 n-alkanes. Compound specific radiocarbon analysis of organic compounds, as well as compound-specific stable isotope analysis, provide valuable information on the origins and carbon cycling in marine system. Above PCGC conditions, we applied compound-specific radiocarbon analysis to the marine sediments from western north Pacific, which showed the possibility of a useful chronology tool for estimating the age of sediment using organic matter in paleoceanographic study, in the area where enough amounts of planktonic foraminifera for radiocarbon analysis by accelerator mass spectrometry (AMS) are difficult to obtain due to dissolution of calcium carbonate. (author)

  18. 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...

  19. 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.

  20. 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 ...

  1. 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 ...

  2. 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…

  3. 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

  4. 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.

  5. 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

  6. Conduct disorders as a result of specific learning disorders

    OpenAIRE

    VOKROJOVÁ, Nela

    2012-01-01

    This thesis focuses on relationship between specific learning disorders and conduct disorders in puberty. The theoretical part explains the basic terms apearing in the thesis such as specific learning disorders, conduct disorders, puberty and prevention of conduct disorder formation. It presents Czech and foreign research which have already been done in this and related areas. The empirical part uses a quantitative method to measure anxiety and occurrence of conduct disorders in second grade ...

  7. 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.

  8. Perceptual learning is specific to the trained structure of information.

    Science.gov (United States)

    Cohen, Yamit; Daikhin, Luba; Ahissar, Merav

    2013-12-01

    What do we learn when we practice a simple perceptual task? Many studies have suggested that we learn to refine or better select the sensory representations of the task-relevant dimension. Here we show that learning is specific to the trained structural regularities. Specifically, when this structure is modified after training with a fixed temporal structure, performance regresses to pretraining levels, even when the trained stimuli and task are retained. This specificity raises key questions as to the importance of low-level sensory modifications in the learning process. We trained two groups of participants on a two-tone frequency discrimination task for several days. In one group, a fixed reference tone was consistently presented in the first interval (the second tone was higher or lower), and in the other group the same reference tone was consistently presented in the second interval. When following training, these temporal protocols were switched between groups, performance of both groups regressed to pretraining levels, and further training was needed to attain postlearning performance. ERP measures, taken before and after training, indicated that participants implicitly learned the temporal regularity of the protocol and formed an attentional template that matched the trained structure of information. These results are consistent with Reverse Hierarchy Theory, which posits that even the learning of simple perceptual tasks progresses in a top-down manner, hence can benefit from temporal regularities at the trial level, albeit at the potential cost that learning may be specific to these regularities.

  9. 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.

  10. 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.

  11. A predictive validity study of the Learning Style Questionnaire (LSQ) using multiple, specific learning criteria

    NARCIS (Netherlands)

    Kappe, F.R.; Boekholt, L.; den Rooyen, C.; van der Flier, H.

    2009-01-01

    Multiple and specific learning criteria were used to examine the predictive validity of the Learning Style Questionnaire (LSQ). Ninety-nine students in a college of higher learning in The Netherlands participated in a naturally occurring field study. The students were categorized into one of four

  12. 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

  13. Academic Achievement and Memory Differences among Specific Learning Disabilities Subtypes

    Science.gov (United States)

    Carmichael, Jessica A.; Fraccaro, Rebecca L.; Miller, Daniel C.; Maricle, Denise E.

    2014-01-01

    Reading, writing, and math are academic skills involving a number of different executive functions, particularly working memory. Children with specific learning disabilities (SLD) may present myriad academic difficulties, depending on their specific area(s) of processing weakness. is study examined differences in academic achievement and working…

  14. Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

    Science.gov (United States)

    Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

    2018-05-24

    The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

  15. 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...

  16. Computational learning on specificity-determining residue-nucleotide interactions

    KAUST Repository

    Wong, Ka-Chun; Li, Yue; Peng, Chengbin; Moses, Alan M.; Zhang, Zhaolei

    2015-01-01

    The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.

  17. Computational learning on specificity-determining residue-nucleotide interactions

    KAUST Repository

    Wong, Ka-Chun

    2015-11-02

    The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.

  18. Specification, authoring and prototyping of personalised workplace learning solutions

    DEFF Research Database (Denmark)

    Dolog, Peter; Kravcik, Milos; Cristea, Alexandra

    2007-01-01

    The main goal of this document is to survey the existing approaches for the authoring and engineering of personalisation and adaptation in e-learning systems. This document enables the comparison of various methods and techniques, and facilitates their integration or reuse. It offers a cohesive r...

  19. Sequence-specific procedural learning deficits in children with specific language impairment.

    Science.gov (United States)

    Hsu, Hsinjen Julie; Bishop, Dorothy V M

    2014-05-01

    This study tested the procedural deficit hypothesis of specific language impairment (SLI) by comparing children's performance in two motor procedural learning tasks and an implicit verbal sequence learning task. Participants were 7- to 11-year-old children with SLI (n = 48), typically developing age-matched children (n = 20) and younger typically developing children matched for receptive grammar (n = 28). In a serial reaction time task, the children with SLI performed at the same level as the grammar-matched children, but poorer than age-matched controls in learning motor sequences. When tested with a motor procedural learning task that did not involve learning sequential relationships between discrete elements (i.e. pursuit rotor), the children with SLI performed comparably with age-matched children and better than younger grammar-matched controls. In addition, poor implicit learning of word sequences in a verbal memory task (the Hebb effect) was found in the children with SLI. Together, these findings suggest that SLI might be characterized by deficits in learning sequence-specific information, rather than generally weak procedural learning. © 2014 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

  20. 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…

  1. 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…

  2. 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

  3. Learning speaker-specific characteristics with a deep neural architecture.

    Science.gov (United States)

    Chen, Ke; Salman, Ahmad

    2011-11-01

    Speech signals convey various yet mixed information ranging from linguistic to speaker-specific information. However, most of acoustic representations characterize all different kinds of information as whole, which could hinder either a speech or a speaker recognition (SR) system from producing a better performance. In this paper, we propose a novel deep neural architecture (DNA) especially for learning speaker-specific characteristics from mel-frequency cepstral coefficients, an acoustic representation commonly used in both speech recognition and SR, which results in a speaker-specific overcomplete representation. In order to learn intrinsic speaker-specific characteristics, we come up with an objective function consisting of contrastive losses in terms of speaker similarity/dissimilarity and data reconstruction losses used as regularization to normalize the interference of non-speaker-related information. Moreover, we employ a hybrid learning strategy for learning parameters of the deep neural networks: i.e., local yet greedy layerwise unsupervised pretraining for initialization and global supervised learning for the ultimate discriminative goal. With four Linguistic Data Consortium (LDC) benchmarks and two non-English corpora, we demonstrate that our overcomplete representation is robust in characterizing various speakers, no matter whether their utterances have been used in training our DNA, and highly insensitive to text and languages spoken. Extensive comparative studies suggest that our approach yields favorite results in speaker verification and segmentation. Finally, we discuss several issues concerning our proposed approach.

  4. An experimental result of estimating an application volume by machine learning techniques.

    Science.gov (United States)

    Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

    2015-01-01

    In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

  5. 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.

  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. 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...

  8. 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.

  9. 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

  10. Vulnerability Assessment by Learning Attack Specifications in Graphs

    NARCIS (Netherlands)

    Nunes Leal Franqueira, V.; Lopes, Raul H.C.

    This paper presents an evolutionary approach for learning attack specifications that describe attack scenarios. The objective is to find vulnerabilities in computer networks which minimise the cost of an attack with maximum impact. Although we focus on Insider Threat, the proposed approach applies

  11. Assessing the Learning Path Specification: a Pragmatic Quality Approach

    NARCIS (Netherlands)

    Janssen, José; Berlanga, Adriana; Heyenrath, Stef; Martens, Harrie; Vogten, Hubert; Finders, Anton; Herder, Eelco; Hermans, Henry; Melero, Javier; Schaeps, Leon; Koper, Rob

    2010-01-01

    Janssen, J., Berlanga, A. J., Heyenrath, S., Martens, H., Vogten, H., Finders, A., Herder, E., Hermans, H., Melero Gallardo, J., Schaeps, L., & Koper, R. (2010). Assessing the Learning Path Specification: a Pragmatic Quality Approach. Journal of Universal Computer Science, 16(21), 3191-3209.

  12. Specific Learning Difficulties--What Teachers Need to Know

    Science.gov (United States)

    Hudson, Diana

    2015-01-01

    This book clearly explains what Specific Learning Difficulties (SpLD) are, and describes the symptoms of conditions most commonly encountered in the mainstream classroom: dyslexia, dyspraxia, dyscalculia, dysgraphia, Autism Spectrum Disorder, ADHD, and OCD. The author provides an overview of the strengths and weaknesses commonly associated with…

  13. 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.

  14. Impact of Computer Aided Learning on Children with Specific Learning Disabilities

    OpenAIRE

    The Spastic Society Of Karnataka , Bangalore

    2004-01-01

    Study conducted by The Spastics Society of Karnataka on behalf of Azim Premji Foundation to assess the effectiveness of computers in enhancing learning for children with specific learning disabilities. Azim Premji Foundation is not liable for any direct or indirect loss or damage whatsoever arising from the use or access of any information, interpretation and conclusions that may be printed in this report.; Study to assess the effectiveness of computers in enhancing learning for children with...

  15. The learning continuum based on student's level of competence and specific pedagogical learning material on physiological aspects from teachers's opinions

    Science.gov (United States)

    Hadi, Ria Fitriyani; Subali, Bambang

    2017-08-01

    The scope of learning continuum at the conceptual knowledge is formulated based on the student's level of competence and specific pedagogical learning material. The purpose of this study is to develop a learning continuum of specific pedagogical material aspects of physiology targeted for students in primary and secondary education. This research was conducted in Province of Yogyakarta Special Region from October 2016 to January 2017. The method used in this study was survey method. The data were collected using questionnaire that had been validated from the aspects of construct validity and experts judgements. Respondents in this study consist of 281 Science/Biology teachers at Public Junior and Senior High Schools in the Province of Yogyakarta Special Region which spread in Yogyakarta city and 4 regencies namely Sleman, Bantul, Kulonprogo, and Gunungkidul. The data were taken using a census. Data were analyzed using a descriptive analysis technique. The results show the learning continuum of physiology based on teachers's opinion from grade VII, VIII, and IX are taught in grade VII, VIII, IX and X on level of C2 (understanding) and the learning continuum of physiology based on teachers's opinion from grade X, XI and XII are taught in grade X and XI on level of C2 (understanding), C3 (applying), and C4 (analyzing) based on teachers's opinions. The conclusion is that many teachers refer to the existing curriculum rather than their own original idea for developing learning continuum.

  16. The specificity of learned parallelism in dual-memory retrieval.

    Science.gov (United States)

    Strobach, Tilo; Schubert, Torsten; Pashler, Harold; Rickard, Timothy

    2014-05-01

    Retrieval of two responses from one visually presented cue occurs sequentially at the outset of dual-retrieval practice. Exclusively for subjects who adopt a mode of grouping (i.e., synchronizing) their response execution, however, reaction times after dual-retrieval practice indicate a shift to learned retrieval parallelism (e.g., Nino & Rickard, in Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 373-388, 2003). In the present study, we investigated how this learned parallelism is achieved and why it appears to occur only for subjects who group their responses. Two main accounts were considered: a task-level versus a cue-level account. The task-level account assumes that learned retrieval parallelism occurs at the level of the task as a whole and is not limited to practiced cues. Grouping response execution may thus promote a general shift to parallel retrieval following practice. The cue-level account states that learned retrieval parallelism is specific to practiced cues. This type of parallelism may result from cue-specific response chunking that occurs uniquely as a consequence of grouped response execution. The results of two experiments favored the second account and were best interpreted in terms of a structural bottleneck model.

  17. Polyunsaturated fatty acids (PUFAs) for children with specific learning disorders.

    Science.gov (United States)

    Tan, May Loong; Ho, Jacqueline J; Teh, Keng Hwang

    2016-09-28

    About 5% of school children have a specific learning disorder, defined as unexpected failure to acquire adequate abilities in reading, writing or mathematics that is not a result of reduced intellectual ability, inadequate teaching or social deprivation. Of these events, 80% are reading disorders. Polyunsaturated fatty acids (PUFAs), in particular, omega-3 and omega-6 fatty acids, which normally are abundant in the brain and in the retina, are important for learning. Some children with specific learning disorders have been found to be deficient in these PUFAs, and it is argued that supplementation of PUFAs may help these children improve their learning abilities. 1. To assess effects on learning outcomes of supplementation of polyunsaturated fatty acids (PUFAs) for children with specific learning disorders.2. To determine whether adverse effects of supplementation of PUFAs are reported in these children. In November 2015, we searched CENTRAL, Ovid MEDLINE, Embase, PsycINFO, 10 other databases and two trials registers. We also searched the reference lists of relevant articles. Randomised controlled trials (RCTs) or quasi-RCTs comparing PUFAs with placebo or no treatment in children younger than 18 years with specific learning disabilities, as diagnosed in accordance with the fifth (or earlier) edition of theDiagnostic and Statistical Manual of Mental Disorders (DSM-5), or the 10th (or earlier) revision of the International Classification of Diseases (ICD-10) or equivalent criteria. We included children with coexisting developmental disorders such as attention deficit hyperactivity disorder (ADHD) or autism. Two review authors (MLT and KHT) independently screened the titles and abstracts of articles identified by the search and eliminated all studies that did not meet the inclusion criteria. We contacted study authors to ask for missing information and clarification, when needed. We used the GRADE approach to assess the quality of evidence. Two small studies

  18. 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.

  19. 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

  20. 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.

  1. Category Specificity in Normal Episodic Learning: Applications to Object Recognition and Category-Specific Agnosia

    Science.gov (United States)

    Bukach, Cindy M.; Bub, Daniel N.; Masson, Michael E. J.; Lindsay, D. Stephen

    2004-01-01

    Studies of patients with category-specific agnosia (CSA) have given rise to multiple theories of object recognition, most of which assume the existence of a stable, abstract semantic memory system. We applied an episodic view of memory to questions raised by CSA in a series of studies examining normal observers' recall of newly learned attributes…

  2. [Specific learning disabilities - from DSM-IV to DSM-5].

    Science.gov (United States)

    Schulte-Körne, Gerd

    2014-09-01

    The publication of the DSM-5 means changes in the classification and recommendations for diagnosis of specific learning disabilities. Dyslexia and dyscalculia have been reintroduced into the DSM. Three specific learning disorders - impairment in reading, impairment in the written expression, and impairment in mathematics, described by subskills - are now part of the DSM-5. Three subcomponents of the reading disorder are expressly differentiated: word reading accuracy, reading rate, and fluency and reading comprehension. Impaired subskills of the specific learning disorder with impairment in written expression are spelling accuracy, grammar and punctuation accuracy, and clarity and organization of written expression. Four subskills are found in the mathematics disorder: number sense, memorization of arithmetic facts, accurate or fluent calculation, and accurate math reasoning. Each impaired academic domain and subskill should be recorded. A description of the severity degree was also included. The diagnosis is based on a variety of methods, including medical history, clinical interview, school report, teacher evaluation, rating scales, and psychometric tests. The IQ discrepancy criterion was abandoned, though that of age or class discrepancy criterion was retained. The application of a discrepancy is recommended by 1 to 2.5 SD. All three specific developmental disorders are common (prevalence 5 %-15 %), occur early during the first years of formal schooling, and persist into adulthood.

  3. Towards sophisticated learning from EHRs: increasing prediction specificity and accuracy using clinically meaningful risk criteria.

    Science.gov (United States)

    Vasiljeva, Ieva; Arandjelovic, Ognjen

    2016-08-01

    Computer based analysis of Electronic Health Records (EHRs) has the potential to provide major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper introduces a novel algorithm that uses machine learning for the discovery of longitudinal patterns in the diagnoses of diseases. Two key technical novelties are introduced: one in the form of a novel learning paradigm which enables greater learning specificity, and another in the form of a risk driven identification of confounding diagnoses. We present a series of experiments which demonstrate the effectiveness of the proposed techniques, and which reveal novel insights regarding the most promising future research directions.

  4. Managing specific learning disability in schools in India.

    Science.gov (United States)

    Karande, Sunil; Sholapurwala, Rukhshana; Kulkarni, Madhuri

    2011-07-01

    Specific learning disability (dyslexia, dysgraphia, and dyscalculia) afflicts 5-15% of school-going children. Over the last decade; awareness about this invisible handicap has grown in India. However, much needs to be done to ensure that each afflicted child gets an opportunity to achieve his or her full academic potential in regular mainstream schools. In order to achieve this ideal scenario, all regular classroom teachers should be sensitized to suspect, and trained to screen for this disability when the child is in primary school. School managements should become proactive to set up resource rooms and employ special educators to ensure that these children receive regular and affordable remedial education; and be diligent in ensuring that these children get the mandatory provisions both during school and board examinations. Once specific learning disability is recognized as a disability by the Government of India, these children with the backing of the Right to Education Act, would be able to benefit significantly.

  5. Lymphogammagraphy. An adaptive technique to specific clinical problems

    International Nuclear Information System (INIS)

    Rojas, Juan Carlos; Llamas, Augusto E; De los Reyes, Amelia; Martinez, Maria Cristina

    2000-01-01

    Lymphoscintigraphy (LS) is an accurate and safe procedure for the evaluation of lymph nodes, many of which remain occult to other imaging techniques. Not only lymphatic pathways, but also the functional condition of the lymphatic channels and the localization of lymphatic basins can be assessed with LS. We will illustrate the utility of the technique in different clinical settings, concerning two patients recently studied at the National Cancer Institute. In the first case a patient previously diagnosed with a testicular teratocarcinoma that presented with ascites. The LS showed a chyloperitoneum, leading the clinicians to with old further treatment given the lymphatic nature of the ascites as opposed to a malignant origin. In the second patient the LS illustrated the adaptability of the lymphatic system to a chronic insult (suture of Pecquet's cistem) by lymphatic flows diversion through paralumbar channels. In this patient LS was combined with a peritoneal scintigraphy to demonstrate permeability through a peritoneovenous bypass; incidentally, a peritoneopleural shunt was diagnosed. LS permit the visualization of lymphatic channels and their functional derangements in an easy, minimally invasive way, not routinely achievable by other imaging techniques |

  6. Enhanced Assessment Technology and Neurocognitive Aspects of Specific Learning Disorder with Impairment in Mathematics.

    Directory of Open Access Journals (Sweden)

    Marios A. Pappas

    2018-02-01

    Full Text Available Specific Learning Disorder with impairment in Mathematics (Developmental Dyscalculia is a complex learning disorder which affects arithmetic skills, symbolic magnitude processing, alertness, flexibility in problem solving and maintained attention. Neuro-cognitive studies revealed that such difficulties in children with DD could be related to poor Working Memory and attention deficits. Furthermore, neuroimaging studies indicate that brain structure differences in children with DD compared to typically developing children could affect mathematical performance. In this study we present the cognitive profile of Dyscalculia, as well as the neuropsychological aspects of the deficit, with special reference to the utilization of enhanced assessment technology such as computerized neuropsychological tools and neuroimaging techniques.

  7. Prediction of drug synergy in cancer using ensemble-based machine learning techniques

    Science.gov (United States)

    Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

    2018-04-01

    Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

  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 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.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. Machine Learning Identification of Protein Properties Useful for Specific Applications

    KAUST Repository

    Khamis, Abdullah

    2016-03-31

    Proteins play critical roles in cellular processes of living organisms. It is therefore important to identify and characterize their key properties associated with their functions. Correlating protein’s structural, sequence and physicochemical properties of its amino acids (aa) with protein functions could identify some of the critical factors governing the specific functionality. We point out that not all functions of even well studied proteins are known. This, complemented by the huge increase in the number of newly discovered and predicted proteins, makes challenging the experimental characterization of the whole spectrum of possible protein functions for all proteins of interest. Consequently, the use of computational methods has become more attractive. Here we address two questions. The first one is how to use protein aa sequence and physicochemical properties to characterize a family of proteins. The second one focuses on how to use transcription factor (TF) protein’s domains to enhance accuracy of predicting TF DNA binding sites (TFBSs). To address the first question, we developed a novel method using computational representation of proteins based on characteristics of different protein regions (N-terminal, M-region and C-terminal) and combined these with the properties of protein aa sequences. We show that this description provides important biological insight about characterization of the protein functional groups. Using feature selection techniques, we identified key properties of proteins that allow for very accurate characterization of different protein families. We demonstrated efficiency of our method in application to a number of antimicrobial peptide families. To address the second question we developed another novel method that uses a combination of aa properties of DNA binding domains of TFs and their TFBS properties to develop machine learning models for predicting TFBSs. Feature selection is used to identify the most relevant characteristics

  17. 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 ...

  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. Optimization of digital radiography techniques for specific application

    International Nuclear Information System (INIS)

    Harara, W.

    2010-12-01

    A low cost digital radiography system (DRS) for testing weld joints and castings in laboratory was assembled. The DRS is composed from X-ray source, scintillator, first surface mirror with Aluminum coating, charged coupled device (CCD) camera and lens. The DRS was used to test flawed carbon steel welded plates with thicknesses up to 12 mm. The comparison between the digital radiographs of the plates weldments and the radiographs of the same plates weldments using medium speed film type had shown that, the detection capability of the weld flaws are nearly identical for the two radiography techniques, while the sensitivity achieved in digital radiography of the plates weldments was one IQI wire less than the sensitivity achieved by conventional radiography of the same plates weldments according to EN 462-1. Further, the DRS was also successfully used to test (100 x 100 x 100) mm Aluminum casting with artificial flaws of varied dimensions and orientations. The resulted digital radiographs of the casting show that, all the flaws had been detected and their dimensions can be measured accurately, this confirm that, The proposed DRS can be used to detect and measure the flaws in the Aluminum and others light metals castings accurately. (author)

  20. 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.

  1. 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...

  2. 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…

  3. 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.

  4. 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.

  5. The future of the IMS Learning Design specification: a critical look

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    P. B. Sloep (2009). The future of the IMS Learning Design specification: a critical look. Presentation at the IMS Learning Design seminar 'The future of IMS Learning Design'. December, 10, 2009, Wollongong, Australia: University of Wollongong.

  6. 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.

  7. A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

    Science.gov (United States)

    Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Huang, Te-Chia; Chuang, Wei-Yao

    2013-06-01

    Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis. The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks. The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619. The

  8. 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.

  9. Specific learning disability in mathematics: a comprehensive review.

    Science.gov (United States)

    Soares, Neelkamal; Evans, Teresa; Patel, Dilip R

    2018-01-01

    Math skills are necessary for success in the childhood educational and future adult work environment. This article reviews the changing terminology for specific learning disabilities (SLD) in math and describes the emerging genetics and neuroimaging studies that relate to individuals with math disability (MD). It is important to maintain a developmental perspective on MD, as presentation changes with age, instruction, and the different models (educational and medical) of identification. Intervention requires a systematic approach to screening and remediation that has evolved with more evidence-based literature. Newer directions in behavioral, educational and novel interventions are described.

  10. Unsupervised behaviour-specific dictionary learning for abnormal event detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2015-01-01

    the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from...... potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms...

  11. 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.

  12. The lasting effects of process-specific versus stimulus-specific learning during infancy.

    Science.gov (United States)

    Hadley, Hillary; Pickron, Charisse B; Scott, Lisa S

    2015-09-01

    The capacity to tell the difference between two faces within an infrequently experienced face group (e.g. other species, other race) declines from 6 to 9 months of age unless infants learn to match these faces with individual-level names. Similarly, the use of individual-level labels can also facilitate differentiation of a group of non-face objects (strollers). This early learning leads to increased neural specialization for previously unfamiliar face or object groups. The current investigation aimed to determine whether early conceptual learning between 6 and 9 months leads to sustained behavioral advantages and neural changes in these same children at 4-6 years of age. Results suggest that relative to a control group of children with no previous training and to children with infant category-level naming experience, children with early individual-level training exhibited faster response times to human faces. Further, individual-level training with a face group - but not an object group - led to more adult-like neural responses for human faces. These results suggest that early individual-level learning results in long-lasting process-specific effects, which benefit categories that continue to be perceived and recognized at the individual level (e.g. human faces). © 2014 John Wiley & Sons Ltd.

  13. Human reinforcement learning subdivides structured action spaces by learning effector-specific values.

    Science.gov (United States)

    Gershman, Samuel J; Pesaran, Bijan; Daw, Nathaniel D

    2009-10-28

    Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable because of the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning-such as prediction error signals for action valuation associated with dopamine and the striatum-can cope with this "curse of dimensionality." We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and blood oxygen level-dependent (BOLD) activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to "divide and conquer" reinforcement learning over high-dimensional action spaces.

  14. Addiction memory as a specific, individually learned memory imprint.

    Science.gov (United States)

    Böning, J

    2009-05-01

    The construct of "addiction memory" (AM) and its importance for relapse occurrence has been the subject of discussion for the past 30 years. Neurobiological findings from "social neuroscience" and biopsychological learning theory, in conjunction with construct-valid behavioral pharmacological animal models, can now also provide general confirmation of addiction memory as a pathomorphological correlate of addiction disorders. Under multifactorial influences, experience-driven neuronal learning and memory processes of emotional and cognitive processing patterns in the specific individual "set" and "setting" play an especially pivotal role in this connection. From a neuropsychological perspective, the episodic (biographical) memory, located at the highest hierarchical level, is of central importance for the formation of the AM in certain structural and functional areas of the brain and neuronal networks. Within this context, neuronal learning and conditioning processes take place more or less unconsciously and automatically in the preceding long-term-memory systems (in particular priming and perceptual memory). They then regulate the individually programmed addiction behavior implicitly and thus subsequently stand for facilitated recollection of corresponding, previously stored cues or context situations. This explains why it is so difficult to treat an addiction memory, which is embedded above all in the episodic memory, from the molecular carrier level via the neuronal pattern level through to the psychological meaning level, and has thus meanwhile become a component of personality.

  15. Psychological Co-morbidity in Children with Specific Learning Disorders.

    Science.gov (United States)

    Sahoo, Manoj K; Biswas, Haritha; Padhy, Susanta Kumar

    2015-01-01

    Children under 19 years of age constitute over 40% of India's population and information about their mental health needs is a national imperative. Children with specific learning disorders (SLDs) exhibit academic difficulties disproportionate to their intellectual capacities. Prevalence of SLD ranges from 2% to 10%. Dyslexia (developmental reading disorder) is the most common type, affecting 80% of all SLD. About 30% of learning disabled children have behavioral and emotional problems, which range from attention deficit hyperactivity disorder (most common) to depression, anxiety, suicide etc., to substance abuse (least common). Co-occurrence of such problems with SLD further adds to the academic difficulty. In such instances, diagnosis is difficult and tricky; improvement in academics demands comprehensive holistic treatment approach. SLD remains a large public health problem because of under-recognition, inadequate treatment and therefore merits greater effort to understand the co-morbidities, especially in the Indian population. As the literature is scarce regarding co-morbid conditions in learning disability in Indian scenario, the present study has tried to focus on Indian population. The educational concessions (recent most) given to such children by Central Board of Secondary Education, New Delhi are referred to. The issues to be addressed by the family physicians are: Low level of awareness among families and teachers, improper dissemination of accurate information about psychological problems, available help seeking avenues, need to develop service delivery models in rural and urban areas and focus on the integration of mental health and primary care keeping such co-morbidity in mind.

  16. d-Cycloserine reduces context specificity of sexual extinction learning.

    Science.gov (United States)

    Brom, Mirte; Laan, Ellen; Everaerd, Walter; Spinhoven, Philip; Trimbos, Baptist; Both, Stephanie

    2015-11-01

    d-Cycloserine (DCS) enhances extinction processes in animals. Although classical conditioning is hypothesized to play a pivotal role in the aetiology of appetitive motivation problems, no research has been conducted on the effect of DCS on the reduction of context specificity of extinction in human appetitive learning, while facilitation hereof is relevant in the context of treatment of problematic reward-seeking behaviors. Female participants were presented with two conditioned stimuli (CSs) that either predicted (CS+) or did not predict (CS-) a potential sexual reward (unconditioned stimulus (US); genital vibrostimulation). Conditioning took place in context A and extinction in context B. Subjects received DCS (125mg) or placebo directly after the experiment on day 1 in a randomized, double-blind, between-subject fashion (Placebo n=31; DCS n=31). Subsequent testing for CS-evoked conditioned responses (CRs) in both the conditioning (A) and the extinction context (B) took place 24h later on day 2. Drug effects on consolidation were then assessed by comparing the recall of sexual extinction memories between the DCS and the placebo groups. Post learning administration of DCS facilitates sexual extinction memory consolidation and affects extinction's fundamental context specificity, evidenced by reduced conditioned genital and subjective sexual responses, relative to placebo, for presentations of the reward predicting cue 24h later outside the extinction context. DCS makes appetitive extinction memories context-independent and prevents the return of conditioned response. NMDA receptor glycine site agonists may be potential pharmacotherapies for the prevention of relapse of appetitive motivation disorders with a learned component. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. M-Learning: Implications in Learning Domain Specificities, Adaptive Learning, Feedback, Augmented Reality, and the Future of Online Learning

    Science.gov (United States)

    Squires, David R.

    2014-01-01

    The aim of this paper is to examine the potential and effectiveness of m-learning in the field of Education and Learning domains. The purpose of this research is to illustrate how mobile technology can and is affecting novel change in instruction, from m-learning and the link to adaptive learning, to the uninitiated learner and capacities of…

  3. Psychological co-morbidity in children with specific learning disorders

    Directory of Open Access Journals (Sweden)

    Manoj K Sahoo

    2015-01-01

    Full Text Available Children under 19 years of age constitute over 40% of India′s population and information about their mental health needs is a national imperative. Children with specific learning disorders (SLDs exhibit academic difficulties disproportionate to their intellectual capacities. Prevalence of SLD ranges from 2% to 10%. Dyslexia (developmental reading disorder is the most common type, affecting 80% of all SLD. About 30% of learning disabled children have behavioral and emotional problems, which range from attention deficit hyperactivity disorder (most common to depression, anxiety, suicide etc., to substance abuse (least common. Co-occurrence of such problems with SLD further adds to the academic difficulty. In such instances, diagnosis is difficult and tricky; improvement in academics demands comprehensive holistic treatment approach. SLD remains a large public health problem because of under-recognition, inadequate treatment and therefore merits greater effort to understand the co-morbidities, especially in the Indian population. As the literature is scarce regarding co-morbid conditions in learning disability in Indian scenario, the present study has tried to focus on Indian population. The educational concessions (recent most given to such children by Central Board of Secondary Education, New Delhi are referred to. The issues to be addressed by the family physicians are: Low level of awareness among families and teachers, improper dissemination of accurate information about psychological problems, available help seeking avenues, need to develop service delivery models in rural and urban areas and focus on the integration of mental health and primary care keeping such co-morbidity in mind.

  4. Specific learning disabilities in children: deficits and neuropsychological profile.

    Science.gov (United States)

    Kohli, Adarsh; Malhotra, Savita; Mohanty, Manju; Khehra, Nitasha; Kaur, Manreet

    2005-06-01

    The public is gradually becoming aware of specific learning disabilities (SLDs), which are very often the cause of academic difficulties. The aim of the study was to assess the SLDs in the clinic population at the Child and Adolescent Psychiatry Clinic at the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh using the National Institute of Mental Health and Neurosciences SLD index and subsequently to assess the children's neuropsychological functions using a battery of tests. Thirty-five children in the age range of 7-14 years (both boys and girls) were recruited as the cohort, diagnosed clinically and assessed using the battery of tests for SLDs and neuropsychological tests consisting of the PGIMER memory scale for children, the Wisconsin card sorting test, the Bender visuo-motor gestalt test and Malin's intelligence scale for Indian children. The study revealed deficits in language and writing skills and impairments in specific areas of memory, executive functions and perceptuo-motor tasks. Identification of SLDs is useful in drawing up a treatment plan specific for a particular child.

  5. 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...

  6. 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.

  7. 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.

  8. Learning the „Look-at-you-go” Moment in Corporate Governance Negotiation Techniques

    Directory of Open Access Journals (Sweden)

    Clara VOLINTIRU

    2015-06-01

    Full Text Available This article explores in an interdisciplinary manner the way concepts are learned or internalized, depending on the varying means of transmission, as well as on the sequencing in which the information is transmitted. In this sense, we build on the constructivist methodology framework in assessing concept acquisition in academic disciplines, at an advanced level. We also present the evolution of certain negotiation techniques, from traditional setting, to less predictable ones. This assessment is compared to a specific Pop Culture case study in which we find an expressive representation of negotiation techniques. Our methodology employs both focus groups and experimental design to test the relative positioning of theoretical concept acquisition (TCA as opposed to expressive concept-acquisition (ECA. Our findings suggest that while expressive concept acquisition (ECA via popular culture representations enhances the students understanding of negotiation techniques, this can only happen in circumstances in which a theoretical concept acquisition (TCA is pre-existent.

  9. Machine Learning Techniques for Arterial Pressure Waveform Analysis

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    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.

  10. Analysis on the Metrics used in Optimizing Electronic Business based on Learning Techniques

    Directory of Open Access Journals (Sweden)

    Irina-Steliana STAN

    2014-09-01

    Full Text Available The present paper proposes a methodology of analyzing the metrics related to electronic business. The drafts of the optimizing models include KPIs that can highlight the business specific, if only they are integrated by using learning-based techniques. Having set the most important and high-impact elements of the business, the models should get in the end the link between them, by automating business flows. The human resource will be found in the situation of collaborating more and more with the optimizing models which will translate into high quality decisions followed by profitability increase.

  11. 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.

  12. Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

    KAUST Repository

    AlQuerm, Ismail A.

    2018-02-21

    There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for

  13. 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.

  14. "Mastery Learning" Como Metodo Psicoeducativo para Ninos con Problemas Especificos de Aprendizaje. ("Mastery Learning" as a Psychoeducational Method for Children with Specific Learning Problems.)

    Science.gov (United States)

    Coya, Liliam de Barbosa; Perez-Coffie, Jorge

    1982-01-01

    "Mastery Learning" was compared with the "conventional" method of teaching reading skills to Puerto Rican children with specific learning disabilities. The "Mastery Learning" group showed significant gains in the cognitive and affective domains. Results suggested Mastery Learning is a more effective method of teaching…

  15. 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

  16. A data-driven predictive approach for drug delivery using machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Li

    Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

  17. Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

    Directory of Open Access Journals (Sweden)

    Jose M. Bernal-de-Lázaro

    2016-05-01

    Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

  18. 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...

  19. 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.

  20. Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

    2017-09-05

    In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  1. Exploring Culture-Specific Learning Styles in Accounting Education

    Science.gov (United States)

    Sikkema, Seth E.; Sauerwein, Joshua A.

    2015-01-01

    Purpose: The purpose of this paper is to review whether culture affects accounting students' learning processes to identify practical guidance for accounting educators facing a culturally diverse classroom. In spite of a significant literature thread in accounting education on student learning, relatively, little emphasis has been placed on…

  2. Anxiety levels in mothers of children with specific learning disability

    Directory of Open Access Journals (Sweden)

    Karande S

    2009-01-01

    Full Text Available Background : Parents of children with specific learning disability (SpLD undergo stress in coping with their child′s condition. Aim : To measure the levels of anxiety and find out the cause of anxiety in mothers of children with SpLD at time of diagnosis. Settings and Design : Prospective rating-scale and interview-based study conducted in our clinic. Materials and Methods : One hundred mothers of children (70 boys, 30 girls with SpLD were interviewed using the Hamilton anxiety rating scale (HAM-A and a semi-structured questionnaire. Detailed clinical and demographic data of mothers were noted. Statistical Analysis : Chi-square test or unpaired student′s t-test was applied wherever applicable. Results : The mean age of mothers was 40.14 years (±SD 4.94, range 25.07-54.0, 73% belonged to upper or upper middle socioeconomic strata of society, 67% were graduates or postgraduates, 58% were full-time home-makers, and 33% lived in joint families. Levels of anxiety were absent in 24%, mild in 75%, and moderate in 1% of mothers. Their mean total anxiety score was 5.65 (±SD 4.75, range 0-21, mean psychic anxiety score was 3.92 (±SD 3.11, range 0-13, and mean somatic anxiety score was 1.76 (±SD 2.05, range 0-10. Their common worries were related to child′s poor school performance (95%, child′s future (90%, child′s behavior (51%, and visits to our clinic (31%. Conclusion : Most mothers of children with SpLD have already developed mild anxiety levels by the time this hidden disability is diagnosed. These anxieties should be addressed by counseling to ensure optimum rehabilitation of these children.

  3. Anxiety levels in mothers of children with specific learning disability.

    Science.gov (United States)

    Karande, S; Kumbhare, N; Kulkarni, M; Shah, N

    2009-01-01

    Parents of children with specific learning disability (SpLD) undergo stress in coping with their child's condition. To measure the levels of anxiety and find out the cause of anxiety in mothers of children with SpLD at time of diagnosis. Prospective rating-scale and interview-based study conducted in our clinic. One hundred mothers of children (70 boys, 30 girls) with SpLD were interviewed using the Hamilton anxiety rating scale (HAM-A) and a semi-structured questionnaire. Detailed clinical and demographic data of mothers were noted. Chi-square test or unpaired student's t-test was applied wherever applicable. The mean age of mothers was 40.14 years (+/-SD 4.94, range 25.07-54.0), 73% belonged to upper or upper middle socioeconomic strata of society, 67% were graduates or postgraduates, 58% were full-time home-makers, and 33% lived in joint families. Levels of anxiety were absent in 24%, mild in 75%, and moderate in 1% of mothers. Their mean total anxiety score was 5.65 (+/-SD 4.75, range 0-21), mean psychic anxiety score was 3.92 (+/-SD 3.11, range 0-13), and mean somatic anxiety score was 1.76 (+/-SD 2.05, range 0-10). Their common worries were related to child's poor school performance (95%), child's future (90%), child's behavior (51%), and visits to our clinic (31%). Most mothers of children with SpLD have already developed mild anxiety levels by the time this hidden disability is diagnosed. These anxieties should be addressed by counseling to ensure optimum rehabilitation of these children.

  4. 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…

  5. 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…

  6. Computer-aided classification of lung nodules on computed tomography images via deep learning technique

    Directory of Open Access Journals (Sweden)

    Hua KL

    2015-08-01

    Full Text Available Kai-Lung Hua,1 Che-Hao Hsu,1 Shintami Chusnul Hidayati,1 Wen-Huang Cheng,2 Yu-Jen Chen3 1Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 2Research Center for Information Technology Innovation, Academia Sinica, 3Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan Abstract: Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. Keywords: nodule classification, deep learning, deep belief network, convolutional neural network

  7. 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

  8. Cultivating Collaborations: Site Specific Design for Embodied Science Learning.

    Science.gov (United States)

    Gill, Katherine; Glazier, Jocelyn; Towns, Betsy

    2018-05-21

    Immersion in well-designed outdoor environments can foster the habits of mind that enable critical and authentic scientific questions to take root in students' minds. Here we share two design cases in which careful, collaborative, and intentional design of outdoor learning environments for informal inquiry provide people of all ages with embodied opportunities to learn about the natural world, developing the capacity for understanding ecology and the ability to empathize, problem-solve and reflect. Embodied learning, as facilitated by and in well-designed outdoor learning environments, leads students to develop new ways of seeing, new scientific questions, new ways to connect with ideas, with others and new ways of thinking about the natural world. Using examples from our collaborative practices as experiential learning designers, we illustrate how creating the habits of mind critical to creating scientists, science-interested, and science-aware individuals benefits from providing students spaces to engage in embodied learning in nature. We show how public landscapes designed in creative partnerships between educators, scientists, designers and the public have potential to amplify science learning for all.

  9. 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

  10. Learning Technology Specification: Principles for Army Training Designers and Developers

    Science.gov (United States)

    2013-09-01

    Bowers & Bowers, 2010; Moreno, 2006; Shönborn, 2011; Watkins & Hufnagel, 2007). • Interactive technologies can help maintain student engagement when...modified to better suit the trainee characteristics, learning objectives, and environmental constraints. • To maintain student engagement when...learning styles (e.g., auditory, visual, tactile) 1 2 3 4 5 Improves student engagement 1 2 3 4 5 Please list any additional factors that are

  11. 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.

  12. Solar adaptive optics: specificities, lessons learned, and open alternatives

    Science.gov (United States)

    Montilla, I.; Marino, J.; Asensio Ramos, A.; Collados, M.; Montoya, L.; Tallon, M.

    2016-07-01

    First on sky adaptive optics experiments were performed on the Dunn Solar Telescope on 1979, with a shearing interferometer and limited success. Those early solar adaptive optics efforts forced to custom-develop many components, such as Deformable Mirrors and WaveFront Sensors, which were not available at that time. Later on, the development of the correlation Shack-Hartmann marked a breakthrough in solar adaptive optics. Since then, successful Single Conjugate Adaptive Optics instruments have been developed for many solar telescopes, i.e. the National Solar Observatory, the Vacuum Tower Telescope and the Swedish Solar Telescope. Success with the Multi Conjugate Adaptive Optics systems for GREGOR and the New Solar Telescope has proved to be more difficult to attain. Such systems have a complexity not only related to the number of degrees of freedom, but also related to the specificities of the Sun, used as reference, and the sensing method. The wavefront sensing is performed using correlations on images with a field of view of 10", averaging wavefront information from different sky directions, affecting the sensing and sampling of high altitude turbulence. Also due to the low elevation at which solar observations are performed we have to include generalized fitting error and anisoplanatism, as described by Ragazzoni and Rigaut, as non-negligible error sources in the Multi Conjugate Adaptive Optics error budget. For the development of the next generation Multi Conjugate Adaptive Optics systems for the Daniel K. Inouye Solar Telescope and the European Solar Telescope we still need to study and understand these issues, to predict realistically the quality of the achievable reconstruction. To improve their designs other open issues have to be assessed, i.e. possible alternative sensing methods to avoid the intrinsic anisoplanatism of the wide field correlation Shack-Hartmann, new parameters to estimate the performance of an adaptive optics solar system, alternatives to

  13. 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.

  14. 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....

  15. 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

  16. The learning continuum of ecology based on teachers' opinion about student's level of competence and specific pedagogical learning material

    Science.gov (United States)

    Pramesti, Indah Cahaya; Subali, Bambang

    2017-08-01

    This study aims at designing learning continuum for developing a curriculum based on teachers' opinion about student's level of competence and specific pedagogical learning material on ecological aspect targeted for students of Primary and Secondary Education. This research is a descriptive research using survey methods. The researchers conducted a census by distributing questionnaires that had been validated from the aspects of construct validity and experts judgements to 147 natural science teachers at junior high school and 134 Biology teachers at senior high school as a population throughout 4 regencies and 1 city in Yogyakarta Special Region.. Data analysis techniques used descriptive analysis. In conclusion, teacher's opinion is influenced by curriculum that exist today. According to the opinions of Natural Science teachers at Junior High School, most of the ecological aspects such as characteristics of biomes, characteristics of ecosystems, characteristics of communities, characteristics of populations, etc. should be taught in grade VII with the level of competence: to understand (C2), while Biology teachers at Senior High School state that the ecological aspect should be taught in class X with the level of competence: to understand (C2), apply (C3) and analyze (C4). Teachers should be a privy in the formulation of the curriculum, so they're not only accept and apply the existing curriculum but also give opinions to improve the curriculum, especially in terms of ecology.

  17. 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.

  18. Tracer technique for measuring specific activity of 63 Ni, using 4πβ-γ

    International Nuclear Information System (INIS)

    Iwahara, A.

    1979-01-01

    The specific activity of a 6 3 Ni solution has been measured by an efficiency tracer technique using a 4 π β - γ coincidence system. 6 3 Ni was chosen. Because it's a very low energy pure beta emitter. Due to chemical compatibility and beta spectral shapes, 6 0 Co has been chosen as tracer. In the determination of 6 3 Ni, the specific activity. As the efficiency tracer techniques requires a previous knowledge of tracer activity, this has been measured by a conventional 4 π β -γ coincidence method. (author)

  19. 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 ...

  20. 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.

  1. Comparing Three Patterns of Strengths and Weaknesses Models for the Identification of Specific Learning Disabilities

    Science.gov (United States)

    Miller, Daniel C.; Maricle, Denise E.; Jones, Alicia M.

    2016-01-01

    Processing Strengths and Weaknesses (PSW) models have been proposed as a method for identifying specific learning disabilities. Three PSW models were examined for their ability to predict expert identified specific learning disabilities cases. The Dual Discrepancy/Consistency Model (DD/C; Flanagan, Ortiz, & Alfonso, 2013) as operationalized by…

  2. 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.

  3. 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.

  4. One-trial overshadowing: Evidence for fast specific fear learning in humans.

    Science.gov (United States)

    Haesen, Kim; Beckers, Tom; Baeyens, Frank; Vervliet, Bram

    2017-03-01

    Adaptive defensive actions necessitate a fear learning system that is both fast and specific. Fast learning serves to minimize the number of threat confrontations, while specific learning ensures that the acquired fears are tied to threat-relevant cues only. In Pavlovian fear conditioning, fear acquisition is typically studied via repetitive pairings of a single cue with an aversive experience, which is not optimal for the examination of fast specific fear learning. In this study, we adopted the one-trial overshadowing procedure from basic learning research, in which a combination of two visual cues is presented once and paired with an aversive electrical stimulation. Using on-line shock expectancy ratings, skin conductance reactivity and startle reflex modulation as indices of fear learning, we found evidence of strong fear after a single conditioning trial (fast learning) as well as attenuated fear responding when only half of the trained stimulus combination was presented (specific learning). Moreover, specificity of fear responding tended to correlate with levels of state and trait anxiety. These results suggest that one-trial overshadowing can be used as a model to study fast specific fear learning in humans and individual differences therein. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning.

    Science.gov (United States)

    Stark-Inbar, Alit; Raza, Meher; Taylor, Jordan A; Ivry, Richard B

    2017-01-01

    In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the

  6. 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…

  7. 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...

  8. 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.

  9. 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…

  10. 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…

  11. 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)

  12. 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.

  13. 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…

  14. 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...

  15. Development and fabrication of patient-specific knee implant using additive manufacturing techniques

    Science.gov (United States)

    Zammit, Robert; Rochman, Arif

    2017-10-01

    Total knee replacement is the most effective treatment to relief pain and restore normal function in a diseased knee joint. The aim of this research was to develop a patient-specific knee implant which can be fabricated using additive manufacturing techniques and has reduced wear rates using a highly wear resistant materials. The proposed design was chosen based on implant requirements, such as reduction in wear rates as well as strong fixation. The patient-specific knee implant improves on conventional knee implants by modifying the articulating surfaces and bone-implant interfaces. Moreover, tribological tests of different polymeric wear couples were carried out to determine the optimal materials to use for the articulating surfaces. Finite element analysis was utilized to evaluate the stresses sustained by the proposed design. Finally, the patient-specific knee implant was successfully built using additive manufacturing techniques.

  16. 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.

  17. Reduced autobiographical memory specificity is associated with impaired discrimination learning in anxiety disorder patients

    Science.gov (United States)

    Lenaert, Bert; Boddez, Yannick; Vervliet, Bram; Schruers, Koen; Hermans, Dirk

    2015-01-01

    Associative learning plays an important role in the development of anxiety disorders, but a thorough understanding of the variables that impact such learning is still lacking. We investigated whether individual differences in autobiographical memory specificity are related to discrimination learning and generalization. In an associative learning task, participants learned the association between two pictures of female faces and a non-aversive outcome. Subsequently, six morphed pictures functioning as generalization stimuli (GSs) were introduced. In a sample of healthy participants (Study 1), we did not find evidence for differences in discrimination learning as a function of memory specificity. In a sample of anxiety disorder patients (Study 2), individuals who were characterized by low memory specificity showed deficient discrimination learning relative to high specific individuals. In contrast to previous findings, results revealed no effect of memory specificity on generalization. These results indicate that impaired discrimination learning, previously shown in patients suffering from an anxiety disorder, may be—in part—due to limited memory specificity. Together, these studies emphasize the importance of incorporating cognitive variables in associative learning theories and their implications for the development of anxiety disorders. In addition, re-analyses of the data (Study 3) showed that patients suffering from panic disorder showed higher outcome expectancies in the presence of the stimulus that was never followed by an outcome during discrimination training, relative to patients suffering from other anxiety disorders and healthy participants. Because we used a neutral, non-aversive outcome (i.e., drawing of a lightning bolt), these data suggest that learning abnormalities in panic disorder may not be restricted to fear learning, but rather reflect a more general associative learning deficit that also manifests in fear irrelevant contexts. PMID

  18. 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…

  19. Classification of breast tumour using electrical impedance and machine learning techniques

    International Nuclear Information System (INIS)

    Amin, Abdullah Al; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

    2014-01-01

    When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy. (paper)

  20. Classification of breast tumour using electrical impedance and machine learning techniques.

    Science.gov (United States)

    Al Amin, Abdullah; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

    2014-06-01

    When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy.

  1. 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)

  2. [Detection and specific studies in procedural learning difficulties].

    Science.gov (United States)

    Magallón, S; Narbona, J

    2009-02-27

    The main disabilities in non-verbal learning disorder (NLD) are: the acquisition and automating of motor and cognitive processes, visual spatial integration, motor coordination, executive functions, difficulty in comprehension of the context, and social skills. AIMS. To review the research to date on NLD, and to discuss whether the term 'procedural learning disorder' (PLD) would be more suitable to refer to NLD. A considerable amount of research suggests a neurological correlate of PLD with dysfunctions in the 'posterior' attention system, or the right hemisphere, or the cerebellum. Even if it is said to be difficult the delimitation between NLD and other disorders or syndromes like Asperger syndrome, certain characteristics contribute to differential diagnosis. Intervention strategies for the PLD must lead to the development of motor automatisms and problem solving strategies, including social skills. The basic dysfunction in NLD affects to implicit learning of routines, automating of motor skills and cognitive strategies that spare conscious resources in daily behaviours. These limitations are partly due to a dysfunction in non-declarative procedural memory. Various dimensions of language are also involved: context comprehension, processing of the spatial and emotional indicators of verbal language, language inferences, prosody, organization of the inner speech, use of language and non-verbal communication; this is why the diagnostic label 'PLD' would be more appropriate, avoiding the euphemistic adjective 'non-verbal'.

  3. 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.

  4. 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 ...

  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. Interference in Ballistic Motor Learning: Specificity and Role of Sensory Error Signals

    Science.gov (United States)

    Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C.; Nielsen, Jens Bo

    2011-01-01

    Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic stimulation (rTMS) of corticospinal motor output at intensities below movement threshold did not cause interference, whereas suprathreshold rTMS evoking motor responses and (re)afferent activation did. Finally, the experiments revealed that suprathreshold repetitive electrical stimulation of the agonist (but not antagonist) peripheral nerve caused interference. The present study is, to our knowledge, the first to demonstrate that peripheral nerve stimulation may cause interference. The finding underscores the importance of sensory feedback as error signals in motor learning. We conclude that interference requires competing plasticity in overlapping circuits. Interference is remarkably specific for circuits involved in a specific movement and it may relate to sensory error signals. PMID:21408054

  8. Visual Perceptual Learning and its Specificity and Transfer: A New Perspective

    Directory of Open Access Journals (Sweden)

    Cong Yu

    2011-05-01

    Full Text Available Visual perceptual learning is known to be location and orientation specific, and is thus assumed to reflect the neuronal plasticity in the early visual cortex. However, in recent studies we created “Double training” and “TPE” procedures to demonstrate that these “fundamental” specificities of perceptual learning are in some sense artifacts and that learning can completely transfer to a new location or orientation. We proposed a rule-based learning theory to reinterpret perceptual learning and its specificity and transfer: A high-level decision unit learns the rules of performing a visual task through training. However, the learned rules cannot be applied to a new location or orientation automatically because the decision unit cannot functionally connect to new visual inputs with sufficient strength because these inputs are unattended or even suppressed during training. It is double training and TPE training that reactivate these new inputs, so that the functional connections can be strengthened to enable rule application and learning transfer. Currently we are investigating the properties of perceptual learning free from the bogus specificities, and the results provide some preliminary but very interesting insights into how training reshapes the functional connections between the high-level decision units and sensory inputs in the brain.

  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 strategies and general cognitive ability as predictors of gender- specific academic achievement.

    Science.gov (United States)

    Ruffing, Stephanie; Wach, F-Sophie; Spinath, Frank M; Brünken, Roland; Karbach, Julia

    2015-01-01

    Recent research has revealed that learning behavior is associated with academic achievement at the college level, but the impact of specific learning strategies on academic success as well as gender differences therein are still not clear. Therefore, the aim of this study was to investigate gender differences in the incremental contribution of learning strategies over general cognitive ability in the prediction of academic achievement. The relationship between these variables was examined by correlation analyses. A set of t-tests was used to test for gender differences in learning strategies, whereas structural equation modeling as well as multi-group analyses were applied to investigate the incremental contribution of learning strategies for male and female students' academic performance. The sample consisted of 461 students (mean age = 21.2 years, SD = 3.2). Correlation analyses revealed that general cognitive ability as well as the learning strategies effort, attention, and learning environment were positively correlated with academic achievement. Gender differences were found in the reported application of many learning strategies. Importantly, the prediction of achievement in structural equation modeling revealed that only effort explained incremental variance (10%) over general cognitive ability. Results of multi-group analyses showed no gender differences in this prediction model. This finding provides further knowledge regarding gender differences in learning research and the specific role of learning strategies for academic achievement. The incremental assessment of learning strategy use as well as gender-differences in their predictive value contributes to the understanding and improvement of successful academic development.

  11. Specific developmental disorders. The language-learning continuum.

    Science.gov (United States)

    Swank, L K

    1999-01-01

    The goal of this article is to inform and educate those who work with children who present with language-learning disorders about phonologic processing deficits, because this area has been shown to have a significant impact on children and adults who exhibit reading disabilities. Mental health professionals who work with children with reading problems need to be aware of what is known about this source of reading disorders and the implications of this knowledge for prevention and treatment. Advocating for appropriate instruction for children with reading problems is an important role mental health professionals can play in working with this population.

  12. IMS Learning Design Specification (version 1.0)

    NARCIS (Netherlands)

    Koper, Rob; Olivier, Bill; Anderson, Thor

    2003-01-01

    Information Model is the core document with the actual specification, the other documents and schema's are derived from the Information Model. When you see any inconsistency between documents, look at the Information Model for the correct interpretation.

  13. Learning Specific Content in Technology Education: Learning Study as a Collaborative Method in Swedish Preschool Class Using Hands-On Material

    Science.gov (United States)

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study…

  14. 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.

  15. "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…

  16. 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…

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption

    OpenAIRE

    Bao, Yukun; Xiong, Tao; Hu, Zhongyi; Kibelloh, Mboni

    2014-01-01

    Reasons for contradictory findings regarding the gender moderate effect on computer self-efficacy in the adoption of e-learning/mobile learning are limited. Recognizing the multilevel nature of the computer self-efficacy (CSE), this study attempts to explore gender differences in the adoption of mobile learning, by extending the Technology Acceptance Model (TAM) with general and specific CSE. Data collected from 137 university students were tested against the research model using the structur...

  3. The Effect of Task-based Teaching on Incidental Vocabulary Learning in English for Specific Purposes

    OpenAIRE

    FALLAHRAFIE, Zahra; RAHMANY, Ramin; SADEGHI, Bahador

    2015-01-01

    Abstract. Learning vocabulary is an essential part of language learning linking the four skills of speaking, listening, reading and writing together. This paper considers the incidental vocabulary teaching and learning within the framework of task-based activities in the hope of improving learners’ vocabulary acquiring in English for Specific Purposes courses (ESP), concentrating on Mechanical Engineering students at Islamic Azad University of Hashtgerd, Iran. A total number of 55 male and fe...

  4. Interference in ballistic motor learning: specificity and role of sensory error signals

    DEFF Research Database (Denmark)

    Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C

    2011-01-01

    Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlap......Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity...... in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non......-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic...

  5. 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.

  6. Development of a PCR technique specific for Demodex injai in biological specimens.

    Science.gov (United States)

    Sastre, N; Ravera, I; Ferreira, D; Altet, L; Sánchez, A; Bardagí, M; Francino, O; Ferrer, L

    2013-09-01

    The identification of Demodex injai as a second Demodex species of dog opened new questions and challenges in the understanding on the Demodex-host relationships. In this paper, we describe the development of a conventional PCR technique based on published genome sequences of D. injai from GenBank that specifically detects DNA from D. injai. This technique amplifies a 238-bp fragment corresponding to a region of the mitochondrial 16S rDNA of D. injai. The PCR was positive in DNA samples obtained from mites identified morphologically as D. injai, which served as positive controls, as well as in samples from three cases of demodicosis associated with proliferation of mites identified as D. injai. Furthermore, the PCR was positive in 2 out of 19 healthy dogs. Samples of Demodex canis and Demodex folliculorum were consistently negative. Skin samples from seven dogs with generalized demodicosis caused by D. canis were all negative in the D. injai-specific PCR, demonstrating that in generalized canine demodicosis, mite proliferation is species-specific. This technique can be a useful tool in the diagnosis and in epidemiologic and pathogenic studies.

  7. 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.

  8. Kernel-Based Learning for Domain-Specific Relation Extraction

    Science.gov (United States)

    Basili, Roberto; Giannone, Cristina; Del Vescovo, Chiara; Moschitti, Alessandro; Naggar, Paolo

    In a specific process of business intelligence, i.e. investigation on organized crime, empirical language processing technologies can play a crucial role. The analysis of transcriptions on investigative activities, such as police interrogatories, for the recognition and storage of complex relations among people and locations is a very difficult and time consuming task, ultimately based on pools of experts. We discuss here an inductive relation extraction platform that opens the way to much cheaper and consistent workflows. The presented empirical investigation shows that accurate results, comparable to the expert teams, can be achieved, and parametrization allows to fine tune the system behavior for fitting domain-specific requirements.

  9. 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.

  10. Seeing the System through the End Users' Eyes: Shadow Expert Technique for Evaluating the Consistency of a Learning Management System

    Science.gov (United States)

    Holzinger, Andreas; Stickel, Christian; Fassold, Markus; Ebner, Martin

    Interface consistency is an important basic concept in web design and has an effect on performance and satisfaction of end users. Consistency also has significant effects on the learning performance of both expert and novice end users. Consequently, the evaluation of consistency within a e-learning system and the ensuing eradication of irritating discrepancies in the user interface redesign is a big issue. In this paper, we report of our experiences with the Shadow Expert Technique (SET) during the evaluation of the consistency of the user interface of a large university learning management system. The main objective of this new usability evaluation method is to understand the interaction processes of end users with a specific system interface. Two teams of usability experts worked independently from each other in order to maximize the objectivity of the results. The outcome of this SET method is a list of recommended changes to improve the user interaction processes, hence to facilitate high consistency.

  11. Placement and Achievement of Urban Hispanic Middle Schoolers with Specific Learning Disabilities

    Science.gov (United States)

    Barrocas, Lisa; Cramer, Elizabeth D.

    2014-01-01

    This study examined achievement gains in reading and math for Hispanic middle school students with specific learning disabilities in inclusive versus segregated settings in a large urban school district. The authors report learning gains for students with and without disabilities in inclusive versus segregated settings. Results indicate no…

  12. Working Memory Functioning in Children with Learning Disorders and Specific Language Impairment

    Science.gov (United States)

    Schuchardt, Kirsten; Bockmann, Ann-Katrin; Bornemann, Galina; Maehler, Claudia

    2013-01-01

    Purpose: On the basis of Baddeley's working memory model (1986), we examined working memory functioning in children with learning disorders with and without specific language impairment (SLI). We pursued the question whether children with learning disorders exhibit similar working memory deficits as children with additional SLI. Method: In…

  13. Principals' Perceptions of Instructional Leadership for Middle School Students of Color with Specific Learning Disabilities

    Science.gov (United States)

    Shannon-Luster, Beverly

    2013-01-01

    Instructional leadership is the most important responsibility for principals and the most vulnerable students in need of productive instructional leadership are students of color with specific learning disabilities. Instructional leaders are challenged with creating supportive learning environments and school cultures that promotes the education…

  14. Understanding Impulsivity among Children with Specific Learning Disabilities in Inclusion Schools

    Science.gov (United States)

    Al-Dababneh, Kholoud Adeeb; Al-Zboon, Eman K.

    2018-01-01

    Impulsive behavior is a characteristic of children with specific learning disabilities (SLD), and is related to learning ability. The present study aims to identify impulsivity behavior in children with SLD who attend inclusion schools, from their resource room teachers' perspectives. A 31-item questionnaire that addressed four subscales was…

  15. Are Language Learning Websites Special? Towards a Research Agenda for Discipline-Specific Usability

    Science.gov (United States)

    Shield, Lesley; Kukulska-Hulme, Agnes

    2006-01-01

    With the intention of defining an initial research agenda for discipline-specific factors in the usability of e-learning websites, this article focuses on the example of foreign language learning. First, general notions and concepts of usability are analyzed, and the term "pedagogical usability" is proposed as a means of focusing on the close…

  16. Context Fear Learning Specifically Activates Distinct Populations of Neurons in Amygdala and Hypothalamus

    Science.gov (United States)

    Trogrlic, Lidia; Wilson, Yvette M.; Newman, Andrew G.; Murphy, Mark

    2011-01-01

    The identity and distribution of neurons that are involved in any learning or memory event is not known. In previous studies, we identified a discrete population of neurons in the lateral amygdala that show learning-specific activation of a c-"fos"-regulated transgene following context fear conditioning. Here, we have extended these studies to…

  17. Identifying Learning Patterns of Children at Risk for Specific Reading Disability

    Science.gov (United States)

    Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E.; Grigorenko, Elena L.

    2016-01-01

    Differences in learning patterns of vocabulary acquisition in children at risk (+SRD) and not at risk (-SRD) for Specific Reading Disability (SRD) were examined using a microdevelopmental paradigm applied to the multi-trial Foreign Language Learning Task (FLLT; Baddeley et al., 1995). The FLLT was administered to 905 children from rural…

  18. D-Cycloserine reduces context specificity of sexual extinction learning

    NARCIS (Netherlands)

    Brom, M.; Laan, E.; Everaerd, W.; Spinhoven, P.; Trimbos, B.; Both, S.

    2015-01-01

    BACKGROUND: d-Cycloserine (DCS) enhances extinction processes in animals. Although classical conditioning is hypothesized to play a pivotal role in the aetiology of appetitive motivation problems, no research has been conducted on the effect of DCS on the reduction of context specificity of

  19. d-Cycloserine reduces context specificity of sexual extinction learning

    NARCIS (Netherlands)

    Brom, Mirte; Laan, Ellen; Everaerd, Walter; Spinhoven, Philip; Trimbos, Baptist; Both, Stephanie

    2015-01-01

    d-Cycloserine (DCS) enhances extinction processes in animals. Although classical conditioning is hypothesized to play a pivotal role in the aetiology of appetitive motivation problems, no research has been conducted on the effect of DCS on the reduction of context specificity of extinction in human

  20. Working Memory Deficits in Children with Specific Learning Disorders

    Science.gov (United States)

    Schuchardt, Kirsten; Maehler, Claudia; Hasselhorn, Marcus

    2008-01-01

    This article examines working memory functioning in children with specific developmental disorders of scholastic skills as defined by ICD-10. Ninety-seven second to fourth graders with a minimum IQ of 80 are compared using a 2 x 2 factorial (dyscalculia vs. no dyscalculia; dyslexia vs. no dyslexia) design. An extensive test battery assesses the…

  1. Self-Esteem among Boys with and without Specific Learning Disabilities.

    Science.gov (United States)

    Bingham, Grace

    1980-01-01

    The self-esteem of 120 males with and without specific learning disabilities, at each of two levels of development (preadolescent and adolescent) was measured using Coopersmith Self-esteem Inventory. (MP)

  2. 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...

  3. Technique of calculating specific capital investments in the fuel extracting sectors of industry

    Energy Technology Data Exchange (ETDEWEB)

    Bugrov, V.A.; Filey, I.A.

    1980-01-01

    An analysis is made of the existing methods of calculating specific capital investments in the fuel extracting sectors of industry. Their shortcomings are shown. It is suggested that specific capital investments for extraction of coal and gas be defined as the ratio of capital investments to the conditional increase in extraction. Coal extraction should take int consideration all the capital investments associated with the input of new facilities, and the maintenance of the attained level of extraction and reconstruction of the enterprise, as well as all the newly introduced facilities both at the new and at the active enterprises associated with an increase in coal extraction and with maintenance of the facilities. The suggested technique completely corresponds to the ''Standard Technique for Developing a Technical-Industrial-Financial Plan,'' which stipulates determination of specific capital investments per unit of introduced facilities with only the difference that it takes into consideration the specific features of the fuel extracting sectors of industry.

  4. Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

    Science.gov (United States)

    Crossley, Scott A.

    2013-01-01

    This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

  5. Aspergillus specific IgE estimation by radioallergosorbent technique (RAST) in obstructive airways disease at Agra

    International Nuclear Information System (INIS)

    Sharma, S.K.; Singh, R.; Mehrotra, M.P.; Patney, N.L.; Sachan, A.S.; Shiromany, A.

    1986-01-01

    The radioallergosorbent technique (RAST) was used to measure the levels of Aspergillus specific IgE in 25 normal controls, 25 cases of extrinsic bronchial asthma and 25 cases of allergic broncho-pulmonary aspergillosis with a view to study the clinical role and its correlation with sputum culture, skin sensitivity and severity of airways obstruction. The test was performed using Pharmacia diagnostic kits with antigen derived from Aspergillus fumigatus. Abnormal levels of Aspergillus specific IgE were observed in 84 per cent cases of bronchial asthma but none of the controls. 86.7 per cent of all cases with positive skin test had positive radioallergosorbent test and there was no false positive reaction. There was a positive correlation of Aspergillus specific IgE with skin test positivity and with FEV 1 /FVC per cent. (author)

  6. Specific Deficit in Implicit Motor Sequence Learning following Spinal Cord Injury.

    Directory of Open Access Journals (Sweden)

    Ayala Bloch

    Full Text Available Physical and psychosocial rehabilitation following spinal cord injury (SCI leans heavily on learning and practicing new skills. However, despite research relating motor sequence learning to spinal cord activity and clinical observations of impeded skill-learning after SCI, implicit procedural learning following spinal cord damage has not been examined.To test the hypothesis that spinal cord injury (SCI in the absence of concomitant brain injury is associated with a specific implicit motor sequence learning deficit that cannot be explained by depression or impairments in other cognitive measures.Ten participants with SCI in T1-T11, unharmed upper limb motor and sensory functioning, and no concomitant brain injury were compared to ten matched control participants on measures derived from the serial reaction time (SRT task, which was used to assess implicit motor sequence learning. Explicit generation of the SRT sequence, depression, and additional measures of learning, memory, and intelligence were included to explore the source and specificity of potential learning deficits.There was no between-group difference in baseline reaction time, indicating that potential differences between the learning curves of the two groups could not be attributed to an overall reduction in response speed in the SCI group. Unlike controls, the SCI group showed no decline in reaction time over the first six blocks of the SRT task and no advantage for the initially presented sequence over the novel interference sequence. Meanwhile, no group differences were found in explicit learning, depression, or any additional cognitive measures.The dissociation between impaired implicit learning and intact declarative memory represents novel empirical evidence of a specific implicit procedural learning deficit following SCI, with broad implications for rehabilitation and adjustment.

  7. 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.

  8. 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.

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

    OpenAIRE

    Akinyelu, Andronicus A.; Adewumi, Aderemi O.

    2013-01-01

    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 learnin...

  10. Subliminal Cues While Teaching: HCI Technique for Enhanced Learning

    Directory of Open Access Journals (Sweden)

    Pierre Chalfoun

    2011-01-01

    Full Text Available This paper presents results from an empirical study conducted with a subliminal teaching technique aimed at enhancing learner's performance in Intelligent Systems through the use of physiological sensors. This technique uses carefully designed subliminal cues (positive and miscues (negative and projects them under the learner's perceptual visual threshold. A positive cue, called answer cue, is a hint aiming to enhance the learner's inductive reasoning abilities and projected in a way to help them figure out the solution faster but more importantly better. A negative cue, called miscue, is also used and aims at obviously at the opposite (distract the learner or lead them to the wrong conclusion. The latest obtained results showed that only subliminal cues, not miscues, could significantly increase learner performance and intuition in a logic-based problem-solving task. Nonintrusive physiological sensors (EEG for recording brainwaves, blood volume pressure to compute heart rate and skin response to record skin conductivity were used to record affective and cerebral responses throughout the experiment. The descriptive analysis, combined with the physiological data, provides compelling evidence for the positive impact of answer cues on reasoning and intuitive decision making in a logic-based problem-solving paradigm.

  11. Evaluation of mesh morphing and mapping techniques in patient specific modeling of the human pelvis.

    Science.gov (United States)

    Salo, Zoryana; Beek, Maarten; Whyne, Cari Marisa

    2013-01-01

    Robust generation of pelvic finite element models is necessary to understand the variation in mechanical behaviour resulting from differences in gender, aging, disease and injury. The objective of this study was to apply and evaluate mesh morphing and mapping techniques to facilitate the creation and structural analysis of specimen-specific finite element (FE) models of the pelvis. A specimen-specific pelvic FE model (source mesh) was generated following a traditional user-intensive meshing scheme. The source mesh was morphed onto a computed tomography scan generated target surface of a second pelvis using a landmarked-based approach, in which exterior source nodes were shifted to target surface vertices, while constrained along a normal. A second copy of the morphed model was further refined through mesh mapping, in which surface nodes of the initial morphed model were selected in patches and remapped onto the surfaces of the target model. Computed tomography intensity based material properties were assigned to each model. The source, target, morphed and mapped models were analyzed under axial compression using linear static FE analysis and their strain distributions evaluated. Morphing and mapping techniques were effectively applied to generate good quality geometrically complex specimen-specific pelvic FE models. Mapping significantly improved strain concurrence with the target pelvis FE model. Copyright © 2012 John Wiley & Sons, Ltd.

  12. Evaluation of mesh morphing and mapping techniques in patient specific modelling of the human pelvis.

    Science.gov (United States)

    Salo, Zoryana; Beek, Maarten; Whyne, Cari Marisa

    2012-08-01

    Robust generation of pelvic finite element models is necessary to understand variation in mechanical behaviour resulting from differences in gender, aging, disease and injury. The objective of this study was to apply and evaluate mesh morphing and mapping techniques to facilitate the creation and structural analysis of specimen-specific finite element (FE) models of the pelvis. A specimen-specific pelvic FE model (source mesh) was generated following a traditional user-intensive meshing scheme. The source mesh was morphed onto a computed tomography scan generated target surface of a second pelvis using a landmarked-based approach, in which exterior source nodes were shifted to target surface vertices, while constrained along a normal. A second copy of the morphed model was further refined through mesh mapping, in which surface nodes of the initial morphed model were selected in patches and remapped onto the surfaces of the target model. Computed tomography intensity-based material properties were assigned to each model. The source, target, morphed and mapped models were analyzed under axial compression using linear static FE analysis, and their strain distributions were evaluated. Morphing and mapping techniques were effectively applied to generate good quality and geometrically complex specimen-specific pelvic FE models. Mapping significantly improved strain concurrence with the target pelvis FE model. Copyright © 2012 John Wiley & Sons, Ltd.

  13. Sculpting with people – An experiential learning technique

    DEFF Research Database (Denmark)

    Andersen, Helle Elisabeth; Larsen, Kirsten Vendelbo

    2015-01-01

    At Department of Nursing, University College Lillebaelt in Denmark we use an experiential technique called sculpting in our simulation program. Sculpting is a kind of non-verbal role play in which participants are given a certain character and create a 'sculpture' by arranging family members......, social circles and professionals in ways which reflect the quality of the relationships of the people involved. The aim of this study is to further describe the sculpting exercise and present a small scale evaluation study using a qualitative descriptive design. An evaluation sheet was formulated...... by the authors and filled out by 114 Danish third-year nursing students. The results show that sculpting is experienced as emotionally demanding, but in a good way. It is experienced as an eye-opener that helps to identify the possible complex and emotional dynamics in a family experiencing critical illness...

  14. 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....

  15. Functional discrimination of membrane proteins using machine learning techniques

    Directory of Open Access Journals (Sweden)

    Yabuki Yukimitsu

    2008-03-01

    Full Text Available Abstract Background Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters. Results We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network method discriminated the channels/pores, electrochemical potential-driven transporters and active transporters with the 5-fold cross validation accuracy of 64% in a data set of 1718 membrane proteins. The application of amino acid occurrence improved the overall accuracy to 68%. In addition, we have discriminated transporters from other α-helical and β-barrel membrane proteins with the accuracy of 85% using k-nearest neighbor method. The classification of transporters and all other proteins (globular and membrane showed the accuracy of 82%. Conclusion The performance of discrimination with amino acid occurrence is better than that with amino acid composition. We suggest that this method could be effectively used to discriminate transporters from all other globular and membrane proteins, and classify them into channels/pores, electrochemical and active transporters.

  16. Working memory deficits in children with specific learning disorders.

    Science.gov (United States)

    Schuchardt, Kirsten; Maehler, Claudia; Hasselhorn, Marcus

    2008-01-01

    This article examines working memory functioning in children with specific developmental disorders of scholastic skills as defined by ICD-10. Ninety-seven second to fourth graders with a minimum IQ of 80 are compared using a 2 x 2 factorial (dyscalculia vs. no dyscalculia; dyslexia vs. no dyslexia) design. An extensive test battery assesses the three subcomponents of working memory described by Baddeley (1986): phonological loop, visual-spatial sketchpad, and central executive. Children with dyscalculia show deficits in visual-spatial memory; children with dyslexia show deficits in phonological and central executive functioning. When controlling for the influence of the phonological loop on the performance of the central executive, however, the effect is no longer significant. Although children with both reading and arithmetic disorders are consistently outperformed by all other groups, there is no significant interaction between the factors dyscalculia and dyslexia.

  17. Learning a specific content in technology education : Learning Study as collaborative method in Swedish preschool class using hands-on material 

    OpenAIRE

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study concerns strong constructions and framed structures. This article describes how this learning study was conducted and discusses reflections made du...

  18. Domain-specific and domain-general constraints on word and sequence learning.

    Science.gov (United States)

    Archibald, Lisa M D; Joanisse, Marc F

    2013-02-01

    The relative influences of language-related and memory-related constraints on the learning of novel words and sequences were examined by comparing individual differences in performance of children with and without specific deficits in either language or working memory. Children recalled lists of words in a Hebbian learning protocol in which occasional lists repeated, yielding improved recall over the course of the task on the repeated lists. The task involved presentation of pictures of common nouns followed immediately by equivalent presentations of the spoken names. The same participants also completed a paired-associate learning task involving word-picture and nonword-picture pairs. Hebbian learning was observed for all groups. Domain-general working memory constrained immediate recall, whereas language abilities impacted recall in the auditory modality only. In addition, working memory constrained paired-associate learning generally, whereas language abilities disproportionately impacted novel word learning. Overall, all of the learning tasks were highly correlated with domain-general working memory. The learning of nonwords was additionally related to general intelligence, phonological short-term memory, language abilities, and implicit learning. The results suggest that distinct associations between language- and memory-related mechanisms support learning of familiar and unfamiliar phonological forms and sequences.

  19. Task-specific singing dystonia: vocal instability that technique cannot fix.

    Science.gov (United States)

    Halstead, Lucinda A; McBroom, Deanna M; Bonilha, Heather Shaw

    2015-01-01

    Singer's dystonia is a rare variation of focal laryngeal dystonia presenting only during specific tasks in the singing voice. It is underdiagnosed since it is commonly attributed to technique problems including increased muscle tension, register transition, or wobble. Singer's dystonia differs from technique-related issues in that it is task- and/or pitch-specific, reproducible and occurs independently from the previously mentioned technical issues.This case series compares and contrasts profiles of four patients with singer's dystonia to increase our knowledge of this disorder. This retrospective case series includes a detailed case history, results of singing evaluations from individual voice teachers, review of singing voice samples by a singing voice specialist, evaluation by a laryngologist with endoscopy and laryngeal electromyography (LEMG), and spectral analysis of the voice samples by a speech-language pathologist. Results demonstrate the similarities and unique differences of individuals with singer's dystonia. Response to treatment and singing status varied from nearly complete relief of symptoms with botulinum toxin injections to minor relief of symptoms and discontinuation of singing. The following are the conclusions from this case series: (1) singer's dystonia exists as a separate entity from technique issues, (2) singer's dystonia is consistent with other focal task-specific dystonias found in musicians, (3) correctly diagnosing singer's dystonia allows singer's access to medical treatment of dystonia and an opportunity to modify their singing repertoire to continue singing with the voice they have, and (4) diagnosis of singer's dystonia requires careful sequential multidisciplinary evaluation to isolate the instability and confirm dystonia by LEMG and spectral voice analysis. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

  20. Uncovering category specificity of genital sexual arousal in women: The critical role of analytic technique.

    Science.gov (United States)

    Pulverman, Carey S; Hixon, J Gregory; Meston, Cindy M

    2015-10-01

    Based on analytic techniques that collapse data into a single average value, it has been reported that women lack category specificity and show genital sexual arousal to a large range of sexual stimuli including those that both match and do not match their self-reported sexual interests. These findings may be a methodological artifact of the way in which data are analyzed. This study examined whether using an analytic technique that models data over time would yield different results. Across two studies, heterosexual (N = 19) and lesbian (N = 14) women viewed erotic films featuring heterosexual, lesbian, and gay male couples, respectively, as their physiological sexual arousal was assessed with vaginal photoplethysmography. Data analysis with traditional methods comparing average genital arousal between films failed to detect specificity of genital arousal for either group. When data were analyzed with smoothing regression splines and a within-subjects approach, both heterosexual and lesbian women demonstrated different patterns of genital sexual arousal to the different types of erotic films, suggesting that sophisticated statistical techniques may be necessary to more fully understand women's genital sexual arousal response. Heterosexual women showed category-specific genital sexual arousal. Lesbian women showed higher arousal to the heterosexual film than the other films. However, within subjects, lesbian women showed significantly different arousal responses suggesting that lesbian women's genital arousal discriminates between different categories of stimuli at the individual level. Implications for the future use of vaginal photoplethysmography as a diagnostic tool of sexual preferences in clinical and forensic settings are discussed. © 2015 Society for Psychophysiological Research.

  1. 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.

  2. Introducing E-Learning in a Norwegian Service Company with Participatory Design and Evolutionary Prototyping Techniques

    OpenAIRE

    Mørch , Anders I.; Engen , Bård Ketil; Hansen Åsand , Hege-René; Brynhildsen , Camilla; Tødenes , Ida

    2004-01-01

    Over a 2-year period, we have participated in the introduction of e-learning in a Norwegian service company, a gas station division of an oil company. This company has an advanced computer network infrastructure for communication and information sharing, but the primary task of the employees is serving customers. We identify some challenges to introducing e-learning in this kind of environment. A primary emphasis has been on using participatory design techniques during the planning stages and...

  3. A framework for detection of malicious software in Android handheld systems using machine learning techniques

    OpenAIRE

    Torregrosa García, Blas

    2015-01-01

    The present study aims at designing and developing new approaches to detect malicious applications in Android-based devices. More precisely, MaLDroide (Machine Learning-based Detector for Android malware), a framework for detection of Android malware based on machine learning techniques, is introduced here. It is devised to identify malicious applications. Este trabajo tiene como objetivo el diseño y el desarrollo de nuevas formas de detección de aplicaciones maliciosas en los dispositivos...

  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. A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

    Science.gov (United States)

    Tsipouras, Markos G; Giannakeas, Nikolaos; Tzallas, Alexandros T; Tsianou, Zoe E; Manousou, Pinelopi; Hall, Andrew; Tsoulos, Ioannis; Tsianos, Epameinondas

    2017-03-01

    Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  6. DIAGNOSIS OF DIABETIC RETINOPATHY USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    R. Priya

    2013-07-01

    Full Text Available Diabetic retinopathy (DR is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely non-proliferative diabetic retinopathy (NPDR and proliferative diabetic retinopathy (PDR. In this paper, to diagnose diabetic retinopathy, three models like Probabilistic Neural network (PNN, Bayesian Classification and Support vector machine (SVM are described and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. The features like blood vessels, haemmoraghes of NPDR image and exudates of PDR image are extracted from the raw images using the image processing techniques and fed to the classifier for classification. A total of 350 fundus images were used, out of which 100 were used for training and 250 images were used for testing. Experimental results show that PNN has an accuracy of 89.6 % Bayes Classifier has an accuracy of 94.4% and SVM has an accuracy of 97.6%. This infers that the SVM model outperforms all other models. Also our system is also run on 130 images available from “DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy” and the results show that PNN has an accuracy of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has an accuracy of 95.38%.

  7. Considerations for Task Analysis Methods and Rapid E-Learning Development Techniques

    Directory of Open Access Journals (Sweden)

    Dr. Ismail Ipek

    2014-02-01

    Full Text Available The purpose of this paper is to provide basic dimensions for rapid training development in e-learning courses in education and business. Principally, it starts with defining task analysis and how to select tasks for analysis and task analysis methods for instructional design. To do this, first, learning and instructional technologies as visions of the future were discussed. Second, the importance of task analysis methods in rapid e-learning was considered, with learning technologies as asynchronous and synchronous e-learning development. Finally, rapid instructional design concepts and e-learning design strategies were defined and clarified with examples, that is, all steps for effective task analysis and rapid training development techniques based on learning and instructional design approaches were discussed, such as m-learning and other delivery systems. As a result, the concept of task analysis, rapid e-learning development strategies and the essentials of online course design were discussed, alongside learner interface design features for learners and designers.

  8. A site-specific slurry application technique on grassland and on arable crops.

    Science.gov (United States)

    Schellberg, Jürgen; Lock, Reiner

    2009-01-01

    There is evidence that unequal slurry application on agricultural land contributes to N losses to the environment. Heterogeneity within fields demands adequate response by means of variable rate application. A technique is presented which allows site-specific application of slurry on grassland and arable land based on pre-defined application maps. The system contains a valve controlling flow rate by an on-board PC. During operation, flow rate is measured and scaled against set point values given in the application map together with the geographic position of the site. The systems worked sufficiently precise at a flow rate between 0 and 25 l s(-1) and an offset of actual slurry flow from set point values between 0.33 and 0.67 l s(-1). Long-term experimentation is required to test if site-specific application de facto reduces N surplus within fields and so significantly contributes to the unloading of N in agricultural areas.

  9. Human reinforcement learning subdivides structured action spaces by learning effector-specific values

    OpenAIRE

    Gershman, Samuel J.; Pesaran, Bijan; Daw, Nathaniel D.

    2009-01-01

    Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable, due to the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning – such as prediction error signals for action valuation associated with dopamine and the striatum – can cope with this “curse of dimensionality...

  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. Projection specificity in heterogeneous locus coeruleus cell populations: implications for learning and memory

    Science.gov (United States)

    Uematsu, Akira; Tan, Bao Zhen

    2015-01-01

    Noradrenergic neurons in the locus coeruleus (LC) play a critical role in many functions including learning and memory. This relatively small population of cells sends widespread projections throughout the brain including to a number of regions such as the amygdala which is involved in emotional associative learning and the medial prefrontal cortex which is important for facilitating flexibility when learning rules change. LC noradrenergic cells participate in both of these functions, but it is not clear how this small population of neurons modulates these partially distinct processes. Here we review anatomical, behavioral, and electrophysiological studies to assess how LC noradrenergic neurons regulate these different aspects of learning and memory. Previous work has demonstrated that subpopulations of LC noradrenergic cells innervate specific brain regions suggesting heterogeneity of function in LC neurons. Furthermore, noradrenaline in mPFC and amygdala has distinct effects on emotional learning and cognitive flexibility. Finally, neural recording data show that LC neurons respond during associative learning and when previously learned task contingencies change. Together, these studies suggest a working model in which distinct and potentially opposing subsets of LC neurons modulate particular learning functions through restricted efferent connectivity with amygdala or mPFC. This type of model may provide a general framework for understanding other neuromodulatory systems, which also exhibit cell type heterogeneity and projection specificity. PMID:26330494

  12. Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2017-01-01

    Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

  13. Vapor generation – atomic spectrometric techniques. Expanding frontiers through specific-species preconcentration. A review

    International Nuclear Information System (INIS)

    Gil, Raúl A.; Pacheco, Pablo H.; Cerutti, Soledad; Martinez, Luis D.

    2015-01-01

    This article reviews 120 articles found in SCOPUS and specific Journal cites corresponding to the terms ‘preconcentration’; ‘speciation’; ‘vapor generation techniques’ and ‘atomic spectrometry techniques’ in the last 5 years. - Highlights: • Recent advances in vapor generation and atomic spectrometry were reviewed. • Species-specific preconcentration strategies after and before VG were discussed. • New preconcentration and speciation analysis were evaluated within this framework. - Abstract: We review recent progress in preconcentration strategies associated to vapor generation techniques coupled to atomic spectrometric (VGT-AS) for specific chemical species detection. This discussion focuses on the central role of different preconcentration approaches, both before and after VG process. The former was based on the classical solid phase and liquid–liquid extraction procedures which, aided by automation and miniaturization strategies, have strengthened the role of VGT-AS in several research fields including environmental, clinical, and others. We then examine some of the new vapor trapping strategies (atom-trapping, hydride trapping, cryotrapping) that entail improvements in selectivity through interference elimination, but also they allow reaching ultra-low detection limits for a large number of chemical species generated in conventional VG systems, including complete separation of several species of the same element. This review covers more than 100 bibliographic references from 2009 up to date, found in SCOPUS database and in individual searches in specific journals. We finally conclude by giving some outlook on future directions of this field

  14. Vapor generation – atomic spectrometric techniques. Expanding frontiers through specific-species preconcentration. A review

    Energy Technology Data Exchange (ETDEWEB)

    Gil, Raúl A.; Pacheco, Pablo H.; Cerutti, Soledad [Área de Química Analítica, Facultad de Química Bioquímica y Farmacia, Universidad Nacional de San Luis, Ciudad de San Luis 5700 (Argentina); Instituto de Química de San Luis, INQUISAL, Centro Científico-Tecnológico de San Luis (CCT-San Luis), Consejo Nacional de Investigaciones Científicas y Universidad Nacional de San Luis, Ciudad de San Luis 5700 (Argentina); Martinez, Luis D., E-mail: ldm@unsl.edu.ar [Área de Química Analítica, Facultad de Química Bioquímica y Farmacia, Universidad Nacional de San Luis, Ciudad de San Luis 5700 (Argentina); Instituto de Química de San Luis, INQUISAL, Centro Científico-Tecnológico de San Luis (CCT-San Luis), Consejo Nacional de Investigaciones Científicas y Universidad Nacional de San Luis, Ciudad de San Luis 5700 (Argentina)

    2015-05-22

    This article reviews 120 articles found in SCOPUS and specific Journal cites corresponding to the terms ‘preconcentration’; ‘speciation’; ‘vapor generation techniques’ and ‘atomic spectrometry techniques’ in the last 5 years. - Highlights: • Recent advances in vapor generation and atomic spectrometry were reviewed. • Species-specific preconcentration strategies after and before VG were discussed. • New preconcentration and speciation analysis were evaluated within this framework. - Abstract: We review recent progress in preconcentration strategies associated to vapor generation techniques coupled to atomic spectrometric (VGT-AS) for specific chemical species detection. This discussion focuses on the central role of different preconcentration approaches, both before and after VG process. The former was based on the classical solid phase and liquid–liquid extraction procedures which, aided by automation and miniaturization strategies, have strengthened the role of VGT-AS in several research fields including environmental, clinical, and others. We then examine some of the new vapor trapping strategies (atom-trapping, hydride trapping, cryotrapping) that entail improvements in selectivity through interference elimination, but also they allow reaching ultra-low detection limits for a large number of chemical species generated in conventional VG systems, including complete separation of several species of the same element. This review covers more than 100 bibliographic references from 2009 up to date, found in SCOPUS database and in individual searches in specific journals. We finally conclude by giving some outlook on future directions of this field.

  15. An Interactive Learning Environment for Teaching the Imperative and Object-Oriented Programming Techniques in Various Learning Contexts

    Science.gov (United States)

    Xinogalos, Stelios

    The acquisition of problem-solving and programming skills in the era of knowledge society seems to be particularly important. Due to the intrinsic difficulty of acquiring such skills various educational tools have been developed. Unfortunately, most of these tools are not utilized. In this paper we present the programming microworlds Karel and objectKarel that support the procedural-imperative and Object-Oriented Programming (OOP) techniques and can be used for supporting the teaching and learning of programming in various learning contexts and audiences. The paper focuses on presenting the pedagogical features that are common to both environments and mainly on presenting the potential uses of these environments.

  16. Statistical word learning in children with autism spectrum disorder and specific language impairment.

    Science.gov (United States)

    Haebig, Eileen; Saffran, Jenny R; Ellis Weismer, Susan

    2017-11-01

    Word learning is an important component of language development that influences child outcomes across multiple domains. Despite the importance of word knowledge, word-learning mechanisms are poorly understood in children with specific language impairment (SLI) and children with autism spectrum disorder (ASD). This study examined underlying mechanisms of word learning, specifically, statistical learning and fast-mapping, in school-aged children with typical and atypical development. Statistical learning was assessed through a word segmentation task and fast-mapping was examined in an object-label association task. We also examined children's ability to map meaning onto newly segmented words in a third task that combined exposure to an artificial language and a fast-mapping task. Children with SLI had poorer performance on the word segmentation and fast-mapping tasks relative to the typically developing and ASD groups, who did not differ from one another. However, when children with SLI were exposed to an artificial language with phonemes used in the subsequent fast-mapping task, they successfully learned more words than in the isolated fast-mapping task. There was some evidence that word segmentation abilities are associated with word learning in school-aged children with typical development and ASD, but not SLI. Follow-up analyses also examined performance in children with ASD who did and did not have a language impairment. Children with ASD with language impairment evidenced intact statistical learning abilities, but subtle weaknesses in fast-mapping abilities. As the Procedural Deficit Hypothesis (PDH) predicts, children with SLI have impairments in statistical learning. However, children with SLI also have impairments in fast-mapping. Nonetheless, they are able to take advantage of additional phonological exposure to boost subsequent word-learning performance. In contrast to the PDH, children with ASD appear to have intact statistical learning, regardless of

  17. Effectiveness of Memantine in Improvement of Cognitive Deficits in Specific Learning Disorder

    Directory of Open Access Journals (Sweden)

    Elham Ahmadi Zahrani

    2016-12-01

    Full Text Available Abstract Background: Specific learning disorder is a neurodevelopmental disorder characterized by persistent difficulties in learning academic skills in reading, written expression, or mathematics. This study was performed to investigate the effectiveness of memantine in the relief of cognitive deficits (selective attention, sustained attention, and working memory in specific learning disorder. Materials and Methods: This study is a clinical trial. Of all children 8-12 years referred to Amir Kabir Hospital 94 patients diagnosed with specific learning disorder based on DSMV diagnostic interview referred by specialist and randomly divided by two groups, memantine and placebo. Cognitive deficits before and after treatment were measured with continuous performance test, Stroop test and Wechsler Digit Span forward and reverse and Corsi test. Results: Multivariate analysis of variance showed a significant difference in error when answering, omission answer and corrected answer in continuous performance test, but this difference is not significant in response time. Difference in forward, reverse and collected auditory was significant and not significant in the auditory span. In active visual working memory at corsi cube test, difference was significant (p <0.05. Conclusion: The results showed that memantine in improvement of sustained attention, auditory working memory and visual working memory, is effective, while in selective attention is not effective and according to similarities of learning disorder and Attention deficit / Hyperactivity disorder (ADHD and the effectiveness of memantine in improvement of symptoms of ADHD, we can also use this drug in improvement of cognitive deficits of specific learning disorder.

  18. Using typing techniques in a specific outbreak: the ethical reflection of public health professionals.

    Science.gov (United States)

    Rump, B; Cornelis, C; Woonink, F; VAN Steenbergen, J; Verweij, M; Hulscher, M

    2017-05-01

    Typing techniques are laboratory methods used in outbreak management to investigate the degree to which microbes found within an outbreak are related. Knowledge about relational patterns between microbes benefits outbreak management, but inevitably also tells us something about the relational patterns of the people hosting them. Since the technique is often used without explicit consent of all individuals involved, this may raise ethical questions. The aim of this study was to unravel the complex ethical deliberation of professionals over the use of such techniques. We organised group discussions (n = 3) with Dutch outbreak managers (n = 23). The topic list was based on previously identified ethical issues and discussions were analysed for recurrent themes. We found that outbreak managers first and foremost reflect on the balance of individual harm with public health benefit. This key question was approached by way of discussing four more specific ethical themes: (1) justification of governmental intervention, (2) responsibility to prevent infections, (3) scientific uncertainty and (4) legal consequences. The themes found in this study, rephrased into accessible questions, represent the shared ethical understanding of professionals and can help to articulate the ethical dimensions of using molecular science in response to infectious disease outbreaks.

  19. Multimodal technique to eliminate humidity interference for specific detection of ethanol.

    Science.gov (United States)

    Jalal, Ahmed Hasnain; Umasankar, Yogeswaran; Gonzalez, Pablo J; Alfonso, Alejandro; Bhansali, Shekhar

    2017-01-15

    Multimodal electrochemical technique incorporating both open circuit potential (OCP) and amperometric techniques have been conceptualized and implemented to improve the detection of specific analyte in systems where more than one analyte is present. This approach has been demonstrated through the detection of ethanol while eliminating the contribution of water in a micro fuel cell sensor system. The sensor was interfaced with LMP91000 potentiostat, controlled through MSP430F5529LP microcontroller to implement an auto-calibration algorithm tailored to improve the detection of alcohol. The sensor was designed and fabricated as a three electrode system with Nafion as a proton exchange membrane (PEM). The electrochemical signal of the interfering phase (water) was eliminated by implementing the multimodal electrochemical detection technique. The results were validated by comparing sensor and potentiostat performances with a commercial sensor and potentiostat respectively. The results suggest that such a sensing system can detect ethanol at concentrations as low as 5ppm. The structure and properties such as low detection limit, selectivity and miniaturized size enables potential application of this device in wearable transdermal alcohol measurements. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis

    Directory of Open Access Journals (Sweden)

    Rita Obeid

    2016-08-01

    Full Text Available Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI and Autism Spectrum Disorder (ASD. Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons and ASD (13 studies, 20 comparisons to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, Probabilistic Classification. Individuals with SLI showed deficits in statistical learning relative to age-matched controls g = .47, 95% CI [.28, .66], p < .001. In contrast, statistical learning was intact in individuals with ASD relative to controls, g = –.13, 95% CI [–.34, .08], p = .22. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman & Pierpont, 2005, impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD.

  1. Code-specific learning rules improve action selection by populations of spiking neurons.

    Science.gov (United States)

    Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

    2014-08-01

    Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

  2. The training and learning process of transseptal puncture using a modified technique.

    Science.gov (United States)

    Yao, Yan; Ding, Ligang; Chen, Wensheng; Guo, Jun; Bao, Jingru; Shi, Rui; Huang, Wen; Zhang, Shu; Wong, Tom

    2013-12-01

    As the transseptal (TS) puncture has become an integral part of many types of cardiac interventional procedures, its technique that was initial reported for measurement of left atrial pressure in 1950s, continue to evolve. Our laboratory adopted a modified technique which uses only coronary sinus catheter as the landmark to accomplishing TS punctures under fluoroscopy. The aim of this study is prospectively to evaluate the training and learning process for TS puncture guided by this modified technique. Guided by the training protocol, TS puncture was performed in 120 consecutive patients by three trainees without previous personal experience in TS catheterization and one experienced trainer as a controller. We analysed the following parameters: one puncture success rate, total procedure time, fluoroscopic time, and radiation dose. The learning curve was analysed using curve-fitting methodology. The first attempt at TS crossing was successful in 74 (82%), a second attempt was successful in 11 (12%), and 5 patients failed to puncture the interatrial septal finally. The average starting process time was 4.1 ± 0.8 min, and the estimated mean learning plateau was 1.2 ± 0.2 min. The estimated mean learning rate for process time was 25 ± 3 cases. Important aspects of learning curve can be estimated by fitting inverse curves for TS puncture. The study demonstrated that this technique was a simple, safe, economic, and effective approach for learning of TS puncture. Base on the statistical analysis, approximately 29 TS punctures will be needed for trainee to pass the steepest area of learning curve.

  3. Integrating SQ4R Technique with Graphic Postorganizers in the Science Learning of Earth and Space

    OpenAIRE

    Djudin, Tomo; Amir, R

    2018-01-01

    This study examined the effect of integrating SQ4R reading technique with graphic post organizers on the students' Earth and Space Science learning achievement and development of metacognitive knowledge. The pretest-posttest non-equivalent control group design was employed in this quasi-experimental method. The sample which consists of 103 seventh grade of secondary school students of SMPN 1 Pontianak was drawn by using intact group random sampling technique. An achievement test and a questio...

  4. Construction of the Questionnaire on Foreign Language Learning Strategies in Specific Croatian Context.

    Science.gov (United States)

    Božinović, Nikolina; Sindik, Joško

    2017-03-01

    Learning strategies are special thoughts or behaviours that individuals use to understand, learn or retain new information, according to the point of view of O’Malley & Chamot. The other view, promoted by Oxford, believes learning strategies are specific actions taken by the learner to make learning easier, faster, more enjoyable, and more transferrable to new situations of language learning and use. The use of appropriate strategies ensures greater success in language learning. The aim of the research was to establish metric characteristics of the Questionnaire on learning strategies created by the author, in line with the template of the original SILL questionnaire (Strategy Inventory for Language Learning). The research was conducted at the Rochester Institute of Technology Croatia on a sample of 201 participants who learned German, Spanish, French and Italian as a foreign language. The results have shown that one-component latent dimensions which describe the space of foreign language learning strategies according to Oxford’s classification, have metric characteristics which are low, but still satisfactory (reliability and validity). All dimensions of learning strategies appeared not to be adequately defined. Therefore, we excluded compensation strategies and merged social and affective strategies into social-affective strategies into the unique dimension. Overall, this version of Oxford’s original questionnaire, based on Oxford’s theoretical construct, applied on Croatian students, clearly shows that current version of the questionnaire has poor metric characteristics. One of the explanations of the results obtained could be positioned in multicultural context and intercultural dialogue. Namely, particular social, political and economic context in Croatia could shape even foreign language learning strategies.

  5. Design and manufacturing of patient-specific orthodontic appliances by computer-aided engineering techniques.

    Science.gov (United States)

    Barone, Sandro; Neri, Paolo; Paoli, Alessandro; Razionale, Armando Viviano

    2018-01-01

    Orthodontic treatments are usually performed using fixed brackets or removable oral appliances, which are traditionally made from alginate impressions and wax registrations. Among removable devices, eruption guidance appliances are used for early orthodontic treatments in order to intercept and prevent malocclusion problems. Commercially available eruption guidance appliances, however, are symmetric devices produced using a few standard sizes. For this reason, they are not able to meet all the specific patient's needs since the actual dental anatomies present various geometries and asymmetric conditions. In this article, a computer-aided design-based methodology for the design and manufacturing of a patient-specific eruption guidance appliances is presented. The proposed approach is based on the digitalization of several steps of the overall process: from the digital reconstruction of patients' anatomies to the manufacturing of customized appliances. A finite element model has been developed to evaluate the temporomandibular joint disks stress level caused by using symmetric eruption guidance appliances with different teeth misalignment conditions. The developed model can then be used to guide the design of a patient-specific appliance with the aim at reducing the patient discomfort. At this purpose, two different customization levels are proposed in order to face both arches and single tooth misalignment issues. A low-cost manufacturing process, based on an additive manufacturing technique, is finally presented and discussed.

  6. Using principles of learning to inform language therapy design for children with specific language impairment.

    Science.gov (United States)

    Alt, Mary; Meyers, Christina; Ancharski, Alexandra

    2012-01-01

    Language treatment for children with specific language impairment (SLI) often takes months to achieve moderate results. Interventions often do not incorporate the principles that are known to affect learning in unimpaired learners. To outline some key findings about learning in typical populations and to suggest a model of how they might be applied to language treatment design as a catalyst for further research and discussion. Three main principles of implicit learning are reviewed: variability, complexity and sleep-dependent consolidation. After explaining these principles, evidence is provided as to how they influence learning tasks in unimpaired learners. Information is reviewed on principles of learning as they apply to impaired populations, current treatment designs are also reviewed that conform to the principles, and ways in which principles of learning might be incorporated into language treatment design are demonstrated. This paper provides an outline for how theoretical knowledge might be applied to clinical practice in an effort to promote discussion. Although the authors look forward to more specific details on how the principles of learning relate to impaired populations, there is ample evidence to suggest that these principles should be considered during treatment design. © 2012 Royal College of Speech and Language Therapists.

  7. Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques.

    Science.gov (United States)

    Cui, De-Mi; Yan, Weizhong; Wang, Xiao-Quan; Lu, Lie-Min

    2017-10-25

    Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT's turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts' interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology's effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction.

  8. Status of the Usage of Active Learning and Teaching Method and Techniques by Social Studies Teachers

    Science.gov (United States)

    Akman, Özkan

    2016-01-01

    The purpose of this study was to determine the active learning and teaching methods and techniques which are employed by the social studies teachers working in state schools of Turkey. This usage status was assessed using different variables. This was a case study, wherein the research was limited to 241 social studies teachers. These teachers…

  9. A Severe Weather Laboratory Exercise for an Introductory Weather and Climate Class Using Active Learning Techniques

    Science.gov (United States)

    Grundstein, Andrew; Durkee, Joshua; Frye, John; Andersen, Theresa; Lieberman, Jordan

    2011-01-01

    This paper describes a new severe weather laboratory exercise for an Introductory Weather and Climate class, appropriate for first and second year college students (including nonscience majors), that incorporates inquiry-based learning techniques. In the lab, students play the role of meteorologists making forecasts for severe weather. The…

  10. 76 FR 45334 - Innovative Techniques for Delivering ITS Learning; Request for Information

    Science.gov (United States)

    2011-07-28

    ... adult learners? 5. Are you aware of any ITS training applications that work on a mobile phone or smart... DEPARTMENT OF TRANSPORTATION Research and Innovative Technology Administration Innovative Techniques for Delivering ITS Learning; Request for Information AGENCY: Research and Innovative Technology...

  11. Learning L2 German vocabulary through reading: the effect of three enhancement techniques compared

    NARCIS (Netherlands)

    Peters, E.; Hulstijn, J.H.; Sercu, L.; Lutjeharms, M.

    2009-01-01

    This study investigated three techniques designed to increase the chances that second language (L2) readers look up and learn unfamiliar words during and after reading an L2 text. Participants in the study, 137 college students in Belgium (L1 = Dutch, L2 = German), were randomly assigned to one of

  12. Phishtest: Measuring the Impact of Email Headers on the Predictive Accuracy of Machine Learning Techniques

    Science.gov (United States)

    Tout, Hicham

    2013-01-01

    The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning…

  13. Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

    Science.gov (United States)

    Chee, Brant Wah Kwong

    2011-01-01

    This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

  14. Critique: Can Children with AD/HD Learn Relaxation and Breathing Techniques through Biofeedback Video Games?

    Science.gov (United States)

    Wright, Craig; Conlon, Elizabeth

    2009-01-01

    This article presents a critique on K. Amon and A. Campbell's "Can children with AD/HD learn relaxation and breathing techniques through biofeedback video games?". Amon and Campbell reported a successful trial of a commercially available biofeedback program, "The Wild Divine", in reducing symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD)…

  15. Using the IGCRA (individual, group, classroom reflective action technique to enhance teaching and learning in large accountancy classes

    Directory of Open Access Journals (Sweden)

    Cristina Poyatos

    2011-02-01

    Full Text Available First year accounting has generally been perceived as one of the more challenging first year business courses for university students. Various Classroom Assessment Techniques (CATs have been proposed to attempt to enrich and enhance student learning, with these studies generally positioning students as learners alone. This paper uses an educational case study approach and examines the implementation of the IGCRA (individual, group, classroom reflective action technique, a Classroom Assessment Technique, on first year accounting students’ learning performance. Building on theoretical frameworks in the areas of cognitive learning, social development, and dialogical learning, the technique uses reports to promote reflection on both learning and teaching. IGCRA was found to promote feedback on the effectiveness of student, as well as teacher satisfaction. Moreover, the results indicated formative feedback can assist to improve the learning and learning environment for a large group of first year accounting students. Clear guidelines for its implementation are provided in the paper.

  16. The EDF catalogue of technical specifications (reference HN), standardization center; Catalogue des specifications techniques EDF (reference HN) centre de normalisation

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-12-31

    A list of EDF technical specifications, valid at the 01/01/1996 date, is presented. Specifications domains such as electrical installations, equipment and materials, uninsulated and insulated conductors, measurement, control and command, electric power generating or transforming equipment, electrical appliances, telecommunications, electronic and computer systems, are covered

  17. Cultivating ICT Students' Interpersonal Soft Skills in Online Learning Environments Using Traditional Active Learning Techniques

    Science.gov (United States)

    Myers, Trina S.; Blackman, Anna; Andersen, Trevor; Hay, Rachel; Lee, Ickjai; Gray, Heather

    2014-01-01

    Flexible online delivery of tertiary ICT programs is experiencing rapid growth. Creating an online environment that develops team building and interpersonal skills is difficult due to factors such as student isolation and the individual-centric model of online learning that encourages discrete study rather than teamwork. Incorporating teamwork…

  18. Splintless orthognathic surgery: a novel technique using patient-specific implants (PSI).

    Science.gov (United States)

    Gander, Thomas; Bredell, Marius; Eliades, Theodore; Rücker, Martin; Essig, Harald

    2015-04-01

    In the past few years, advances in three-dimensional imaging have conducted to breakthrough in the diagnosis, treatment planning and result assessment in orthognathic surgery. Hereby error-prone and time-consuming planning steps, like model surgery and transfer of the face bow, can be eluded. Numerous positioning devices, in order to transfer the three-dimensional treatment plan to the intraoperative site, have been described. Nevertheless the use of positioning devices and intraoperative splints are failure-prone and time-consuming steps, which have to be performed during the operation and during general anesthesia of the patient. We describe a novel time-sparing and failsafe technique using patient-specific implants (PSI) as positioning guides and concurrently as rigid fixation of the maxilla in the planned position. This technique avoids elaborate positioning and removal of manufactured positioning devices and allows maxillary positioning without the use of occlusal splints. Copyright © 2014 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

  19. The 'Nefertiti lift': a new technique for specific re-contouring of the jawline.

    Science.gov (United States)

    Levy, Phillip M

    2007-12-01

    Botulinum toxin type A (BoNTA) is now used extensively for rejuvenation of the forehead, glabellar and periocular regions and there is increasing focus on treatment of the lower face. Although there is well-documented evidence for the efficacy of botulinum toxin in the correction of platysmal bands, little work has been done to explore its potential role in rejuvenation of the jawline. To date, effects in this area have been reported as a consequence of platysmal banding treatment and are inconsistent. Hesitancy to explore treatment may be due to evidence of a greater, more durable response to the toxin in the lower facial muscles as well as reports of increased potential migration and subsequent side effects. This paper describes a new technique using BoNTA (Vistabel); Allergan, Irvine, CA, USA) to drape the skin of the jawline contour and provide the visual effect of a 'mini lift'. Experience with 130 patients with doses of BoNTA up to 20 U is described. Patient satisfaction is extremely high and the specificity of dosing and technique has led to a low incidence of adverse effects. The 'Nefertiti lift' is a minimally invasive, effective and acceptable alternative for those patients seeking an effective way to push back surgery.

  20. 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.

  1. Qualitative and quantitative revaluation of specific learning disabilities: a multicentric study.

    Science.gov (United States)

    Operto, Francesca F; Mazza, Roberta; Buttiglione, Maura; Craig, Francesco; Frolli, Alessandro; Pisano, Simone; Margari, Lucia; Coppola, Giangennaro

    2018-04-12

    Specific learning disabilities are disorders that affect the instrumental skills of academic learning, leaving intact the general intellectual functioning. It is possible to distinguish: dyslexia, dysorthography, dysgraphia, and dyscalculia. The diagnosis is made according to DSMV. The aim of this study is to evaluate the implementation of Law N° 170 following a diagnosis of specific learning disabilities in children and their evolution over time. The sample under examination consists of 75 children, 56 males and 18 females aged 7,8 to 16 years, with a diagnosis of specific learning disabilities; a revaluation was carried outthrough the use of standardized instruments according to age and school attended. A twopart questionnaire was proposed: the first part turned to the parents/carers of the child and the second part turned to the boy himself. The improvement parameter has been linked, through a statistical analysis of univarianza with intelligence quotient, age, application of the law 10 October 2010 n 170, rehabilitative paths and attending afterschool program. Most of the guys are followed at school by the application of the law 170 and, outside school, by attending speech and neuropsychological therapy and after school. Going to investigate the actual use of the measures put in place by the school, it is evident a partial and incomplete application of Law 170. The most suitable measures for these children are pedagogical measures in order to make them integrate with the group class and strengthen their capacities through specific measures provided by a specific legislative decree.

  2. Perceptual learning of basic visual features remains task specific with Training-Plus-Exposure (TPE) training.

    Science.gov (United States)

    Cong, Lin-Juan; Wang, Ru-Jie; Yu, Cong; Zhang, Jun-Yun

    2016-01-01

    Visual perceptual learning is known to be specific to the trained retinal location, feature, and task. However, location and feature specificity can be eliminated by double-training or TPE training protocols, in which observers receive additional exposure to the transfer location or feature dimension via an irrelevant task besides the primary learning task Here we tested whether these new training protocols could even make learning transfer across different tasks involving discrimination of basic visual features (e.g., orientation and contrast). Observers practiced a near-threshold orientation (or contrast) discrimination task. Following a TPE training protocol, they also received exposure to the transfer task via performing suprathreshold contrast (or orientation) discrimination in alternating blocks of trials in the same sessions. The results showed no evidence for significant learning transfer to the untrained near-threshold contrast (or orientation) discrimination task after discounting the pretest effects and the suprathreshold practice effects. These results thus do not support a hypothetical task-independent component in perceptual learning of basic visual features. They also set the boundary of the new training protocols in their capability to enable learning transfer.

  3. The technique for 3D printing patient-specific models for auricular reconstruction.

    Science.gov (United States)

    Flores, Roberto L; Liss, Hannah; Raffaelli, Samuel; Humayun, Aiza; Khouri, Kimberly S; Coelho, Paulo G; Witek, Lukasz

    2017-06-01

    Currently, surgeons approach autogenous microtia repair by creating a two-dimensional (2D) tracing of the unaffected ear to approximate a three-dimensional (3D) construct, a difficult process. To address these shortcomings, this study introduces the fabrication of patient-specific, sterilizable 3D printed auricular model for autogenous auricular reconstruction. A high-resolution 3D digital photograph was captured of the patient's unaffected ear and surrounding anatomic structures. The photographs were exported and uploaded into Amira, for transformation into a digital (.stl) model, which was imported into Blender, an open source software platform for digital modification of data. The unaffected auricle as digitally isolated and inverted to render a model for the contralateral side. The depths of the scapha, triangular fossa, and cymba were deepened to accentuate their contours. Extra relief was added to the helical root to further distinguish this structure. The ear was then digitally deconstructed and separated into its individual auricular components for reconstruction. The completed ear and its individual components were 3D printed using polylactic acid filament and sterilized following manufacturer specifications. The sterilized models were brought to the operating room to be utilized by the surgeon. The models allowed for more accurate anatomic measurements compared to 2D tracings, which reduced the degree of estimation required by surgeons. Approximately 20 g of the PLA filament were utilized for the construction of these models, yielding a total material cost of approximately $1. Using the methodology detailed in this report, as well as departmentally available resources (3D digital photography and 3D printing), a sterilizable, patient-specific, and inexpensive 3D auricular model was fabricated to be used intraoperatively. This technique of printing customized-to-patient models for surgeons to use as 'guides' shows great promise. Copyright © 2017 European

  4. 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.

  5. 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.

  6. Learning to spell from reading: general knowledge about spelling patterns influences memory for specific words.

    Science.gov (United States)

    Pacton, Sébastien; Borchardt, Gaëlle; Treiman, Rebecca; Lété, Bernard; Fayol, Michel

    2014-05-01

    Adults often learn to spell words during the course of reading for meaning, without intending to do so. We used an incidental learning task in order to study this process. Spellings that contained double n, r and t which are common doublets in French, were learned more readily by French university students than spellings that contained less common but still legal doublets. When recalling or recognizing the latter, the students sometimes made transposition errors, doubling a consonant that often doubles in French rather than the consonant that was originally doubled (e.g., tiddunar recalled as tidunnar). The results, found in three experiments using different nonwords and different types of instructions, show that people use general knowledge about the graphotactic patterns of their writing system together with word-specific knowledge to reconstruct spellings that they learn from reading. These processes contribute to failures and successes in memory for spellings, as in other domains.

  7. Developing an instrument to measure emotional behaviour abilities of meaningful learning through the Delphi technique.

    Science.gov (United States)

    Cadorin, Lucia; Bagnasco, Annamaria; Tolotti, Angela; Pagnucci, Nicola; Sasso, Loredana

    2017-09-01

    To identify items for a new instrument that measures emotional behaviour abilities of meaningful learning, according to Fink's Taxonomy. Meaningful learning is an active process that promotes a wider and deeper understanding of concepts. It is the result of an interaction between new and previous knowledge and produces a long-term change of knowledge and skills. To measure meaningful learning capability, it is very important in the education of health professionals to identify problems or special learning needs. For this reason, it is necessary to create valid instruments. A Delphi Study technique was implemented in four phases by means of e-mail. The study was conducted from April-September 2015. An expert panel consisting of ten researchers with experience in Fink's Taxonomy was established to identify the items of the instrument. Data were analysed for conceptual description and item characteristics and attributes were rated. Expert consensus was sought in each of these phases. An 87·5% consensus cut-off was established. After four rounds, consensus was obtained for validation of the content of the instrument 'Assessment of Meaningful learning Behavioural and Emotional Abilities'. This instrument consists of 56 items evaluated on a 6-point Likert-type scale. Foundational Knowledge, Application, Integration, Human Dimension, Caring and Learning How to Learn were the six major categories explored. This content validated tool can help educators (teachers, trainers and tutors) to identify and improve the strategies to support students' learning capability, which could increase their awareness of and/or responsibility in the learning process. © 2017 John Wiley & Sons Ltd.

  8. Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization.

    Science.gov (United States)

    Gao, Shenghua; Tsang, Ivor Wai-Hung; Ma, Yi

    2014-02-01

    This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

  9. A Learning Model for L/M Specificity in Ganglion Cells

    Science.gov (United States)

    Ahumada, Albert J.

    2016-01-01

    An unsupervised learning model for developing LM specific wiring at the ganglion cell level would support the research indicating LM specific wiring at the ganglion cell level (Reid and Shapley, 2002). Removing the contributions to the surround from cells of the same cone type improves the signal-to-noise ratio of the chromatic signals. The unsupervised learning model used is Hebbian associative learning, which strengthens the surround input connections according to the correlation of the output with the input. Since the surround units of the same cone type as the center are redundant with the center, their weights end up disappearing. This process can be thought of as a general mechanism for eliminating unnecessary cells in the nervous system.

  10. ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    VIMALA C.

    2015-05-01

    Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

  11. Big data - modelling of midges in Europa using machine learning techniques and satellite imagery

    DEFF Research Database (Denmark)

    Cuellar, Ana Carolina; Kjær, Lene Jung; Skovgaard, Henrik

    2017-01-01

    coordinates of each trap, start and end dates of trapping. We used 120 environmental predictor variables together with Random Forest machine learning algorithms to predict the overall species distribution (probability of occurrence) and monthly abundance in Europe. We generated maps for every month...... and the Obsoletus group, although abundance was generally higher for a longer period of time for C. imicula than for the Obsoletus group. Using machine learning techniques, we were able to model the spatial distribution in Europe for C. imicola and the Obsoletus group in terms of abundance and suitability...

  12. Computer-Assisted Mathematics Instruction for Students with Specific Learning Disability: A Review of the Literature

    Science.gov (United States)

    Stultz, Sherry L.

    2017-01-01

    This review was conducted to evaluate the current body of scholarly research regarding the use of computer-assisted instruction (CAI) to teach mathematics to students with specific learning disability (SLD). For many years, computers are utilized for educational purposes. However, the effectiveness of CAI for teaching mathematics to this specific…

  13. The Effectiveness of Computer-Assisted Instruction for Teaching Mathematics to Students with Specific Learning Disability

    Science.gov (United States)

    Stultz, Sherry L.

    2013-01-01

    Using computers to teach students is not a new idea. Computers have been utilized for educational purposes for over 80 years. However, the effectiveness of these programs for teaching mathematics to students with specific learning disability is unclear. This study was undertaken to determine if computer-assisted instruction was as effective as…

  14. Defining the Undefinable: Operationalization of Methods to Identify Specific Learning Disabilities among Practicing School Psychologists

    Science.gov (United States)

    Cottrell, Joseph M.; Barrett, Courtenay A.

    2016-01-01

    Accurate and consistent identification of students with specific learning disabilities (SLDs) is crucial; however, state and district guidelines regarding identification methods lack operationalization and are inconsistent throughout the United States. In the current study, the authors surveyed 471 school psychologists about "school" SLD…

  15. Extracurricular Activities and the Development of Social Skills in Children with Intellectual and Specific Learning Disabilities

    Science.gov (United States)

    Brooks, B. A.; Floyd, F.; Robins, D. L.; Chan, W. Y.

    2015-01-01

    Background: Children with intellectual disability and specific learning disabilities often lack age-appropriate social skills, which disrupts their social functioning. Because of the limited effectiveness of classroom mainstreaming and social skills training for these children, it is important to explore alternative opportunities for social skill…

  16. Graduation Prospects of College Students with Specific Learning Disorder and Students with Mental Health Related Disabilities

    Science.gov (United States)

    Jorgensen, Mary; Budd, Jillian; Fichten, Catherine S.; Nguyen, Mai N.; Havel, Alice

    2018-01-01

    This study's goal was to compare aspects related to academic persistence of two groups of college students with non-visible disabilities: 110 Canadian two and four-year college students--55 with mental health related disabilities and 55 with Specific Learning Disorder (LD). Results show that students with mental health related disabilities were…

  17. Personalization for Specific Users : Designing Decision Support Systems to Support Stimulating Learning Environments

    NARCIS (Netherlands)

    Maruster, Laura; Faber, Niels R.; van Haren, Rob J.; Salvendy, G; Smith, MJ

    2009-01-01

    Creating adaptive systems becomes increasingly attractive in the context of specific groups of users, such as agricultural users. This group of users seems to differ with respect to information processing, knowledge management and learning styles. In this work we aim to offer directions toward

  18. Parents' Perspectives on Coping with Duchenne Muscular Dystrophy and Concomitant Specific Learning Disabilities

    Science.gov (United States)

    Webb, Carol L.

    2005-01-01

    This study addresses parental perspectives and coping strategies related to Duchenne muscular dystrophy and specific learning disabilities. Data were collected through individual semi-structured in-depth interviews with fifteen sets of parents. Participants were selected based on variables such as age of children, number of children with both…

  19. The Legal Meaning of Specific Learning Disability for IDEA Eligibility: The Latest Case Law

    Science.gov (United States)

    Zirkel, Perry A.

    2013-01-01

    Specific learning disability (SLD), although moderately declining in recent years, continues to be the largest of the eligibility classifications under the Individuals with Disabilities Education Act (IDEA; NCES, 2012). The recognition of response to intervention (RTI) in the 2004 amendments of the IDEA as an approach for identifying students with…

  20. Executive Functioning and Psychopathology in Psychotherapy for Adolescents with Specific Learning Disorders

    Science.gov (United States)

    Kopelman-Rubin, Daphne; Klomek, Anat Brunstein; Al-Yagon, Michal; Mufson, Laura; Apter, Alan; Mikulincer, Mario

    2017-01-01

    This study examined the contribution of executive functioning (EF) to improvements in psychiatric symptomatology following I Can Succeed (ICS; Kopelman-Rubin, 2012) psychotherapy, a skill-enhancement intervention designed to target EF and socio-emotional aspects of specific learning disabilities (SLD). Forty adolescents with SLD underwent ICS in…

  1. Working Memory and Learning in Children with Developmental Coordination Disorder and Specific Language Impairment

    Science.gov (United States)

    Alloway, Tracy Packiam; Archibald, Lisa

    2008-01-01

    The authors compared 6- to 11-year-olds with developmental coordination disorder (DCD) and those with specific language impairment (SLI) on measures of memory (verbal and visuospatial short-term and working memory) and learning (reading and mathematics). Children with DCD with typical language skills were impaired in all four areas of memory…

  2. Internet-Specific Epistemic Beliefs and Self-Regulated Learning in Online Academic Information Searching

    Science.gov (United States)

    Chiu, Yen-Lin; Liang, Jyh-Chong; Tsai, Chin-Chung

    2013-01-01

    Epistemic beliefs have been considered as important components of the self-regulatory model; however, their relationships with self-regulated learning processes in the Internet context need further research. The main purpose of this study was to examine the relationships between Internet-specific epistemic belief dimensions and self-regulated…

  3. Evaluating the Impact of Dyslexia Laws on the Identification of Specific Learning Disability and Dyslexia

    Science.gov (United States)

    Phillips, B. Anne Barber; Odegard, Timothy N.

    2017-01-01

    Dyslexia is a specific learning disability that impacts word reading accuracy and/or reading fluency. Over half of the states in the USA have passed legislation intended to promote better identification of individuals with dyslexia. To date, no study has been conducted to investigate the potential impact of state laws on the identification of…

  4. Training School Psychologists to Identify Specific Learning Disabilities: A Content Analysis of Syllabi

    Science.gov (United States)

    Barrett, Courtenay A.; Cottrell, Joseph M.; Newman, Daniel S.; Pierce, Benjamin G.; Anderson, Alisha

    2015-01-01

    Approximately 2.4 million children receive special education services for specific learning disabilities (SLDs), and school psychologists are key contributors to the SLD eligibility decision-making process. The Individuals with Disabilities Education Act (2004) enabled local education agencies to use response to intervention (RTI) instead of the…

  5. The effects of inspecting and constructing part-task-specific visualizations on team and individual learning

    NARCIS (Netherlands)

    Slof, Bert; Erkens, Gijsbert; Kirschner, Paul A.; Helms-Lorenz, Michelle

    This study examined whether inspecting and constructing different part-task-specific visualizations differentially affects learning. To this end, a complex business-economics problem was structured into three phase-related part-tasks: (1) determining core concepts, (2) proposing multiple solutions,

  6. Specific Learning Disabilities in DSM-5: Are the Changes for Better or Worse?

    Science.gov (United States)

    Tannock, Rosemary

    2013-01-01

    DSM-5, the fifth edition of the American Psychiatric Association's "Diagnostic and Statistical Manual of Mental Disorders," was published in May 2013, amidst a storm of controversy. This article focuses on changes made to the diagnostic criteria for Specific Learning Disorders (SLD). Primary criticisms of the changes in the SLD concern…

  7. Fostering Self-Concept and Interest for Statistics through Specific Learning Environments

    Science.gov (United States)

    Sproesser, Ute; Engel, Joachim; Kuntze, Sebastian

    2016-01-01

    Supporting motivational variables such as self-concept or interest is an important goal of schooling as they relate to learning and achievement. In this study, we investigated whether specific interest and self-concept related to the domains of statistics and mathematics can be fostered through a four-lesson intervention focusing on statistics.…

  8. Suicide Attempts among Individuals with Specific Learning Disorders: An Underrecognized Issue

    Science.gov (United States)

    Fuller-Thomson, Esme; Carroll, Samara Z.; Yang, Wook

    2018-01-01

    Several studies have linked specific learning disorders (SLDs) with suicidal ideation, but less is known about the disorders' association with suicide attempts. This gap in the literature is addressed via the 2012 nationally representative Canadian Community Health Survey (n = 21,744). The prevalence of lifetime suicide attempts among those with…

  9. E-Learning Lifecycles:How Communities and Context can affect E-learning Specifications and Tool Design

    Directory of Open Access Journals (Sweden)

    Michael Magee

    2004-10-01

    Full Text Available The development of a large body of e-learning specifications, such as IMS and SCORM, has led to the proposal for a new way to facilitate content workflow. This involves the movement of educational digital content and the knowledge of pedagogical communities into an online space. Several projects have looked at the theoretical structure of these specifications. They implemented a series of tools in order to examine and research the issues around the actual usage of these specifications. The CAREO, ALOHA and ALOHA 2 projects were designed to expose both individual users and whole institutions to these ideas. Initial research into the result of those interactions indicates that there is some utility in the adoption of e-learning specifications. The future success of them will depend on their ability to adapt and meet the needs of the educational community as they begin to adopt, use and evolve the way they use the specifications and the tools created around them.

  10. White matter tract-specific quantitative analysis in multiple sclerosis: Comparison of optic radiation reconstruction techniques.

    Directory of Open Access Journals (Sweden)

    Chenyu Wang

    Full Text Available The posterior visual pathway is commonly affected by multiple sclerosis (MS pathology that results in measurable clinical and electrophysiological impairment. Due to its highly structured retinotopic mapping, the visual pathway represents an ideal substrate for investigating patho-mechanisms in MS. Therefore, a reliable and robust imaging segmentation method for in-vivo delineation of the optic radiations (OR is needed. However, diffusion-based tractography approaches, which are typically used for OR segmentation are confounded by the presence of focal white matter lesions. Current solutions require complex acquisition paradigms and demand expert image analysis, limiting application in both clinical trials and clinical practice. In the current study, using data acquired in a clinical setting on a 3T scanner, we optimised and compared two approaches for optic radiation (OR reconstruction: individual probabilistic tractography-based and template-based methods. OR segmentation results were applied to subjects with MS and volumetric and diffusivity parameters were compared between OR segmentation techniques. Despite differences in reconstructed OR volumes, both OR lesion volume and OR diffusivity measurements in MS subjects were highly comparable using optimised probabilistic tractography-based, and template-based, methods. The choice of OR reconstruction technique should be determined primarily by the research question and the nature of the available dataset. Template-based approaches are particularly suited to the semi-automated analysis of large image datasets and have utility even in the absence of dMRI acquisitions. Individual tractography methods, while more complex than template based OR reconstruction, permit measurement of diffusivity changes along fibre bundles that are affected by specific MS lesions or other focal pathologies.

  11. Statistical and Machine-Learning Data Mining Techniques for Better Predictive Modeling and Analysis of Big Data

    CERN Document Server

    Ratner, Bruce

    2011-01-01

    The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has

  12. Game Design Narrative for Learning: Appropriating Adventure Game Design Narrative Devices and Techniques for the Design of Interactive Learning Environments

    Science.gov (United States)

    Dickey, Michele D.

    2006-01-01

    The purpose of this conceptual analysis is to investigate how contemporary video and computer games might inform instructional design by looking at how narrative devices and techniques support problem solving within complex, multimodal environments. Specifically, this analysis presents a brief overview of game genres and the role of narrative in…

  13. Compilation Techniques Specific for a Hardware Cryptography-Embedded Multimedia Mobile Processor

    Directory of Open Access Journals (Sweden)

    Masa-aki FUKASE

    2007-12-01

    Full Text Available The development of single chip VLSI processors is the key technology of ever growing pervasive computing to answer overall demands for usability, mobility, speed, security, etc. We have so far developed a hardware cryptography-embedded multimedia mobile processor architecture, HCgorilla. Since HCgorilla integrates a wide range of techniques from architectures to applications and languages, one-sided design approach is not always useful. HCgorilla needs more complicated strategy, that is, hardware/software (H/S codesign. Thus, we exploit the software support of HCgorilla composed of a Java interface and parallelizing compilers. They are assumed to be installed in servers in order to reduce the load and increase the performance of HCgorilla-embedded clients. Since compilers are the essence of software's responsibility, we focus in this article on our recent results about the design, specifications, and prototyping of parallelizing compilers for HCgorilla. The parallelizing compilers are composed of a multicore compiler and a LIW compiler. They are specified to abstract parallelism from executable serial codes or the Java interface output and output the codes executable in parallel by HCgorilla. The prototyping compilers are written in Java. The evaluation by using an arithmetic test program shows the reasonability of the prototyping compilers compared with hand compilers.

  14. Radon reduction techniques for suspended timber floors and pressure field extension assessment of hardcore specifications

    International Nuclear Information System (INIS)

    Gregory, T.J.; Stephen, R.K.

    1994-01-01

    This paper comprises two case studies. The first describes a series of mitigation measures carried out in a small primary school fitted with a suspended timber floor. Radon levels had been successfully reduced but the floor subsequently collapsed due to an outbreak of dry-rot. The floor was replaced with a ground-bearing concrete slab fitted with a typical example of one of 200 or so sump-and-fan systems fitted by Cornwall County Council (CCC). Following consultation with the Building Research Establishment (BRE) a network of small bore pipes was fitted below the floor during construction to record variations in radon levels and pressures. The second case study describes the floor replacement at a second, similar school but with a permeable layer of material under the concrete slab and more pressure measurement points. The pressure measurements and their subsequent analysis are described and the performance of the two installations compared. Using BRE and CCC expertise, this technique is now being applied to a number of other replacement floors in order to assess pressure field extension in a variety of hardcore and blinding materials. It is hoped that by careful selection of hardcore and blinding specifications the increased pressure field extension obtained could result in a new-build properties requiring fewer underfloor suction points and/or a reduction in fan power consumption with a greater degree of confidence of success than at present. The selection and design of suction systems to date has been on a very pragmatic basis. (author)

  15. Effects of practice schedule and task specificity on the adaptive process of motor learning.

    Science.gov (United States)

    Barros, João Augusto de Camargo; Tani, Go; Corrêa, Umberto Cesar

    2017-10-01

    This study investigated the effects of practice schedule and task specificity based on the perspective of adaptive process of motor learning. For this purpose, tasks with temporal and force control learning requirements were manipulated in experiments 1 and 2, respectively. Specifically, the task consisted of touching with the dominant hand the three sequential targets with specific movement time or force for each touch. Participants were children (N=120), both boys and girls, with an average age of 11.2years (SD=1.0). The design in both experiments involved four practice groups (constant, random, constant-random, and random-constant) and two phases (stabilisation and adaptation). The dependent variables included measures related to the task goal (accuracy and variability of error of the overall movement and force patterns) and movement pattern (macro- and microstructures). Results revealed a similar error of the overall patterns for all groups in both experiments and that they adapted themselves differently in terms of the macro- and microstructures of movement patterns. The study concludes that the effects of practice schedules on the adaptive process of motor learning were both general and specific to the task. That is, they were general to the task goal performance and specific regarding the movement pattern. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. First-order and higher order sequence learning in specific language impairment.

    Science.gov (United States)

    Clark, Gillian M; Lum, Jarrad A G

    2017-02-01

    A core claim of the procedural deficit hypothesis of specific language impairment (SLI) is that the disorder is associated with poor implicit sequence learning. This study investigated whether implicit sequence learning problems in SLI are present for first-order conditional (FOC) and higher order conditional (HOC) sequences. Twenty-five children with SLI and 27 age-matched, nonlanguage-impaired children completed 2 serial reaction time tasks. On 1 version, the sequence to be implicitly learnt comprised a FOC sequence and on the other a HOC sequence. Results showed that the SLI group learned the HOC sequence (η p ² = .285, p = .005) but not the FOC sequence (η p ² = .099, p = .118). The control group learned both sequences (FOC η p ² = .497, HOC η p 2= .465, ps < .001). The SLI group's difficulty learning the FOC sequence is consistent with the procedural deficit hypothesis. However, the study provides new evidence that multiple mechanisms may underpin the learning of FOC and HOC sequences. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  17. A Cultural Psychological Approach to Analyze Intercultural Learning: Potential and Limits of the Structure Formation Technique

    Directory of Open Access Journals (Sweden)

    Doris Weidemann

    2009-01-01

    Full Text Available Despite the huge interest in sojourner adjustment, there is still a lack of qualitative as well as of longitudinal research that would offer more detailed insights into intercultural learning processes during overseas stays. The present study aims to partly fill that gap by documenting changes in knowledge structures and general living experiences of fifteen German sojourners in Taiwan in a longitudinal, cultural-psychological study. As part of a multimethod design a structure formation technique was used to document subjective theories on giving/losing face and their changes over time. In a second step results from this study are compared to knowledge-structures of seven long-term German residents in Taiwan, and implications for the conceptualization of intercultural learning will be proposed. Finally, results from both studies serve to discuss the potential and limits of structure formation techniques in the field of intercultural communication research. URN: urn:nbn:de:0114-fqs0901435

  18. Agent-specific learning signals for self-other distinction during mentalising.

    Directory of Open Access Journals (Sweden)

    Sam Ereira

    2018-04-01

    Full Text Available Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self-other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG enabled us to track neural representations of prediction errors (PEs and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self-other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self-other distinction also had a reduced behavioural capacity for self-other distinction and displayed more marked subclinical psychopathological traits. The neural self-other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self-other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker.

  19. Approximate multi-state reliability expressions using a new machine learning technique

    International Nuclear Information System (INIS)

    Rocco S, Claudio M.; Muselli, Marco

    2005-01-01

    The machine-learning-based methodology, previously proposed by the authors for approximating binary reliability expressions, is now extended to develop a new algorithm, based on the procedure of Hamming Clustering, which is capable to deal with multi-state systems and any success criterion. The proposed technique is presented in details and verified on literature cases: experiment results show that the new algorithm yields excellent predictions

  20. Grammar predicts procedural learning and consolidation deficits in children with Specific Language Impairment.

    Science.gov (United States)

    Hedenius, Martina; Persson, Jonas; Tremblay, Antoine; Adi-Japha, Esther; Veríssimo, João; Dye, Cristina D; Alm, Per; Jennische, Margareta; Bruce Tomblin, J; Ullman, Michael T

    2011-01-01

    The Procedural Deficit Hypothesis (PDH) posits that Specific Language Impairment (SLI) can be largely explained by abnormalities of brain structures that subserve procedural memory. The PDH predicts impairments of procedural memory itself, and that such impairments underlie the grammatical deficits observed in the disorder. Previous studies have indeed reported procedural learning impairments in SLI, and have found that these are associated with grammatical difficulties. The present study extends this research by examining consolidation and longer-term procedural sequence learning in children with SLI. The Alternating Serial Reaction Time (ASRT) task was given to children with SLI and typically developing (TD) children in an initial learning session and an average of three days later to test for consolidation and longer-term learning. Although both groups showed evidence of initial sequence learning, only the TD children showed clear signs of consolidation, even though the two groups did not differ in longer-term learning. When the children were re-categorized on the basis of grammar deficits rather than broader language deficits, a clearer pattern emerged. Whereas both the grammar impaired and normal grammar groups showed evidence of initial sequence learning, only those with normal grammar showed consolidation and longer-term learning. Indeed, the grammar-impaired group appeared to lose any sequence knowledge gained during the initial testing session. These findings held even when controlling for vocabulary or a broad non-grammatical language measure, neither of which were associated with procedural memory. When grammar was examined as a continuous variable over all children, the same relationships between procedural memory and grammar, but not vocabulary or the broader language measure, were observed. Overall, the findings support and further specify the PDH. They suggest that consolidation and longer-term procedural learning are impaired in SLI, but that these

  1. Grammar Predicts Procedural Learning and Consolidation Deficits in Children with Specific Language Impairment

    Science.gov (United States)

    Hedenius, Martina; Persson, Jonas; Tremblay, Antoine; Adi-Japha, Esther; Veríssimo, João; Dye, Cristina D.; Alm, Per; Jennische, Margareta; Tomblin, J. Bruce; Ullman, Michael T.

    2011-01-01

    The Procedural Deficit Hypothesis (PDH) posits that Specific Language Impairment (SLI) can be largely explained by abnormalities of brain structures that subserve procedural memory. The PDH predicts impairments of procedural memory itself, and that such impairments underlie the grammatical deficits observed in the disorder. Previous studies have indeed reported procedural learning impairments in SLI, and have found that these are associated with grammatical difficulties. The present study extends this research by examining the consolidation and longer-term procedural sequence learning in children with SLI. The Alternating Serial Reaction Time (ASRT) task was given to children with SLI and typically-developing (TD) children in an initial learning session and an average of three days later to test for consolidation and longer-term learning. Although both groups showed evidence of initial sequence learning, only the TD children showed clear signs of consolidation, even though the two groups did not differ in longer-term learning. When the children were re-categorized on the basis of grammar deficits rather than broader language deficits, a clearer pattern emerged. Whereas both the grammar impaired and normal grammar groups showed evidence of initial sequence learning, only those with normal grammar showed consolidation and longer-term learning. Indeed, the grammar-impaired group appeared to lose any sequence knowledge gained during the initial testing session. These findings held even when controlling for vocabulary or a broad non-grammatical language measure, neither of which were associated with procedural memory. When grammar was examined as a continuous variable over all children, the same relationships between procedural memory and grammar, but not vocabulary or the broader language measure, were observed. Overall, the findings support and further specify the PDH. They suggest that consolidation and longer-term procedural learning are impaired in SLI, but that

  2. Different levels of food restriction reveal genotype-specific differences in learning a visual discrimination task.

    Directory of Open Access Journals (Sweden)

    Kalina Makowiecki

    Full Text Available In behavioural experiments, motivation to learn can be achieved using food rewards as positive reinforcement in food-restricted animals. Previous studies reduce animal weights to 80-90% of free-feeding body weight as the criterion for food restriction. However, effects of different degrees of food restriction on task performance have not been assessed. We compared learning task performance in mice food-restricted to 80 or 90% body weight (BW. We used adult wildtype (WT; C57Bl/6j and knockout (ephrin-A2⁻/⁻ mice, previously shown to have a reverse learning deficit. Mice were trained in a two-choice visual discrimination task with food reward as positive reinforcement. When mice reached criterion for one visual stimulus (80% correct in three consecutive 10 trial sets they began the reverse learning phase, where the rewarded stimulus was switched to the previously incorrect stimulus. For the initial learning and reverse phase of the task, mice at 90%BW took almost twice as many trials to reach criterion as mice at 80%BW. Furthermore, WT 80 and 90%BW groups significantly differed in percentage correct responses and learning strategy in the reverse learning phase, whereas no differences between weight restriction groups were observed in ephrin-A2⁻/⁻ mice. Most importantly, genotype-specific differences in reverse learning strategy were only detected in the 80%BW groups. Our results indicate that increased food restriction not only results in better performance and a shorter training period, but may also be necessary for revealing behavioural differences between experimental groups. This has important ethical and animal welfare implications when deciding extent of diet restriction in behavioural studies.

  3. Task-specific effect of transcranial direct current stimulation on motor learning

    Directory of Open Access Journals (Sweden)

    Cinthia Maria Saucedo Marquez

    2013-07-01

    Full Text Available Transcranial direct current stimulation (tDCS is a relatively new non-invasive brain stimulation technique that modulates neural processes. When applied to the human primary motor cortex (M1, tDCS has beneficial effects on motor skill learning and consolidation in healthy controls and in patients. However, it remains unclear whether tDCS improves motor learning in a general manner or whether these effects depend on which motor task is acquired. Here we compare whether the effect of tDCS differs when the same individual acquires (1 a Sequential Finger Tapping Task (SEQTAP and (2 a Visual Isometric Pinch Force Task (FORCE. Both tasks have been shown to be sensitive to tDCS applied over M1, however, the underlying processes mediating learning and memory formation might benefit differently from anodal-tDCS. Thirty healthy subjects were randomly assigned to an anodal-tDCS group or sham-group. Using a double-blind, sham-controlled cross-over design, tDCS was applied over M1 while subjects acquired each of the motor tasks over 3 consecutive days, with the order being randomized across subjects. We found that anodal-tDCS affected each task differently: The SEQTAP task benefited from anodal-tDCS during learning, whereas the FORCE task showed improvements only at retention. These findings suggest that anodal tDCS applied over M1 appears to have a task-dependent effect on learning and memory formation.

  4. The Context-Specific Conceptions of Learning in Case-Based Accounting Assignments, Students' Characteristics and Performance

    Science.gov (United States)

    Moilanen, Sinikka

    2017-01-01

    The present study contributes to accounting education literature by describing context-specific conceptions of learning related to case assignments, and by exploring the associations between the conceptions of learning, students' characteristics and performance. The data analysed consist of 1320 learning diaries of 336 students, connected with…

  5. Exploring Graduate Students' Perspectives towards Using Gamification Techniques in Online Learning

    Directory of Open Access Journals (Sweden)

    Daniah ALABBASI

    2017-07-01

    Full Text Available Teachers and educational institutions are attempting to find an appropriate strategy to motivate as well as engage students in the learning process. Institutions are encouraging the use of gamification in education for the purpose of improving the intrinsic motivation as well as engagement. However, the students’ perspective of the issue is under-investigated. The purpose of this research study was to explore graduate students’ perspectives toward the use of gamification techniques in online learning. The study used exploratory research and survey as the data collection tool. Forty-seven graduate students (n = 47 enrolled in an instructional technology program studied in a learning management system that supports gamification (TalentLMS. The average total percentages were calculated for each survey section to compose the final perspective of the included students. The results showed a positive perception toward the use of gamification tools in online learning among graduate students. Students require effort-demanding, challenging, sophisticated learning systems that increase competency, enhance recall memory, concentration, attentiveness, commitment, and social interaction. Limitations of the study are identified, which highlights the need for further research on the subject matter.

  6. Extracurricular activities and the development of social skills in children with intellectual and specific learning disabilities.

    Science.gov (United States)

    Brooks, B A; Floyd, F; Robins, D L; Chan, W Y

    2015-07-01

    Children with intellectual disability and specific learning disabilities often lack age-appropriate social skills, which disrupts their social functioning. Because of the limited effectiveness of classroom mainstreaming and social skills training for these children, it is important to explore alternative opportunities for social skill acquisition. Participation in social activities is positively related to children's social adjustment, but little is known about the benefits of activity participation for children with intellectual and specific learning disabilities. This study investigated the association between frequency and type of social activity participation and the social competence of 8-11-year-old children with intellectual disability (n = 40) and specific learning disabilities (n = 53), in comparison with typically developing peers (n = 24). More time involved in unstructured activities, but not structured activities, was associated with higher levels of social competence for all children. This association was strongest for children with intellectual disability, suggesting that participation in unstructured social activities was most beneficial for these children. Future research on the quality of involvement is necessary to further understand specific aspects of unstructured activities that might facilitate social development. © 2014 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

  7. Ants use partner specific odors to learn to recognize a mutualistic partner.

    Directory of Open Access Journals (Sweden)

    Masaru K Hojo

    Full Text Available Regulation via interspecific communication is an important for the maintenance of many mutualisms. However, mechanisms underlying the evolution of partner communication are poorly understood for many mutualisms. Here we show, in an ant-lycaenid butterfly mutualism, that attendant ants selectively learn to recognize and interact cooperatively with a partner. Workers of the ant Pristomyrmex punctatus learn to associate cuticular hydrocarbons of mutualistic Narathura japonica caterpillars with food rewards and, as a result, are more likely to tend the caterpillars. However, the workers do not learn to associate the cuticular hydrocarbons of caterpillars of a non-ant-associated lycaenid, Lycaena phlaeas, with artificial food rewards. Chemical analysis revealed cuticular hydrocarbon profiles of the mutualistic caterpillars were complex compared with those of non-ant-associated caterpillars. Our results suggest that partner-recognition based on partner-specific chemical signals and cognitive abilities of workers are important mechanisms underlying the evolution and maintenance of mutualism with ants.

  8. Learning trajectories for speech motor performance in children with specific language impairment.

    Science.gov (United States)

    Richtsmeier, Peter T; Goffman, Lisa

    2015-01-01

    Children with specific language impairment (SLI) often perform below expected levels, including on tests of motor skill and in learning tasks, particularly procedural learning. In this experiment we examined the possibility that children with SLI might also have a motor learning deficit. Twelve children with SLI and thirteen children with typical development (TD) produced complex nonwords in an imitation task. Productions were collected across three blocks, with the first and second blocks on the same day and the third block one week later. Children's lip movements while producing the nonwords were recorded using an Optotrak camera system. Movements were then analyzed for production duration and stability. Movement analyses indicated that both groups of children produced shorter productions in later blocks (corroborated by an acoustic analysis), and the rate of change was comparable for the TD and SLI groups. A nonsignificant trend for more stable productions was also observed in both groups. SLI is regularly accompanied by a motor deficit, and this study does not dispute that. However, children with SLI learned to make more efficient productions at a rate similar to their peers with TD, revealing some modification of the motor deficit associated with SLI. The reader will learn about deficits commonly associated with specific language impairment (SLI) that often occur alongside the hallmark language deficit. The authors present an experiment showing that children with SLI improved speech motor performance at a similar rate compared to typically developing children. The implication is that speech motor learning is not impaired in children with SLI. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Machine Learning or Information Retrieval Techniques for Bug Triaging: Which is better?

    Directory of Open Access Journals (Sweden)

    Anjali Goyal

    2017-07-01

    Full Text Available Bugs are the inevitable part of a software system. Nowadays, large software development projects even release beta versions of their products to gather bug reports from users. The collected bug reports are then worked upon by various developers in order to resolve the defects and make the final software product more reliable. The high frequency of incoming bugs makes the bug handling a difficult and time consuming task. Bug assignment is an integral part of bug triaging that aims at the process of assigning a suitable developer for the reported bug who corrects the source code in order to resolve the bug. There are various semi and fully automated techniques to ease the task of bug assignment. This paper presents the current state of the art of various techniques used for bug report assignment. Through exhaustive research, the authors have observed that machine learning and information retrieval based bug assignment approaches are most popular in literature. A deeper investigation has shown that the trend of techniques is taking a shift from machine learning based approaches towards information retrieval based approaches. Therefore, the focus of this work is to find the reason behind the observed drift and thus a comparative analysis is conducted on the bug reports of the Mozilla, Eclipse, Gnome and Open Office projects in the Bugzilla repository. The results of the study show that the information retrieval based technique yields better efficiency in recommending the developers for bug reports.

  10. Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

    Directory of Open Access Journals (Sweden)

    Manoja Kumar Behera

    2018-06-01

    Full Text Available Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC maximum power point tracking (MPPT technique that is based on proportional integral (PI controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN, ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO techniques and their performance are compared with existing models like back propagation (BP forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network

  11. Group Guidance Services with Self-Regulation Technique to Improve Student Learning Motivation in Junior High School (JHS)

    Science.gov (United States)

    Pranoto, Hadi; Atieka, Nurul; Wihardjo, Sihadi Darmo; Wibowo, Agus; Nurlaila, Siti; Sudarmaji

    2016-01-01

    This study aims at: determining students motivation before being given a group guidance with self-regulation technique, determining students' motivation after being given a group counseling with self-regulation technique, generating a model of group counseling with self-regulation technique to improve motivation of learning, determining the…

  12. Estimating Global Seafloor Total Organic Carbon Using a Machine Learning Technique and Its Relevance to Methane Hydrates

    Science.gov (United States)

    Lee, T. R.; Wood, W. T.; Dale, J.

    2017-12-01

    Empirical and theoretical models of sub-seafloor organic matter transformation, degradation and methanogenesis require estimates of initial seafloor total organic carbon (TOC). This subsurface methane, under the appropriate geophysical and geochemical conditions may manifest as methane hydrate deposits. Despite the importance of seafloor TOC, actual observations of TOC in the world's oceans are sparse and large regions of the seafloor yet remain unmeasured. To provide an estimate in areas where observations are limited or non-existent, we have implemented interpolation techniques that rely on existing data sets. Recent geospatial analyses have provided accurate accounts of global geophysical and geochemical properties (e.g. crustal heat flow, seafloor biomass, porosity) through machine learning interpolation techniques. These techniques find correlations between the desired quantity (in this case TOC) and other quantities (predictors, e.g. bathymetry, distance from coast, etc.) that are more widely known. Predictions (with uncertainties) of seafloor TOC in regions lacking direct observations are made based on the correlations. Global distribution of seafloor TOC at 1 x 1 arc-degree resolution was estimated from a dataset of seafloor TOC compiled by Seiter et al. [2004] and a non-parametric (i.e. data-driven) machine learning algorithm, specifically k-nearest neighbors (KNN). Built-in predictor selection and a ten-fold validation technique generated statistically optimal estimates of seafloor TOC and uncertainties. In addition, inexperience was estimated. Inexperience is effectively the distance in parameter space to the single nearest neighbor, and it indicates geographic locations where future data collection would most benefit prediction accuracy. These improved geospatial estimates of TOC in data deficient areas will provide new constraints on methane production and subsequent methane hydrate accumulation.

  13. Towards large-scale FAME-based bacterial species identification using machine learning techniques.

    Science.gov (United States)

    Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

    2009-05-01

    In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species

  14. Later learning stages in procedural memory are impaired in children with Specific Language Impairment.

    Science.gov (United States)

    Desmottes, Lise; Meulemans, Thierry; Maillart, Christelle

    2016-01-01

    According to the Procedural Deficit Hypothesis (PDH), difficulties in the procedural memory system may contribute to the language difficulties encountered by children with Specific Language Impairment (SLI). Most studies investigating the PDH have used the sequence learning paradigm; however these studies have principally focused on initial sequence learning in a single practice session. The present study sought to extend these investigations by assessing the consolidation stage and longer-term retention of implicit sequence-specific knowledge in 42 children with or without SLI. Both groups of children completed a serial reaction time task and were tested 24h and one week after practice. Results showed that children with SLI succeeded as well as children with typical development (TD) in the early acquisition stage of the sequence learning task. However, as training blocks progressed, only TD children improved their sequence knowledge while children with SLI did not appear to evolve any more. Moreover, children with SLI showed a lack of the consolidation gains in sequence knowledge displayed by the TD children. Overall, these results were in line with the predictions of the PDH and suggest that later learning stages in procedural memory are impaired in SLI. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. [Multilingualism and child psychiatry: on differential diagnoses of language disorder, specific learning disorder, and selective mutism].

    Science.gov (United States)

    Tamiya, Satoshi

    2014-01-01

    Multilingualism poses unique psychiatric problems, especially in the field of child psychiatry. The author discusses several linguistic and transcultural issues in relation to Language Disorder, Specific Learning Disorder and Selective Mutism. Linguistic characteristics of multiple language development, including so-called profile effects and code-switching, need to be understood for differential diagnosis. It is also emphasized that Language Disorder in a bilingual person is not different or worse than that in a monolingual person. Second language proficiency, cultural background and transfer from the first language all need to be considered in an evaluation for Specific Learning Disorder. Selective Mutism has to be differentiated from the silent period observed in the normal successive bilingual development. The author concludes the review by remarking on some caveats around methods of language evaluation in a multilingual person.

  16. The Goal Specificity Effect on Strategy Use and Instructional Efficiency during Computer-Based Scientific Discovery Learning

    Science.gov (United States)

    Kunsting, Josef; Wirth, Joachim; Paas, Fred

    2011-01-01

    Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the "effects of goal specificity" (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on "strategy use" and "instructional efficiency". Our empirical findings close an important research gap,…

  17. Teachers' opinion about learning continuum based on student's level of competence and specific pedagogical material in classification topics

    Science.gov (United States)

    Andriani, Aldina Eka; Subali, Bambang

    2017-08-01

    This research discusses learning continuum development for designing a curriculum. The objective of this study is to gather the opinion of public junior and senior high school teachers about learning continuum based on student's level of competence and specific pedagogical material in classification topics. This research was conducted in Yogyakarta province from October 2016 to January 2017. This research utilizes a descriptive survey method. Respondents in this study consist of 281 science teachers at junior and senior high school in Yogyakarta city and 4 regencies namely Sleman, Bantul, Kulonprogo, and Gunung Kidul. The sample were taken using a census. The collection of data used questionnaire that had been validated from the aspects of construct validity and experts judgements. Data were analyzed using a descriptive analysis technique. The results of the analysis show that the opinions of teachers regarding specific pedagogical material in classification topics of living things at the junior high school taught in grade VII to the ability level of C2 (Understanding). At senior high school level, it is taught in grade X with the ability level C2 (Understanding). Based on these results, it can be concluded that the opinions of teachers still refer to the current syllabus and curriculum so that the teachers do not have pure opinions about the student's competence level in classification topics that should be taught at the level of the grade in accordance with the level of corresponding competency.

  18. 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.

  19. Pitch Discrimination Learning: Specificity for Pitch and Harmonic Resolvability, and Electrophysiological Correlates

    OpenAIRE

    Carcagno, Samuele; Plack, Christopher J.

    2011-01-01

    Multiple-hour training on a pitch discrimination task dramatically decreases the threshold for detecting a pitch difference between two harmonic complexes. Here, we investigated the specificity of this perceptual learning with respect to the pitch and the resolvability of the trained harmonic complex, as well as its cortical electrophysiological correlates. We trained 24 participants for 12 h on a pitch discrimination task using one of four different harmonic complexes. The complexes differed...

  20. Nitric oxide regulates input specificity of long-term depression and context dependence of cerebellar learning.

    Directory of Open Access Journals (Sweden)

    Hideaki Ogasawara

    2007-01-01

    Full Text Available Recent studies have shown that multiple internal models are acquired in the cerebellum and that these can be switched under a given context of behavior. It has been proposed that long-term depression (LTD of parallel fiber (PF-Purkinje cell (PC synapses forms the cellular basis of cerebellar learning, and that the presynaptically synthesized messenger nitric oxide (NO is a crucial "gatekeeper" for LTD. Because NO diffuses freely to neighboring synapses, this volume learning is not input-specific and brings into question the biological significance of LTD as the basic mechanism for efficient supervised learning. To better characterize the role of NO in cerebellar learning, we simulated the sequence of electrophysiological and biochemical events in PF-PC LTD by combining established simulation models of the electrophysiology, calcium dynamics, and signaling pathways of the PC. The results demonstrate that the local NO concentration is critical for induction of LTD and for its input specificity. Pre- and postsynaptic coincident firing is not sufficient for a PF-PC synapse to undergo LTD, and LTD is induced only when a sufficient amount of NO is provided by activation of the surrounding PFs. On the other hand, above-adequate levels of activity in nearby PFs cause accumulation of NO, which also allows LTD in neighboring synapses that were not directly stimulated, ruining input specificity. These findings lead us to propose the hypothesis that NO represents the relevance of a given context and enables context-dependent selection of internal models to be updated. We also predict sparse PF activity in vivo because, otherwise, input specificity would be lost.

  1. A Survey on Domain-Specific Languages for Machine Learning in Big Data

    OpenAIRE

    Portugal, Ivens; Alencar, Paulo; Cowan, Donald

    2016-01-01

    The amount of data generated in the modern society is increasing rapidly. New problems and novel approaches of data capture, storage, analysis and visualization are responsible for the emergence of the Big Data research field. Machine Learning algorithms can be used in Big Data to make better and more accurate inferences. However, because of the challenges Big Data imposes, these algorithms need to be adapted and optimized to specific applications. One important decision made by software engi...

  2. Affordances of Telecollaboration tools for English for Specific Purposes online learning

    OpenAIRE

    Sevilla Pavón, Ana

    2016-01-01

    This paper explores students’ perceptions of the affordances of different telecollaboration tools used in an innovation project for English for Specific Purposes online learning carried out between the University of Valencia (Spain) and Wofford College (South Carolina, United States) during the school year 2015-2016. Different tools for synchronous and asynchronous communication were used. The asynchronous tools included a discussion forum, a wiki, social networking websites and Google forms;...

  3. Impact of an education program on parental knowledge of specific learning disability

    OpenAIRE

    Karande Sunil; Mehta Vishal; Kulkarni Madhuri

    2007-01-01

    Background :A supportive home environment is one of the factors that can favorably determine the outcome of specific learning disability (SpLD) in a school-going child. However, there is no reliable information available on parental knowledge about SpLD. Aims :To investigate parental knowledge of SpLD and to evaluate the impact of an educational intervention on it. Settings and Design : Prospective questionnaire-based study conducted in our clinic. Materials and Methods : From April to Novemb...

  4. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    Science.gov (United States)

    Gueth, P.; Dauvergne, D.; Freud, N.; Létang, J. M.; Ray, C.; Testa, E.; Sarrut, D.

    2013-07-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.

  5. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    International Nuclear Information System (INIS)

    Gueth, P; Freud, N; Létang, J M; Sarrut, D; Dauvergne, D; Ray, C; Testa, E

    2013-01-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations. (paper)

  6. Evaluation of undergraduate clinical learning experiences in the subject of pediatric dentistry using critical incident technique

    Directory of Open Access Journals (Sweden)

    S Vyawahare

    2013-01-01

    Full Text Available Introduction: In pediatric dentistry, the experiences of dental students may help dental educators better prepare graduates to treat the children. Research suggests that student′s perceptions should be considered in any discussion of their education, but there has been no systematic examination of India′s undergraduate dental students learning experiences. Aim: This qualitative investigation aimed to gather and analyze information about experiences in pediatric dentistry from the students′ viewpoint using critical incident technique (CIT. Study Design: The sample group for this investigation came from all 240 3 rd and 4 th year dental students from all the four dental colleges in Indore. Using CIT, participants were asked to describe at least one positive and one negative experience in detail. Results: They described 308 positive and 359 negative experiences related to the pediatric dentistry clinic. Analysis of the data resulted in the identification of four key factors related to their experiences: 1 The instructor; 2 the patient; 3 the learning process; and 4 the learning environment. Conclusion: The CIT is a useful data collection and analysis technique that provides rich, useful data and has many potential uses in dental education.

  7. Evaluation of undergraduate clinical learning experiences in the subject of pediatric dentistry using critical incident technique.

    Science.gov (United States)

    Vyawahare, S; Banda, N R; Choubey, S; Parvekar, P; Barodiya, A; Dutta, S

    2013-01-01

    In pediatric dentistry, the experiences of dental students may help dental educators better prepare graduates to treat the children. Research suggests that student's perceptions should be considered in any discussion of their education, but there has been no systematic examination of India's undergraduate dental students learning experiences. This qualitative investigation aimed to gather and analyze information about experiences in pediatric dentistry from the students' viewpoint using critical incident technique (CIT). The sample group for this investigation came from all 240 3rd and 4th year dental students from all the four dental colleges in Indore. Using CIT, participants were asked to describe at least one positive and one negative experience in detail. They described 308 positive and 359 negative experiences related to the pediatric dentistry clinic. Analysis of the data resulted in the identification of four key factors related to their experiences: 1) The instructor; 2) the patient; 3) the learning process; and 4) the learning environment. The CIT is a useful data collection and analysis technique that provides rich, useful data and has many potential uses in dental education.

  8. 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

  9. Combined Acquisition Technique (CAT) for Neuroimaging of Multiple Sclerosis at Low Specific Absorption Rates (SAR)

    Science.gov (United States)

    Biller, Armin; Choli, Morwan; Blaimer, Martin; Breuer, Felix A.; Jakob, Peter M.; Bartsch, Andreas J.

    2014-01-01

    Purpose To compare a novel combined acquisition technique (CAT) of turbo-spin-echo (TSE) and echo-planar-imaging (EPI) with conventional TSE. CAT reduces the electromagnetic energy load transmitted for spin excitation. This radiofrequency (RF) burden is limited by the specific absorption rate (SAR) for patient safety. SAR limits restrict high-field MRI applications, in particular. Material and Methods The study was approved by the local Medical Ethics Committee. Written informed consent was obtained from all participants. T2- and PD-weighted brain images of n = 40 Multiple Sclerosis (MS) patients were acquired by CAT and TSE at 3 Tesla. Lesions were recorded by two blinded, board-certificated neuroradiologists. Diagnostic equivalence of CAT and TSE to detect MS lesions was evaluated along with their SAR, sound pressure level (SPL) and sensations of acoustic noise, heating, vibration and peripheral nerve stimulation. Results Every MS lesion revealed on TSE was detected by CAT according to both raters (Cohen’s kappa of within-rater/across-CAT/TSE lesion detection κCAT = 1.00, at an inter-rater lesion detection agreement of κLES = 0.82). CAT reduced the SAR burden significantly compared to TSE (pCAT were 29.0 (±5.7) % for the T2-contrast and 32.7 (±21.9) % for the PD-contrast (expressed as percentages of the effective SAR limit of 3.2 W/kg for head examinations). Average SPL of CAT was no louder than during TSE. Sensations of CAT- vs. TSE-induced heating, noise and scanning vibrations did not differ. Conclusion T2−/PD-CAT is diagnostically equivalent to TSE for MS lesion detection yet substantially reduces the RF exposure. Such SAR reduction facilitates high-field MRI applications at 3 Tesla or above and corresponding protocol standardizations but CAT can also be used to scan faster, at higher resolution or with more slices. According to our data, CAT is no more uncomfortable than TSE scanning. PMID:24608106

  10. Combined acquisition technique (CAT for neuroimaging of multiple sclerosis at low specific absorption rates (SAR.

    Directory of Open Access Journals (Sweden)

    Armin Biller

    Full Text Available PURPOSE: To compare a novel combined acquisition technique (CAT of turbo-spin-echo (TSE and echo-planar-imaging (EPI with conventional TSE. CAT reduces the electromagnetic energy load transmitted for spin excitation. This radiofrequency (RF burden is limited by the specific absorption rate (SAR for patient safety. SAR limits restrict high-field MRI applications, in particular. MATERIAL AND METHODS: The study was approved by the local Medical Ethics Committee. Written informed consent was obtained from all participants. T2- and PD-weighted brain images of n = 40 Multiple Sclerosis (MS patients were acquired by CAT and TSE at 3 Tesla. Lesions were recorded by two blinded, board-certificated neuroradiologists. Diagnostic equivalence of CAT and TSE to detect MS lesions was evaluated along with their SAR, sound pressure level (SPL and sensations of acoustic noise, heating, vibration and peripheral nerve stimulation. RESULTS: Every MS lesion revealed on TSE was detected by CAT according to both raters (Cohen's kappa of within-rater/across-CAT/TSE lesion detection κCAT = 1.00, at an inter-rater lesion detection agreement of κLES = 0.82. CAT reduced the SAR burden significantly compared to TSE (p<0.001. Mean SAR differences between TSE and CAT were 29.0 (± 5.7 % for the T2-contrast and 32.7 (± 21.9 % for the PD-contrast (expressed as percentages of the effective SAR limit of 3.2 W/kg for head examinations. Average SPL of CAT was no louder than during TSE. Sensations of CAT- vs. TSE-induced heating, noise and scanning vibrations did not differ. CONCLUSION: T2-/PD-CAT is diagnostically equivalent to TSE for MS lesion detection yet substantially reduces the RF exposure. Such SAR reduction facilitates high-field MRI applications at 3 Tesla or above and corresponding protocol standardizations but CAT can also be used to scan faster, at higher resolution or with more slices. According to our data, CAT is no more uncomfortable than TSE scanning.

  11. 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.

  12. Fostering students’ thinking skill and social attitude through STAD cooperative learning technique on tenth grade students of chemistry class

    Science.gov (United States)

    Kriswintari, D.; Yuanita, L.; Widodo, W.

    2018-04-01

    The aim of this study was to develop chemistry learning package using Student Teams Achievement Division (STAD) cooperative learning technique to foster students’ thinking skills and social attitudes. The chemistry learning package consisting of lesson plan, handout, students’ worksheet, thinking skill test, and observation sheet of social attitude was developed using the Dick and Carey model. Research subject of this study was chemistry learning package using STAD which was tried out on tenth grade students of SMA Trimurti Surabaya. The tryout was conducted using the one-group pre-test post-test design. Data was collected through observation, test, and questionnaire. The obtained data were analyzed using descriptive qualitative analysis. The findings of this study revealed that the developed chemistry learning package using STAD cooperative learning technique was categorized valid, practice and effective to be implemented in the classroom to foster students’ thinking skill and social attitude.

  13. Alignment-free genome tree inference by learning group-specific distance metrics.

    Science.gov (United States)

    Patil, Kaustubh R; McHardy, Alice C

    2013-01-01

    Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods--9 alignment-free and 1 alignment-based.

  14. Laparoscopic colorectal surgery in learning curve: Role of implementation of a standardized technique and recovery protocol. A cohort study

    Directory of Open Access Journals (Sweden)

    Gaetano Luglio

    2015-06-01

    Conclusion: Proper laparoscopic colorectal surgery is safe and leads to excellent results in terms of recovery and short term outcomes, even in a learning curve setting. Key factors for better outcomes and shortening the learning curve seem to be the adoption of a standardized technique and training model along with the strict supervision of an expert colorectal surgeon.

  15. Quality specifications in postgraduate medical e-learning: an integrative literature review leading to a postgraduate medical e-learning model.

    Science.gov (United States)

    De Leeuw, R A; Westerman, Michiel; Nelson, E; Ket, J C F; Scheele, F

    2016-07-08

    E-learning is driving major shifts in medical education. Prioritizing learning theories and quality models improves the success of e-learning programs. Although many e-learning quality standards are available, few are focused on postgraduate medical education. We conducted an integrative review of the current postgraduate medical e-learning literature to identify quality specifications. The literature was thematically organized into a working model. Unique quality specifications (n = 72) were consolidated and re-organized into a six-domain model that we called the Postgraduate Medical E-learning Model (Postgraduate ME Model). This model was partially based on the ISO-19796 standard, and drew on cognitive load multimedia principles. The domains of the model are preparation, software design and system specifications, communication, content, assessment, and maintenance. This review clarified the current state of postgraduate medical e-learning standards and specifications. It also synthesized these specifications into a single working model. To validate our findings, the next-steps include testing the Postgraduate ME Model in controlled e-learning settings.

  16. 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.

  17. Particle identification at LHCb: new calibration techniques and machine learning classification algorithms

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic uncertainties. This talk highlights recent developments in PID that use innovative machine learning techniques, as well as novel data-driven approaches which ensure that PID performance is well reproduced in simulation.

  18. Research On C4.5 As One Of The Inductive Learning Techniques

    OpenAIRE

    Yıldırım, Savaş

    2003-01-01

    The thesis in hand deals with C4.5 (Decision Tree Construction Algorithm) as one of the most significant techniques of machine learning, and how it differs from its older version ID3. With this aim in mind, not only the approaches provided by C4.5 but also other approaches are examined. The decision tree algorithms are useful in a variety of spheres from defense to medicine or economics; and bear a vital importance for decision support systems in these areas. Written by Quinlan in 1993 in C p...

  19. Talker-specific learning in amnesia: Insight into mechanisms of adaptive speech perception.

    Science.gov (United States)

    Trude, Alison M; Duff, Melissa C; Brown-Schmidt, Sarah

    2014-05-01

    A hallmark of human speech perception is the ability to comprehend speech quickly and effortlessly despite enormous variability across talkers. However, current theories of speech perception do not make specific claims about the memory mechanisms involved in this process. To examine whether declarative memory is necessary for talker-specific learning, we tested the ability of amnesic patients with severe declarative memory deficits to learn and distinguish the accents of two unfamiliar talkers by monitoring their eye-gaze as they followed spoken instructions. Analyses of the time-course of eye fixations showed that amnesic patients rapidly learned to distinguish these accents and tailored perceptual processes to the voice of each talker. These results demonstrate that declarative memory is not necessary for this ability and points to the involvement of non-declarative memory mechanisms. These results are consistent with findings that other social and accommodative behaviors are preserved in amnesia and contribute to our understanding of the interactions of multiple memory systems in the use and understanding of spoken language. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

    Directory of Open Access Journals (Sweden)

    Palika Chopra

    2018-01-01

    Full Text Available A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT model, random forest (RF model, and neural network (NN model have been used and compared with the help of coefficient of determination (R2 and root-mean-square error (RMSE, and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

  1. Teaching Sustainability Using an Active Learning Constructivist Approach: Discipline-Specific Case Studies in Higher Education

    Directory of Open Access Journals (Sweden)

    Maria Kalamas Hedden

    2017-07-01

    Full Text Available In this paper we present our rationale for using an active learning constructivist approach to teach sustainability-related topics in a higher education. To push the boundaries of ecological literacy, we also develop a theoretical model for sustainability knowledge co-creation. Drawing on the experiences of faculty at a major Southeastern University in the United States, we present case studies in architecture, engineering, geography, and marketing. Four Sustainability Faculty Fellows describe their discipline-specific case studies, all of which are project-based learning experiences, and include details regarding teaching and assessment. Easily replicated in other educational contexts, these case studies contribute to the advancement of sustainability education.

  2. Current techniques in postmortem imaging with specific attention to paediatric applications

    International Nuclear Information System (INIS)

    Sieswerda-Hoogendoorn, Tessa; Rijn, Rick R. van

    2010-01-01

    In this review we discuss the decline of and current controversies regarding conventional autopsies and the use of postmortem radiology as an adjunct to and a possible alternative for the conventional autopsy. We will address the radiological techniques and applications for postmortem imaging in children. (orig.)

  3. Current techniques in postmortem imaging with specific attention to paediatric applications

    Energy Technology Data Exchange (ETDEWEB)

    Sieswerda-Hoogendoorn, Tessa; Rijn, Rick R. van [Academic Medical Centre Amsterdam, Department of Radiology, Amsterdam Zuid-Oost (Netherlands); Netherlands Forensic Institute, Department of Pathology and Toxicology, The Hague (Netherlands)

    2010-02-15

    In this review we discuss the decline of and current controversies regarding conventional autopsies and the use of postmortem radiology as an adjunct to and a possible alternative for the conventional autopsy. We will address the radiological techniques and applications for postmortem imaging in children. (orig.)

  4. 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.

  5. 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.

  6. Prevalence of specific learning disabilities among primary school children in a South Indian city.

    Science.gov (United States)

    Mogasale, Vijayalaxmi V; Patil, Vishwanath D; Patil, Nanasaheb M; Mogasale, Vittal

    2012-03-01

    To measure the prevalence of specific learning disabilities (SpLDs) such as dyslexia, dysgraphia and dyscalculia among primary school children in a South Indian city. A cross-sectional multi-staged stratified randomized cluster sampling study was conducted among children aged 8-11 years from third and fourth standard. A six level screening approach that commenced with identification of scholastic backwardness followed by stepwise exclusion of impaired vision and hearing, chronic medical conditions and subnormal intelligence was carried out among these children. In the final step, the remaining children were subjected to specific tests for reading, comprehension, writing and mathematical calculation. The prevalence of specific learning disabilities was 15.17% in sampled children, whereas 12.5%, 11.2% and 10.5% had dysgraphia, dyslexia and dyscalculia respectively. This study suggests that the prevalence of SpLDs is at the higher side of previous estimations in India. The study is unique due to its large geographically representative design and identification of the problem using simplified screening approach and tools, which minimizes the number and time of specialist requirement and spares the expensive investigation. This approach and tools are suitable for field situations and resource scarce settings. Based on the authors' experience, they express the need for more prevalence studies, remedial education and policy interventions to manage SpLDs at main stream educational system to improve the school performance in Indian children.

  7. Teacher's opinions about learning continuum based on the student's level of competence and specific pedagogical materials on anatomical aspects

    Science.gov (United States)

    Astuti, Laili Dwi; Subali, Bambang

    2017-08-01

    This research deals with designing learning continuum for developing a curriculum. The objective of this study is to gather the opinion of public junior and high school teachers about Learning Continuum based on Student's Level of Competence and Specific Pedagogical Material on Anatomical Aspects. This is a survey research. The population of the research is natural science teachers at junior high school and biology teacher at senior high school in Yogyakarta Special Region. Data were collected using a questionnaire. Data were analyzed using a descriptive analysis technique. Based on the results of the survey, the teachers opinion are in accordance with the level of the students they teach. Junior high school teachers argued that anatomical aspects were taught in grade VII,VIII, IX and X on the level of C2 (understanding), the high school teacher argued that anatomical aspects were taught in grade VIII, X and XI on the level of C2 (understanding) and C3 (apply). While according to the opinions of primary school teachers about aspects of anatomy resulted from the research of Subali (2016), anatomy is mostly not taught at the elementary school level, only some of the materials that are taught in this school level. Therefore, the results of the survey can be inferred that the opinions of teachers is still based on the existing curriculum.

  8. Development of self-learning Monte Carlo technique for more efficient modeling of nuclear logging measurements

    International Nuclear Information System (INIS)

    Zazula, J.M.

    1988-01-01

    The self-learning Monte Carlo technique has been implemented to the commonly used general purpose neutron transport code MORSE, in order to enhance sampling of the particle histories that contribute to a detector response. The parameters of all the biasing techniques available in MORSE, i.e. of splitting, Russian roulette, source and collision outgoing energy importance sampling, path length transformation and additional biasing of the source angular distribution are optimized. The learning process is iteratively performed after each batch of particles, by retrieving the data concerning the subset of histories that passed the detector region and energy range in the previous batches. This procedure has been tested on two sample problems in nuclear geophysics, where an unoptimized Monte Carlo calculation is particularly inefficient. The results are encouraging, although the presented method does not directly minimize the variance and the convergence of our algorithm is restricted by the statistics of successful histories from previous random walk. Further applications for modeling of the nuclear logging measurements seem to be promising. 11 refs., 2 figs., 3 tabs. (author)

  9. 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.

  10. Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

    Science.gov (United States)

    Wang, Ding; Liu, Derong

    2018-06-01

    The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Articular dysfunction patterns in patients with mechanical low back pain: A clinical algorithm to guide specific mobilization and manipulation techniques.

    Science.gov (United States)

    Dewitte, V; Cagnie, B; Barbe, T; Beernaert, A; Vanthillo, B; Danneels, L

    2015-06-01

    Recent systematic reviews have demonstrated reasonable evidence that lumbar mobilization and manipulation techniques are beneficial. However, knowledge on optimal techniques and doses, and its clinical reasoning is currently lacking. To address this, a clinical algorithm is presented so as to guide therapists in their clinical reasoning to identify patients who are likely to respond to lumbar mobilization and/or manipulation and to direct appropriate technique selection. Key features in subjective and clinical examination suggestive of mechanical nociceptive pain probably arising from articular structures, can categorize patients into distinct articular dysfunction patterns. Based on these patterns, specific mobilization and manipulation techniques are suggested. This clinical algorithm is merely based on empirical clinical expertise and complemented through knowledge exchange between international colleagues. The added value of the proposed articular dysfunction patterns should be considered within a broader perspective. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Pitch discrimination learning: specificity for pitch and harmonic resolvability, and electrophysiological correlates.

    Science.gov (United States)

    Carcagno, Samuele; Plack, Christopher J

    2011-08-01

    Multiple-hour training on a pitch discrimination task dramatically decreases the threshold for detecting a pitch difference between two harmonic complexes. Here, we investigated the specificity of this perceptual learning with respect to the pitch and the resolvability of the trained harmonic complex, as well as its cortical electrophysiological correlates. We trained 24 participants for 12 h on a pitch discrimination task using one of four different harmonic complexes. The complexes differed in pitch and/or spectral resolvability of their components by the cochlea, but were filtered into the same spectral region. Cortical-evoked potentials and a behavioral measure of pitch discrimination were assessed before and after training for all the four complexes. The change in these measures was compared to that of two control groups: one trained on a level discrimination task and one without any training. The behavioral results showed that learning was partly specific to both pitch and resolvability. Training with a resolved-harmonic complex improved pitch discrimination for resolved complexes more than training with an unresolved complex. However, we did not find evidence that training with an unresolved complex leads to specific learning for unresolved complexes. Training affected the P2 component of the cortical-evoked potentials, as well as a later component (250-400 ms). No significant changes were found on the mismatch negativity (MMN) component, although a separate experiment showed that this measure was sensitive to pitch changes equivalent to the pitch discriminability changes induced by training. This result suggests that pitch discrimination training affects processes not measured by the MMN, for example, processes higher in level or parallel to those involved in MMN generation.

  13. Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence.

    Science.gov (United States)

    Ahn, Woo-Young; Vassileva, Jasmin

    2016-04-01

    Recent animal and human studies reveal distinct cognitive and neurobiological differences between opiate and stimulant addictions; however, our understanding of the common and specific effects of these two classes of drugs remains limited due to the high rates of polysubstance-dependence among drug users. The goal of the current study was to identify multivariate substance-specific markers classifying heroin dependence (HD) and amphetamine dependence (AD), by using machine-learning approaches. Participants included 39 amphetamine mono-dependent, 44 heroin mono-dependent, 58 polysubstance dependent, and 81 non-substance dependent individuals. The majority of substance dependent participants were in protracted abstinence. We used demographic, personality (trait impulsivity, trait psychopathy, aggression, sensation seeking), psychiatric (attention deficit hyperactivity disorder, conduct disorder, antisocial personality disorder, psychopathy, anxiety, depression), and neurocognitive impulsivity measures (Delay Discounting, Go/No-Go, Stop Signal, Immediate Memory, Balloon Analogue Risk, Cambridge Gambling, and Iowa Gambling tasks) as predictors in a machine-learning algorithm. The machine-learning approach revealed substance-specific multivariate profiles that classified HD and AD in new samples with high degree of accuracy. Out of 54 predictors, psychopathy was the only classifier common to both types of addiction. Important dissociations emerged between factors classifying HD and AD, which often showed opposite patterns among individuals with HD and AD. These results suggest that different mechanisms may underlie HD and AD, challenging the unitary account of drug addiction. This line of work may shed light on the development of standardized and cost-efficient clinical diagnostic tests and facilitate the development of individualized prevention and intervention programs for HD and AD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  14. Is there a need for a specific educational scholarship for using e-learning in medical education?

    Science.gov (United States)

    Sandars, John; Goh, Poh Sun

    2016-10-01

    We propose the need for a specific educational scholarship when using e-learning in medical education. Effective e-learning has additional factors that require specific critical attention, including the design and delivery of e-learning. An important aspect is the recognition that e-learning is a complex intervention, with several interconnecting components that have to be aligned. This alignment requires an essential iterative development process with usability testing. Effectiveness of e-learning in one context may not be fully realized in another context unless there is further consideration of applicability and scalability. We recommend a participatory approach for an educational scholarship for using e-learning in medical education, such as by action research or design-based research.

  15. Markerless gating for lung cancer radiotherapy based on machine learning techniques

    International Nuclear Information System (INIS)

    Lin Tong; Li Ruijiang; Tang Xiaoli; Jiang, Steve B; Dy, Jennifer G

    2009-01-01

    In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks-ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.

  16. Shape-specific perceptual learning in a figure-ground segregation task.

    Science.gov (United States)

    Yi, Do-Joon; Olson, Ingrid R; Chun, Marvin M

    2006-03-01

    What does perceptual experience contribute to figure-ground segregation? To study this question, we trained observers to search for symmetric dot patterns embedded in random dot backgrounds. Training improved shape segmentation, but learning did not completely transfer either to untrained locations or to untrained shapes. Such partial specificity persisted for a month after training. Interestingly, training on shapes in empty backgrounds did not help segmentation of the trained shapes in noisy backgrounds. Our results suggest that perceptual training increases the involvement of early sensory neurons in the segmentation of trained shapes, and that successful segmentation requires perceptual skills beyond shape recognition alone.

  17. Protocol for chromosome-specific probe construction using PRINS, micromanipulation and DOP-PCR techniques

    Directory of Open Access Journals (Sweden)

    PAULO Z. PASSAMANI

    2017-12-01

    Full Text Available ABSTRACT Chromosome-specific probes have been widely used in molecular cytogenetics, being obtained with different methods. In this study, a reproducible protocol for construction of chromosome-specific probes is proposed which associates in situ amplification (PRINS, micromanipulation and degenerate oligonucleotide-primed PCR (DOP-PCR. Human lymphocyte cultures were used to obtain metaphases from male and female individuals. The chromosomes were amplified via PRINS, and subcentromeric fragments of the X chromosome were microdissected using microneedles coupled to a phase contrast microscope. The fragments were amplified by DOP-PCR and labeled with tetramethyl-rhodamine-5-dUTP. The probes were used in fluorescent in situ hybridization (FISH procedure to highlight these specific regions in the metaphases. The results show one fluorescent red spot in male and two in female X chromosomes and interphase nuclei.

  18. The Impact of Using Note Taking's Techniques on the Students' Learning

    Directory of Open Access Journals (Sweden)

    Asrar Jabir Edan

    2017-03-01

    Full Text Available It is often said that the worst pen is better than the best memory and regardless of how good the students' memory might be, they need to take notes during the lesson or lecture because it is impossible to remember all the details later on. This is so easy to use technique which requires a brief record of important information can help students not only recall what has been said in the class, but also to achieve their learning goals and provide a useful summary of the material to be revised especially before the test. Unfortunately, it is noticed that most of the students, especially at the secondary stage, neglect this important skill. Most of them don’t often write notes unless they are told to do so by the teacher or depend only on the textbooks forgetting that not all the material mentioned during the lesson found in them as some are explanations to the complex and abstract ones and others are related to the teacher's experience in the subject matter. In fact, note taking skill is part of the learning process and to be useful, students need to learn how to do it effectively and what to record because not all what is said is important. This requires acquiring more than one skill on the part of the learners and more effort on the part of the teacher to teach them how to do it properly. For the above reasons, more light will be shed in this research on this topic followed by an experiment and a test to evaluate its effectiveness in learning

  19. Annual increments, specific gravity and energy of Eucalyptus grandis by gamma-ray attenuation technique

    International Nuclear Information System (INIS)

    Rezende, M.A.; Guerrini, I.A.; Ferraz, E.S.B.

    1990-01-01

    Specific gravity annual increments in volume, mass and energy of Eucalyptus grandis at thirteen years of age were made taking into account measurements of the calorific value for wood. It was observed that the calorific value for wood decrease slightly, while the specific gravity increase significantly with age. The so-called culmination age for the Annual Volume Increment was determined to be around fourth year of growth while for the Annual Mass and Energy Increment was around the eighty year. These results show that a tree in a particular age may not have a significant growth in volume, yet one is mass and energy. (author)

  20. Accuracy comparison among different machine learning techniques for detecting malicious codes

    Science.gov (United States)

    Narang, Komal

    2016-03-01

    In this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Naïve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.

  1. [Motor capacities involved in the psychomotor skills of the cardiopulmonary resuscitation technique: recommendations for the teaching-learning process].

    Science.gov (United States)

    Miyadahira, A M

    2001-12-01

    It is a bibliographic study about the identification of the motor capacities involved in the psychomotor skills of the cardiopulmonary resuscitation (CPR) which aims to obtain subsidies to the planning of the teaching-learning process of this skill. It was found that: the motor capacities involved in the psychomotor skill of the CPR technique are predominantly cognitive and motor, involving 9 perceptive-motor capacities and 8 physical proficiency capacities. The CPR technique is a psychomotor skill classified as open, done in series and categorized as a thin and global skill and the teaching-learning process of the CPR technique has an elevated degree of complexity.

  2. Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

    Science.gov (United States)

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

    2015-09-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

  3. Medical students benefit from the use of ultrasound when learning peripheral IV techniques.

    Science.gov (United States)

    Osborn, Scott R; Borhart, Joelle; Antonis, Michael S

    2012-03-06

    Recent studies support high success rates after a short learning period of ultrasound IV technique, and increased patient and provider satisfaction when using ultrasound as an adjunct to peripheral IV placement. No study to date has addressed the efficacy for instructing ultrasound-naive providers. We studied the introduction of ultrasound to the teaching technique of peripheral IV insertion on first- and second-year medical students. This was a prospective, randomized, and controlled trial. A total of 69 medical students were randomly assigned to the control group with a classic, landmark-based approach (n = 36) or the real-time ultrasound-guided group (n = 33). Both groups observed a 20-min tutorial on IV placement using both techniques and then attempted vein cannulation. Students were given a survey to report their results and observations by a 10-cm visual analog scale. The survey response rate was 100%. In the two groups, 73.9% stated that they attempted an IV previously, and 63.7% of students had used an ultrasound machine prior to the study. None had used ultrasound for IV access prior to our session. The average number of attempts at cannulation was 1.42 in either group. There was no difference between the control and ultrasound groups in terms of number of attempts (p = 0.31). In both groups, 66.7% of learners were able to cannulate in one attempt, 21.7% in two attempts, and 11.6% in three attempts. The study group commented that they felt they gained more knowledge from the experience (p students feel they learn more when using ultrasound after a 20-min tutorial to place IVs and cannulation of the vein feels easier. Success rates are comparable between the traditional and ultrasound teaching approaches.

  4. Methods and Techniques for the Design and Implementation of Domain-Specific Languages

    NARCIS (Netherlands)

    Hemel, Z.

    2012-01-01

    Domain-Specific Languages (DSLs) are programming language aimed at a particular problem domain, e.g. banking, database querying or website page lay-outs. Through the use of high-level concepts, a DSL raises the level of abstraction and expressive power of the programmer, and reduces the size of

  5. Emotional and Meta-Emotional Intelligence as Predictors of Adjustment Problems in Students with Specific Learning Disorders

    Science.gov (United States)

    D'Amico, Antonella; Guastaferro, Teresa

    2017-01-01

    The purpose of this study was to analyse adjustment problems in a group of adolescents with a Specific Learning Disorder (SLD), examining to what extent they depend on the severity level of the learning disorder and/or on the individual's level of emotional intelligence. Adjustment problems,, perceived severity levels of SLD, and emotional and…

  6. The immediate effect of a brief energy psychology intervention (Emotional Freedom Techniques) on specific phobias: a pilot study.

    Science.gov (United States)

    Salas, Martha M; Brooks, Audrey J; Rowe, Jack E

    2011-01-01

    Specific phobia is one of the most prevalent anxiety disorders. Emotional Freedom Techniques (EFT) has been shown to improve anxiety symptoms; however, their application to specific phobias has received limited attention. This pilot study examined whether EFT, a brief exposure therapy that combines cognitive and somatic elements, had an immediate effect on the reduction of anxiety and behavior associated with specific phobias. The study utilized a crossover design with participants randomly assigned to either diaphragmatic breathing or EFT as the first treatment. The study was conducted at a regional university in the Southwestern United States. Twenty-two students meeting criteria for a phobic response to a specific stimulus (≥8 on an 11-point subjective units of distress scale). Participants completed a total of five two-minute rounds in each treatment intervention. Study measures included a behavioral approach test (BAT), Subjective Units of Distress Scale (SUDS), and Beck Anxiety Inventory (BAI). Emotional Freedom Techniques significantly reduced phobia-related anxiety (BAI P = .042; SUDS P = .002) and ability to approach the feared stimulus (BAT P = .046) whether presented as an initial treatment or following diaphragmatic breathing. When presented as the initial treatment, the effects of EFT remained through the presentation of the comparison intervention. The efficacy of EFT in treating specific phobias demonstrated in several earlier studies is corroborated by the current investigation. Comparison studies between EFT and the most effective established therapies for treating specific phobias are recommended. Copyright © 2011 Elsevier Inc. All rights reserved.

  7. Application of Specific Theory of Constraints Technique for the Identification of Main Causes of Negative Consequences within Procurement Logistics

    Directory of Open Access Journals (Sweden)

    Kapustina Larisa M.

    2017-05-01

    Full Text Available The paper presents a concrete example of the selected Theory of Constraints (TOC technique implementation in order to identify the main causes of undesirable consequences in the context of supply logistics issues. Determining the undesirable consequences of supply logistics is primarily related to the adverse impact on costs, profitability and quality of outsourcing enterprise which provide services in supply chain field. Particularly, this implementation includes individual steps of the process related to the creation of the specific TOC technique. The outcome is to identify the main causes which have the most significant impact on the negative consequences.

  8. The implications of technological learning on the prospects of specific renewable energy technologies in Europe

    International Nuclear Information System (INIS)

    Uyterlinde, M.A.; De Vries, H.J.; Junginger, H.M.

    2005-05-01

    The objective of this chapter is to examine the impact of technological learning on the diffusion of specific renewable energy technologies into the electricity market of the EU-25 until 2020, using a market simulation model (ADMIRE REBUS). It is assumed that from 2012 a harmonized trading system for renewable energy certificates will be implemented. Also it is assumed that a target of 24% renewable electricity (RES-E) in 2020 is set and met. By comparing optimistic and pessimistic endogenous technological learning scenarios, it is found that the diffusion of onshore wind energy into the market is relatively robust, regardless of technological development. However the diffusion rates of offshore wind energy and biomass gasification greatly depend on their technological development. Competition between these two options and already existing biomass combustion options largely determines the overall costs of electricity from renewables and the choice of technologies for the individual member countries. In the optimistic learning scenario, in 2020 the market price for RES-E is 1 euroct/kWh lower than in the pessimistic scenario (about 7 vs. 8 euroct/kWh). As a result, the total expenditures for RES-E market stimulation are 30% lower in the optimistic scenario. For comparison, instead of introducing a harmonized trading system, also continuation of present policies to support renewables was evaluated, assuming that the member states of the EU can fulfil their ambition levels only by exploiting their domestic renewable energy potentials (i.e. exclusion of international trade). This would require many member states to use their offshore wind potential, making the diffusion of offshore wind much less dependent on both the rate of technological learning and competition from biomass options, compared to the harmonization policy scenario

  9. Implications of the Declarative/Procedural Model for Improving Second Language Learning: The Role of Memory Enhancement Techniques

    Science.gov (United States)

    Ullman, Michael T.; Lovelett, Jarrett T.

    2018-01-01

    The declarative/procedural (DP) model posits that the learning, storage, and use of language critically depend on two learning and memory systems in the brain: declarative memory and procedural memory. Thus, on the basis of independent research on the memory systems, the model can generate specific and often novel predictions for language. Till…

  10. Learning to see the difference specifically alters the most informative V4 neurons.

    Science.gov (United States)

    Raiguel, Steven; Vogels, Rufin; Mysore, Santosh G; Orban, Guy A

    2006-06-14

    Perceptual learning is an instance of adult plasticity whereby training in a sensory (e.g., a visual task) results in neuronal changes leading to an improved ability to perform the task. Yet studies in primary visual cortex have found that changes in neuronal response properties were relatively modest. The present study examines the effects of training in an orientation discrimination task on the response properties of V4 neurons in awake rhesus monkeys. Results indicate that the changes induced in V4 are indeed larger than those in V1. Nonspecific effects of training included a decrease in response variance, and an increase in overall orientation selectivity in V4. The orientation-specific changes involved a local steepening in the orientation tuning curve around the trained orientation that selectively improved orientation discriminability at the trained orientation. Moreover, these changes were largely confined to the population of neurons whose orientation tuning was optimal for signaling small differences in orientation at the trained orientation. Finally, the modifications were restricted to the part of the tuning curve close to the trained orientation. Thus, we conclude that it is the most informative V4 neurons, those most directly involved in the discrimination, that are specifically modified by perceptual learning.

  11. Post-Learning Sleep Transiently Boosts Context Specific Operant Extinction Memory

    Directory of Open Access Journals (Sweden)

    Marion Inostroza

    2017-04-01

    Full Text Available Operant extinction is learning to supress a previously rewarded behavior. It is known to be strongly associated with the specific context in which it was acquired, which limits the therapeutic use of operant extinction in behavioral treatments, e.g., of addiction. We examined whether sleep influences contextual memory of operant extinction over time, using two different recall tests (Recent and Remote. Rats were trained in an operant conditioning task (lever press in context A, then underwent extinction training in context B, followed by a 3-h retention period that contained either spontaneous morning sleep, morning sleep deprivation, or spontaneous evening wakefulness. A recall test was performed either immediately after the 3-h experimental retention period (Recent recall or after 48 h (Remote, in the extinction context B and in a novel context C. The two main findings were: (i at the Recent recall test, sleep in comparison with sleep deprivation and spontaneous wakefulness enhanced extinction memory but, only in the extinction context B; (ii at the Remote recall, extinction performance after sleep was enhanced in both contexts B and C to an extent comparable to levels at Recent recall in context B. Interestingly, extinction performance at Remote recall was also improved in the sleep deprivation groups in both contexts, with no difference to performance in the sleep group. Our results suggest that 3 h of post-learning sleep transiently facilitate the context specificity of operant extinction at a Recent recall. However, the improvement and contextual generalization of operant extinction memory observed in the long-term, i.e., after 48 h, does not require immediate post-learning sleep.

  12. Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

    Science.gov (United States)

    Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

    2017-08-01

    The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

  13. A Sensitive, Rapid, and Specific Technique for the Detection of Collagenase Using Zymography.

    Science.gov (United States)

    Prasad, Shivcharan; Roy, Ipsita

    2017-01-01

    In-gel zymography is a commonly employed tool to identify active enzymes in a quantitative and qualitative manner. In this work, apart from the incorporation of substrate which is traditionally employed in zymography, the identification of collagenase by incubation of the enzyme resolved on a polyacrylamide gel with substrate solution is described. The two methods are quite fast and result in specific detection of bacterial collagenase.

  14. Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

    Science.gov (United States)

    Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

    The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

  15. LEARNING MATERIALS SELECTION FOR DIFFERENTIATED INSTRUCTION OF ENGLISH FOR SPECIFIC PURPOSES OF FUTURE PROFESSIONALS IN THE FIELD OF INFORMATION TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    Oksana Synekop

    2017-09-01

    Full Text Available In conditions of differentiation the learning materials selection will optimize the training English for Specific Purposes of the future professionals in the field of information technology at university level. The purpose of the article is to define the basic unit of learning material, the factors of influence on the learning material selection, principles, criteria and the procedure of learning material selection in this paper. Reviewing the scientific achievements in the learning material selection in teaching English has become a basis for defining the factors of influence, principles and criteria in the research. The basic unit of learning material (learning English text for professional purposes is outlined. The factors of influence and principles (correspondence of learning materials to professional interests and needs of information technology students; necessary ability and accessibility; regarding the linguistic and stylistic necessity and sufficiency; availability of Internet sources information of the learning material selection are defined. Also, the qualitative criteria (authenticity; professional significance, relevance and informativeness; conformity of foreign language level and intellectual development of students; variety of genres and forms of speech, their sufficient filling by linguistic material; coherence, integrity, consistency, semantic completeness; topic conformity; situation conformity; unlimited access, reliability and exemplarity of Internet sources and the quantitative criteria (the amount of material of the learning material selection are highlighted. The process of English for Specific Purposes material selection (defining the disciplines of different cycles; defining spheres and related topics; outlining situations, communicative roles and intentions of professional communication; specifying the sources of selection; evaluating the texts; analysis of the knowledge, skills and sub-skills required for the

  16. Quality of life in mucopolysaccharidoses: construction of a specific measure using the focus group technique.

    Science.gov (United States)

    Oliveira, M R; Schwartz, I; Costa, L S; Maia, H; Ribeiro, M; Guerreiro, L B; Acosta, A; Rocha, N S

    2018-01-15

    To describe the perceptions of patients, their caregivers, and their healthcare providers to the development of a new specific instrument for assessment of the quality of life (QoL) in patients with mucopolysaccharidoses (MPS) using a qualitative focus group (FG) design. FGs were held in two Brazilian states (Rio Grande do Sul and Rio de Janeiro). Three versions of the new instrument were developed, each for a different age group: children (age 8-12 years), adolescents (age 13-17), and adults (age ≥ 18). The FGs mostly confirmed the relevance of items. All FGs unanimously agreed on the facets: School, Happiness, Life Prospects, Religiosity, Pain, Continuity of Treatment, Trust in Treatment, Relationship with Family, Relationship with Healthcare Providers, Acceptance, and Meaning of Life. The overall concept of QoL (as proposed by the WHO-World Health Organization) and its facets apply to this patient population. However, other specific facets-particularly concerning clinical manifestations and the reality of the disease-were suggested, confirming the need for the development of a specific QoL instrument for MPS.

  17. Perception and coping with the specific learning disabilities impacts on everyday life of children with this diagnosis

    OpenAIRE

    Vilímová, Zuzana

    2015-01-01

    TITLE: Perception and coping with the specific learning disabilities impacts on everyday life of children with this diagnosis. ABSTRACT This text is focused on recognition of impacts of the specific learning disabilities on everyday life as the children with this diagnosis themselves see it and the strategies used by these children in order to cope with these disabilities. The theoretical part summarizes the necessary knowledge of the early school age developmental stage, the interaction of a...

  18. Efficacy of two sperm preparation techniques in reducing non-specific bacterial species from human semen

    Directory of Open Access Journals (Sweden)

    Prabath K Abeysundara

    2013-01-01

    Full Text Available Context: Artificial reproductive techniques using seminal preparations with bacteria may cause pelvic inflammatory disease and its sequalae. Aims: To assess efficacy of two sperm preparation techniques to clear bacteria and the effect of bacteriospermia on sperm recovery rates. Settings and Design: A descriptive cross-sectional study was carried out among males of subfertile couples. Subjects and Methods: Semen samples were randomly allocated into swim-up method (group S, n = 68 and density gradient method (group D, n = 50 for sperm preparation. Seminal fluid analysis and bacterial cultures were performed in each sample before and after sperm preparation. Statistical Analysis: McNemar′s chi-squared test and independent samples t-test in SPSS version 16.0 were used. Results: Organisms were found in 86 (72.88% out of 118 samples, before sperm preparation; Streptococcus species (n = 40, 46.51% of which 14 were Group D Streptococcus species, Coagulase negative Staphylococcus species (n = 17, 19.76%, Staphylococcus aureus (n = 13, 15.11%, Coliform species (n = 11, 12.79% of which 09 were Escherichia coli and Corynebacterium species (n = 5, 5.81%. There was a statistically significant reduction of culture positive samples in raw vs. processed samples; in group S, 49 (72.05% vs. 16 (23.52% and in group D, 37 (74% vs. 18 (36%. In group S and D, mean (SD recovery rates of culture positive vs. culture negative samples were 39.44% (SD-14.02 vs. 44.22% (SD-22.38, P = 0.39 and 52.50% (SD-37.16 vs. 49.58% (SD-40.32, P = 0.82 respectively. Conclusions: Both sperm preparation methods significantly reduced bacteria in semen, but total clearance was not achieved. Sperm recovery rate was not affected by bacteriospermia.

  19. The simulated early learning of cervical spine manipulation technique utilising mannequins.

    Science.gov (United States)

    Chapman, Peter D; Stomski, Norman J; Losco, Barrett; Walker, Bruce F

    2015-01-01

    Trivial pain or minor soreness commonly follows neck manipulation and has been estimated at one in three treatments. In addition, rare catastrophic events can occur. Some of these incidents have been ascribed to poor technique where the neck is rotated too far. The aims of this study were to design an instrument to measure competency of neck manipulation in beginning students when using a simulation mannequin, and then examine the suitability of using a simulation mannequin to teach the early psychomotor skills for neck chiropractic manipulative therapy. We developed an initial set of questionnaire items and then used an expert panel to assess an instrument for neck manipulation competency among chiropractic students. The study sample comprised all 41 fourth year 2014 chiropractic students at Murdoch University. Students were randomly allocated into either a usual learning or mannequin group. All participants crossed over to undertake the alternative learning method after four weeks. A chi-square test was used to examine differences between groups in the proportion of students achieving an overall pass mark at baseline, four weeks, and eight weeks. This study was conducted between January and March 2014. We successfully developed an instrument of measurement to assess neck manipulation competency in chiropractic students. We then randomised 41 participants to first undertake either "usual learning" (n = 19) or "mannequin learning" (n = 22) for early neck manipulation training. There were no significant differences between groups in the overall pass rate at baseline (χ(2) = 0.10, p = 0.75), four weeks (χ(2) = 0.40, p = 0.53), and eight weeks (χ(2) = 0.07, p = 0.79). This study demonstrates that the use of a mannequin does not affect the manipulation competency grades of early learning students at short term follow up. Our findings have potentially important safety implications as the results indicate that students could initially

  20. 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.

  1. Safe genetic modification of cardiac stem cells using a site-specific integration technique.

    Science.gov (United States)

    Lan, Feng; Liu, Junwei; Narsinh, Kazim H; Hu, Shijun; Han, Leng; Lee, Andrew S; Karow, Marisa; Nguyen, Patricia K; Nag, Divya; Calos, Michele P; Robbins, Robert C; Wu, Joseph C

    2012-09-11

    Human cardiac progenitor cells (hCPCs) are a promising cell source for regenerative repair after myocardial infarction. Exploitation of their full therapeutic potential may require stable genetic modification of the cells ex vivo. Safe genetic engineering of stem cells, using facile methods for site-specific integration of transgenes into known genomic contexts, would significantly enhance the overall safety and efficacy of cellular therapy in a variety of clinical contexts. We used the phiC31 site-specific recombinase to achieve targeted integration of a triple fusion reporter gene into a known chromosomal context in hCPCs and human endothelial cells. Stable expression of the reporter gene from its unique chromosomal integration site resulted in no discernible genomic instability or adverse changes in cell phenotype. Namely, phiC31-modified hCPCs were unchanged in their differentiation propensity, cellular proliferative rate, and global gene expression profile when compared with unaltered control hCPCs. Expression of the triple fusion reporter gene enabled multimodal assessment of cell fate in vitro and in vivo using fluorescence microscopy, bioluminescence imaging, and positron emission tomography. Intramyocardial transplantation of genetically modified hCPCs resulted in significant improvement in myocardial function 2 weeks after cell delivery, as assessed by echocardiography (P=0.002) and MRI (P=0.001). We also demonstrated the feasibility and therapeutic efficacy of genetically modifying differentiated human endothelial cells, which enhanced hind limb perfusion (Pmodification system is a safe, efficient tool to enable site-specific integration of reporter transgenes in progenitor and differentiated cell types.

  2. Analysis of the phonon surface specific heat using Green function techniques

    International Nuclear Information System (INIS)

    Carrico, A.S.; Albuquerque, E.L.

    1980-01-01

    Green functions are derived for the displacement associated with acoustic vibrations in isotropic elastic media and used to evaluate the surface specific heat in the harmonic approximation. We consider only the low-temperature limit case since, provided K B 1/h is very samll, we can replace the dispersion relation for the three acoustic branches by its long-wavelenghts form. The contributions of surface elastic waves ot the Rayleigh and Love types are pointed out and their features discussed. The nature of the result and their relations to previous work in this field is also presented and discussed. (author) [pt

  3. Modification-specific proteomics: strategies for characterization of post-translational modifications using enrichment techniques

    DEFF Research Database (Denmark)

    Zhao, Yingming; Jensen, Ole N

    2009-01-01

    More than 300 different types of protein post-translational modifications (PTMs) have been described, many of which are known to have pivotal roles in cellular physiology and disease. Nevertheless, only a handful of PTMs have been extensively investigated at the proteome level. Knowledge of protein...... substrates and their PTM sites is key to dissection of PTM-mediated cellular processes. The past several years have seen a tremendous progress in developing MS-based proteomics technologies for global PTM analysis, including numerous studies of yeast and other microbes. Modification-specific enrichment...

  4. Analysis of the phonon surface specific heat using Green function techniques

    International Nuclear Information System (INIS)

    Silva Carrico, A. da; Albuquerque, E.L. de

    1981-01-01

    Green functions are derived for the displacement associated with acoustic vibrations in isotropic elastic media and used to evaluate the surface specific heat in the harmonic approximation. Only the low-temperature limit case is considered since, provided K sub(B) T/h is very small, the dispersion relation for the three acoustic branches can be replaced by its long-wavelenght form. The contributions of surface elastic waves of the Rayleigh and Love types are pointed out and their features discussed. The nature of the result and their relations to previous work in this field is also presented and discussed. (Author) [pt

  5. 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.

  6. 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.

  7. Articular dysfunction patterns in patients with mechanical neck pain: a clinical algorithm to guide specific mobilization and manipulation techniques.

    Science.gov (United States)

    Dewitte, Vincent; Beernaert, Axel; Vanthillo, Bart; Barbe, Tom; Danneels, Lieven; Cagnie, Barbara

    2014-02-01

    In view of a didactical approach for teaching cervical mobilization and manipulation techniques to students as well as their use in daily practice, it is mandatory to acquire sound clinical reasoning to optimally apply advanced technical skills. The aim of this Masterclass is to present a clinical algorithm to guide (novice) therapists in their clinical reasoning to identify patients who are likely to respond to mobilization and/or manipulation. The presented clinical reasoning process is situated within the context of pain mechanisms and is narrowed to and applicable in patients with a dominant input pain mechanism. Based on key features in subjective and clinical examination, patients with mechanical nociceptive pain probably arising from articular structures can be categorized into specific articular dysfunction patterns. Pending on these patterns, specific mobilization and manipulation techniques are warranted. The proposed patterns are illustrated in 3 case studies. This clinical algorithm is the corollary of empirical expertise and is complemented by in-depth discussions and knowledge exchange with international colleagues. Consequently, it is intended that a carefully targeted approach contributes to an increase in specificity and safety in the use of cervical mobilizations and manipulation techniques as valuable adjuncts to other manual therapy modalities. Copyright © 2013 Elsevier Ltd. All rights reserved.

  8. Exploration of machine learning techniques in predicting multiple sclerosis disease course.

    Directory of Open Access Journals (Sweden)

    Yijun Zhao

    Full Text Available To explore the value of machine learning methods for predicting multiple sclerosis disease course.1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening or not (non-worsening at up to five years after baseline visit. Support vector machines (SVM were used to build the classifier, and compared to logistic regression (LR using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%. Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

  9. A hybrid stock trading framework integrating technical analysis with machine learning techniques

    Directory of Open Access Journals (Sweden)

    Rajashree Dash

    2016-03-01

    Full Text Available In this paper, a novel decision support system using a computational efficient functional link artificial neural network (CEFLANN and a set of rules is proposed to generate the trading decisions more effectively. Here the problem of stock trading decision prediction is articulated as a classification problem with three class values representing the buy, hold and sell signals. The CEFLANN network used in the decision support system produces a set of continuous trading signals within the range 0–1 by analyzing the nonlinear relationship exists between few popular technical indicators. Further the output trading signals are used to track the trend and to produce the trading decision based on that trend using some trading rules. The novelty of the approach is to engender the profitable stock trading decision points through integration of the learning ability of CEFLANN neural network with the technical analysis rules. For assessing the potential use of the proposed method, the model performance is also compared with some other machine learning techniques such as Support Vector Machine (SVM, Naive Bayesian model, K nearest neighbor model (KNN and Decision Tree (DT model.

  10. An analysis of a digital variant of the Trail Making Test using machine learning techniques.

    Science.gov (United States)

    Dahmen, Jessamyn; Cook, Diane; Fellows, Robert; Schmitter-Edgecombe, Maureen

    2017-01-01

    The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility. This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities. Using digital Trail Making Test (dTMT) data collected from (N = 54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features. Predicted TMT scores correlate well with clinical digital test scores (r = 0.98) and paper time to completion scores (r = 0.65). Predicted TICS exhibited a small correlation with clinically derived TICS scores (r = 0.12 Part A, r = 0.10 Part B). Predicted FAB scores exhibited a small correlation with clinically derived FAB scores (r = 0.13 Part A, r = 0.29 for Part B). Digitally derived features were also used to predict diagnosis (AUC of 0.65). Our findings indicate that the dTMT is capable of measuring the same aspects of cognition as the paper-based TMT. Furthermore, the dTMT's additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone.

  11. Overview of manifold learning techniques for the investigation of disruptions on JET

    International Nuclear Information System (INIS)

    Cannas, B; Fanni, A; Pau, A; Sias, G; Murari, A

    2014-01-01

    Identifying a low-dimensional embedding of a high-dimensional data set allows exploration of the data structure. In this paper we tested some existing manifold learning techniques for discovering such embedding within the multidimensional operational space of a nuclear fusion tokamak. Among the manifold learning methods, the following approaches have been investigated: linear methods, such as principal component analysis and grand tour, and nonlinear methods, such as self-organizing map and its probabilistic variant, generative topographic mapping. In particular, the last two methods allow us to obtain a low-dimensional (typically two-dimensional) map of the high-dimensional operational space of the tokamak. These maps provide a way of visualizing the structure of the high-dimensional plasma parameter space and allow discrimination between regions characterized by a high risk of disruption and those with a low risk of disruption. The data for this study comes from plasma discharges selected from 2005 and up to 2009 at JET. The self-organizing map and generative topographic mapping provide the most benefits in the visualization of very large and high-dimensional datasets. Some measures have been used to evaluate their performance. Special emphasis has been put on the position of outliers and extreme points, map composition, quantization errors and topological errors. (paper)

  12. Assessing Uncertainty in Deep Learning Techniques that Identify Atmospheric Rivers in Climate Simulations

    Science.gov (United States)

    Mahesh, A.; Mudigonda, M.; Kim, S. K.; Kashinath, K.; Kahou, S.; Michalski, V.; Williams, D. N.; Liu, Y.; Prabhat, M.; Loring, B.; O'Brien, T. A.; Collins, W. D.

    2017-12-01

    Atmospheric rivers (ARs) can be the difference between CA facing drought or hurricane-level storms. ARs are a form of extreme weather defined as long, narrow columns of moisture which transport water vapor outside the tropics. When they make landfall, they release the vapor as rain or snow. Convolutional neural networks (CNNs), a machine learning technique that uses filters to recognize features, are the leading computer vision mechanism for classifying multichannel images. CNNs have been proven to be effective in identifying extreme weather events in climate simulation output (Liu et. al. 2016, ABDA'16, http://bit.ly/2hlrFNV). Here, we compare three different CNN architectures, tuned with different hyperparameters and training schemes. We compare two-layer, three-layer, four-layer, and sixteen-layer CNNs' ability to recognize ARs in Community Atmospheric Model version 5 output, and we explore the ability of data augmentation and pre-trained models to increase the accuracy of the classifier. Because pre-training the model with regular images (i.e. benches, stoves, and dogs) yielded the highest accuracy rate, this strategy, also known as transfer learning, may be vital in future scientific CNNs, which likely will not have access to a large labelled training dataset. By choosing the most effective CNN architecture, climate scientists can build an accurate historical database of ARs, which can be used to develop a predictive understanding of these phenomena.

  13. The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Jianning Wu

    2015-01-01

    Full Text Available The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

  14. The novel quantitative technique for assessment of gait symmetry using advanced statistical learning algorithm.

    Science.gov (United States)

    Wu, Jianning; Wu, Bin

    2015-01-01

    The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

  15. Deep learning ensemble with asymptotic techniques for oscillometric blood pressure estimation.

    Science.gov (United States)

    Lee, Soojeong; Chang, Joon-Hyuk

    2017-11-01

    This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and Monte-Carlo approach. While the former is used to estimate SBP and DBP, the latter attempts to determine confidence intervals (CIs) for SBP and DBP based on oscillometric BP measurements. This work originally employs deep belief networks (DBN)-deep neural networks (DNN) to effectively estimate BPs based on oscillometric measurements. However, there are some inherent problems with these methods. First, it is not easy to determine the best DBN-DNN estimator, and worthy information might be omitted when selecting one DBN-DNN estimator and discarding the others. Additionally, our input feature vectors, obtained from only five measurements per subject, represent a very small sample size; this is a critical weakness when using the DBN-DNN technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To address these problems, an ensemble with an asymptotic approach (based on combining the bootstrap with the DBN-DNN technique) is utilized to generate the pseudo features needed to estimate the SBP and DBP. In the first stage, the bootstrap-aggregation technique is used to create ensemble parameters. Afterward, the AdaBoost approach is employed for the second-stage SBP and DBP estimation. We then use the bootstrap and Monte-Carlo techniques in order to determine the CIs based on the target BP estimated using the DBN-DNN ensemble regression estimator with the asymptotic technique in the third stage. The proposed method can mitigate the estimation uncertainty such as large the standard deviation of error (SDE) on comparing the proposed DBN-DNN ensemble regression estimator with the DBN-DNN single regression estimator, we identify that the SDEs of the SBP and DBP are reduced by 0.58 and 0.57  mmHg, respectively. These

  16. A Simple Ensemble Simulation Technique for Assessment of Future Variations in Specific High-Impact Weather Events

    Science.gov (United States)

    Taniguchi, Kenji

    2018-04-01

    To investigate future variations in high-impact weather events, numerous samples are required. For the detailed assessment in a specific region, a high spatial resolution is also required. A simple ensemble simulation technique is proposed in this paper. In the proposed technique, new ensemble members were generated from one basic state vector and two perturbation vectors, which were obtained by lagged average forecasting simulations. Sensitivity experiments with different numbers of ensemble members, different simulation lengths, and different perturbation magnitudes were performed. Experimental application to a global warming study was also implemented for a typhoon event. Ensemble-mean results and ensemble spreads of total precipitation, atmospheric conditions showed similar characteristics across the sensitivity experiments. The frequencies of the maximum total and hourly precipitation also showed similar distributions. These results indicate the robustness of the proposed technique. On the other hand, considerable ensemble spread was found in each ensemble experiment. In addition, the results of the application to a global warming study showed possible variations in the future. These results indicate that the proposed technique is useful for investigating various meteorological phenomena and the impacts of global warming. The results of the ensemble simulations also enable the stochastic evaluation of differences in high-impact weather events. In addition, the impacts of a spectral nudging technique were also examined. The tracks of a typhoon were quite different between cases with and without spectral nudging; however, the ranges of the tracks among ensemble members were comparable. It indicates that spectral nudging does not necessarily suppress ensemble spread.

  17. 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...

  18. Smart Training, Smart Learning: The Role of Cooperative Learning in Training for Youth Services.

    Science.gov (United States)

    Doll, Carol A.

    1997-01-01

    Examines cooperative learning in youth services and adult education. Discusses characteristics of cooperative learning techniques; specific cooperative learning techniques (brainstorming, mini-lecture, roundtable technique, send-a-problem problem solving, talking chips technique, and three-step interview); and the role of the trainer. (AEF)

  19. Application of the modulated temperature differential scanning calorimetry technique for the determination of the specific heat of copper nanofluids

    International Nuclear Information System (INIS)

    De Robertis, E.; Cosme, E.H.H.; Neves, R.S.; Kuznetsov, A.Yu.; Campos, A.P.C.; Landi, S.M.; Achete, C.A.

    2012-01-01

    The purpose of this work is to investigate the applicability of the modulated temperature differential scanning calorimetry technique to measure specific heat of copper nanofluids by using the ASTM E2719 standard procedure, which is generally applied to thermally stable solids and liquids. The one-step method of preparation of copper nanofluid samples is described. The synthesized nanoparticles were separated from the base fluid and examined by X-ray diffraction and transmission electron microscopy in order to evaluate their structure, morphology and chemical nature. The presence of copper nanoparticles in the base fluid alters the characteristics of crystallization and melting processes and reduces the specific heat values of nanofluids in the whole studied temperature range. - Highlights: ► Copper nanofluids prepared by one-step method. ► Methodology of synthesis improved nanofluid stability. ► Specific heat determinations using modulated temperature differential scanning calorimetry. ► Good agreement between theoretical and experimental values.

  20. Assessing the Effectiveness of Inquiry-based Learning Techniques Implemented in Large Classroom Settings

    Science.gov (United States)

    Steer, D. N.; McConnell, D. A.; Owens, K.

    2001-12-01

    assessments of knowledge-level learning included evaluations of student responses to pre- and post-instruction conceptual test questions, short group exercises and content-oriented exam questions. Higher level thinking skills were assessed when students completed exercises that required the completion of Venn diagrams, concept maps and/or evaluation rubrics both during class periods and on exams. Initial results indicate that these techniques improved student attendance significantly and improved overall retention in the course by 8-14% over traditional lecture formats. Student scores on multiple choice exam questions were slightly higher (1-3%) for students taught in the active learning environment and short answer questions showed larger gains (7%) over students' scores in a more traditional class structure.

  1. 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.

  2. A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

    Directory of Open Access Journals (Sweden)

    Pengfei Jia

    2016-03-01

    Full Text Available When an electronic nose (E-nose is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde. Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

  3. Investigation of distinctive characteristics of children with specific learning disorder and borderline intellectual functioning

    Directory of Open Access Journals (Sweden)

    Selcuk Ozkan

    Full Text Available Abstract Background Borderline intelligence function (BIF and specific learning disorder (SLD are common diagnoses in children who are brought up for learning problems and school failure. Objective The aim of our study was to determine whether there were distinctive aspects of cognitive testing routinely used in evaluating SLD and BIF and investigate emotion regulation skills and minor neurologic symptoms. Method Sixty children (30 SLD and 30 BIF who are currently attending primary school are selected for study. Visual Aural Digit Span Test – Form B, Gessel Figure Drawing Test, Bender Gestalt Visual Motor Perception Test, WISC-R, Emotion Regulation Scale (ERS and Neurological Evaluation Scale (NES was administered. Results There was no statistically significant difference between groups in cognitive tests. The emotional regulation ability measured by the emotional regulation subscale was better in the SLD group than the BIF group (p = 0.014. In the NES, sensory integration (p = 0.008, motor coordination (p = 0.047 and other (p < 0.001 subscales showed higher scores in the BIF group. Discussion It has been shown that cognitive tests don’t have distinguishing features in the evaluation of SLD and BIF. Emotion regulation subscale score of ERS and sensory integration, motor coordination, and total scores of NES can be used in both discrimination of groups.

  4. Perinatal exposure to dioxins perturbs learning performance of the rat in a dose-specific fashion

    Energy Technology Data Exchange (ETDEWEB)

    Hojo, R.; Rieko, H.; Masaki, K.; Junzo, Y.; Chiharu, T. [National Inst. for Environmental Studies, Tsukuba (Japan)

    2004-09-15

    Dioxins (chlorinated dibenzo-p-dioxin congeners and related compounds including coplanar PCBs) are transferred transplacentally and lactationally from mothers to the developing brain of offspring. Maternal exposure to dioxins are suspected to cause adverse effects on the advanced brain function of offspring, because Previous studies indicate that the most toxic dioxin congener, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (TCDD), affected the advanced brain function of rats, even when mothers had been exposed to a relatively low level of dioxins that would not affect themselves. In coplanar PCBs, which are dioxin-like, toxic equivalency factors (TEFs) are based on similar toxicity to TCDD and on a common mechanism of action, mediated by the aromatic hydrocarbon receptor (AhR). However, non-coplanar PCBs, which are considered to be non-dioxin-like PCBs, also show adverse effects on the learning and memory functions of offspring. In the present study, we hypothesize that coplanar PCBs have two types of toxicities, one is the similar to TCDD and the other is the specific toxicity of PCB itself. To address this hypothesis, effects of maternal exposure to one of the coplanar PCBs, 3,3',4,4',5-pentachlorobiphenyl (PCB126, 1997 WHO TEF = 0.1), on learning and behavioural performance of rats assessed by schedule-controlled operant behavior (SCOB) were examined and compared to TCDD.

  5. Reporting intellectual capital in health care organizations: specifics, lessons learned, and future research perspectives.

    Science.gov (United States)

    Veltri, Stefania; Bronzetti, Giovanni; Sicoli, Graziella

    2011-01-01

    This article analyzes the concept of intellectual capital (IC) in the health sector sphere by studying the case of a major nonprofit research organization in this sector, which has for some time been publishing IC reports. In the last few years, health care organizations have been the object of great attention in the implementation and transfer of managerial models and tools; however, there is still a lack of attention paid to the strategic management of IC as a fundamental resource for supporting and enhancing performance improvement dynamics. The main aim of this article is to examine the IC reporting model used by the Center of Molecular Medicine (CMM), a Swedish health organization which is an outstanding benchmark in reporting its IC. We also consider the specifics of IC reporting for health organizations, the lessons learned by analyzing CMM's IC reporting, and future perspectives for research.

  6. Health-related quality of life of children with newly diagnosed specific learning disability.

    Science.gov (United States)

    Karande, Sunil; Bhosrekar, Kirankumar; Kulkarni, Madhuri; Thakker, Arpita

    2009-06-01

    The objective of this study was to measure health-related quality of life (HRQL) of children with newly diagnosed specific learning disability (SpLD) using the Child Health Questionnaire-Parent Form 50. We detected clinically significant deficits (effect size > or = -0.5) in 9 out of 12 domains: limitations in family activities, emotional impact on parents, social limitations as a result of emotional-behavioral problems, time impact on parents, general behavior, physical functioning, social limitations as a result of physical health, general health perceptions and mental health; and in both summary scores (psychosocial > physical). Multivariate analysis revealed having > or = 1 non-academic problem(s) (p or =1 non-academic problem(s) (p = 0.006) or first-born status (p = 0.035) predicted a poor physical summary score. HRQL is significantly compromised in children having newly diagnosed SpLD.

  7. Femtosecond laser-assisted cataract surgery with bimanual technique: learning curve for an experienced cataract surgeon.

    Science.gov (United States)

    Cavallini, Gian Maria; Verdina, Tommaso; De Maria, Michele; Fornasari, Elisa; Volpini, Elisa; Campi, Luca

    2017-11-29

    To describe the intraoperative complications and the learning curve of microincision cataract surgery assisted by femtosecond laser (FLACS) with bimanual technique performed by an experienced surgeon. It is a prospective, observational, comparative case series. A total of 120 eyes which underwent bimanual FLACS by the same experienced surgeon during his first experience were included in the study; we considered the first 60 cases as Group A and the second 60 cases as Group B. In both groups, only nuclear sclerosis of grade 2 or 3 was included; an intraocular lens was implanted through a 1.4-mm incision. Best-corrected visual acuity (BCVA), surgically induced astigmatism (SIA), central corneal thickness and endothelial cell loss (ECL) were evaluated before and at 1 and 3 months after surgery. Intraoperative parameters, and intra- and post-operative complications were recorded. In Group A, we had femtosecond laser-related minor complications in 11 cases (18.3%) and post-operative complications in 2 cases (3.3%); in Group B, we recorded 2 cases (3.3%) of femtosecond laser-related minor complications with no post-operative complications. Mean effective phaco time (EPT) was 5.32 ± 3.68 s in Group A and 4.34 ± 2.39 s in Group B with a significant difference (p = 0.046). We recorded a significant mean BCVA improvement at 3 months in both groups (p  0.05). Finally, we found significant ECL in both groups with a significant difference between the two groups (p = 0.042). FLACS with bimanual technique and low-energy LDV Z8 is associated with a necessary initial learning curve. After the first adjustments in the surgical technique, this technology seems to be safe and effective with rapid visual recovery and it helps surgeons to standardize the crucial steps of cataract surgery.

  8. Investigating quality of life and self-stigma in Hong Kong children with specific learning disabilities.

    Science.gov (United States)

    Chan, Yi; Chan, Yim Yuk; Cheng, Sui Lam; Chow, Man Yin; Tsang, Yau Wai; Lee, Clara; Lin, Chung-Ying

    2017-09-01

    Children with specific learning disabilities (SpLD) are likely to develop self-stigma and have a poor quality of life (QoL) because of their poor academic performance. Although both self-stigma and poor QoL issues are likely to be found in low academic achievers without SpLD, children with SpLD have worse situation because their diagnosis of SpLD suggests that their learning struggles are biological and permanent. Specifically, students' perception of own capabilities may be affected more by the diagnosis of SpLD than their own actual performance. We examined the self-stigma and QoL of children with SpLD in Hong Kong, a region with an academics-focused culture. Children with SpLD (n=49,M age ±SD=9.55±1.21; SpLD group) and typically developing children (n=32,M age ±SD=9.81±1.40; TD group) completed a Kid-KINDL to measure QoL and a Modified Self-Stigma Scale to measure self-stigma. All parents completed a parallel Kid-KINDL to measure QoL of their children. Compared with the TD group, the SpLD group had a higher level of self-stigma (p=0.027) and lower QoL (child-reported Kid-KINDL: p=0.001; parent-reported Kid-KINDL: plearning process of children with SpLD may be designed to overcome self-stigma and to improve QoL. In addition, the program may involve parents of the children with SpLD or other people (e.g., the peer of the children with SpLD) for improving their understanding and perceptions of SpLD. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Do students with Down syndrome have a specific learning profile for reading?

    Science.gov (United States)

    Ratz, Christoph

    2013-12-01

    The present study assessed achieved reading stages of 190 school-aged children with Down syndrome (DS, age 6-20) in Bavaria, one of the most populated federal states in Germany. Teachers described the reading stages of their students in a questionnaire. The achieved stages of reading according to the developmental model of Frith are compared to a sample of 1419 students with intellectual disability (ID) regardless of etiology, but excluding DS; thereafter parallelized ID-groups were compared. Results of the questionnaire addressed to the students' teachers showed that 20.2% of the students with DS do not read at all, 7.6% read at a logographic stage, 49.4% at an alphabetic and 22.8% at an orthographic level. Alongside these findings among the whole sample, correlations are described concerning age, gender, IQ and sociocultural background. The students with DS are then compared to other students with ID with mixed etiologies. This comparison stresses the emphasis on the alphabetic level amongst students with DS. This emphasis also exists when DS and non-DS students are parallelized in groups of ID, thus showing that students with DS and severe ID are ahead in reading, but those with mild ID are behind. Knowledge about specific literacy attainment of students with DS is vital for planning instruction, for creating learning environments, and for formulating future fields of research. Especially students with DS need specific teaching which takes their impaired verbal short term memory into account, such as learning to read in syllables. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. Cognitive deficits are a matter of emotional context: inflexible strategy use mediates context-specific learning impairments in OCD.

    Science.gov (United States)

    Zetsche, Ulrike; Rief, Winfried; Westermann, Stefan; Exner, Cornelia

    2015-01-01

    The present study examines the interplay between cognitive deficits and emotional context in obsessive-compulsive disorder (OCD) and social phobia (SP). Specifically, this study examines whether the inflexible use of efficient learning strategies in an emotional context underlies impairments in probabilistic classification learning (PCL) in OCD, and whether PCL impairments are specific to OCD. Twenty-three participants with OCD, 30 participants with SP and 30 healthy controls completed a neutral and an OCD-specific PCL task. OCD participants failed to adopt efficient learning strategies and showed fewer beneficial strategy switches than controls only in an OCD-specific context, but not in a neutral context. Additionally, OCD participants did not show any explicit memory impairments. Number of beneficial strategy switches in the OCD-specific task mediated the difference in PCL performance between OCD and control participants. Individuals with SP were impaired in both PCL tasks. In contrast to neuropsychological models postulating general cognitive impairments in OCD, the present findings suggest that it is the interaction between cognition and emotion that is impaired in OCD. Specifically, activated disorder-specific fears may impair the flexible adoption of efficient learning strategies and compromise otherwise unimpaired PCL. Impairments in PCL are not specific to OCD.

  11. Fast patient-specific Monte Carlo brachytherapy dose calculations via the correlated sampling variance reduction technique

    Energy Technology Data Exchange (ETDEWEB)

    Sampson, Andrew; Le Yi; Williamson, Jeffrey F. [Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298 (United States)

    2012-02-15

    heterogeneous doses. On an AMD 1090T processor, computing times of 38 and 21 sec were required to achieve an average statistical uncertainty of 2% within the prostate (1 x 1 x 1 mm{sup 3}) and breast (0.67 x 0.67 x 0.8 mm{sup 3}) CTVs, respectively. Conclusions: CMC supports an additional average 38-60 fold improvement in average efficiency relative to conventional uncorrelated MC techniques, although some voxels experience no gain or even efficiency losses. However, for the two investigated case studies, the maximum variance within clinically significant structures was always reduced (on average by a factor of 6) in the therapeutic dose range generally. CMC takes only seconds to produce an accurate, high-resolution, low-uncertainly dose distribution for the low-energy PSB implants investigated in this study.

  12. A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

    Energy Technology Data Exchange (ETDEWEB)

    Xu Xiaoying; Ho, Shirley; Trac, Hy; Schneider, Jeff; Ntampaka, Michelle [McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States); Poczos, Barnabas [School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States)

    2013-08-01

    We investigate machine learning (ML) techniques for predicting the number of galaxies (N{sub gal}) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N{sub gal}. In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N{sub gal} by training our algorithms on the following six halo properties: number of particles, M{sub 200}, {sigma}{sub v}, v{sub max}, half-mass radius, and spin. For Millennium, our predicted N{sub gal} values have a mean-squared error (MSE) of {approx}0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to {approx}5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N{sub gal}. Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M{sub star}, low M{sub star}). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs.

  13. A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

    International Nuclear Information System (INIS)

    Xu Xiaoying; Ho, Shirley; Trac, Hy; Schneider, Jeff; Ntampaka, Michelle; Poczos, Barnabas

    2013-01-01

    We investigate machine learning (ML) techniques for predicting the number of galaxies (N gal ) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for analyses of large-scale structure. The ML techniques proposed here distinguish themselves from traditional halo occupation distribution (HOD) modeling as they do not assume a prescribed relationship between halo properties and N gal . In addition, our ML approaches are only dependent on parent halo properties (like HOD methods), which are advantageous over subhalo-based approaches as identifying subhalos correctly is difficult. We test two algorithms: support vector machines (SVM) and k-nearest-neighbor (kNN) regression. We take galaxies and halos from the Millennium simulation and predict N gal by training our algorithms on the following six halo properties: number of particles, M 200 , σ v , v max , half-mass radius, and spin. For Millennium, our predicted N gal values have a mean-squared error (MSE) of ∼0.16 for both SVM and kNN. Our predictions match the overall distribution of halos reasonably well and the galaxy correlation function at large scales to ∼5%-10%. In addition, we demonstrate a feature selection algorithm to isolate the halo parameters that are most predictive, a useful technique for understanding the mapping between halo properties and N gal . Lastly, we investigate these ML-based approaches in making mock catalogs for different galaxy subpopulations (e.g., blue, red, high M star , low M star ). Given its non-parametric nature as well as its powerful predictive and feature selection capabilities, ML offers an interesting alternative for creating mock catalogs

  14. Identifying tropical dry forests extent and succession via the use of machine learning techniques

    Science.gov (United States)

    Li, Wei; Cao, Sen; Campos-Vargas, Carlos; Sanchez-Azofeifa, Arturo

    2017-12-01

    Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages.

  15. Techniques of material-flow-specific residual waste treatment; Techniken der stoffstromspezifischen Restabfallbehandlung

    Energy Technology Data Exchange (ETDEWEB)

    Maak, D.; Collins, H.J. [Technische Univ. Braunschweig, Leichtweiss - Inst. fuer Wasserbau (Germany)

    1998-09-01

    The success achieved with large-scale plants for mechanical-biological residual waste treatment has led to a change of course in waste pretreatment. In view of the low emissions via the water and gas routes from landfilled wastes and the low costs of waste treatment some authorising authorities have meanwhile issued special licences pursuant to clause no. 2.4 of the Technical Code on Household Waste, thus enabling mechanical-biological residual waste treatment plants to continue operations beyond the year 2005. Beside offering a means of treatment and disposal, cost-effective mechanical-biological pretreatment also provides an opportunity for going over to material-flow-specific residual waste treatment. These process stages permit recirculating valuable materials and using other materials for energy production. They can be retrofitted on a modular basis in existing plants. If these advantages of the present innovative pretreatment methods are not used, then mechanical-biological pretreatment can still serve as a preparatory stage for thermal treatment. To date there has been no practical experience with this innovative method of residual waste treatment. However, industrial-scale trials have shown that each individual treatment stage is capable of being carried out successfully. [Deutsch] Die guten Erfolge im grosstechnischen Betrieb von Anlagen zur mechanisch-biologischen Restabfallbehandlung haben zu einer Kursaenderung bei der Vorbehandlung von Abfaellen gefuehrt. Geringe Emissionen der deponierten Abfaelle auf dem Gas- und Wasserpfad sowie geringe Kosten fuer die Behandlung der Abfaelle haben dazu gefuehrt, dass inzwischen bereits einige Genehmigungsbehoerden eine Ausnahmegenehmigung nach Nr. 2.4 der TA Siedlungsabfall erteilt haben und damit der Betrieb von mechanisch-biologischen Restabfallbehandlungsanlagen auch nach 2005 ermoeglicht wird. Neben der alleinigen Behandlung und Deponierung bietet die kostenguenstige Vorbehandlung mit mechanisch

  16. Does Structured Quizzing with Process Specific Feedback Lead to Learning Gains in an Active Learning Geoscience Classroom?

    Science.gov (United States)

    Palsole, S.; Serpa, L. F.

    2013-12-01

    There is a great realization that efficient teaching in the geosciences has the potential to have far reaching effects in outreach to decision and policy makers (Herbert, 2006; Manduca & Mogk, 2006). This research in turn informs educators that the geosciences by the virtue of their highly integrative nature play an important role in serving as an entry point into STEM disciplines and helping developing a new cadre of geoscientists, scientists and a general population with an understanding of science. Keeping these goals in mind we set to design introductory geoscience courses for non-majors and majors that move away from the traditional lecture models which don't necessarily contribute well to knowledge building and retention ((Handelsman et al., 2007; Hake, 1997) to a blended active learning classroom where basic concepts and didactic information is acquired online via webquests, lecturettes and virtual field trips and the face to face portions of the class are focused on problem solving exercises. The traditional way to ensure that students are prepared for the in-class activity is to have the students take a quiz online to demonstrate basic competency. In the process of redesign, we decided to leverage the technology to build quizzes that are highly structured and map to a process (formation of divergent boundaries for example) or sets of earth processes that we needed the students to know before in-class activities. The quizzes can be taken multiple times and provide process specific feedback, thus serving as a heuristic to the students to ensure they have acquired the necessary competency. The heuristic quizzes were developed and deployed over a year with the student data driving the redesign process to ensure synchronicity. Preliminary data analysis indicates a positive correlation between higher student scores on in-class application exercises and time spent on the process quizzes. An assessment of learning gains also indicate a higher degree of self

  17. 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.

  18. Numerical Capacities as Domain-Specific Predictors beyond Early Mathematics Learning: A Longitudinal Study

    Science.gov (United States)

    Reigosa-Crespo, Vivian; González-Alemañy, Eduardo; León, Teresa; Torres, Rosario; Mosquera, Raysil; Valdés-Sosa, Mitchell

    2013-01-01

    The first aim of the present study was to investigate whether numerical effects (Numerical Distance Effect, Counting Effect and Subitizing Effect) are domain-specific predictors of mathematics development at the end of elementary school by exploring whether they explain additional variance of later mathematics fluency after controlling for the effects of general cognitive skills, focused on nonnumerical aspects. The second aim was to address the same issues but applied to achievement in mathematics curriculum that requires solutions to fluency in calculation. These analyses assess whether the relationship found for fluency are generalized to mathematics content beyond fluency in calculation. As a third aim, the domain specificity of the numerical effects was examined by analyzing whether they contribute to the development of reading skills, such as decoding fluency and reading comprehension, after controlling for general cognitive skills and phonological processing. Basic numerical capacities were evaluated in children of 3rd and 4th grades (n=49). Mathematics and reading achievements were assessed in these children one year later. Results showed that the size of the Subitizing Effect was a significant domain-specific predictor of fluency in calculation and also in curricular mathematics achievement, but not in reading skills, assessed at the end of elementary school. Furthermore, the size of the Counting Effect also predicted fluency in calculation, although this association only approached significance. These findings contrast with proposals that the core numerical competencies measured by enumeration will bear little relationship to mathematics achievement. We conclude that basic numerical capacities constitute domain-specific predictors and that they are not exclusively “start-up” tools for the acquisition of Mathematics; but they continue modulating this learning at the end of elementary school. PMID:24255710

  19. Numerical capacities as domain-specific predictors beyond early mathematics learning: a longitudinal study.

    Science.gov (United States)

    Reigosa-Crespo, Vivian; González-Alemañy, Eduardo; León, Teresa; Torres, Rosario; Mosquera, Raysil; Valdés-Sosa, Mitchell

    2013-01-01

    The first aim of the present study was to investigate whether numerical effects (Numerical Distance Effect, Counting Effect and Subitizing Effect) are domain-specific predictors of mathematics development at the end of elementary school by exploring whether they explain additional variance of later mathematics fluency after controlling for the effects of general cognitive skills, focused on nonnumerical aspects. The second aim was to address the same issues but applied to achievement in mathematics curriculum that requires solutions to fluency in calculation. These analyses assess whether the relationship found for fluency are generalized to mathematics content beyond fluency in calculation. As a third aim, the domain specificity of the numerical effects was examined by analyzing whether they contribute to the development of reading skills, such as decoding fluency and reading comprehension, after controlling for general cognitive skills and phonological processing. Basic numerical capacities were evaluated in children of 3(rd) and 4(th) grades (n=49). Mathematics and reading achievements were assessed in these children one year later. Results showed that the size of the Subitizing Effect was a significant domain-specific predictor of fluency in calculation and also in curricular mathematics achievement, but not in reading skills, assessed at the end of elementary school. Furthermore, the size of the Counting Effect also predicted fluency in calculation, although this association only approached significance. These findings contrast with proposals that the core numerical competencies measured by enumeration will bear little relationship to mathematics achievement. We conclude that basic numerical capacities constitute domain-specific predictors and that they are not exclusively "start-up" tools for the acquisition of Mathematics; but they continue modulating this learning at the end of elementary school.

  20. Visual paired-associate learning: in search of material-specific effects in adult patients who have undergone temporal lobectomy.

    Science.gov (United States)

    Smith, Mary Lou; Bigel, Marla; Miller, Laurie A

    2011-02-01

    The mesial temporal lobes are important for learning arbitrary associations. It has previously been demonstrated that left mesial temporal structures are involved in learning word pairs, but it is not yet known whether comparable lesions in the right temporal lobe impair visually mediated associative learning. Patients who had undergone left (n=16) or right (n=18) temporal lobectomy for relief of intractable epilepsy and healthy controls (n=13) were administered two paired-associate learning tasks assessing their learning and memory of pairs of abstract designs or pairs of symbols in unique locations. Both patient groups had deficits in learning the designs, but only the right temporal group was impaired in recognition. For the symbol location task, differences were not found in learning, but again a recognition deficit was found for the right temporal group. The findings implicate the mesial temporal structures in relational learning. They support a material-specific effect for recognition but not for learning and recall of arbitrary visual and visual-spatial associative information. Copyright © 2010 Elsevier Inc. All rights reserved.

  1. Contingency learning is not affected by conflict experience: Evidence from a task conflict-free, item-specific Stroop paradigm.

    Science.gov (United States)

    Levin, Yulia; Tzelgov, Joseph

    2016-02-01

    A contingency learning account of the item-specific proportion congruent effect has been described as an associative stimulus-response learning process that has nothing to do with controlling the Stroop conflict. As supportive evidence, contingency learning has been demonstrated with response conflict-free stimuli, such as neutral words. However, what gives rise to response conflict and to Stroop interference in general is task conflict. The present study investigated whether task conflict can constitute a trigger or, alternatively, a booster to the contingency learning process. This was done by employing a "task conflict-free" condition (i.e., geometric shapes) and comparing it with a "task conflict" condition (i.e., neutral words). The results showed a significant contingency learning effect in both conditions, refuting the possibility that contingency learning is triggered by the presence of a task conflict. Contingency learning was also not enhanced by the task conflict experience, indicating its complete insensitivity to Stroop conflict(s). Thus, the results showed no evidence that performance optimization as a result of contingency learning is greater under conflict, implying that contingency learning is not recruited to assist the control system to overcome conflict. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. 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.

  3. 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 ...

  4. A specific implicit sequence learning deficit as an underlying cause of dyslexia? Investigating the role of attention in implicit learning tasks.

    Science.gov (United States)

    Staels, Eva; Van den Broeck, Wim

    2017-05-01

    Recently, a general implicit sequence learning deficit was proposed as an underlying cause of dyslexia. This new hypothesis was investigated in the present study by including a number of methodological improvements, for example, the inclusion of appropriate control conditions. The second goal of the study was to explore the role of attentional functioning in implicit and explicit learning tasks. In a 2 × 2 within-subjects design 4 tasks were administered in 30 dyslexic and 38 control children: an implicit and explicit serial reaction time (RT) task and an implicit and explicit contextual cueing task. Attentional functioning was also administered. The entire learning curves of all tasks were analyzed using latent growth curve modeling in order to compare performances between groups and to examine the role of attentional functioning on the learning curves. The amount of implicit learning was similar for both groups. However, the dyslexic group showed slower RTs throughout the entire task. This group difference reduced and became nonsignificant after controlling for attentional functioning. Both implicit learning tasks, but none of the explicit learning tasks, were significantly affected by attentional functioning. Dyslexic children do not suffer from a specific implicit sequence learning deficit. The slower RTs of the dyslexic children throughout the entire implicit sequence learning process are caused by their comorbid attention problems and overall slowness. A key finding of the present study is that, in contrast to what was assumed for a long time, implicit learning relies on attentional resources, perhaps even more than explicit learning does. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  5. Modern Languages and Specific Learning Difficulties (SpLD): Implications of Teaching Adult Learners with Dyslexia in Distance Learning

    Science.gov (United States)

    Gallardo, Matilde; Heiser, Sarah; Arias McLaughlin, Ximena

    2015-01-01

    In modern language (ML) distance learning programmes, teachers and students use online tools to facilitate, reinforce and support independent learning. This makes it essential for teachers to develop pedagogical expertise in using online communication tools to perform their role. Teachers frequently raise questions of how best to support the needs…

  6. Current Techniques of Teaching and Learning in Bariatric Surgical Procedures: A Systematic Review.

    Science.gov (United States)

    Kaijser, Mirjam; van Ramshorst, Gabrielle; van Wagensveld, Bart; Pierie, Jean-Pierre

    The gastric sleeve resection and gastric bypass are the 2 most commonly performed bariatric procedures. This article provides an overview of current teaching and learning methods of those techniques in resident and fellow training. A database search was performed on Pubmed, Embase, and the Education Resources Information Center (ERIC) to identify the methods used to provide training in bariatric surgery worldwide. After exclusion based on titles and abstracts, full texts of the selected articles were assessed. Included articles were reviewed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. In total, 2442 titles were identified and 14 full text articles met inclusion criteria. Four publications described an ex vivo training course, and 6 focused on at least 1 step of the gastric bypass procedure. Two randomized controlled trials (RCT) provided high-quality evidence on training aspects. Surgical coaching caused significant improvement of Bariatric Objective Structured Assessment of Technical Skills (BOSATS) scores (3.60 vs. 3.90, p = 0.017) and reduction of technical errors (18 vs. 10, p = 0.003). A preoperative warm-up increased global rating scales (GRS) scores on depth perception (p = 0.02), bimanual dexterity (p = 0.01), and efficiency of movements (p = 0.03). Stepwise education, surgical coaching, warming up, Internet-based knowledge modules, and ex vivo training courses are effective in relation to bariatric surgical training of residents and fellows, possibly shortening their learning curves. Copyright © 2018 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

  7. Region and task-specific activation of Arc in primary motor cortex of rats following motor skill learning.

    Science.gov (United States)

    Hosp, J A; Mann, S; Wegenast-Braun, B M; Calhoun, M E; Luft, A R

    2013-10-10

    Motor learning requires protein synthesis within the primary motor cortex (M1). Here, we show that the immediate early gene Arc/Arg3.1 is specifically induced in M1 by learning a motor skill. Arc mRNA was quantified using a fluorescent in situ hybridization assay in adult Long-Evans rats learning a skilled reaching task (SRT), in rats performing reaching-like forelimb movement without learning (ACT) and in rats that were trained in the operant but not the motor elements of the task (controls). Apart from M1, Arc expression was assessed within the rostral motor area (RMA), primary somatosensory cortex (S1), striatum (ST) and cerebellum. In SRT animals, Arc mRNA levels in M1 contralateral to the trained limb were 31% higher than ipsilateral (pmotor skill learning in rats. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  8. Sixth Grade Students' Content-Specific Competencies and Challenges in Learning the Seasons Through Modeling

    Science.gov (United States)

    Sung, Ji Young; Oh, Phil Seok

    2017-06-01

    Recent science education reform initiatives suggest that learning in science should be organized on the basis of scientists' actual practices including the development and use of models. In line with this, the current study adapted three types of modeling practices to teach two Korean 6th grade science classes the causes of the Earth's seasons. Specifically, the study aimed to identify the students' content-specific competencies and challenges based on fine-grained descriptions and analyses of two target groups' cases. Data included digital recordings of modeling-based science lessons in the two classes, the teacher's and students' artifacts, and interviews with the students. These multiple types of data were analyzed complementarily and qualitatively. It was revealed that the students had a competency in constructing models to generate the desired phenomenon (i.e., seasons). They had difficulty, however, in considering the tilt of the Earth's rotation axis as a cause of the seasons and in finding a proper way of representing the Sun's meridian altitude on a globe. But, when the students were helped and guided by the teacher and peers' interventions, they were able to revise their models in alignment with the scientific understanding of the seasons. Based on these findings, the teacher's pedagogical roles, which include using student competencies as resources, asking physical questions, and explicit guidance on experimentation skills, were recommended to support successful incorporations of modeling practices in the science classroom.

  9. We'll Make You a Better Teacher: Learning from Guitar Techniques

    Science.gov (United States)

    Greenbowe, Thomas J.

    2008-02-01

    It is worth noting that there are more resources and more uses of technology available world-wide to help individuals become better guitar players than there are resources available to help individuals become better science teachers. Providing resources and services to help individuals become effective chemistry teachers and improve their chemistry teaching and expand their range of techniques is a worthwhile endeavor. This commentary proposes that a new magazine should be developed and designed to complement and augment the Journal of Chemical Education , the Examinations Institute, the BCCEs, and programming at regional, national, and international meetings. We need to be making use of the expertise of chemical educators from around the world to convey the best practices of teaching chemistry. This magazine would feature topics directly relating to teaching chemistry in the classroom and it would include master teachers explaining and discussing chemistry education techniques. A Web site and perhaps a DVD would have digital movies of master chemistry teachers illustrating how they implement a specific technique with students. The Web site would serve as a repository for resources. It would serve as an alternative site for professional development.

  10. Machine learning techniques for medical diagnosis of diabetes using iris images.

    Science.gov (United States)

    Samant, Piyush; Agarwal, Ravinder

    2018-04-01

    Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for disease diagnosis is reliable and fastest growing field in biomedical. Proposed model is an attempt to evaluate diagnostic validity of an old complementary and alternative medicine technique, iridology for diagnosis of type-2 diabetes using soft computing methods. Investigation was performed over a close group of total 338 subjects (180 diabetic and 158 non-diabetic). Infra-red images of both the eyes were captured simultaneously. The region of interest from the iris image was cropped as zone corresponds to the position of pancreas organ according to the iridology chart. Statistical, texture and discrete wavelength transformation features were extracted from the region of interest. The results show best classification accuracy of 89.63% calculated from RF classifier. Maximum specificity and sensitivity were absorbed as 0.9687 and 0.988, respectively. Results have revealed the effectiveness and diagnostic significance of proposed model for non-invasive and automatic diabetes diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

  11. Novel-word learning deficits in Mandarin-speaking preschool children with specific language impairments.

    Science.gov (United States)

    Chen, Yuchun; Liu, Huei-Mei

    2014-01-01

    Children with SLI exhibit overall deficits in novel word learning compared to their age-matched peers. However, the manifestation of the word learning difficulty in SLI was not consistent across tasks and the factors affecting the learning performance were not yet determined. Our aim is to examine the extent of word learning difficulties in Mandarin-speaking preschool children with SLI, and to explore the potent influence of existing lexical knowledge on to the word learning process. Preschool children with SLI (n=37) and typical language development (n=33) were exposed to novel words for unfamiliar objects embedded in stories. Word learning tasks including the initial mapping and short-term repetitive learning were designed. Results revealed that Mandarin-speaking preschool children with SLI performed as well as their age-peers in the initial form-meaning mapping task. Their word learning difficulty was only evidently shown in the short-term repetitive learning task under a production demand, and their learning speed was slower than the control group. Children with SLI learned the novel words with a semantic head better in both the initial mapping and repetitive learning tasks. Moderate correlations between stand word learning performances and scores on standardized vocabulary were found after controlling for children's age and nonverbal IQ. The results suggested that the word learning difficulty in children with SLI occurred in the process of establishing a robust phonological representation at the beginning stage of word learning. Also, implicit compound knowledge is applied to aid word learning process for children with and without SLI. We also provide the empirical data to validate the relationship between preschool children's word learning performance and their existing receptive vocabulary ability. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. 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

  13. Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.

    Science.gov (United States)

    Meyers, Alysha R; Al-Tarawneh, Ibraheem S; Wurzelbacher, Steven J; Bushnell, P Timothy; Lampl, Michael P; Bell, Jennifer L; Bertke, Stephen J; Robins, David C; Tseng, Chih-Yu; Wei, Chia; Raudabaugh, Jill A; Schnorr, Teresa M

    2018-01-01

    This study leveraged a state workers' compensation claims database and machine learning techniques to target prevention efforts by injury causation and industry. Injury causation auto-coding methods were developed to code more than 1.2 million Ohio Bureau of Workers' Compensation claims for this study. Industry groups were ranked for soft-tissue musculoskeletal claims that may have been preventable with biomechanical ergonomic (ERGO) or slip/trip/fall (STF) interventions. On the basis of the average of claim count and rate ranks for more than 200 industry groups, Skilled Nursing Facilities (ERGO) and General Freight Trucking (STF) were the highest risk for lost-time claims (>7 days). This study created a third, major causation-specific U.S. occupational injury surveillance system. These findings are being used to focus prevention resources on specific occupational injury types in specific industry groups, especially in Ohio. Other state bureaus or insurers may use similar methods.

  14. Analysis of classical time-trial performance and technique-specific physiological determinants in elite female cross-country skiers

    Directory of Open Access Journals (Sweden)

    Øyvind Sandbakk

    2016-08-01

    Full Text Available The present study investigated the contribution of performance on uphill, flat, and downhill sections to overall performance in an international 10-km classical time-trial in elite female cross-country skiers, as well as the relationships between performance on snow and laboratory-measured physiological variables in the double poling (DP and diagonal (DIA techniques. Ten elite female cross-country skiers were continuously measured by a global positioning system device during an international 10-km cross-country skiing time-trial in the classical technique. One month prior to the race, all skiers performed a 5-min submaximal and 3-min self-paced performance test while roller skiing on a treadmill, both in the DP and DIA techniques. The time spent on uphill (r=0.98 and flat (r=0.91 sections of the race correlated most strongly with the overall 10-km performance (both p<0.05. Approximately 56% of the racing time was spent uphill, and stepwise multiple regression revealed that uphill time explained 95.5% of the variance in overall performance (p<0.001. Distance covered during the 3-min roller-skiing test and body-mass normalized peak oxygen uptake (VO2peak in both techniques showed the strongest correlations with overall time-trial performance (r=0.66-0.78, with DP capacity tending to have greatest impact on the flat and DIA capacity on uphill terrain (all p<0.05. Our present findings reveal that the time spent uphill most strongly determine classical time-trial performance, and that the major portion of the performance differences among elite female cross-country skiers can be explained by variations in technique-specific aerobic power.

  15. 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

  16. Linking actions and objects: Context-specific learning of novel weight priors.

    Science.gov (United States)

    Trewartha, Kevin M; Flanagan, J Randall

    2017-06-01

    Distinct explicit and implicit memory processes support weight predictions used when lifting objects and making perceptual judgments about weight, respectively. The first time that an object is encountered weight is predicted on the basis of learned associations, or priors, linking size and material to weight. A fundamental question is whether the brain maintains a single, global representation of priors, or multiple representations that can be updated in a context specific way. A second key question is whether the updating of priors, or the ability to scale lifting forces when repeatedly lifting unusually weighted objects requires focused attention. To investigate these questions we compared the adaptability of weight predictions used when lifting objects and judging their weights in different groups of participants who experienced size-weight inverted objects passively (with the objects placed on the hands) or actively (where participants lift the objects) under full or divided attention. To assess weight judgments we measured the size-weight illusion after every 20 trials of experience with the inverted objects both passively and actively. The attenuation of the illusion that arises when lifting inverted object was found to be context-specific such that the attenuation was larger when the mode of interaction with the inverted objects matched the method of assessment of the illusion. Dividing attention during interaction with the inverted objects had no effect on attenuation of the illusion, but did slow the rate at which lifting forces were scaled to the weight inverted objects. These findings suggest that the brain stores multiple representations of priors that are context specific, and that focused attention is important for scaling lifting forces, but not for updating weight predictions used when judging object weight. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Enhancing Peer Acceptance of Children with Learning Difficulties: Classroom Goal Orientation and Effects of a Storytelling Programme with Drama Techniques

    Science.gov (United States)

    Law, Yin-kum; Lam, Shui-fong; Law, Wilbert; Tam, Zoe W. Y.

    2017-01-01

    Peer acceptance is an important facilitator for the success of inclusive education. The aim of the current study is twofold: (1) to examine how classroom goal orientation is associated with children's acceptance of peers with learning difficulties; and (2) to evaluate the effectiveness of a storytelling programme with drama techniques on…

  18. Classification of Cytochrome P450 1A2 Inhibitors and Non-Inhibitors by Machine Learning Techniques

    DEFF Research Database (Denmark)

    Vasanthanathan, Poongavanam; Taboureau, Olivier; Oostenbrink, Chris

    2009-01-01

    of CYP1A2 inhibitors and non-inhibitors. Training and test sets consisted of about 400 and 7000 compounds, respectively. Various machine learning techniques, like binary QSAR, support vector machine (SVM), random forest, kappa nearest neighbors (kNN), and decision tree methods were used to develop...

  19. Children's Negotiations of Visualization Skills during a Design-Based Learning Experience Using Nondigital and Digital Techniques

    Science.gov (United States)

    Smith, Shaunna

    2018-01-01

    In the context of a 10-day summer camp makerspace experience that employed design-based learning (DBL) strategies, the purpose of this descriptive case study was to better understand the ways in which children use visualization skills to negotiate design as they move back and forth between the world of nondigital design techniques (i.e., drawing,…

  20. Comparison of Two Different Techniques of Cooperative Learning Approach: Undergraduates' Conceptual Understanding in the Context of Hormone Biochemistry

    Science.gov (United States)

    Mutlu, Ayfer

    2018-01-01

    The purpose of the research was to compare the effects of two different techniques of the cooperative learning approach, namely Team-Game Tournament and Jigsaw, on undergraduates' conceptual understanding in a Hormone Biochemistry course. Undergraduates were randomly assigned to Group 1 (N = 23) and Group 2 (N = 29). Instructions were accomplished…

  1. Effect of Ability Grouping in Reciprocal Teaching Technique of Collaborative Learning on Individual Achievements and Social Skills

    Science.gov (United States)

    Sumadi; Degeng, I Nyoman S.; Sulthon; Waras

    2017-01-01

    This research focused on effects of ability grouping in reciprocal teaching technique of collaborative learning on individual achievements dan social skills. The results research showed that (1) there are differences in individual achievement significantly between high group of homogeneous, middle group of homogeneous, low group of homogeneous,…

  2. Examining Mobile Learning Trends 2003-2008: A Categorical Meta-Trend Analysis Using Text Mining Techniques

    Science.gov (United States)

    Hung, Jui-Long; Zhang, Ke

    2012-01-01

    This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four…

  3. The Effectiveness of Using WhatsApp Messenger as One of Mobile Learning Techniques to Develop Students' Writing Skills

    Science.gov (United States)

    Fattah, Said Fathy El Said Abdul

    2015-01-01

    The present study was an attempt to determine the effectiveness of using a WhatsApp Messenger as one of mobile learning techniques to develop students' writing skills. Participants were 30 second year college students, English department from a private university in Saudi Arabia. The experimental group (N = 15) used WhatsApp technology to develop…

  4. Negotiating the Rules of Engagement: Exploring Perceptions of Dance Technique Learning through Bourdieu's Concept of "Doxa"

    Science.gov (United States)

    Rimmer, Rachel

    2017-01-01

    This article presents the findings from a focus group discussion conducted with first year undergraduate dance students in March 2015. The focus group concluded a cycle of action research during which the researcher explored the use of enquiry-based learning approaches to teaching dance technique in higher education. Grounded in transformative and…

  5. Conventional and Piecewise Growth Modeling Techniques: Applications and Implications for Investigating Head Start Children's Early Literacy Learning

    Science.gov (United States)

    Hindman, Annemarie H.; Cromley, Jennifer G.; Skibbe, Lori E.; Miller, Alison L.

    2011-01-01

    This article reviews the mechanics of conventional and piecewise growth models to demonstrate the unique affordances of each technique for examining the nature and predictors of children's early literacy learning during the transition from preschool through first grade. Using the nationally representative Family and Child Experiences Survey…

  6. Using the IGCRA (Individual, Group, Classroom Reflective Action) Technique to Enhance Teaching and Learning in Large Accountancy Classes

    Science.gov (United States)

    Poyatos Matas, Cristina; Ng, Chew; Muurlink, Olav

    2011-01-01

    First year accounting has generally been perceived as one of the more challenging first year business courses for university students. Various Classroom Assessment Techniques (CATs) have been proposed to attempt to enrich and enhance student learning, with these studies generally positioning students as learners alone. This paper uses an…

  7. Is There a Relationship between the Usage of Active and Collaborative Learning Techniques and International Students' Study Anxiety?

    Science.gov (United States)

    Khoshlessan, Rezvan

    2013-01-01

    This study was designed to explore the relationships between the international students' perception of professors' instructional practices (the usage of active and collaborative learning techniques in class) and the international students' study anxiety. The dominant goal of this research was to investigate whether the professors' usage of active…

  8. A screening on Specific Learning Disorders in an Italian speaking high genetic homogeneity area.

    Science.gov (United States)

    Cappa, Claudia; Giulivi, Sara; Schilirò, Antonino; Bastiani, Luca; Muzio, Carlo; Meloni, Fabrizio

    2015-01-01

    The aim of the present research is to investigate the prevalence of Specific Learning Disorders (SLD) in Ogliastra, an area of the island of Sardinia, Italy. Having experienced centuries of isolation, Ogliastra has become a high genetic homogeneity area, and is considered particularly interesting for studies on different kinds of pathologies. Here we are going to describe the results of a screening carried out throughout 2 consecutive years in 49 second grade classes (24 considered in the first year and 25 in the second year of the study) of the Ogliastra region. A total of 610 pupils (average age 7.54 years; 293 female, 317 male) corresponding to 68.69% of all pupils who were attending second grade in the area, took part in the study. The tool used for the screening was "RSR-DSA. Questionnaire for the detection of learning difficulties and disorders", which allowed the identification of 83 subjects at risk (13.61% of the whole sample involved in the study). These subjects took part in an enhancement training program of about 6 months. After the program, pupils underwent assessment for reading, writing and calculation abilities, as well as cognitive assessment. According to the results of the assessment, the prevalence of SLDs is 6.06%. For what concerns dyslexia, 4.75% of the total sample manifested this disorder either in isolation or in comorbidity with other disorders. According to the first national epidemiological investigation carried out in Italy, the prevalence of dyslexia is 3.1-3.2%, which is lower than the prevalence obtained in the present study. Given the genetic basis of SLDs, this result, together with the presence of several cases of SLD in isolation (17.14%) and with a 3:1 ratio of males to females diagnosed with a SLD, was to be expected in a sample coming from a high genetic homogeneity area. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Quality of life of parents of children with newly diagnosed specific learning disability

    Directory of Open Access Journals (Sweden)

    Karande S

    2009-01-01

    Full Text Available Background: Poor school performance in children causes significant stress to parents. Aims: To analyze the quality of life (QOL of parents having a child with newly diagnosed specific learning disability (SpLD and to evaluate the impact of clinical and socio-demographic characteristics on their QOL. Design: Cross-sectional questionnaire-based study. Setting: Learning disability clinic in tertiary care hospital. Materials and Methods: From June 2006 to February 2007, 150 parents (either mother or father of children consecutively diagnosed as having SpLD were enrolled. Parent′s QOL was measured by the WHOQOL-100 instrument which is a generic instrument containing 25 facets of QOL organized in six domains. Statistical Analysis Used: Independent samples t-test, one-way analysis of variance, and multiple regression analysis were carried out for statistical significance. Results: Mean age of parents was 42.6 years (SD 5.5; mothers to fathers ratio 1.3:1; and 19 (12.7% were currently ill. Only four WHOQOL-100 domains (psychological > social relationships > environment > spiritual and five WHOQOL-100 facets (leisur > pfeel > energy > esteem > sex contributed significantly to their "overall" QOL. Female gender, being currently ill, being in paid work, and having a male child were characteristics that independently predicted a poor domain/facet QOL score. Conclusions: The present study has identified domains and facets that need to be addressed by counselors for improving overall QOL of these parents. Initiating these measures would also improve the home environment and help in the rehabilitation of children with SpLD.

  10. The Effect of Semantic Mapping as a Vocabulary Instruction Technique on EFL Learners with Different Perceptual Learning Styles

    Directory of Open Access Journals (Sweden)

    Esmaeel Abdollahzadeh

    2009-05-01

    Full Text Available Traditional and modern vocabulary instruction techniques have been introduced in the past few decades to improve the learners’ performance in reading comprehension. Semantic mapping, which entails drawing learners’ attention to the interrelationships among lexical items through graphic organizers, is claimed to enhance vocabulary learning significantly. However, whether this technique suits all types of learners has not been adequately investigated. This study examines the effectiveness of employing semantic mapping versus traditional approaches in vocabulary instruction to EFL learners with different perceptual modalities. A modified version of Reid’s (1987 perceptual learning style questionnaire was used to determine the learners’ modality types. The results indicate that semantic mapping in comparison to the traditional approaches significantly enhances vocabulary learning of EFL learners. However, although visual learners slightly outperformed other types of learners on the post-test, no significant differences were observed among intermediate learners with different perceptual modalities employing semantic mapping for vocabulary practice.

  11. Physical injury assessment of male versus female chiropractic students when learning and performing various adjustive techniques: a preliminary investigative study

    Directory of Open Access Journals (Sweden)

    Huber Laura L

    2006-08-01

    Full Text Available Abstract Background Reports of musculoskeletal injuries that some chiropractic students experienced while in the role of adjustor became increasingly evident and developed into the basis of this study. The main objective of this study was to survey a select student population and identify, by gender, the specific types of musculoskeletal injuries they experienced when learning adjustive techniques in the classroom, and performing them in the clinical setting. Methods A survey was developed to record musculoskeletal injuries that students reported to have sustained while practicing chiropractic adjustment set-ups and while delivering adjustments. The survey was modeled from similar instruments used in the university's clinic as well as those used in professional practice. Stratified sampling was used to obtain participants for the study. Data reported the anatomical areas of injury, adjustive technique utilized, the type of injury received, and the recovery time from sustained injuries. The survey also inquired as to the type and area of any past physical injuries as well as the mechanism(s of injury. Results Data obtained from the study identified injuries of the shoulder, wrist, elbow, neck, low back, and mid-back. The low back was the most common injury site reported by females, and the neck was the most common site reported by males. The reported wrist injuries in both genders were 1% male complaints and 17% female complaints. A total of 13% of female respondents reported shoulder injuries, whereas less than 1% of male respondents indicated similar complaints. Conclusion The data collected from the project indicated that obtaining further information on the subject would be worthwhile, and could provide an integral step toward developing methods of behavior modification in an attempt to reduce and/or prevent the incidence of musculoskeletal injuries.

  12. Comparison of executive functions in students with and without specific learning disability with the characteristic reading and writing

    Directory of Open Access Journals (Sweden)

    Saba Hasanvandi

    2017-03-01

    Full Text Available Background: The aim of present study was to investigate executive functions included of working memory, organization-planning and reasoning in the children with and without specific learning disability with the characteristic reading and writing. Materials and methods: The design of this research was Ex-Post Facto design. Statistical population was all male students of third grade primary schools in Tehran which were referred to education institution with diagnosis special learning disorders in educational centers. The sample included of 90 students chosen and assigned into 3 groups of 30 students, included of: children who had specific learning disability with characteristic reading, children who had specific learning disability with characteristic writing, normal children were selected by systematic randomized sampling and 3 groups were compared. The data instruments were: Wechsler’ subtests of similarities and digit differences, Andre Ray test, in formal (unofficial reading and dictation test. The obtained data were analyzed with ANOVA. Results: The results showed that there was difference between the group of normal children and other group in executive functions including working memory, organization-planning and reasoning (P<0.05. Also there was difference between two children groups with specific learning disability with  characteristic reading and writing in working memory and reasoning, whereas for organization-planning parameter there were not seen any differences between these two groups (P<0.05. Conclusion: Regarding to obtained results, it is recommended to adoption some ways for improvements of working memory, organization-planning and reasoning

  13. The learning curve of the three-port two-instrument complete thoracoscopic lobectomy for lung cancer—A feasible technique worthy of popularization

    Directory of Open Access Journals (Sweden)

    Yu-Jen Cheng

    2015-07-01

    Conclusion: Three-port complete thoracoscopic lobectomy with the two-instrument technique is feasible for lung cancer treatment. The length of the learning curve consisted of 28 cases. This TPTI technique should be popularized.

  14. A Preliminary Investigation of General and Technique-specific Assessments for the Evaluation of Laparoscopic Technical Skills.

    Science.gov (United States)

    Vergis, Ashley; Steigerwald, Sarah

    2017-10-07

    Background  Both general and technique-specific assessments of technical skill have been validated in surgical education. The purpose of this study was to assess the correlation between the objective structured assessment of technical skills (OSATS) and the global operative assessment of laparoscopic skills (GOALS) rating scales using a high-fidelity porcine laparoscopic cholecystectomy model. Methods Post-graduate year-one general surgery and urology residents (n=14) performed a live laparoscopic porcine cholecystectomy. Trained surgeons rated their performance using OSATS and GOALS assessment scales. Results Pearson's correlation coefficient between OSATS and GOALS was 0.96 for overall scores. It ranged from 0.78 - 0.89 for domains that overlapped between the two scales. Conclusion There is a very high correlation between OSATS and GOALS. This implies that they likely measure similar constructs and that either may be used for summative-type assessments of trainee skill. However, further investigation is needed to determine if technique-specific assessments may provide more useful feedback in formative evaluation.

  15. Shoulder strengthening exercises adapted to specific shoulder pathologies can be selected using new simulation techniques: a pilot study.

    Science.gov (United States)

    Charbonnier, Caecilia; Lädermann, Alexandre; Kevelham, Bart; Chagué, Sylvain; Hoffmeyer, Pierre; Holzer, Nicolas

    2018-02-01

    Shoulder strength training exercises represent a major component of rehabilitation protocols designed for conservative or postsurgical management of shoulder pathologies. Numerous methods are described for exercising each shoulder muscle or muscle group. Limited information is available to assess potential deleterious effects of individual methods with respect to specific shoulder pathologies. Thus, the goal of this pilot study was to use a patient-specific 3D measurement technique coupling medical imaging and optical motion capture for evaluation of a set of shoulder strength training exercises regarding glenohumeral, labral and subacromial compression, as well as elongation of the rotator cuff muscles. One volunteer underwent magnetic resonance imaging (MRI) and motion capture of the shoulder. Motion data from the volunteer were recorded during three passive rehabilitation exercises and twenty-nine strengthening exercises targeting eleven of the most frequently trained shoulder muscles or muscle groups and using four different techniques when available. For each exercise, glenohumeral and labral compression, subacromial space height and rotator cuff muscles elongation were measured on the entire range of motion. Significant differences in glenohumeral, subacromial and labral compressions were observed between sets of exercises targeting individual shoulder muscles. Muscle lengths computed by simulation compared to MRI measurements showed differences of 0-5%. This study represents the first screening of shoulder strengthening exercises to identify potential deleterious effects on the shoulder joint. Motion capture combined with medical imaging allows for reliable assessment of glenohumeral, labral and subacromial compression, as well as muscle-tendon elongation during shoulder strength training exercises.

  16. A comparison of machine learning techniques for detection of drug target articles.

    Science.gov (United States)

    Danger, Roxana; Segura-Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

    2010-12-01

    Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure. Copyright © 2010 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    Lee, Hanbong

    2016-01-01

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

  18. Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

    KAUST Repository

    Fernandes, José Antonio

    2015-01-01

    The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.

  19. Combining machine learning and matching techniques to improve causal inference in program evaluation.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Program evaluations often utilize various matching approaches to emulate the randomization process for group assignment in experimental studies. Typically, the matching strategy is implemented, and then covariate balance is assessed before estimating treatment effects. This paper introduces a novel analytic framework utilizing a machine learning algorithm called optimal discriminant analysis (ODA) for assessing covariate balance and estimating treatment effects, once the matching strategy has been implemented. This framework holds several key advantages over the conventional approach: application to any variable metric and number of groups; insensitivity to skewed data or outliers; and use of accuracy measures applicable to all prognostic analyses. Moreover, ODA accepts analytic weights, thereby extending the methodology to any study design where weights are used for covariate adjustment or more precise (differential) outcome measurement. One-to-one matching on the propensity score was used as the matching strategy. Covariate balance was assessed using standardized difference in means (conventional approach) and measures of classification accuracy (ODA). Treatment effects were estimated using ordinary least squares regression and ODA. Using empirical data, ODA produced results highly consistent with those obtained via the conventional methodology for assessing covariate balance and estimating treatment effects. When ODA is combined with matching techniques within a treatment effects framework, the results are consistent with conventional approaches. However, given that it provides additional dimensions and robustness to the analysis versus what can currently be achieved using conventional approaches, ODA offers an appealing alternative. © 2016 John Wiley & Sons, Ltd.

  20. Learning-based computing techniques in geoid modeling for precise height transformation

    Science.gov (United States)

    Erol, B.; Erol, S.

    2013-03-01

    Precise determination of local geoid is of particular importance for establishing height control in geodetic GNSS applications, since the classical leveling technique is too laborious. A geoid model can be accurately obtained employing properly distributed benchmarks having GNSS and leveling observations using an appropriate computing algorithm. Besides the classical multivariable polynomial regression equations (MPRE), this study attempts an evaluation of learning based computing algorithms: artificial neural networks (ANNs), adaptive network-based fuzzy inference system (ANFIS) and especially the wavelet neural networks (WNNs) approach in geoid surface approximation. These algorithms were developed parallel to advances in computer technologies and recently have been used for solving complex nonlinear problems of many applications. However, they are rather new in dealing with precise modeling problem of the Earth gravity field. In the scope of the study, these methods were applied to Istanbul GPS Triangulation Network data. The performances of the methods were assessed considering the validation results of the geoid models at the observation points. In conclusion the ANFIS and WNN revealed higher prediction accuracies compared to ANN and MPRE methods. Beside the prediction capabilities, these methods were also compared and discussed from the practical point of view in conclusions.