WorldWideScience

Sample records for non-traditional machining techniques

  1. Developing an efficient decision support system for non-traditional machine selection: an application of MOORA and MOOSRA

    Directory of Open Access Journals (Sweden)

    Asis Sarkar

    2015-01-01

    Full Text Available The purpose of this paper is to find out an efficient decision support method for non-traditional machine selection. It seeks to analyze potential non-traditional machine selection attributes with a relatively new MCDM approach of MOORA and MOOSRA method. The use of MOORA and MOOSRA method has been adopted to tackle subjective evaluation of information collected from an expert group. An example case study is shown here for better understanding of the said selection module which can be effectively applied to any other decision-making scenario. The method is not only computationally very simple, easily comprehensible, and robust, but also believed to have numerous subjective attributes. The rankings are expected to provide good guidance to the managers of an organization to select a feasible non-traditional machine. It shall also provide a good insight for the non-traditional machine manufacturer who might encourage research work concerning non-traditional machine selection.

  2. Application of PROMETHEE-GAIA method for non-traditional machining processes selection

    Directory of Open Access Journals (Sweden)

    Prasad Karande

    2012-10-01

    Full Text Available With ever increasing demand for manufactured products of hard alloys and metals with high surface finish and complex shape geometry, more interest is now being paid to non-traditional machining (NTM processes, where energy in its direct form is used to remove material from workpiece surface. Compared to conventional machining processes, NTM processes possess almost unlimited capabilities and there is a strong believe that use of NTM processes would go on increasing in diverse range of applications. Presence of a large number of NTM processes along with complex characteristics and capabilities, and lack of experts in NTM process selection domain compel for development of a structured approach for NTM process selection for a given machining application. Past researchers have already attempted to solve NTM process selection problems using various complex mathematical approaches which often require a profound knowledge in mathematics/artificial intelligence from the part of process engineers. In this paper, four NTM process selection problems are solved using an integrated PROMETHEE (preference ranking organization method for enrichment evaluation and GAIA (geometrical analysis for interactive aid method which would act as a visual decision aid to the process engineers. The observed results are quite satisfactory and exactly match with the expected solutions.

  3. Airfoil shape optimization using non-traditional optimization technique and its validation

    Directory of Open Access Journals (Sweden)

    R. Mukesh

    2014-07-01

    Full Text Available Computational fluid dynamics (CFD is one of the computer-based solution methods which is more widely employed in aerospace engineering. The computational power and time required to carry out the analysis increase as the fidelity of the analysis increases. Aerodynamic shape optimization has become a vital part of aircraft design in the recent years. Generally if we want to optimize an airfoil we have to describe the airfoil and for that, we need to have at least hundred points of x and y co-ordinates. It is really difficult to optimize airfoils with this large number of co-ordinates. Nowadays many different schemes of parameter sets are used to describe general airfoil such as B-spline, and PARSEC. The main goal of these parameterization schemes is to reduce the number of needed parameters as few as possible while controlling the important aerodynamic features effectively. Here the work has been done on the PARSEC geometry representation method. The objective of this work is to introduce the knowledge of describing general airfoil using twelve parameters by representing its shape as a polynomial function. And also we have introduced the concept of Genetic Algorithm to optimize the aerodynamic characteristics of a general airfoil for specific conditions. A MATLAB program has been developed to implement PARSEC, Panel Technique, and Genetic Algorithm. This program has been tested for a standard NACA 2411 airfoil and optimized to improve its coefficient of lift. Pressure distribution and co-efficient of lift for airfoil geometries have been calculated using the Panel method. The optimized airfoil has improved co-efficient of lift compared to the original one. The optimized airfoil is validated using wind tunnel data.

  4. Non-Traditional Wraps

    Science.gov (United States)

    Owens, Buffy

    2009-01-01

    This article presents a recipe for non-traditional wraps. In this article, the author describes how adults and children can help with the recipe and the skills involved with this recipe. The bigger role that children can play in the making of the item the more they are apt to try new things and appreciate the texture and taste.

  5. Non-traditional inheritance

    International Nuclear Information System (INIS)

    Hall, J.G.

    1992-01-01

    In the last few years, several non-traditional forms of inheritance have been recognized. These include mosaicism, cytoplasmic inheritance, uniparental disomy, imprinting, amplification/anticipation, and somatic recombination. Genomic imprinting (GI) is the dependence of the phenotype on the sex of the transmitting parent. GI in humans seems to involve growth, behaviour, and survival in utero. The detailed mechanism of genomic imprinting is not known, but it seems that some process is involved in turning a gene off; this probably involves two genes, one of which produces a product that turns a gene off, and the gene that is itself turned off. The process of imprinting (turning off) may be associated with methylation. Erasure of imprinting can occur, and seems to be associated with meiosis. 10 refs

  6. Machine safety: proper safeguarding techniques.

    Science.gov (United States)

    Martin, K J

    1992-06-01

    1. OSHA mandates certain safeguarding of machinery to prevent accidents and protect machine operators. OSHA specifies moving parts that must be guarded and sets criteria for the guards. 2. A 1989 OSHA standard for lockout/tagout requires locking the energy source during maintenance, periodically inspecting for power transmission, and training maintenance workers. 3. In an amputation emergency, first aid for cardiopulmonary resuscitation, shock, and bleeding are the first considerations. The amputated part should be wrapped in moist gauze, placed in a sealed plastic bag, and placed in a container of 50% water and 50% ice for transport. 4. The role of the occupational health nurse in machine safety is to conduct worksite analyses to identify proper safeguarding and to communicate deficiencies to appropriate personnel; to train workers in safe work practices and observe compliance in the use of machine guards; to provide care to workers injured by machines; and to reinforce safe work practices among machine operators.

  7. 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...

  8. 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...

  9. 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

  10. 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.

  11. 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....

  12. 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)

  13. 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.

  14. 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

  15. 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

  16. 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...

  17. 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.

  18. 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...

  19. 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...

  20. 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....

  1. 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.

  2. 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...

  3. Machine monitoring via current signature analysis techniques

    International Nuclear Information System (INIS)

    Smith, S.F.; Castleberry, K.N.; Nowlin, C.H.

    1992-01-01

    A significant need in the effort to provide increased production quality is to provide improved plant equipment monitoring capabilities. Unfortunately, in today's tight economy, even such monitoring instrumentation must be implemented in a recognizably cost effective manner. By analyzing the electric current drawn by motors, actuator, and other line-powered industrial equipment, significant insights into the operations of the movers, driven equipment, and even the power source can be obtained. The generic term 'current signature analysis' (CSA) has been coined to describe several techniques for extracting useful equipment or process monitoring information from the electrical power feed system. A patented method developed at Oak Ridge National Laboratory is described which recognizes the presence of line-current modulation produced by motors and actuators driving varying loads. The in-situ application of applicable linear demodulation techniques to the analysis of numerous motor-driven systems is also discussed. The use of high-quality amplitude and angle-demodulation circuitry has permitted remote status monitoring of several types of medium and high-power gas compressors in (US DOE facilities) driven by 3-phase induction motors rated from 100 to 3,500 hp, both with and without intervening speed increasers. Flow characteristics of the compressors, including various forms of abnormal behavior such as surging and rotating stall, produce at the output of the specialized detectors specific time and frequency signatures which can be easily identified for monitoring, control, and fault-prevention purposes. The resultant data are similar in form to information obtained via standard vibration-sensing techniques and can be analyzed using essentially identical methods. In addition, other machinery such as refrigeration compressors, brine pumps, vacuum pumps, fans, and electric motors have been characterized

  4. 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.

  5. Technique for Increasing Accuracy of Positioning System of Machine Tools

    Directory of Open Access Journals (Sweden)

    Sh. Ji

    2014-01-01

    Full Text Available The aim of research is to improve the accuracy of positioning and processing system using a technique for optimization of pressure diagrams of guides in machine tools. The machining quality is directly related to its accuracy, which characterizes an impact degree of various errors of machines. The accuracy of the positioning system is one of the most significant machining characteristics, which allow accuracy evaluation of processed parts.The literature describes that the working area of the machine layout is rather informative to characterize the effect of the positioning system on the macro-geometry of the part surfaces to be processed. To enhance the static accuracy of the studied machine, in principle, two groups of measures are possible. One of them points toward a decrease of the cutting force component, which overturns the slider moments. Another group of measures is related to the changing sizes of the guide facets, which may lead to their profile change.The study was based on mathematical modeling and optimization of the cutting zone coordinates. And we find the formula to determine the surface pressure of the guides. The selected parameters of optimization are vectors of the cutting force and values of slides and guides. Obtained results show that a technique for optimization of coordinates in the cutting zone was necessary to increase a processing accuracy.The research has established that to define the optimal coordinates of the cutting zone we have to change the sizes of slides, value and coordinates of applied forces, reaching the pressure equalization and improving the accuracy of positioning system of machine tools. In different points of the workspace a vector of forces is applied, pressure diagrams are found, which take into account the changes in the parameters of positioning system, and the pressure diagram equalization to provide the most accuracy of machine tools is achieved.

  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. Adapting Virtual Machine Techniques for Seamless Aspect Support

    NARCIS (Netherlands)

    Bockisch, Christoph; Arnold, Matthew; Dinkelaker, Tom; Mezini, Mira

    2006-01-01

    Current approaches to compiling aspect-oriented programs are inefficient. This inefficiency has negative effects on the productivity of the development process and is especially prohibitive for dynamic aspect deployment. In this work, we present how well-known virtual machine techniques can be used

  8. Memory Based Machine Intelligence Techniques in VLSI hardware

    OpenAIRE

    James, Alex Pappachen

    2012-01-01

    We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high ...

  9. 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.

  10. 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.

  11. Affordable non-traditional source data mining for context assessment to improve distributed fusion system robustness

    Science.gov (United States)

    Bowman, Christopher; Haith, Gary; Steinberg, Alan; Morefield, Charles; Morefield, Michael

    2013-05-01

    This paper describes methods to affordably improve the robustness of distributed fusion systems by opportunistically leveraging non-traditional data sources. Adaptive methods help find relevant data, create models, and characterize the model quality. These methods also can measure the conformity of this non-traditional data with fusion system products including situation modeling and mission impact prediction. Non-traditional data can improve the quantity, quality, availability, timeliness, and diversity of the baseline fusion system sources and therefore can improve prediction and estimation accuracy and robustness at all levels of fusion. Techniques are described that automatically learn to characterize and search non-traditional contextual data to enable operators integrate the data with the high-level fusion systems and ontologies. These techniques apply the extension of the Data Fusion & Resource Management Dual Node Network (DNN) technical architecture at Level 4. The DNN architecture supports effectively assessment and management of the expanded portfolio of data sources, entities of interest, models, and algorithms including data pattern discovery and context conformity. Affordable model-driven and data-driven data mining methods to discover unknown models from non-traditional and `big data' sources are used to automatically learn entity behaviors and correlations with fusion products, [14 and 15]. This paper describes our context assessment software development, and the demonstration of context assessment of non-traditional data to compare to an intelligence surveillance and reconnaissance fusion product based upon an IED POIs workflow.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. Learning How to Learn: Implications for Non Traditional Adult Students

    Science.gov (United States)

    Tovar, Lynn A.

    2008-01-01

    In this article, learning how to learn for non traditional adult students is discussed with a focus on police officers and firefighters. Learning how to learn is particularly relevant for all returning non-traditional adults; however in the era of terrorism it is critical for the public safety officers returning to college after years of absence…

  17. Development of an evaluation technique for human-machine interface

    International Nuclear Information System (INIS)

    Min, Dae Hwan; Koo, Sang Hui; Ahn, Won Yeong; Ryu, Yeong Shin

    1997-07-01

    The purpose of this study is two-fold : firstly to establish an evaluation technique for HMI(Human Machine Interface) in NPPs(Nuclear Power Plants) and secondly to develop an architecture of a support system which can be used for the evaluation of HMI. In order to establish an evaluation technique, this study conducted literature review on basic theories of cognitive science studies and summarized the cognitive characteristics of humans. This study also surveyed evaluation techniques of HMI in general, and reviewed studies on the evaluation of HMI in NPPs. On the basis of this survey, the study established a procedure for the evaluation of HMI in NPPs in Korea and laid a foundation for empirical verification

  18. Development of an evaluation technique for human-machine interface

    Energy Technology Data Exchange (ETDEWEB)

    Min, Dae Hwan; Koo, Sang Hui; Ahn, Won Yeong; Ryu, Yeong Shin [Korea Univ., Seoul (Korea, Republic of)

    1997-07-15

    The purpose of this study is two-fold : firstly to establish an evaluation technique for HMI(Human Machine Interface) in NPPs(Nuclear Power Plants) and secondly to develop an architecture of a support system which can be used for the evaluation of HMI. In order to establish an evaluation technique, this study conducted literature review on basic theories of cognitive science studies and summarized the cognitive characteristics of humans. This study also surveyed evaluation techniques of HMI in general, and reviewed studies on the evaluation of HMI in NPPs. On the basis of this survey, the study established a procedure for the evaluation of HMI in NPPs in Korea and laid a foundation for empirical verification.

  19. 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 ...

  20. 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…

  1. 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...

  2. 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.

  3. Adoption of agricultural innovations through non-traditional financial ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Adoption of agricultural innovations through non-traditional financial services ... donors, banks, and financial institutions to explore new kinds of financial services to ... enterprises, and others in the production process to connect with markets.

  4. Practice Location Characteristics of Non-Traditional Dental Practices.

    Science.gov (United States)

    Solomon, Eric S; Jones, Daniel L

    2016-04-01

    Current and future dental school graduates are increasingly likely to choose a non-traditional dental practice-a group practice managed by a dental service organization or a corporate practice with employed dentists-for their initial practice experience. In addition, the growth of non-traditional practices, which are located primarily in major urban areas, could accelerate the movement of dentists to those areas and contribute to geographic disparities in the distribution of dental services. To help the profession understand the implications of these developments, the aim of this study was to compare the location characteristics of non-traditional practices and traditional dental practices. After identifying non-traditional practices across the United States, the authors located those practices and traditional dental practices geographically by zip code. Non-traditional dental practices were found to represent about 3.1% of all dental practices, but they had a greater impact on the marketplace with almost twice the average number of staff and annual revenue. Virtually all non-traditional dental practices were located in zip codes that also had a traditional dental practice. Zip codes with non-traditional practices had significant differences from zip codes with only a traditional dental practice: the populations in areas with non-traditional practices had higher income levels and higher education and were slightly younger and proportionally more Hispanic; those practices also had a much higher likelihood of being located in a major metropolitan area. Dental educators and leaders need to understand the impact of these trends in the practice environment in order to both prepare graduates for practice and make decisions about planning for the workforce of the future.

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. Using support vector machines in the multivariate state estimation technique

    International Nuclear Information System (INIS)

    Zavaljevski, N.; Gross, K.C.

    1999-01-01

    One approach to validate nuclear power plant (NPP) signals makes use of pattern recognition techniques. This approach often assumes that there is a set of signal prototypes that are continuously compared with the actual sensor signals. These signal prototypes are often computed based on empirical models with little or no knowledge about physical processes. A common problem of all data-based models is their limited ability to make predictions on the basis of available training data. Another problem is related to suboptimal training algorithms. Both of these potential shortcomings with conventional approaches to signal validation and sensor operability validation are successfully resolved by adopting a recently proposed learning paradigm called the support vector machine (SVM). The work presented here is a novel application of SVM for data-based modeling of system state variables in an NPP, integrated with a nonlinear, nonparametric technique called the multivariate state estimation technique (MSET), an algorithm developed at Argonne National Laboratory for a wide range of nuclear plant applications

  10. Machine Learning Techniques for Arterial Pressure Waveform Analysis

    Directory of Open Access Journals (Sweden)

    João Cardoso

    2013-05-01

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

  11. Application of Fuzzy TOPSIS for evaluating machining techniques using sustainability metrics

    Science.gov (United States)

    Digalwar, Abhijeet K.

    2018-04-01

    Sustainable processes and techniques are getting increased attention over the last few decades due to rising concerns over the environment, improved focus on productivity and stringency in environmental as well as occupational health and safety norms. The present work analyzes the research on sustainable machining techniques and identifies techniques and parameters on which sustainability of a process is evaluated. Based on the analysis these parameters are then adopted as criteria’s to evaluate different sustainable machining techniques such as Cryogenic Machining, Dry Machining, Minimum Quantity Lubrication (MQL) and High Pressure Jet Assisted Machining (HPJAM) using a fuzzy TOPSIS framework. In order to facilitate easy arithmetic, the linguistic variables represented by fuzzy numbers are transformed into crisp numbers based on graded mean representation. Cryogenic machining was found to be the best alternative sustainable technique as per the fuzzy TOPSIS framework adopted. The paper provides a method to deal with multi criteria decision making problems in a complex and linguistic environment.

  12. 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.

  13. 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%.

  14. A critical survey of live virtual machine migration techniques

    Directory of Open Access Journals (Sweden)

    Anita Choudhary

    2017-11-01

    Full Text Available Abstract Virtualization techniques effectively handle the growing demand for computing, storage, and communication resources in large-scale Cloud Data Centers (CDC. It helps to achieve different resource management objectives like load balancing, online system maintenance, proactive fault tolerance, power management, and resource sharing through Virtual Machine (VM migration. VM migration is a resource-intensive procedure as VM’s continuously demand appropriate CPU cycles, cache memory, memory capacity, and communication bandwidth. Therefore, this process degrades the performance of running applications and adversely affects efficiency of the data centers, particularly when Service Level Agreements (SLA and critical business objectives are to be met. Live VM migration is frequently used because it allows the availability of application service, while migration is performed. In this paper, we make an exhaustive survey of the literature on live VM migration and analyze the various proposed mechanisms. We first classify the types of Live VM migration (single, multiple and hybrid. Next, we categorize VM migration techniques based on duplication mechanisms (replication, de-duplication, redundancy, and compression and awareness of context (dependency, soft page, dirty page, and page fault and evaluate the various Live VM migration techniques. We discuss various performance metrics like application service downtime, total migration time and amount of data transferred. CPU, memory and storage data is transferred during the process of VM migration and we identify the category of data that needs to be transferred in each case. We present a brief discussion on security threats in live VM migration and categories them in three different classes (control plane, data plane, and migration module. We also explain the security requirements and existing solutions to mitigate possible attacks. Specific gaps are identified and the research challenges in improving

  15. Reaching the Non-Traditional Stopout Population: A Segmentation Approach

    Science.gov (United States)

    Schatzel, Kim; Callahan, Thomas; Scott, Crystal J.; Davis, Timothy

    2011-01-01

    An estimated 21% of 25-34-year-olds in the United States, about eight million individuals, have attended college and quit before completing a degree. These non-traditional students may or may not return to college. Those who return to college are referred to as stopouts, whereas those who do not return are referred to as stayouts. In the face of…

  16. Do Ghanaian non-traditional exporters understand the importance of ...

    African Journals Online (AJOL)

    Do Ghanaian non-traditional exporters understand the importance of sales ... The older the firm in export business, the more likely it was for management to put in ... taking into consideration other factors like internet use and planning of sales ...

  17. The Pleasures and Pitfalls of a Non-traditional Occupation.

    Science.gov (United States)

    Scott, Robert E.

    Both men and women who engage in non-traditional occupations (occupations in which 80 percent or more of the participants are of the opposite sex) are generally happy with their occupational choice, according to interviews with seventy such women and ten men. The women, however, experienced more discrimination and sexual harassment, while the men…

  18. Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

    NARCIS (Netherlands)

    Misra, Ankita; Vojinovic, Zoran; Ramakrishnan, Balaji; Luijendijk, Arjen; Ranasinghe, Roshanka

    2018-01-01

    Satellite imagery along with image processing techniques prove to be efficient tools for bathymetry retrieval as they provide time and cost-effective alternatives to traditional methods of water depth estimation. In this article, a nonlinear machine learning technique of Support Vector Machine (SVM)

  19. Wire electric-discharge machining and other fabrication techniques

    Science.gov (United States)

    Morgan, W. H.

    1983-01-01

    Wire electric discharge machining and extrude honing were used to fabricate a two dimensional wing for cryogenic wind tunnel testing. Electric-discharge cutting is done with a moving wire electrode. The cut track is controlled by means of a punched-tape program and the cutting feed is regulated according to the progress of the work. Electric-discharge machining involves no contact with the work piece, and no mechanical force is exerted. Extrude hone is a process for honing finish-machined surfaces by the extrusion of an abrasive material (silly putty), which is forced through a restrictive fixture. The fabrication steps are described and production times are given.

  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. Machine throughput improvement achieved using innovative control technique

    International Nuclear Information System (INIS)

    Sharma, V.; Acharya, S.; Mittal, K.C.

    2012-01-01

    In any type of fully or semi automatic machine the control systems plays an important role. The control system on the one hand has to consider the human psychology, intelligence requirement for an operator, and attention needed from him. On the other hand the complexity of the control has also to be understood well before designing a control system that can be handled comfortably and safely by the operator. As far as the user experience/comfort is concerned the design of control system GUI is vital. Considering these two aspects related to the user of the machine it is evident that the control system design is very important because it is has to accommodate the human behaviour and skill sets required/available as well as the capability of the machine under the control of the control system. An intelligently designed control system can enhance the productivity of the machine. (author)

  2. 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....

  3. Energy and non-traditional security (NTS) in Asia

    Energy Technology Data Exchange (ETDEWEB)

    Caballero-Anthony, Mely [Nanyang Technological Univ., Singapore (SG). Centre for Non-Traditional Security (NTS) Studies; Chang, Youngho [Nanyang Technological Univ., Singapore (Singapore). Division of Economics; Putra, Nur Azha (eds.) [National Univ. of Singapore (Singapore). Energy Security Division

    2012-07-01

    Traditional notions of security are premised on the primacy of state security. In relation to energy security, traditional policy thinking has focused on ensuring supply without much emphasis on socioeconomic and environmental impacts. Non-traditional security (NTS) scholars argue that threats to human security have become increasingly prominent since the end of the Cold War, and that it is thus critical to adopt a holistic and multidisciplinary approach in addressing rising energy needs. This volume represents the perspectives of scholars from across Asia, looking at diverse aspects of energy security through a non-traditional security lens. The issues covered include environmental and socioeconomic impacts, the role of the market, the role of civil society, energy sustainability and policy trends in the ASEAN region.

  4. 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.

  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. 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

  7. 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

  8. 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…

  9. Relevance vector machine technique for the inverse scattering problem

    International Nuclear Information System (INIS)

    Wang Fang-Fang; Zhang Ye-Rong

    2012-01-01

    A novel method based on the relevance vector machine (RVM) for the inverse scattering problem is presented in this paper. The nonlinearity and the ill-posedness inherent in this problem are simultaneously considered. The nonlinearity is embodied in the relation between the scattered field and the target property, which can be obtained through the RVM training process. Besides, rather than utilizing regularization, the ill-posed nature of the inversion is naturally accounted for because the RVM can produce a probabilistic output. Simulation results reveal that the proposed RVM-based approach can provide comparative performances in terms of accuracy, convergence, robustness, generalization, and improved performance in terms of sparse property in comparison with the support vector machine (SVM) based approach. (general)

  10. Predicting breast screening attendance using machine learning techniques.

    Science.gov (United States)

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

    2011-03-01

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

  11. 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 ...

  12. A comparison of machine learning techniques for predicting downstream acid mine drainage

    CSIR Research Space (South Africa)

    van Zyl, TL

    2014-07-01

    Full Text Available windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear...

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Automatic pellet density checking machine using vision technique

    International Nuclear Information System (INIS)

    Kumar, Suman; Raju, Y.S.; Raj Kumar, J.V.; Sairam, S.; Sheela; Hemantha Rao, G.V.S.

    2012-01-01

    Uranium di-oxide powder prepared through chemical process is converted to green pellets through the powder metallurgy route of precompaction and final compaction operations. These green pellets are kept in a molybdenum boat, which consists of a molybdenum base and a shroud. The boats are passed through the high temperature sintering furnaces to achieve required density of pellets. At present MIL standard 105 E is followed for measuring density of sintered pellets in the boat. As per AQL 2.5 of MIL standard, five pellets are collected from each boat, which contains approximately 800 nos of pellets. The densities of these collected pellets are measured. If anyone pellet density is less than the required value, the entire boat of pellets are rejected and sent back for dissolution for further processing. An Automatic Pellet Density Checking Machine (APDCM) was developed to salvage the acceptable density pellets from the rejected boat of pellets

  19. 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 ...

  20. 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.

  1. 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.

  2. Nuclear forensics of a non-traditional sample: Neptunium

    International Nuclear Information System (INIS)

    Doyle, Jamie L.; Schwartz, Daniel; Tandon, Lav

    2016-01-01

    Recent nuclear forensics cases have focused primarily on plutonium (Pu) and uranium (U) materials. By definition however, nuclear forensics can apply to any diverted nuclear material. This includes neptunium (Np), an internationally safeguarded material like Pu and U, that could offer a nuclear security concern if significant quantities were found outside of regulatory control. This case study couples scanning electron microscopy (SEM) with quantitative analysis using newly developed specialized software, to evaluate a non-traditional nuclear forensic sample of Np. Here, the results of the morphological analyses were compared with another Np sample of known pedigree, as well as other traditional actinide materials in order to determine potential processing and point-of-origin

  3. Application of Artificial Intelligence Techniques for the Control of the Asynchronous Machine

    Directory of Open Access Journals (Sweden)

    F. Khammar

    2016-01-01

    Full Text Available The induction machine is experiencing a growing success for two decades by gradually replacing the DC machines and synchronous in many industrial applications. This paper is devoted to the study of advanced methods applied to the command of the asynchronous machine in order to obtain a system of control of high performance. While the criteria for response time, overtaking, and static error can be assured by the techniques of conventional control, the criterion of robustness remains a challenge for researchers. This criterion can be satisfied only by applying advanced techniques of command. After mathematical modeling of the asynchronous machine, it defines the control strategies based on the orientation of the rotor flux. The results of the different simulation tests highlight the properties of robustness of algorithms proposed and suggested to compare the different control strategies.

  4. 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.

  5. 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...

  6. Vacuum system and cleaning techniques in the FTU machines

    International Nuclear Information System (INIS)

    Alessandrini, C.; Apicella, M.L.; Ferro, C.

    1988-01-01

    FTU (Frascati Tokamak Upgrade) is a high magnetic field (8T) tokamak under construction at the Frascati Energy Research Center (ENEA). Its vacuum systems has been already manifactured and is presently being assembled. It consist of an all metallic fully welded vessel, pumped by six turbomolecular pumps. The vacuum system has been dimensioned to allow a base pressure lower than 2.6 x 10 -6 Pa. The paper reports the design philosophy of the vacuum system. The results of the cleaning techniques performed on a 1:1 scale toroidal sector of FTU are also presented and discussed

  7. A technique to identify some typical radio frequency interference using support vector machine

    Science.gov (United States)

    Wang, Yuanchao; Li, Mingtao; Li, Dawei; Zheng, Jianhua

    2017-07-01

    In this paper, we present a technique to automatically identify some typical radio frequency interference from pulsar surveys using support vector machine. The technique has been tested by candidates. In these experiments, to get features of SVM, we use principal component analysis for mosaic plots and its classification accuracy is 96.9%; while we use mathematical morphology operation for smog plots and horizontal stripes plots and its classification accuracy is 86%. The technique is simple, high accurate and useful.

  8. 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...

  9. The Gritty: Grit and Non-traditional Doctoral Student Success

    Directory of Open Access Journals (Sweden)

    Ted M. Cross

    2014-07-01

    Full Text Available As higher education is changing to reach larger numbers of students via online modalities, the issue of student attrition and other measures of student success become increasingly important. While research has focused largely on undergraduate online students, less has been done in the area of online non-traditional doctoral student success, particularly from the student trait perspective. The concept of grit, passion and persistence for long-term goals, has been identified as an important element of the successful attainment of long-term goals. As doctoral education is a long-term goal the purpose of this study was to examine the impact of doctoral student grit scores on student success. Success was measured by examining current student GPA and other factors. Significant relationships were found between grit and current student GPA, grit and the average number of hours students spent on their program of study weekly, and grit and age. The results of this research maybe important for informing how doctoral education is structured and how students might be better prepared for doctoral work.

  10. Improvement of engineering soil properties using non -traditional additives

    Directory of Open Access Journals (Sweden)

    Waheed Mohanned

    2018-01-01

    Full Text Available Laboratory experiments are conducted to evaluate the effect of some non-traditional additives on the engineering properties of clayey soil, which show problematic phenomenon when used as a construction material. The conducted tests covered the influence of these additives on various parameters like consistency limits, compaction characteristics and CBR value. Two nontraditional stabilizers are selected in this study, polymers and phosphoric acid at three different percent which are (1%, 3% and 5% of the dry soil weight. It is concluded that addition of the polymer to the clayey soil results in a slight increase in plastic limit while the liquid limit is not affected accompanied by a marginal decrease in the dry unit weight while the optimum moisture content remains unaffected. The addition of phosphoric acid to the clayey soil has no effect on its Atterberg limits. In general, it is observed that polymer is found to be ineffective as a stabilizer to improve clayey soils, especially in small amounts of about (3%. The phosphoric acid treated soil gained better improvement for all amounts of additive used. For (3% acid treated soil the CBR is about (360% compared to that of untreated soil, for that, it can be concluded that the improvement using phosphoric acid in the clay soils is a promising option and can be applied to solve the geotechnical stabilization problems.

  11. Complex technique for studying the machine part wear

    International Nuclear Information System (INIS)

    Grishko, V.A.; Zhushma, V.F.

    1981-01-01

    A technique to determine the wear of steel details rolling with sliding with circulatory lubrication is suggested. The functional diagram of the experimental device and structural diagrams of equipment to register the wear of tested samples and forming the lubricating layer between them, are considered. Results of testing three conples of disc samples and the data characterizing the dependence of sample wear on the value of contact stress are presented. The peculiarity of the device used is synchronous registering of the lubricating layer formation in the place of contact and detail mass loss in time which is realized correspondingly over discharge voltage on the lubricating layer and the intensity of radiation from detail wear products activated by neutrons. On the basis, of the investigation the conclusion is made that MEhF-1 oil has a greater antiwear effectiveness than the universal TAD-17 1 oil used presently [ru

  12. Performance Evaluation of Eleven-Phase Induction Machine with Different PWM Techniques

    Directory of Open Access Journals (Sweden)

    M.I. Masoud

    2015-06-01

    Full Text Available Multiphase induction machines are used extensively in low and medium voltage (MV drives. In MV drives, power switches have a limitation associated with switching frequency. This paper is a comparative study of the eleven-phase induction machine’s performance when used as a prototype and fed sinusoidal pulse-width-modulation (SPWM with a low switching frequency, selective harmonic elimination (SHE, and single pulse modulation (SPM techniques. The comparison depends on voltage/frequency controls for the same phase of voltage applied on the machine terminals for all previous techniques. The comparative study covers torque ripple, stator and harmonic currents, and motor efficiency.

  13. 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.

  14. Teaching Climate Science in Non-traditional Classrooms

    Science.gov (United States)

    Strybos, J.

    2015-12-01

    San Antonio College is the oldest, largest and centrally-located campus of Alamo Colleges, a network of five community colleges based around San Antonio, Texas with a headcount enrollment of approximately 20,000 students. The student population is diverse in ethnicity, age and income; and the Colleges understand that they play a salient role in educating its students on the foreseen impacts of climate change. This presentation will discuss the key investment Alamo Colleges has adopted to incorporate sustainability and climate science into non-traditional classrooms. The established courses that cover climate-related course material have historically had low enrollments. One of the most significant challenges is informing the student population of the value of this class both in their academic career and in their personal lives. By hosting these lessons in hands-on simulations and demonstrations that are accessible and understandable to students of any age, and pursuing any major, we have found an exciting way to teach all students about climate change and identify solutions. San Antonio College (SAC) hosts the Bill R. Sinkin Eco Centro Community Center, completed in early 2014, that serves as an environmental hub for Alamo Colleges' staff and students as well as the San Antonio community. The center actively engages staff and faculty during training days in sustainability by presenting information on Eco Centro, personal sustainability habits, and inviting faculty to bring their classes for a tour and sustainability primer for students. The Centro has hosted professors from diverse disciplines that include Architecture, Psychology, Engineering, Science, English, Fine Arts, and International Studies to bring their classes to center to learn about energy, water conservation, landscaping, and green building. Additionally, Eco Centro encourages and assists students with research projects, including a solar-hydroponic project currently under development with the support

  15. Non-traditional Stable Isotope Systematics of Seafloor Hydrothermal Systems

    Science.gov (United States)

    Rouxel, O. J.

    2009-05-01

    Seafloor hydrothermal activity at mid-ocean ridges is one of the fundamental processes controlling the chemistry of the oceans and the altered oceanic crust. Past studies have demonstrated the complexity and diversity of seafloor hydrothermal systems and have highlighted the importance of subsurface environments in controlling the composition of hydrothermal fluids and mineralization types. Traditionally, the behavior of metals in seafloor hydrothermal systems have been investigated by integrating results from laboratory studies, theoretical models, mineralogy and fluid and mineral chemistry. Isotope ratios of various metals and metalloids, such as Fe, Cu, Zn, Se, Cd and Sb have recently provided new approaches for the study of seafloor hydrothermal systems. Despite these initial investigations, the cause of the isotopic variability of these elements remains poorly constrained. We have little understanding of the isotope variations between vent types (black or white smokers) as well as the influence of source rock composition (basalt, felsic or ultrabasic rocks) and alteration types. Here, I will review and present new results of metal isotope systematics of seafloor hydrothermal systems, in particular: (1) determination of empirical isotope fractionation factors for Zn, Fe and Cu-isotopes through isotopic analysis of mono-mineralic sulfide grains lining the internal chimney wall in contact with hydrothermal fluid; (2) comparison of Fe- and Cu-isotope signatures of vent fluids from mid- oceanic and back-arc hydrothermal fields, spanning wide ranges of pH, temperature, metal concentrations and contributions of magmatic fluids enriched in SO2. Ultimately, the use of complementary non-traditional stable isotope systems may help identify and constrain the complex interactions between fluids,minerals, and organisms in seafloor hydrothermal systems.

  16. A non-traditional multinational approach to construction inspection program

    International Nuclear Information System (INIS)

    Ram, Srinivasan; Smith, M.E.; Walker, T.F.

    2007-01-01

    The next generation of nuclear plants would be fabricated, constructed and licensed in markedly different ways than the present light water reactors. Non-traditional commercial nuclear industry suppliers, shipyards in Usa and international fabricators, would be a source to supply major components and subsystems. The codes of construction may vary depending upon the prevailing codes and standards used by the respective supplier. Such codes and standards need to be reconciled with the applicable regulations (e.g., 10 CFR 52). A Construction Inspection Program is an integral part of the Quality Assurance Measures required during the Construction Phase of the power plant. In order to achieve the stated cost and schedule goals of the new build plants, a nontraditional multi-national approach would be required. In lieu of the traditional approach of individual utility inspecting the quality of fabrication and construction, a multi-utility team approach is a method that will be discussed. Likewise, a multinational cooperative licensing approach is suggested taking advantage of inspectors of the regulatory authority where the component would be built. The multi-national approach proposed here is based on the principle of forming teaming agreements between the utilities, vendors and the regulators. For instance, rather than sending Country A's inspectors all over the world, inspectors of the regulator in Country B where a particular component is being fabricated would in fact be performing the required inspections for Country A's regulator. Similarly teaming arrangements could be set up between utilities and vendors in different countries. The required oversight for the utility or the vendor could be performed by their counterparts in the country where a particular item is being fabricated

  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. 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....

  19. 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…

  20. 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.

  1. Techniques and applications for binaural sound manipulation in human-machine interfaces

    Science.gov (United States)

    Begault, Durand R.; Wenzel, Elizabeth M.

    1992-01-01

    The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

  2. 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.

  3. 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...

  4. Non-traditional shape GFRP rebars for concrete reinforcement

    Science.gov (United States)

    Claure, Guillermo G.

    The use of glass-fiber-reinforced-polymer (GFRP) composites as internal reinforcement (rebars) for concrete structures has proven to be an alternative to traditional steel reinforcement due to significant advantages such as magnetic transparency and, most importantly, corrosion resistance equating to durability and structural life extension. In recent years, the number of projects specifying GFRP reinforcement has increased dramatically leading the construction industry towards more sustainable practices. Typically, GFRP rebars are similar to their steel counterparts having external deformations or surface enhancements designed to develop bond to concrete, as well as having solid circular cross-sections; but lately, the worldwide composites industry has taken advantage of the pultrusion process developing GFRP rebars with non-traditional cross-sectional shapes destined to optimize their mechanical, physical, and environmental attributes. Recently, circular GFRP rebars with a hollow-core have also become available. They offer advantages such as a larger surface area for improved bond, and the use of the effective cross-sectional area that is engaged to carry load since fibers at the center of a solid cross-section are generally not fully engaged. For a complete understanding of GFRP rebar physical properties, a study on material characterization regarding a quantitative cross-sectional area analysis of different GFRP rebars was undertaken with a sample population of 190 GFRP specimens with rebar denomination ranging from #2 to #6 and with different cross-sectional shapes and surface deformations manufactured by five pultruders from around the world. The water displacement method was applied as a feasible and reliable way to conduct the investigation. In addition to developing a repeatable protocol for measuring cross-sectional area, the objectives of establishing critical statistical information related to the test methodology and recommending improvements to

  5. Process acceptance and adjustment techniques for Swiss automatic screw machine parts. Final report

    International Nuclear Information System (INIS)

    Robb, J.M.

    1976-01-01

    Product tolerance requirements for small, cylindrical, piece parts produced on swiss automatic screw machines have progressed to the reliability limits of inspection equipment. The miniature size, configuration, and tolerance requirements (plus or minus 0.0001 in.) (0.00254 mm) of these parts preclude the use of screening techniques to accept product or adjust processes during setup and production runs; therefore, existing means of product acceptance and process adjustment must be refined or new techniques must be developed. The purpose of this endeavor has been to determine benefits gained through the implementation of a process acceptance technique (PAT) to swiss automatic screw machine processes. PAT is a statistical approach developed for the purpose of accepting product and centering processes for parts produced by selected, controlled processes. Through this endeavor a determination has been made of the conditions under which PAT can benefit a controlled process and some specific types of screw machine processes upon which PAT could be applied. However, it was also determined that PAT, if used indiscriminately, may become a record keeping burden when applied to more than one dimension at a given machining operation

  6. 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.

  7. A Computer Program for Simplifying Incompletely Specified Sequential Machines Using the Paull and Unger Technique

    Science.gov (United States)

    Ebersole, M. M.; Lecoq, P. E.

    1968-01-01

    This report presents a description of a computer program mechanized to perform the Paull and Unger process of simplifying incompletely specified sequential machines. An understanding of the process, as given in Ref. 3, is a prerequisite to the use of the techniques presented in this report. This process has specific application in the design of asynchronous digital machines and was used in the design of operational support equipment for the Mariner 1966 central computer and sequencer. A typical sequential machine design problem is presented to show where the Paull and Unger process has application. A description of the Paull and Unger process together with a description of the computer algorithms used to develop the program mechanization are presented. Several examples are used to clarify the Paull and Unger process and the computer algorithms. Program flow diagrams, program listings, and a program user operating procedures are included as appendixes.

  8. 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

  9. 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.

  10. Electric-Discharge Machining Techniques for Evaluating Tritium Effects on Materials

    International Nuclear Information System (INIS)

    Morgan, M.J.

    2003-01-01

    In this investigation, new ways to evaluate the long-term effects of tritium on the structural properties of components were developed. Electric-discharge machining (EDM) techniques for cutting tensile and fracture toughness samples from tritium exposed regions of returned reservoirs were demonstrated. An existing electric discharge machine was used to cut sub-size tensile and fracture toughness samples from the inside surfaces of reservoir mockups. Tensile properties from the EDM tensile samples were similar to those measured using full-size samples cut from similar stock. Although the existing equipment could not be used for machining tritium-exposed hardware, off-the shelf EDM units are available that could. With the right equipment and the required radiological controls in place, similar machining and testing techniques could be used to directly measure the effects of tritium on the properties of material cut from reservoir returns. Stress-strain properties from tritium-exposed reservoirs would improve finite element modeling of reservoir performance because the data would be representative of the true state of the reservoir material in the field. Tensile data from samples cut directly from reservoirs would also complement existing shelf storage and burst test data of the Life Storage Program and help answer questions about a specific reservoir's processing history and properties

  11. Non-traditional Sensor Tasking for SSA: A Case Study

    Science.gov (United States)

    Herz, A.; Herz, E.; Center, K.; Martinez, I.; Favero, N.; Clark, C.; Therien, W.; Jeffries, M.

    Industry has recognized that maintaining SSA of the orbital environment going forward is too challenging for the government alone. Consequently there are a significant number of commercial activities in various stages of development standing-up novel sensors and sensor networks to assist in SSA gathering and dissemination. Use of these systems will allow government and military operators to focus on the most sensitive space control issues while allocating routine or lower priority data gathering responsibility to the commercial side. The fact that there will be multiple (perhaps many) commercial sensor capabilities available in this new operational model begets a common access solution. Absent a central access point to assert data needs, optimized use of all commercial sensor resources is not possible and the opportunity for coordinated collections satisfying overarching SSA-elevating objectives is lost. Orbit Logic is maturing its Heimdall Web system - an architecture facilitating “data requestor” perspectives (allowing government operations centers to assert SSA data gathering objectives) and “sensor operator” perspectives (through which multiple sensors of varying phenomenology and capability are integrated via machine -machine interfaces). When requestors submit their needs, Heimdall’s planning engine determines tasking schedules across all sensors, optimizing their use via an SSA-specific figure-of-merit. ExoAnalytic was a key partner in refining the sensor operator interfaces, working with Orbit Logic through specific details of sensor tasking schedule delivery and the return of observation data. Scant preparation on both sides preceded several integration exercises (walk-then-run style), which culminated in successful demonstration of the ability to supply optimized schedules for routine public catalog data collection – then adapt sensor tasking schedules in real-time upon receipt of urgent data collection requests. This paper will provide a

  12. 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.

  13. 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

  14. ROBUSTNESS OF A FACE-RECOGNITION TECHNIQUE BASED ON SUPPORT VECTOR MACHINES

    OpenAIRE

    Prashanth Harshangi; Koshy George

    2010-01-01

    The ever-increasing requirements of security concerns have placed a greater demand for face recognition surveillance systems. However, most current face recognition techniques are not quite robust with respect to factors such as variable illumination, facial expression and detail, and noise in images. In this paper, we demonstrate that face recognition using support vector machines are sufficiently robust to different kinds of noise, does not require image pre-processing, and can be used with...

  15. 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

  16. 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.

  17. 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.

  18. Perceived constraints by non-traditional users on the Mt. Baker-Snoqualmie National Forest

    Science.gov (United States)

    Elizabeth A. Covelli; Robert C. Burns; Alan Graefe

    2007-01-01

    The purpose of this study was to investigate the constraints that non-traditional users face, along with the negotiation strategies that are employed in order to start, continue, or increase participation in recreation on a national forest. Non-traditional users were defined as respondents who were not Caucasian. Additionally, both constraints and negotiation...

  19. An Investigation of the Perceptions of Business Students Regarding Non-Traditional Business Education Formats.

    Science.gov (United States)

    Barnes, John W.; Hadjimarcou, John

    1999-01-01

    A survey of 118 undergraduate business students at a major southwestern university found that most consider non-traditional education as a viable option to traditional education. However, respondents also identified disadvantages of non-traditional programs, such as cost, external validity of degrees, and impersonalized learning environment.…

  20. Andragogical Teaching Methods to Enhance Non-Traditional Student Classroom Engagement

    Science.gov (United States)

    Allen, Pamela; Withey, Paul; Lawton, Deb; Aquino, Carlos Tasso

    2016-01-01

    The aim of this study was to provide a reflection of current trends in higher education, identify some of the changes in student behavior, and potential identification of non-traditional classroom facilitation with the purpose of strengthening active learning and use of technology in the classroom. Non-traditional teaching is emerging in the form…

  1. Exploring Non-Traditional Learning Methods in Virtual and Real-World Environments

    Science.gov (United States)

    Lukman, Rebeka; Krajnc, Majda

    2012-01-01

    This paper identifies the commonalities and differences within non-traditional learning methods regarding virtual and real-world environments. The non-traditional learning methods in real-world have been introduced within the following courses: Process Balances, Process Calculation, and Process Synthesis, and within the virtual environment through…

  2. 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%.

  3. Optimization of Coolant Technique Conditions for Machining A319 Aluminium Alloy Using Response Surface Method (RSM)

    Science.gov (United States)

    Zainal Ariffin, S.; Razlan, A.; Ali, M. Mohd; Efendee, A. M.; Rahman, M. M.

    2018-03-01

    Background/Objectives: The paper discusses about the optimum cutting parameters with coolant techniques condition (1.0 mm nozzle orifice, wet and dry) to optimize surface roughness, temperature and tool wear in the machining process based on the selected setting parameters. The selected cutting parameters for this study were the cutting speed, feed rate, depth of cut and coolant techniques condition. Methods/Statistical Analysis Experiments were conducted and investigated based on Design of Experiment (DOE) with Response Surface Method. The research of the aggressive machining process on aluminum alloy (A319) for automotive applications is an effort to understand the machining concept, which widely used in a variety of manufacturing industries especially in the automotive industry. Findings: The results show that the dominant failure mode is the surface roughness, temperature and tool wear when using 1.0 mm nozzle orifice, increases during machining and also can be alternative minimize built up edge of the A319. The exploration for surface roughness, productivity and the optimization of cutting speed in the technical and commercial aspects of the manufacturing processes of A319 are discussed in automotive components industries for further work Applications/Improvements: The research result also beneficial in minimizing the costs incurred and improving productivity of manufacturing firms. According to the mathematical model and equations, generated by CCD based RSM, experiments were performed and cutting coolant condition technique using size nozzle can reduces tool wear, surface roughness and temperature was obtained. Results have been analyzed and optimization has been carried out for selecting cutting parameters, shows that the effectiveness and efficiency of the system can be identified and helps to solve potential problems.

  4. 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.

  5. 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...

  6. LARA. Localization of an automatized refueling machine by acoustical sounding in breeder reactors - implementation of artificial intelligence techniques

    International Nuclear Information System (INIS)

    Lhuillier, C.; Malvache, P.

    1987-01-01

    The automatic control of the machine which handles the nuclear subassemblies in fast neutron reactors requires autonomous perception and decision tools. An acoustical device allows the machine to position in the work area. Artificial intelligence techniques are implemented to interpret the data: pattern recognition, scene analysis. The localization process is managed by an expert system. 6 refs.; 8 figs

  7. 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.

  8. 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

  9. Analysis of machining and machine tools

    CERN Document Server

    Liang, Steven Y

    2016-01-01

    This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

  10. Digital Mayhem 3D machine techniques where inspiration, techniques and digital art meet

    CERN Document Server

    Evans, Duncan

    2014-01-01

    From Icy Tundras to Desert savannahs, master the art of landscape and environment design for 2D and 3D digital content. Make it rain, shower your digital scene with a snow storm or develop a believable urban scene with a critical eye for modeling, lighting and composition. Move beyond the limitations of gallery style coffee table books with Digital Mayhem: 3D Landscapes-offering leading professional techniques, groundbreaking inspiration, and artistic mastery from some of the greatest digital artists. More than just a gallery book - each artist has written a breakdown overview, with supporting

  11. An overview of non-traditional nuclear threats

    International Nuclear Information System (INIS)

    Geelhood, B.D.; Wogman, N.A.

    2005-01-01

    In view of the terrorist threats to the United States, the country needs to consider new vectors and weapons related to nuclear and radiological threats against our homeland. The traditional threat vectors, missiles and bombers, have expanded to include threats arriving through the flow of commerce. The new commerce-related vectors include: sea cargo, truck cargo, rail cargo, air cargo, and passenger transport. The types of weapons have also expanded beyond nuclear warheads to include radiation dispersal devices (RDD) or 'dirty' bombs. The consequences of these nuclear and radiological threats are both economic and life threatening. The defense against undesirable materials entering our borders involves extensive radiation monitoring at ports of entry. The radiation and other signatures of potential nuclear and radiological threats are examined along with potential sensors to discover undesirable items in the flow of commerce. Techniques to improve radiation detection are considered. A strategy of primary and secondary screening is proposed to rapidly clear most cargo and carefully examine suspect cargo. (author)

  12. Social Capital of Non-Traditional Students at a German University. Do Traditional and Non-Traditional Students Access Different Social Resources?

    Science.gov (United States)

    Brändle, Tobias; Häuberer, Julia

    2015-01-01

    Social capital is of particular value for the acquisition of education. Not only does it prevent scholars from dropping out but it improves the educational achievement. The paper focuses on access to social resources by traditional and non-traditional students at a German university and asks if there are group differences considering this…

  13. 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

  14. Engagement techniques and playing level impact the biomechanical demands on rugby forwards during machine-based scrummaging

    OpenAIRE

    Preatoni, Ezio; Stokes, Keith A.; England, Michael E.; Trewartha, Grant

    2014-01-01

    Objectives This cross-sectional study investigated the factors that may influence the physical loading on rugby forwards performing a scrum by studying the biomechanics of machine-based scrummaging under different engagement techniques and playing levels.Methods 34 forward packs from six playing levels performed repetitions of five different types of engagement techniques against an instrumented scrum machine under realistic training conditions. Applied forces and body movements were recorded...

  15. The influence of cooling techniques on cutting forces and surface roughness during cryogenic machining of titanium alloys

    Directory of Open Access Journals (Sweden)

    Wstawska Iwona

    2016-12-01

    Full Text Available Titanium alloys are one of the materials extensively used in the aerospace industry due to its excellent properties of high specific strength and corrosion resistance. On the other hand, they also present problems wherein titanium alloys are extremely difficult materials to machine. In addition, the cost associated with titanium machining is also high due to lower cutting velocities and shorter tool life. The main objective of this work is a comparison of different cooling techniques during cryogenic machining of titanium alloys. The analysis revealed that applied cooling technique has a significant influence on cutting force and surface roughness (Ra parameter values. Furthermore, in all cases observed a positive influence of cryogenic machining on selected aspects after turning and milling of titanium alloys. This work can be also the starting point to the further research, related to the analysis of cutting forces and surface roughness during cryogenic machining of titanium alloys.

  16. A relevance vector machine technique for the automatic detection of clustered microcalcifications (Honorable Mention Poster Award)

    Science.gov (United States)

    Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.

    2005-04-01

    Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

  17. Fuzzy classification for strawberry diseases-infection using machine vision and soft-computing techniques

    Science.gov (United States)

    Altıparmak, Hamit; Al Shahadat, Mohamad; Kiani, Ehsan; Dimililer, Kamil

    2018-04-01

    Robotic agriculture requires smart and doable techniques to substitute the human intelligence with machine intelligence. Strawberry is one of the important Mediterranean product and its productivity enhancement requires modern and machine-based methods. Whereas a human identifies the disease infected leaves by his eye, the machine should also be capable of vision-based disease identification. The objective of this paper is to practically verify the applicability of a new computer-vision method for discrimination between the healthy and disease infected strawberry leaves which does not require neural network or time consuming trainings. The proposed method was tested under outdoor lighting condition using a regular DLSR camera without any particular lens. Since the type and infection degree of disease is approximated a human brain a fuzzy decision maker classifies the leaves over the images captured on-site having the same properties of human vision. Optimizing the fuzzy parameters for a typical strawberry production area at a summer mid-day in Cyprus produced 96% accuracy for segmented iron deficiency and 93% accuracy for segmented using a typical human instant classification approximation as the benchmark holding higher accuracy than a human eye identifier. The fuzzy-base classifier provides approximate result for decision making on the leaf status as if it is healthy or not.

  18. 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.

  19. 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.

  20. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    KAUST Repository

    Harrou, Fouzi; Ramahaleomiarantsoa, Jacques F.; Nounou, Mohamed N.; Nounou, Hazem N.

    2016-01-01

    Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM

  1. Traditional and non-traditional educational outcomes : Trade-off or complementarity?

    NARCIS (Netherlands)

    van der Wal, Marieke; Waslander, Sietske

    2007-01-01

    Recently, schools have increasingly been charged with enhancing non-traditional academic competencies, in addition to traditional academic competencies. This article raises the question whether schools can implement these new educational goals in their curricula and simultaneously realise the

  2. Renewable energy sources. Non-traditional actors on the international market

    International Nuclear Information System (INIS)

    1999-01-01

    Five of Sweden's technical attaches have investigated the non-traditional actors activity within the field of renewable energy sources. Countries studied are USA, Japan, France, Germany and Great Britain

  3. CONSUMERS’ BRAND EQUITY PERCEPTIONS OF TRADITIONAL AND NON-TRADITIONAL BRANDS

    OpenAIRE

    Catli, Ozlem; Ermec Sertoglu, Aysegul; Ors, Husniye

    2017-01-01

    Thisstudy aims to compare consumers' brand perception of traditional brands withbrand perceptions of non-traditional brands.  Consumers livingin Ankara constitute the universe of work, and data were gathered in aface-to-face interview using the survey method. the demographic characteristicsof the participants was prepared with the aim of evaluating and comparing onetraditional brand and one non traditional brand of brand equity related to thebrand equity by the participants. According to...

  4. Conceptualisation of learning satisfaction experienced by non-traditional learners in Singapore

    OpenAIRE

    Khiat, Henry

    2013-01-01

    This study uncovered the different factors that make up the learning satisfaction of non-traditional learners in Singapore. Data was collected from a component of the student evaluation exercise in a Singapore university in 2011. A mixed-methods approach was adopted in the analysis. The study stated that non-traditional learners’ learning satisfaction can be generally grouped into four main categories: a) Desirable Learning Deliverables; b) Directed Learning Related Factors; c) Lecturer/Tutor...

  5. An Innovative System for the Efficient and Effective Treatment of Non-Traditional Waters for Reuse in Thermoelectric Power Generation

    Energy Technology Data Exchange (ETDEWEB)

    John Rodgers; James Castle

    2008-08-31

    This study assessed opportunities for improving water quality associated with coal-fired power generation including the use of non-traditional waters for cooling, innovative technology for recovering and reusing water within power plants, novel approaches for the removal of trace inorganic compounds from ash pond effluents, and novel approaches for removing biocides from cooling tower blowdown. This research evaluated specifically designed pilot-scale constructed wetland systems for treatment of targeted constituents in non-traditional waters for reuse in thermoelectric power generation and other purposes. The overall objective of this project was to decrease targeted constituents in non-traditional waters to achieve reuse criteria or discharge limitations established by the National Pollutant Discharge Elimination System (NPDES) and Clean Water Act (CWA). The six original project objectives were completed, and results are presented in this final technical report. These objectives included identification of targeted constituents for treatment in four non-traditional water sources, determination of reuse or discharge criteria for treatment, design of constructed wetland treatment systems for these non-traditional waters, and measurement of treatment of targeted constituents in non-traditional waters, as well as determination of the suitability of the treated non-traditional waters for reuse or discharge to receiving aquatic systems. The four non-traditional waters used to accomplish these objectives were ash basin water, cooling water, flue gas desulfurization (FGD) water, and produced water. The contaminants of concern identified in ash basin waters were arsenic, chromium, copper, mercury, selenium, and zinc. Contaminants of concern in cooling waters included free oxidants (chlorine, bromine, and peroxides), copper, lead, zinc, pH, and total dissolved solids. FGD waters contained contaminants of concern including arsenic, boron, chlorides, selenium, mercury

  6. 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.

  7. Efficiency improvement of the maximum power point tracking for PV systems using support vector machine technique

    International Nuclear Information System (INIS)

    Kareim, Ameer A; Mansor, Muhamad Bin

    2013-01-01

    The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P and O), and incremental conductance (IC) algorithms were used to compare with proposed SVM algorithm. MATLAB models for PV module, theoretical, SVM, P and O, and IC algorithms are implemented. The improved MPPT uses the SVM method to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The SVM technique used two inputs which are solar radiation and ambient temperature of the modeled PV module. The results show that the proposed SVM technique has less Root Mean Square Error (RMSE) and higher efficiency than P and O and IC methods.

  8. Non-traditional neutron activation analysis by use of a nuclear reactor

    International Nuclear Information System (INIS)

    Mukhammedov, S.

    2003-01-01

    Full text: Traditional reactor neutron activation analysis (NAA) based on (n, γ) - thermal neutron capture nuclear reaction has been developed into a reliable and powerful analytical method, for trace element analysis, allowing the determination of over 60 chemical elements, with good accuracy and low detection limits. Considering all possibilities of activation and a radiochemical separation of the indicator radionuclide, the majority of the elements of this group can be determined at the ppm concentration level and below. However, for solving a number of analytical problems NAA technique is not well suited or it cannot be used at all. An important limitation is that all light elements, some medium and heavy elements cannot be determined even at ppm concentration level by this method, for example, H, Be, Li, B, C, N, O, Ti, Nb, Pb, etc. Accurate determination of lithium, oxygen and other light elements in sub-microgram level is of importance in geochemical and material studies. Such examples are great many. On such instances, several non-traditional reactor activation analysis can be used which have increasingly been developed and applied to several fields of semiconductor industry, biology, geology in recent years. The purpose of this presentation is to review the modern status of non-traditional nuclear reactor activation analysis based on use of nuclear reactions excited by the flow of secondary charged particles which are produced by two methods. In first method the triton flow is produced by thermal neutrons flux which excites the nuclear reaction 6 Li(n, α)T on lithium. The neutron activation analysis associated with two consecutive reactions 6 Li(n, α)T + 16 O(T, n) 18 F is established to determine trace amounts either of lithium or of oxygen in different geological, ecological and technological samples. Besides, the triton flow can be used for the determination of other light elements, for instance, B, N, S, Mg. This nuclear reactor triton activation

  9. 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

  10. 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)

  11. 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.

  12. 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

  13. 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.

  14. Hubble Tarantula Treasury Project - VI. Identification of Pre-Main-Sequence Stars using Machine Learning techniques

    Science.gov (United States)

    Ksoll, Victor F.; Gouliermis, Dimitrios A.; Klessen, Ralf S.; Grebel, Eva K.; Sabbi, Elena; Anderson, Jay; Lennon, Daniel J.; Cignoni, Michele; de Marchi, Guido; Smith, Linda J.; Tosi, Monica; van der Marel, Roeland P.

    2018-05-01

    The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire star-burst region of 30 Doradus down to the half Solar mass limit. We use the deep stellar catalogue of HTTP to identify all the pre-main-sequence (PMS) stars of the region, i.e., stars that have not started their lives on the main-sequence yet. The photometric distinction of these stars from the more evolved populations is not a trivial task due to several factors that alter their colour-magnitude diagram positions. The identification of PMS stars requires, thus, sophisticated statistical methods. We employ Machine Learning Classification techniques on the HTTP survey of more than 800,000 sources to identify the PMS stellar content of the observed field. Our methodology consists of 1) carefully selecting the most probable low-mass PMS stellar population of the star-forming cluster NGC2070, 2) using this sample to train classification algorithms to build a predictive model for PMS stars, and 3) applying this model in order to identify the most probable PMS content across the entire Tarantula Nebula. We employ Decision Tree, Random Forest and Support Vector Machine classifiers to categorise the stars as PMS and Non-PMS. The Random Forest and Support Vector Machine provided the most accurate models, predicting about 20,000 sources with a candidateship probability higher than 50 percent, and almost 10,000 PMS candidates with a probability higher than 95 percent. This is the richest and most accurate photometric catalogue of extragalactic PMS candidates across the extent of a whole star-forming complex.

  15. Automatic Quality Inspection of Percussion Cap Mass Production by Means of 3D Machine Vision and Machine Learning Techniques

    Science.gov (United States)

    Tellaeche, A.; Arana, R.; Ibarguren, A.; Martínez-Otzeta, J. M.

    The exhaustive quality control is becoming very important in the world's globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement. Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, machine learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.

  16. 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.

  17. 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.

  18. Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique

    Directory of Open Access Journals (Sweden)

    Rakan Kh. Antar

    2017-12-01

    Full Text Available In this paper, Artificial Neural Network (ANN technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm and mechanical torque (±7 N.m in steady state and dynamic conditions. The simulation results illustratethe effectiveness of the proposed controller without speed or torque sensors.

  19. 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.

  20. 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

  1. A comparison of machine learning techniques for survival prediction in breast cancer.

    Science.gov (United States)

    Vanneschi, Leonardo; Farinaccio, Antonella; Mauri, Giancarlo; Antoniotti, Mauro; Provero, Paolo; Giacobini, Mario

    2011-05-11

    The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

  2. A comparison of machine learning techniques for survival prediction in breast cancer

    Directory of Open Access Journals (Sweden)

    Vanneschi Leonardo

    2011-05-01

    Full Text Available Abstract Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

  3. Controlling the Adhesion of Superhydrophobic Surfaces Using Electrolyte Jet Machining Techniques

    Science.gov (United States)

    Yang, Xiaolong; Liu, Xin; Lu, Yao; Zhou, Shining; Gao, Mingqian; Song, Jinlong; Xu, Wenji

    2016-01-01

    Patterns with controllable adhesion on superhydrophobic areas have various biomedical and chemical applications. Electrolyte jet machining technique (EJM), an electrochemical machining method, was firstly exploited in constructing dimples with various profiles on the superhydrophobic Al alloy surface using different processing parameters. Sliding angles of water droplets on those dimples firstly increased and then stabilized at a certain value with the increase of the processing time or the applied voltages of the EJM, indicating that surfaces with different adhesion force could be obtained by regulating the processing parameters. The contact angle hysteresis and the adhesion force that restricts the droplet from sliding off were investigated through experiments. The results show that the adhesion force could be well described using the classical Furmidge equation. On account of this controllable adhesion force, water droplets could either be firmly pinned to the surface, forming various patterns or slide off at designed tilting angles at specified positions on a superhydrophobic surface. Such dimples on superhydrophopbic surfaces can be applied in water harvesting, biochemical analysis and lab-on-chip devices. PMID:27046771

  4. Influence of Heartwood on Wood Density and Pulp Properties Explained by Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Carla Iglesias

    2017-01-01

    Full Text Available The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470:2008, fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S and E-W directions were measured on transversal stem sections of A. melanoxylon R. Br. The relative position of the samples with respect to the total tree height was also considered as an input variable. Different configurations were tested until the maximum correlation coefficient was achieved. A classical mathematical technique (multiple linear regression and machine learning methods (classification and regression trees, multi-layer perceptron and support vector machines were tested. Classification and regression trees (CART was the most accurate model for the prediction of pulp ISO brightness (R = 0.85. The other parameters could be predicted with fair results (R = 0.64–0.75 by CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and pulp properties, and should be taken as a quality trait when assessing a pulpwood resource.

  5. Modeling, Control and Analyze of Multi-Machine Drive Systems using Bond Graph Technique

    Directory of Open Access Journals (Sweden)

    J. Belhadj

    2006-03-01

    Full Text Available In this paper, a system viewpoint method has been investigated to study and analyze complex systems using Bond Graph technique. These systems are multimachine multi-inverter based on Induction Machine (IM, well used in industries like rolling mills, textile, and railway traction. These systems are multi-domains, multi-scales time and present very strong internal and external couplings, with non-linearity characterized by a high model order. The classical study with analytic model is difficult to manipulate and it is limited to some performances. In this study, a “systemic approach” is presented to design these kinds of systems, using an energetic representation based on Bond Graph formalism. Three types of multimachine are studied with their control strategies. The modeling is carried out by Bond Graph and results are discussed to show the performances of this methodology

  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. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

    Science.gov (United States)

    del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

    2015-06-17

    Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

  8. HTGR Metallic Reactor Internals Core Shell Cutting & Machining Antideformation Technique Study

    International Nuclear Information System (INIS)

    Xing Huiping; Xue Song

    2014-01-01

    The reactor shell assembly of HTGR nuclear power station demonstration project metallic reactor internals is key components of reactor, remains with high-precision large component with large-sized thin-walled straight cylinder-shaped structure, and is the first manufacture in China. As compared with other reactor shell, it has a larger ID (Φ5360mm), a longer length (19000mm), a smaller wall thickness (40mm) and a higher precision requirement. During the process of manufacture, the deformation due to cutting & machining will directly affect the final result of manufacture, the control of structural deformation and cutting deformation shall be throughout total manufacture process of such assembly. To realize the control of entire core shell assembly geometry, the key is to innovate and make breakthroughs on anti-deformation technique and then provide reliable technological foundations for the manufacture of HTGR metallic reactor internals. (author)

  9. 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.

  10. Development of Experimental Setup of Metal Rapid Prototyping Machine using Selective Laser Sintering Technique

    Science.gov (United States)

    Patil, S. N.; Mulay, A. V.; Ahuja, B. B.

    2018-04-01

    Unlike in the traditional manufacturing processes, additive manufacturing as rapid prototyping, allows designers to produce parts that were previously considered too complex to make economically. The shift is taking place from plastic prototype to fully functional metallic parts by direct deposition of metallic powders as produced parts can be directly used for desired purpose. This work is directed towards the development of experimental setup of metal rapid prototyping machine using selective laser sintering and studies the various parameters, which plays important role in the metal rapid prototyping using SLS technique. The machine structure in mainly divided into three main categories namely, (1) Z-movement of bed and table, (2) X-Y movement arrangement for LASER movements and (3) feeder mechanism. Z-movement of bed is controlled by using lead screw, bevel gear pair and stepper motor, which will maintain the accuracy of layer thickness. X-Y movements are controlled using timing belt and stepper motors for precise movements of LASER source. Feeder mechanism is then developed to control uniformity of layer thickness metal powder. Simultaneously, the study is carried out for selection of material. Various types of metal powders can be used for metal RP as Single metal powder, mixture of two metals powder, and combination of metal and polymer powder. Conclusion leads to use of mixture of two metals powder to minimize the problems such as, balling effect and porosity. Developed System can be validated by conducting various experiments on manufactured part to check mechanical and metallurgical properties. After studying the results of these experiments, various process parameters as LASER properties (as power, speed etc.), and material properties (as grain size and structure etc.) will be optimized. This work is mainly focused on the design and development of cost effective experimental setup of metal rapid prototyping using SLS technique which will gives the feel of

  11. Performance optimization in electro- discharge machining using a suitable multiresponse optimization technique

    Directory of Open Access Journals (Sweden)

    I. Nayak

    2017-06-01

    Full Text Available In the present research work, four different multi response optimization techniques, viz. multiple response signal-to-noise (MRSN ratio, weighted signal-to-noise (WSN ratio, Grey relational analysis (GRA and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian methods have been used to optimize the electro-discharge machining (EDM performance characteristics such as material removal rate (MRR, tool wear rate (TWR and surface roughness (SR simultaneously. Experiments have been planned on a D2 steel specimen based on L9 orthogonal array. Experimental results are analyzed using the standard procedure. The optimum level combinations of input process parameters such as voltage, current, pulse-on-time and pulse-off-time, and percentage contributions of each process parameter using ANOVA technique have been determined. Different correlations have been developed between the various input process parameters and output performance characteristics. Finally, the optimum performances of these four methods are compared and the results show that WSN ratio method is the best multiresponse optimization technique for this process. From the analysis, it is also found that the current has the maximum effect on the overall performance of EDM operation as compared to other process parameters.

  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. Student Media Usage Patterns and Non-Traditional Learning in Higher Education

    Directory of Open Access Journals (Sweden)

    Olaf Zawacki-Richter

    2015-04-01

    Full Text Available A total of 2,338 students at German universities participated in a survey, which investigated media usage patterns of so-called traditional and non-traditional students (Schuetze & Wolter, 2003. The students provided information on the digital devices that they own or have access to, and on their usage of media and e-learning tools and services for their learning. A distinction was made between external, formal and internal, informal tools and services. Based on the students’ responses, a typology of media usage patterns was established by means of a latent class analysis (LCA. Four types or profiles of media usage patterns were identified. These types were labeled entertainment users, peripheral users, advanced users and instrumental users. Among non-traditional students, the proportion of instrumental users was rather high. Based on the usage patterns of traditional and non-traditional students, implications for media selection in the instructional design process are outlined in the paper.

  14. Machine Learning Techniques for Optical Performance Monitoring from Directly Detected PDM-QAM Signals

    DEFF Research Database (Denmark)

    Thrane, Jakob; Wass, Jesper; Piels, Molly

    2017-01-01

    Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal...... detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical...

  15. 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.

  16. Machine learning techniques in disease forecasting: a case study on rice blast prediction

    Directory of Open Access Journals (Sweden)

    Kapoor Amar S

    2006-11-01

    Full Text Available Abstract Background Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG approach achieved an average correlation coefficient (r of 0.50, which increased to 0.60 and percent mean absolute error (%MAE decreased from 65.42 to 52.24 when back-propagation neural network (BPNN was used. With generalized regression neural network (GRNN, the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also

  17. Lighting the Gym: A Guide to Illuminating Non-Traditional Spaces.

    Science.gov (United States)

    Womack, Jennifer; Nelson, Steve

    2000-01-01

    Covers all the steps needed to light an open, non-traditional performance space--everything from where to locate lights, support towers, and power sources, to cable and dimmer requirements. Covers safety issues, equipment costs, what students should and should not be allowed to do, and how to deal with electricians and rental companies. (SC)

  18. Women into Non-Traditional Sectors: Addressing Gender Segregation in the Northern Ireland Workplace

    Science.gov (United States)

    Potter, Michael; Hill, Myrtle

    2009-01-01

    The horizontal segregation of the workforce along gender lines tends to assign women to lower paid, lower status employment. Consequently, schemes to address segregation have focused on preparing women to enter non-traditional occupations through training and development processes. This article examines models to encourage women into…

  19. Student Media Usage Patterns and Non-Traditional Learning in Higher Education

    Science.gov (United States)

    Zawacki-Richter, Olaf; Müskens, Wolfgang; Krause, Ulrike; Alturki, Uthman; Aldraiweesh, Ahmed

    2015-01-01

    A total of 2,338 students at German universities participated in a survey, which investigated media usage patterns of so-called traditional and non-traditional students (Schuetze & Wolter, 2003). The students provided information on the digital devices that they own or have access to, and on their usage of media and e-learning tools and…

  20. The Revival of Non-Traditional State Actors' Interests in Africa

    DEFF Research Database (Denmark)

    Kragelund, Peter

    2012-01-01

    credit ratings make external finance available for African governments. This article examines how non-traditional state actors affect the possibility of African governments setting and funding their own development priorities. It argues that while the current situation may increase the policy autonomy...

  1. Differences Do Make a Difference: Recruitment Strategies for the Non-Traditional Student.

    Science.gov (United States)

    Zamanou, Sonia

    Many colleges and universities lack a comprehensive, fully integrated marketing plan to combat high attrition rates in programs offered to non-traditional students. A clear understanding of the needs of the marketplace is crucial to an effective marketing program. Research suggests that life transitions are what motivate adults to pursue…

  2. Non-Traditional Students and Critical Pedagogy: Transformative Practice and the Teaching of Criminal Law

    Science.gov (United States)

    Menis, Susanna

    2017-01-01

    This article explores the practical implication of adopting critical pedagogy, and more specifically critical legal pedagogy, in the teaching of non-traditional students in higher education context. It is based on the teaching of criminal law at Birkbeck School of Law, addressing learning tasks which have been designed to enhance students'…

  3. Barriers to Blended Digital Distance Vocational Learning for Non-Traditional Students

    Science.gov (United States)

    Safford, Kimberly; Stinton, Julia

    2016-01-01

    This research identifies and examines the challenges of blending digital distance and vocational learning for non-traditional and low-socio-economic status students who are new to university education. A survey of students in vocational primary education and early years qualifications in a distance university is illuminated by interviews with…

  4. Export contracts for non-traditional products: Chayote from Costa Rica

    NARCIS (Netherlands)

    Saénz, F.; Ruben, R.

    2004-01-01

    This paper focuses on the determinants of market and contract choice for non-traditional crops and the possibilities for involving local producers in global agro-food chains through delivery relationships with packers and brokers. Main attention is given to the importance of quality for entering the

  5. Motivational Orientations of Non-Traditional Adult Students to Enroll in a Degree-Seeking Program

    Science.gov (United States)

    Francois, Emmanuel Jean

    2014-01-01

    The purpose of this research was to investigate the motivational orientations of non-traditional adult students to enroll in a degree-seeking program based on their academic goal. The Education Participation Scale (EPS) was used to measure the motivational orientations of participants. Professional advancement, cognitive interest, and educational…

  6. Enhancing Critical Thinking Skills and Writing Skills through the Variation in Non-Traditional Writing Task

    Science.gov (United States)

    Sinaga, Parlindungan; Feranie, Shelly

    2017-01-01

    The research aims to identify the impacts of embedding non-traditional writing tasks within the course of modern physics conducted to the students of Physics Education and Physics Study Programs. It employed a quasi-experimental method with the pretest-posttest control group design. The used instruments were tests on conceptual mastery, tests on…

  7. Access to and Use of Export Market Information by Non- Traditional ...

    African Journals Online (AJOL)

    Ghana has traditionally depended on a number of export commodities such as cocoa, timber, gold and diamonds for its economic and social development. Recent economic policies of government have aimed to expand the country's exports to include non-traditional exports such as horticultural products, textiles, fishery ...

  8. Testing Algorithmic Skills in Traditional and Non-Traditional Programming Environments

    Science.gov (United States)

    Csernoch, Mária; Biró, Piroska; Máth, János; Abari, Kálmán

    2015-01-01

    The Testing Algorithmic and Application Skills (TAaAS) project was launched in the 2011/2012 academic year to test first year students of Informatics, focusing on their algorithmic skills in traditional and non-traditional programming environments, and on the transference of their knowledge of Informatics from secondary to tertiary education. The…

  9. Using Virtual Reality for Task-Based Exercises in Teaching Non-Traditional Students of German

    Science.gov (United States)

    Libbon, Stephanie

    2004-01-01

    Using task-based exercises that required web searches and online activities, this course introduced non-traditional students to the sights and sounds of the German culture and language and simultaneously to computer technology. Through partner work that required negotiation of the net as well as of the language, these adult beginning German…

  10. 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.

  11. Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

    Directory of Open Access Journals (Sweden)

    Ivana Šemanjski

    2015-12-01

    Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

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

    OpenAIRE

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

    2005-01-01

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

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Online laboratory evaluation of seeding-machine application by an acoustic technique

    Energy Technology Data Exchange (ETDEWEB)

    Karimi, H.; Navid, H.; Mahmoudi, A.

    2015-07-01

    Researchers and planter manufacturers have been working closely to develop an automated system for evaluating performance of seeding. In the present study, an innovative use of acoustic signal for laboratory evaluation of seeding-machine application is described. Seed detection technique of the proposed system was based on a rising voltage value that a microphone sensed in each impaction of seeds to a steel plate. Online determining of seed spacing was done with a script which was written in MATLAB software. To evaluate the acoustic system with desired seed spacing, a testing rig was designed. Seeds of wheat, corn and pelleted tomato were used as experimental material. Typical seed patterns were positioned manually on a belt stand with different spacing patterns. When the belt was running, the falling seeds from the end point of the belt impacted to the steel plate, and their acoustic signal was sensed by the microphone. In each impact, data was processed and spacing between the seeds was automatically obtained. Coefficient of determination of gathered data from the belt system and the corresponding seeds spacing measured with the acoustic system in all runs was about 0.98. This strong correlation indicates that the acoustic system worked well in determining the seeds spacing. (Author)

  18. 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.

  19. 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.

  20. Advanced Digitization Techniques in Retrieval of Mechanism and Machine Science Resources

    Science.gov (United States)

    Lovasz, E.-Ch.; Gruescu, C. M.; Ciupe, V.; Carabas, I.; Margineanu, D.; Maniu, I.; Dehelean, N.

    The European project thinkMOTION works on the purpose of retrieving all-times content regarding mechanisms and machine science by means of creating a digital library, accessible to a broad public through the portal Europeana. DMG-Lib is intended to display the development in the field, from its very beginning up to now days. There is a large range of significant objects available, physically very heterogeneous and needing all to be digitized. The paper presents the workflow, the equipments and specific techniques used in digitization of documents featuring very different characteristics (size, texture, color, degree of preservation, resolution and so on). Once the workflow established on very detailed steps, the development of the workstation is treated. Special equipments designed and assembled at Universitatea "Politehnica" Timisoara are presented. A large series of software applications, including original programs, work for digitization itself, processing of images, management of files, automatic optoelectronic control of capture, storage of information in different stages of processing. An illustrating example is explained, showing the steps followed in order to obtain a clear, high-resolution image from an old original document (very valuable as a historical proof but very poor in quality regarding clarity, contrast and resolution).

  1. A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

    OpenAIRE

    Ademujimi , Toyosi ,; Brundage , Michael ,; Prabhu , Vittaldas ,

    2017-01-01

    Part 6: Intelligent Diagnostics and Maintenance Solutions; International audience; Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learnin...

  2. 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…

  3. Current breathomics-a review on data pre-processing techniques and machine learning in metabolomics breath analysis

    DEFF Research Database (Denmark)

    Smolinska, A.; Hauschild, A. C.; Fijten, R. R. R.

    2014-01-01

    been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start...... different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus...

  4. Modelling risk of tick exposure in southern Scandinavia using machine learning techniques, satellite imagery, and human population density maps

    DEFF Research Database (Denmark)

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

    30 sites (forests and meadows) in each of Denmark, southern Norway and south-eastern Sweden. At each site we measured presence/absence of ticks, and used the data obtained along with environmental satellite images to run Boosted Regression Tree machine learning algorithms to predict overall spatial...... and Sweden), areas with high population densities tend to overlap with these zones.Machine learning techniques allow us to predict for larger areas without having to perform extensive sampling all over the region in question, and we were able to produce models and maps with high predictive value. The results...

  5. The impact of gender ideologies on men's and women's desire for a traditional or non-traditional partner

    OpenAIRE

    Thomae, M.; Houston, Diane

    2016-01-01

    Two studies examine preferences for a long-term partner who conforms to traditional or non- traditional gender\\ud roles. The studies both demonstrate a link between benevolent sexism and preference for a traditional partner.\\ud However, Study 1 also demonstrates a strong preference among women for a non-traditional partner. We measured\\ud ambivalent sexist ideologies before introducing participants to either a stereotypically traditional or stereotypically non-traditional character of the opp...

  6. 3D CT cerebral angiography technique using a 320-detector machine with a time–density curve and low contrast medium volume: Comparison with fixed time delay technique

    International Nuclear Information System (INIS)

    Das, K.; Biswas, S.; Roughley, S.; Bhojak, M.; Niven, S.

    2014-01-01

    Aim: To describe a cerebral computed tomography angiography (CTA) technique using a 320-detector CT machine and a small contrast medium volume (35 ml, 15 ml for test bolus). Also, to compare the quality of these images with that of the images acquired using a larger contrast medium volume (90 or 120 ml) and a fixed time delay (FTD) of 18 s using a 16-detector CT machine. Materials and methods: Cerebral CTA images were acquired using a 320-detector machine by synchronizing the scanning time with the time of peak enhancement as determined from the time–density curve (TDC) using a test bolus dose. The quality of CTA images acquired using this technique was compared with that obtained using a FTD of 18 s (by 16-detector CT), retrospectively. Average densities in four different intracranial arteries, overall opacification of arteries, and the degree of venous contamination were graded and compared. Results: Thirty-eight patients were scanned using the TDC technique and 40 patients using the FTD technique. The arterial densities achieved by the TDC technique were higher (significant for supraclinoid and basilar arteries, p < 0.05). The proportion of images deemed as having “good” arterial opacification was 95% for TDC and 90% for FTD. The degree of venous contamination was significantly higher in images produced by the FTD technique (p < 0.001%). Conclusion: Good diagnostic quality CTA images with significant reduction of venous contamination can be achieved with a low contrast medium dose using a 320-detector machine by coupling the time of data acquisition with the time of peak enhancement

  7. Non-Traditional Authorship Attribution Studies of William Shakespeare’s Canon: Some Caveats

    Directory of Open Access Journals (Sweden)

    Joseph Rudman

    2016-03-01

    Full Text Available The paper looks at the problems in conducting non-traditional authorship attribution studies on the canon of William Shakespeare. After a short introduction, the case is put forth that these studies are ‘scientific’ and must adhere to the tenets of the scientific method. By showing that a complete and valid experimental plan is necessary and pointing out the many and varied pitfalls (e.g., the text, the control groups, the treatment of errors, it becomes clear what a valid study of Shakespearean non-traditional authorship attribution demands. I then come to the conclusion that such a valid study is not attainable with the limits of present-day knowledge.

  8. Rethinking energy security in Asia. A non-traditional view of human security

    Energy Technology Data Exchange (ETDEWEB)

    Caballero-Anthony, Mely [Nanyang Technological Univ., Singapore (SG). Centre for Non-Traditional Security (NTS) Studies; Chang, Youngho [Nanyang Technological Univ., Singapore (Singapore). Division of Economics; Putra, Nur Azha (eds.) [National Univ. of Singapore (Singapore). Energy Security Division

    2012-07-01

    Traditional notions of security are premised on the primacy of state security. In relation to energy security, traditional policy thinking has focused on ensuring supply without much emphasis on socioeconomic and environmental impacts. Non-traditional security (NTS) scholars argue that threats to human security have become increasingly prominent since the end of the Cold War, and that it is thus critical to adopt a holistic and multidisciplinary approach in addressing rising energy needs. This volume represents the perspectives of scholars from across Asia, looking at diverse aspects of energy security through a non-traditional security lens. The issues covered include environmental and socioeconomic impacts, the role of the market, the role of civil society, energy sustainability and policy trends in the ASEAN region.

  9. Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

    Science.gov (United States)

    Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

    2017-08-04

    Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the

  10. Syrian Refugees: Are They a Non Traditional Threat to Water Supplies in Lebanon and Jordan

    Science.gov (United States)

    2016-09-01

    effects of Syrian refugees on the water supplies of each country as a non-traditional security threat. Political stability is the ultimate goal of each...security.html. 11 against Syrians sets the stage for political instability because the Syrians represent an increasing portion of the population, if...of political instability could send shockwaves through the region and drastically alter U.S. foreign policy in the Middle East. Though the stakes

  11. Export contracts for non-traditional products: Chayote from Costa Rica

    OpenAIRE

    Saénz, F.; Ruben, R.

    2004-01-01

    This paper focuses on the determinants of market and contract choice for non-traditional crops and the possibilities for involving local producers in global agro-food chains through delivery relationships with packers and brokers. Main attention is given to the importance of quality for entering the export market and the impact of contractual arrangements on loyal behaviour. Core stipulations in the contract regarding the frequency of delivery and the provision of technical assistance are med...

  12. Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

    DEFF Research Database (Denmark)

    Zibar, Darko; de Carvalho, Luis Henrique Hecker; Piels, Molly

    2015-01-01

    In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms...

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

    OpenAIRE

    Zhao, Yijun; Healy, Brian C.; Rotstein, Dalia; Guttmann, Charles R. G.; Bakshi, Rohit; Weiner, Howard L.; Brodley, Carla E.; Chitnis, Tanuja

    2017-01-01

    Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 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. Results Baseline data...

  14. 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

  15. In vitro biological characterization of macroporous 3D Bonelike structures prepared through a 3D machining technique

    International Nuclear Information System (INIS)

    Laranjeira, M.S.; Dias, A.G.; Santos, J.D.; Fernandes, M.H.

    2009-01-01

    3D bioactive macroporous structures were prepared using a 3D machining technique. A virtual 3D structure model was created and a computer numerically controlled (CNC) milling device machined Bonelike samples. The resulting structures showed a reproducible macroporosity and interconnective structure. Macropores size after sintering was approximately 2000 μm. In vitro testing using human bone marrow stroma showed that cells were able to adhere and proliferate on 3D structures surface and migrate into all macropore channels. In addition, these cells were able to differentiate, since mineralized globular structures associated with cell layer were identified. Results obtained showed that 3D structures of Bonelike successfully allow cell migration into all macropores, and allow human bone marrow stromal cells to proliferate and differentiate. This innovative technique may be considered as a step-forward preparation for 3D interconnective macroporous structures that allow bone ingrowth while maintaining mechanical integrity.

  16. The Impact of Intrinsic and Extrinsic Motivation on the Academic Achievement of Non-Traditional Undergraduate Students

    Science.gov (United States)

    Arce, Alma Lorenia

    2017-01-01

    Non-traditional students have become a growing component of the student population in today's college systems. Research has shown that non-traditional students are less likely to achieve academically and complete their degree programs compared to traditional students. The purpose of this quantitative, correlational study was to investigate the…

  17. The smart aerial release machine, a universal system for applying the sterile insect technique: Manuscript Draft

    International Nuclear Information System (INIS)

    Mubarqui, Leal Ruben; Perez, Rene Cano; Klad, Roberto Angulo; Lopez, Jose L. Zavale; Parker, Andrew; Seck, Momar Talla; Sall, Baba; Bouyer, Jeremy

    2014-01-01

    Beyond insecticides, alternative methods to control insect pests for agriculture and vectors of diseases are needed. Management strategies involving the mass-release of living control agents have been developed, including genetic control with sterile insects and biological control with parasitoids, for which aerial release of insects is often required. Aerial release in genetic control programmes often involves the use of chilled sterile insects, which can improve dispersal, survival and competitiveness of sterile males. Currently available means of aerially releasing chilled fruit flies are however insufficiently precise to ensure homogeneous distribution at low release rates and no device is available for tsetse. Here we present the smart aerial release machine, a new design by the Mubarqui Company, based on the use of vibrating conveyors. The machine is controlled through Bluetooth by a tablet with Android Operating System including a completely automatic guidance and navigation system (MaxNav software). The tablet is also connected to an online relational database facilitating the preparation of flight schedules and automatic storage of flight reports. The new machine was compared with a conveyor release machine in Mexico using two fruit flies species (Anastrepha ludens and Ceratitis capitata) and we obtained better dispersal homogeneity (% of positive traps, p < 0.001) for both species and better recapture rates for Anastrepha ludens (p < 0.001), especially at low release densities (<1500 per ha). We also demonstrated that the machine can replace paper boxes for aerial release of tsetse in Senegal.This technology limits damages to insects and allows a large range of release rates from 10 flies/km"2 for tsetse flies up to 600 000 flies/km"2 for fruit flies. The potential of this machine to release other species like mosquitoes is discussed. Plans and operating of the machine are provided to allow its use worldwide.

  18. The smart aerial release machine, a universal system for applying the sterile insect technique.

    Directory of Open Access Journals (Sweden)

    Ruben Leal Mubarqui

    Full Text Available Beyond insecticides, alternative methods to control insect pests for agriculture and vectors of diseases are needed. Management strategies involving the mass-release of living control agents have been developed, including genetic control with sterile insects and biological control with parasitoids, for which aerial release of insects is often required. Aerial release in genetic control programmes often involves the use of chilled sterile insects, which can improve dispersal, survival and competitiveness of sterile males. Currently available means of aerially releasing chilled fruit flies are however insufficiently precise to ensure homogeneous distribution at low release rates and no device is available for tsetse.Here we present the smart aerial release machine, a new design by the Mubarqui Company, based on the use of vibrating conveyors. The machine is controlled through Bluetooth by a tablet with Android Operating System including a completely automatic guidance and navigation system (MaxNav software. The tablet is also connected to an online relational database facilitating the preparation of flight schedules and automatic storage of flight reports. The new machine was compared with a conveyor release machine in Mexico using two fruit flies species (Anastrepha ludens and Ceratitis capitata and we obtained better dispersal homogeneity (% of positive traps, p<0.001 for both species and better recapture rates for Anastrepha ludens (p<0.001, especially at low release densities (<1500 per ha. We also demonstrated that the machine can replace paper boxes for aerial release of tsetse in Senegal.This technology limits damages to insects and allows a large range of release rates from 10 flies/km2 for tsetse flies up to 600,000 flies/km2 for fruit flies. The potential of this machine to release other species like mosquitoes is discussed. Plans and operating of the machine are provided to allow its use worldwide.

  19. Machine-learning techniques for family demography: an application of random forests to the analysis of divorce determinants in Germany

    OpenAIRE

    Arpino, Bruno; Le Moglie, Marco; Mencarini, Letizia

    2018-01-01

    Demographers often analyze the determinants of life-course events with parametric regression-type approaches. Here, we present a class of nonparametric approaches, broadly defined as machine learning (ML) techniques, and discuss advantages and disadvantages of a popular type known as random forest. We argue that random forests can be useful either as a substitute, or a complement, to more standard parametric regression modeling. Our discussion of random forests is intuitive and...

  20. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques

    OpenAIRE

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-01-01

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content...

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

    Science.gov (United States)

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

    2017-06-14

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

  2. ADAPTING HYBRID MACHINE TRANSLATION TECHNIQUES FOR CROSS-LANGUAGE TEXT RETRIEVAL SYSTEM

    Directory of Open Access Journals (Sweden)

    P. ISWARYA

    2017-03-01

    Full Text Available This research work aims in developing Tamil to English Cross - language text retrieval system using hybrid machine translation approach. The hybrid machine translation system is a combination of rule based and statistical based approaches. In an existing word by word translation system there are lot of issues and some of them are ambiguity, Out-of-Vocabulary words, word inflections, and improper sentence structure. To handle these issues, proposed architecture is designed in such a way that, it contains Improved Part-of-Speech tagger, machine learning based morphological analyser, collocation based word sense disambiguation procedure, semantic dictionary, and tense markers with gerund ending rules, and two pass transliteration algorithm. From the experimental results it is clear that the proposed Tamil Query based translation system achieves significantly better translation quality over existing system, and reaches 95.88% of monolingual performance.

  3. Possibilities of radiation technique application in machine-building industry of Bulgaria

    International Nuclear Information System (INIS)

    Petrov, A.; Avramov, D.; Kostov, St.

    1979-01-01

    In last ten years, in development of machine-building industry, tendency has been outlined for creation of machines and constructions having minimum weight and elevated reliability from one side due to improvement of design and technology of production and from the other side due to application of materials with improved parameters. Solution of these problems is closely connected with application of the radiation methods. State-of-art of the radiation technology application in the machine-building industry is analyzed and mainly for investigation of wear resistance of friction machineparts. Use of spatial radioactive labelling in investigation of materials and application of radiation methods for optimization of technological processes in metallurgy, foundry and so on is considered. Estimation is give of perspectives of further growth of introduction of radiation methods in Bulgaria [ru

  4. Chemically intuited, large-scale screening of MOFs by machine learning techniques

    Science.gov (United States)

    Borboudakis, Giorgos; Stergiannakos, Taxiarchis; Frysali, Maria; Klontzas, Emmanuel; Tsamardinos, Ioannis; Froudakis, George E.

    2017-10-01

    A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

  5. 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.

  6. NON-TRADITIONAL SPORTS AT SCHOOL. BENEFITS FOR PHYSICAL AND MOTOR DEVELOPMENT

    Directory of Open Access Journals (Sweden)

    AMADOR J. LARA-SÁNCHEZ

    2010-12-01

    Full Text Available Physical Education teachers have been using some very classic team sports, like football, basketball, handball, volleyball, etc. for many years in order to develop their education work at school. As a consequence of this, the benefits of this kind of activities on Physical Education lessons have not been as notable as we mighthave expected, since, even if they are increasing, their development and application are still low. There are many and very varied new non-traditional sports that have emerged and extended across Spain in recent years. To mention an example, we could refer to a newly created non-traditional sport such as kin-ball. This sport wascreated for the purpose of achieving a way to combine several factors such as health, team-work and competitiveness. Three teams of four players each participate. This way, every player can participate to a great extent in all the moves of the match, for each of them must defend one area of their half in order to achieve a common objective. Besides, kin-ball helps to develop motor skills at school in an easy way; that is, coordination, balance and perception. There is a large variety of non-traditional games and sports that are similar to kin-ball, such as floorball, intercrosse, mazaball, tchoukball, ultimate, indiaca, shuttleball... All of them show many physical, psychic and social advantages, and can help us to make the Physical Education teaching-learning process more motivating, acquiring the recreational component that it showed some years ago and which hasnow disappeared

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

    Directory of Open Access Journals (Sweden)

    Steren Chabert

    2017-01-01

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

  8. Book review: OF OTHER THOUGHTS: NON-TRADITIONAL WAYS TO THE

    Directory of Open Access Journals (Sweden)

    Johan Verbeke

    2014-12-01

    Full Text Available Research paradigms in the fields of architecture and arts have been developing and changing during the last decade. Part of this development is a shift to include design work and artistic work into the knowledge processes of doctoral work. This work evidently also needs supervision. At the same time doctoral degrees have been developing in relation to indigenous ways of thinking. The book Other Thoughts: Non-Traditional Ways to the Doctorate discusses the challenges one is facing, either as a PhD student or as a supervisor, when doing or supervising a PhD in a less established field.

  9. Cardiometabolic Risks in Polycystic Ovary Syndrome: Non-Traditional Risk Factors and the Impact of Obesity.

    Science.gov (United States)

    Chiu, Wei-Ling; Boyle, Jacqueline; Vincent, Amanda; Teede, Helena; Moran, Lisa J

    2017-01-01

    Polycystic ovary syndrome (PCOS) is a common and complex endocrinopathy with reproductive, metabolic, and psychological features and significantly increased cardiometabolic risks. PCOS is underpinned by inherent insulin resistance and hyperandrogenism. Obesity, more common in PCOS, plays an important role in the pathophysiology, exacerbating hyperinsulinaemia and hyperandrogenism, leading to recommended first-line lifestyle intervention. Significant traditional and non-traditional risk factors are implicated in PCOS in addition to obesity-exacerbated cardiometabolic risks and are explored in this review to promote the understanding of this common metabolic and reproductive condition. © 2016 S. Karger AG, Basel.

  10. Elevating Virtual Machine Introspection for Fine-Grained Process Monitoring: Techniques and Applications

    Science.gov (United States)

    Srinivasan, Deepa

    2013-01-01

    Recent rapid malware growth has exposed the limitations of traditional in-host malware-defense systems and motivated the development of secure virtualization-based solutions. By running vulnerable systems as virtual machines (VMs) and moving security software from inside VMs to the outside, the out-of-VM solutions securely isolate the anti-malware…

  11. Engagement techniques and playing level impact the biomechanical demands on rugby forwards during machine-based scrummaging.

    Science.gov (United States)

    Preatoni, Ezio; Stokes, Keith A; England, Michael E; Trewartha, Grant

    2015-04-01

    This cross-sectional study investigated the factors that may influence the physical loading on rugby forwards performing a scrum by studying the biomechanics of machine-based scrummaging under different engagement techniques and playing levels. 34 forward packs from six playing levels performed repetitions of five different types of engagement techniques against an instrumented scrum machine under realistic training conditions. Applied forces and body movements were recorded in three orthogonal directions. The modification of the engagement technique altered the load acting on players. These changes were in a similar direction and of similar magnitude irrespective of the playing level. Reducing the dynamics of the initial engagement through a fold-in procedure decreased the peak compression force, the peak downward force and the engagement speed in excess of 30%. For example, peak compression (horizontal) forces in the professional teams changed from 16.5 (baseline technique) to 8.6 kN (fold-in procedure). The fold-in technique also reduced the occurrence of combined high forces and head-trunk misalignment during the absorption of the impact, which was used as a measure of potential hazard, by more than 30%. Reducing the initial impact did not decrease the ability of the teams to produce sustained compression forces. De-emphasising the initial impact against the scrum machine decreased the mechanical stresses acting on forward players and may benefit players' welfare by reducing the hazard factors that may induce chronic degeneration of the spine. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  12. Scoping Study of Machine Learning Techniques for Visualization and Analysis of Multi-source Data in Nuclear Safeguards

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Yonggang

    2018-05-07

    In implementation of nuclear safeguards, many different techniques are being used to monitor operation of nuclear facilities and safeguard nuclear materials, ranging from radiation detectors, flow monitors, video surveillance, satellite imagers, digital seals to open source search and reports of onsite inspections/verifications. Each technique measures one or more unique properties related to nuclear materials or operation processes. Because these data sets have no or loose correlations, it could be beneficial to analyze the data sets together to improve the effectiveness and efficiency of safeguards processes. Advanced visualization techniques and machine-learning based multi-modality analysis could be effective tools in such integrated analysis. In this project, we will conduct a survey of existing visualization and analysis techniques for multi-source data and assess their potential values in nuclear safeguards.

  13. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

    Science.gov (United States)

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-08-15

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

  14. New and non-traditional mineral raw materials deposits, perspectives of use

    International Nuclear Information System (INIS)

    Beyseev, O.; Beyseev, A.; Baichigasov, I.; Sergev, E.; Shakirova, G.

    1996-01-01

    Deposits of new and non-traditional kinds of mineral raw material are revealed, explored and prepared to industrial recovery in Kazakstan, that can be used in frames of conversion process to create new materials with unique properties, to prepare base for new technologies elaboration, and to achieve appreciable economic benefit. These deposits are located mostly in geographic and economic conditions of advanced infrastructure and mining works network, favorable for recovery.On the tests results the following is of heaviest interest: RHODUCITE, NEMALITE-CONTAINING CHRYSOTILE-ASBESTOS, NICKEL-CONTAINING SILICATE-ASBOLAN ORES, MEDICINAL MINERALS, SHUNGITES, FULLERENES, RAW QUARTZ MINERALS - the group of deposits containing 5 min tons of high quality quartz good for manufacture of cut-glass and fibre-optical articles, is explored in details. There are also deposits of other kinds of non-traditional strategic mineral raw material in the Republic of Kazakstan - natural fillers, that can be used in the national economy of the country and bring considerable economic benefit: chrysotile-asbestos, amphibole-asbestos, talk, vollastonite, tremolite, actinolite, vermiculite, zeolite, etc

  15. A Quantitative Proteomics Approach to Clinical Research with Non-Traditional Samples

    Directory of Open Access Journals (Sweden)

    Rígel Licier

    2016-10-01

    Full Text Available The proper handling of samples to be analyzed by mass spectrometry (MS can guarantee excellent results and a greater depth of analysis when working in quantitative proteomics. This is critical when trying to assess non-traditional sources such as ear wax, saliva, vitreous humor, aqueous humor, tears, nipple aspirate fluid, breast milk/colostrum, cervical-vaginal fluid, nasal secretions, bronco-alveolar lavage fluid, and stools. We intend to provide the investigator with relevant aspects of quantitative proteomics and to recognize the most recent clinical research work conducted with atypical samples and analyzed by quantitative proteomics. Having as reference the most recent and different approaches used with non-traditional sources allows us to compare new strategies in the development of novel experimental models. On the other hand, these references help us to contribute significantly to the understanding of the proportions of proteins in different proteomes of clinical interest and may lead to potential advances in the emerging field of precision medicine.

  16. A Quantitative Proteomics Approach to Clinical Research with Non-Traditional Samples.

    Science.gov (United States)

    Licier, Rígel; Miranda, Eric; Serrano, Horacio

    2016-10-17

    The proper handling of samples to be analyzed by mass spectrometry (MS) can guarantee excellent results and a greater depth of analysis when working in quantitative proteomics. This is critical when trying to assess non-traditional sources such as ear wax, saliva, vitreous humor, aqueous humor, tears, nipple aspirate fluid, breast milk/colostrum, cervical-vaginal fluid, nasal secretions, bronco-alveolar lavage fluid, and stools. We intend to provide the investigator with relevant aspects of quantitative proteomics and to recognize the most recent clinical research work conducted with atypical samples and analyzed by quantitative proteomics. Having as reference the most recent and different approaches used with non-traditional sources allows us to compare new strategies in the development of novel experimental models. On the other hand, these references help us to contribute significantly to the understanding of the proportions of proteins in different proteomes of clinical interest and may lead to potential advances in the emerging field of precision medicine.

  17. Non-traditional stable isotope behaviors in immiscible silica-melts in a mafic magma chamber.

    Science.gov (United States)

    Zhu, Dan; Bao, Huiming; Liu, Yun

    2015-12-01

    Non-traditional stable isotopes have increasingly been applied to studies of igneous processes including planetary differentiation. Equilibrium isotope fractionation of these elements in silicates is expected to be negligible at magmatic temperatures (δ(57)Fe difference often less than 0.2 per mil). However, an increasing number of data has revealed a puzzling observation, e.g., the δ(57)Fe for silicic magmas ranges from 0‰ up to 0.6‰, with the most positive δ(57)Fe almost exclusively found in A-type granitoids. Several interpretations have been proposed by different research groups, but these have so far failed to explain some aspects of the observations. Here we propose a dynamic, diffusion-induced isotope fractionation model that assumes Si-melts are growing and ascending immiscibly in a Fe-rich bulk magma chamber. Our model offers predictions on the behavior of non-traditional stable isotope such as Fe, Mg, Si, and Li that are consistent with observations from many A-type granitoids, especially those associated with layered intrusions. Diffusion-induced isotope fractionation may be more commonly preserved in magmatic rocks than was originally predicted.

  18. An Investigation of Women Engineers in Non-Traditional Occupations in the Thai Construction Industry

    Directory of Open Access Journals (Sweden)

    Nuanthip Kaewsri

    2011-06-01

    Full Text Available For over a decade, the public and the private sectors have carried out research aimed at attracting women engineers to the construction industry and retaining them. However, studies on women engineers working in other types of construction-related businesses apart from contractor companies such as consultancies, developers, etc., have not been many. This paper aims to examine the experiences of women engineers in non-traditional careers and the implications for their turnover. A literature search on women’s careers in construction was performed in conjunction with semi-structured interviews with a sampling of 141 individuals. Results from three viewpoints, viz those of professional men and women engineers in contractor companies, and women engineers in non-contractor companies, were found to differ in many respects, including their opinions about career advancement, career path and the difficulties involved. It was also found that women engineers in contractor companies were much more affected by problems such as sexual harassment, work-life conflicts and equal opportunity than women engineers in non-contractor companies. Turnover rates of women engineers and their reasons for leaving were examined. Women engineers, particularly those in contractor companies, had to confront more barriers in non-traditional careers than their male counterparts.  Nonetheless, working in non-contractor companies provides a viable alternative for women engineers who want to have successful careers in the Thai construction industry.

  19. Injury survey of a non-traditional 'soft-edged' trampoline designed to lower equipment hazards.

    Science.gov (United States)

    Eager, David B; Scarrott, Carl; Nixon, Jim; Alexander, Keith

    2013-01-01

    In Australia trampolines contribute one quarter of all childhood play equipment injuries. The objective of this study was to gather and evaluate injury data from a non-traditional, 'soft-edged', consumer trampoline, where the design aimed to minimise injuries from the equipment and from falling off. The manufacturer of the non-traditional trampoline provided the University of Technology Sydney with their Australian customer database. The study involved surveys in Queensland and New South Wales, between May 2007 and March 2010. Initially injury data was gathered by a phone interview pilot study, then in the full study, through an email survey. The 3817 respondents were the carers of child users of the 'soft-edge' trampolines. Responses were compared with Australian and US emergency department data. In both countries the proportion of injuries caused by the equipment and falling off was compared with the proportion caused by the jumpers to themselves or each other. The comparisons showed a significantly lower proportion resulted from falling-off or hitting the equipment for this design when compared to traditional trampolines, both in Australia and the US. This research concludes that equipment-induced and falling-off injuries, the more severe injuries on traditional trampolines, can be significantly reduced with appropriate trampoline design.

  20. 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.

  1. Free focus radiography with miniaturized dental x-ray machines: a comparison of ''midline'' and ''lateral'' techniques

    International Nuclear Information System (INIS)

    Jensen, T.W.

    1983-01-01

    The use of free focus radiography (FFR) employing miniaturized dental x-ray machines with radiation probes has never been generally accepted in dentistry despite its recognized radiographic potential. The present investigation studied ways to improve imaging and lower radiation burdens in dental free focus radiography. Relatively high air exposures ranging from 42,050 mR per film for high-resolution images to 3,214 mR per film for lower-resolution images using a current midline radiographic technique for panoramic FFR were found. In a proposed lateral FFR panoramic technique, reduced exposures ranged from 420 mR per film for high-resolution images to 14 mR per film for lower-resolution images. In each technique the lower exposure was obtained with a rare earth imaging system. A proposed modification of the current midline FFR technique using a rare earth imaging system and heavy added copper filtration was found to produce exposures in the range normally used in dentistry (207 mr), and the resultant image was high in contrast with relatively low detail. A comparison of essential characteristics of midline and lateral FFR techniques failed to identify specific advantages for the midline technique in current use. Lateral exposure modes in dental FFR should receive increased attention in the interest of good imaging and radiation control. It was noted that existing miniaturized dental x-ray machines may have been designed specifically for use of the midline FFR exposure technique, and modification of this equipment to support reliable lateral exposure modes was recommended

  2. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    KAUST Repository

    Harrou, Fouzi

    2016-05-09

    Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM). The proposed fault detection approach is based on the use of principal components analysis (PCA). However, conventional PCA-based detection indices, such as the T2T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA) control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes.

  3. The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine

    Science.gov (United States)

    Taha, Z.; Razman, M. A. M.; Adnan, F. A.; Ghani, A. S. Abdul; Majeed, A. P. P. Abdul; Musa, R. M.; Sallehudin, M. F.; Mukai, Y.

    2018-03-01

    Fish Hunger behaviour is one of the important element in determining the fish feeding routine, especially for farmed fishes. Inaccurate feeding routines (under-feeding or over-feeding) lead the fishes to die and thus, reduces the total production of fishes. The excessive food which is not eaten by fish will be dissolved in the water and thus, reduce the water quality (oxygen quantity in the water will be reduced). The reduction of oxygen (water quality) leads the fish to die and in some cases, may lead to fish diseases. This study correlates Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique. The behaviour is clustered with respect to the position of the centre of gravity of the school of fish prior feeding, during feeding and after feeding. The clustered fish behaviour is then classified by means of a machine learning technique namely Support vector machine (SVM). It has been shown from the study that the Fine Gaussian variation of SVM is able to provide a reasonably accurate classification of fish feeding behaviour with a classification accuracy of 79.7%. The proposed integration technique may increase the usefulness of the captured data and thus better differentiates the various behaviour of farmed fishes.

  4. High Classification Rates for Continuous Cow Activity Recognition using Low-cost GPS Positioning Sensors and Standard Machine Learning Techniques

    DEFF Research Database (Denmark)

    Godsk, Torben; Kjærgaard, Mikkel Baun

    2011-01-01

    activities. By preprocessing the raw cow position data, we obtain high classification rates using standard machine learning techniques to recognize cow activities. Our objectives were to (i) determine to what degree it is possible to robustly recognize cow activities from GPS positioning data, using low...... and their activities manually logged to serve as ground truth. For our dataset we managed to obtain an average classification success rate of 86.2% of the four activities: eating/seeking (90.0%), walking (100%), lying (76.5%), and standing (75.8%) by optimizing both the preprocessing of the raw GPS data...

  5. Technique to reduce the shaft torque stress at an induction machine

    Directory of Open Access Journals (Sweden)

    Adrian Tulbure

    2005-10-01

    Full Text Available For the active attenuation at load stress in the drive shaft, the control system should receive as input signal the instantaneous shaft torque value. In this context an intelligent observer for shaft tongue of mains operatea induction machine, which is able to responding by variation of LIF (Load Input Function[1] must be developed. Extensive computer simulation prove the effectiveness of the proposed solution. In order to obtain a practical validation, the stimulated regulator has been designed and tested in the Institute of Electrical Engineering in Clausthal/Germany [2]. This paper contains following parts: Developing the mathematical model, Practical realisation, Simulations and measurements, Evaluating the control solutions and Conclusions.

  6. Non-Traditional Security: The Case of Water Security in the Mekong Subregion

    Directory of Open Access Journals (Sweden)

    Haefner, Andrea

    2013-09-01

    Full Text Available In the first decade of the twenty-first century Non-Traditional Security (NTS challenges are of rising importance due to their increasing impact on daily life and broader national interests. This paper focuses on the Mekong Region as an important subregion due to its significance for more than 70 million people living directly on the river banks and its importance for the economic development of the six riparian countries. This paper investigates NTS challenges in the Mekong Subregion with a focus on environmental challenges and argues that NTS are of increasing importance in the region and will increase in the future. Whereas economic growth is crucial for the improvements of the livelihoods on the Mekong River and the overall economic performance of the riparian states, environmental protection cannot be disregarded as doing so would have devastating impact on the subregion and the wider region in the future.

  7. Non-Traditional Systemic Treatments for Diabetic Retinopathy: An
Evidence-Based Review

    Science.gov (United States)

    Simó, Rafael; Ballarini, Stefania; Cunha-Vaz, José; Ji, Linong; Haller, Hermann; Zimmet, Paul; Wong, Tien Y.

    2015-01-01

    The rapid escalation in the global prevalence diabetes, with more than 30% being afflicted with diabetic retinopathy (DR), means it is likely that associated vision-threatening conditions will also rise substantially. This means that new therapeutic approaches need to be found that go beyond the current standards of diabetic care, and which are effective in the early stages of the disease. In recent decades several new pharmacological agents have been investigated for their effectiveness in preventing the appearance and progression of DR or in reversing DR; some with limited success while others appear promising. This up-to-date critical review of non-traditional systemic treatments for DR is based on the published evidence in MEDLINE spanning 1980-December 2014. It discusses a number of therapeutic options, paying particular attention to the mechanisms of action and the clinical evidence for the use of renin-angiotensin system blockade, fenofibrate and calcium dobesilate monohydrate in DR. PMID:25989912

  8. The type specimens of Calyptratae (Diptera) housed in non-traditional institutions in Argentina.

    Science.gov (United States)

    Patitucci, Luciano Damián; Mulieri, Pablo Ricardo; Domínguez, M Cecilia; Mariluis, Juan Carlos

    2015-01-14

    The type material of species of Calyptratae Diptera belonging to Anthomyiidae, Calliphoridae, Fanniidae, Muscidae, Sarcophagidae, and Tachinidae, housed in the collections of non-traditional institutions in Argentina were examined. These collections were included in the recently created "Sistema Nacional de Datos Biológicos" (National Biological Data System). We examined four collections: "Administración Nacional de Laboratorios e Institutos de Salud 'Dr. Carlos G. Malbrán'" (ANLIS), "Instituto Nacional de Tecnología Agropecuaria, Castelar, Buenos Aires" (INTA), "Instituto Argentino de Investigaciones de las Zonas Áridas" (IADIZA); and "Fundación Félix de Azara" (CFA). Comparison of the original descriptions of these species with the label information revealed the existence of 24 holotypes, 5 lectotypes, 11 syntypes, and 441 paratypes/paralectotypes. Complete information is given for each type, including reference to the original description, label data, and preservation condition. 

  9. Extraction and properties of starches from the non-traditional vegetables Yam and Taro

    Energy Technology Data Exchange (ETDEWEB)

    Andrade, Luan Alberto; Barbosa, Natalia Alves; Pereira, Joelma, E-mail: luandrade87@yahoo.com.br [Universidade Federal de Lavras (UFLA), Lavras, MG (Brazil)

    2017-04-15

    The objective of this study was to assess the chemical, physical, morphological, crystalline and thermal properties of starch from two non-traditional vegetables, yam and taro. The analyses included proximate composition percent, amylose and mineral content, water absorption capacity, absolute density, morphological properties, X-ray diffractometry, thermal properties, pasting properties and infrared spectrum. The extracted starch exhibited a high purity level with low lipid, fiber and ash contents. The electron micrographs suggested that the taro starch granules were smaller than the yam starch granules. The results for the experimental conditions used in this study indicated that the studied starches differed, especially the amylose content, granule size and crystallinity degree and the pattern of the starches. Due to the high amylose content of yam starch, this type of starch can be used for film preparation, whereas the taro starch can be used as a fat substitute due to its small granule size. (author)

  10. Cathedral outreach: student-led workshops for school curriculum enhancement in non-traditional environments

    Science.gov (United States)

    Posner, Matthew T.; Jantzen, Alexander; van Putten, Lieke D.; Ravagli, Andrea; Donko, Andrei L.; Soper, Nathan; Wong, Nicholas H. L.; John, Pearl V.

    2017-08-01

    Universities in the United Kingdom have been driven to work with a larger pool of potential students than just the more traditional student (middle-class white male), in order to tackle the widely-accepted skills-shortage in the fields of science, technology, engineering and mathematics (STEM), whilst honoring their commitment to fair access to higher education. Student-led outreach programs have contributed significantly to this drive. Two such programs run by postgraduate students at the University of Southampton are the Lightwave Roadshow and Southampton Accelerate!, which focus on photonics and particle physics, respectively. The program ambassadors have developed activities to enhance areas of the national curriculum through presenting fundamental physical sciences and their applications to optics and photonics research. The activities have benefitted significantly from investment from international organizations, such as SPIE, OSA and the IEEE Photonics Society, and UK research councils, in conjunction with university recruitment and outreach strategies. New partnerships have been formed to expand outreach programs to work in non-traditional environments to challenge stereotypes of scientists. This paper presents two case studies of collaboration with education learning centers at Salisbury Cathedral and Winchester Cathedral. The paper outlines workshops and shows developed for pupils aged 6-14 years (UK key stages 2-4) on the electromagnetic spectrum, particle physics, telecommunications and the human eye using a combination of readily obtainable items, hand-built kits and elements from the EYEST Photonics Explorer kit. The activities are interactive to stimulate learning through active participation, complement the UK national curriculum and link the themes of science with the non-traditional setting of a cathedral. We present methods to evaluate the impact of the activity and tools to obtain qualitative feedback for continual program improvement. We also

  11. Diffusion of non-traditional cookstoves across western Honduras: A social network analysis

    International Nuclear Information System (INIS)

    Ramirez, Sebastian; Dwivedi, Puneet; Ghilardi, Adrian; Bailis, Robert

    2014-01-01

    A third of the world's population uses inefficient biomass stoves, contributing to severe health problems, forest degradation, and climate change. Clean burning, fuel-efficient, non-traditional cookstoves (NTCS) are a promising solution; however, numerous projects fail during the diffusion process. We use social network analysis to reveal patterns driving a successful stove intervention in western Honduras. The intervention lacks formal marketing, but has spread across a wide area in just a few years. To understand the process, we map the social network of active community members who drove diffusion across a large swath of the country. We find that most ACMs heard about stoves twice before sharing information about it with others and introducing the stove into their own communities. On average, the social distance between ACMs and the project team is 3 degrees of separation. Both men and women are critical to the diffusion process, but men tend to communicate over longer distances, while women principally communicate over shorter distances. Government officials are also crucial to diffusion. Understanding how information moves through social networks and across geographic space allows us to theorize how knowledge about beneficial technologies spreads in the absence of formal marketing and inform policies for NTCS deployment worldwide. - Highlights: • We build a chain of referrals to track spread of information about non traditional cookstoves. • We find differences among gender and occupations that should inform policy. • People hear about the stoves twice before becoming suppliers of information. • Government officials play a substantial role in the diffusion. • Males play leading role in diffusion over long distances, females in short distances

  12. A bit of both science and economics: a non-traditional STEM identity narrative

    Science.gov (United States)

    Mark, Sheron L.

    2017-10-01

    Black males, as one non-dominant population, remain underrepresented and less successful in science, technology, engineering, and mathematics (STEM). Researchers focused on non-dominant populations are advised against generalizations and to examine cultural intersections (i.e. race, ethnicity, gender, and more) and also to explore cases of success, in addition to cases of under-achievement and underrepresentation. This study has focused on one African American male, Randy, who expressed high-achieving STEM career goals in computer science and engineering. Furthermore, recognizing that culture and identity development underlie STEM engagement and persistence, this long-term case study focused on how Randy developed a STEM identity during the course of the study and the implications of that process for his STEM career exploration. Étienne Wenger's (1999) communities-of-practice (CoP) was employed as a theoretical framework and, in doing so, (1) the informal STEM program in which Randy participated was characterized as a STEM-for-social-justice CoP and (2) Randy participated in ways that consistently utilized an "economics" lens from beyond the boundaries of the CoP. In doing so, Randy functioned as a broker within the CoP and developed a non-traditional STEM identity-in-practice which integrated STEM, "economics", and community engagement. Randy's STEM identity-in-practice is discussed in terms of the contextual factors that support scientific identity development (Hazari et al. in J Res Sci Teach 47:978-1003, 2010), the importance of recognizing and supporting the development of holistic and non-traditional STEM identities, especially for diverse populations in STEM, and the implications of this new understanding of Randy's STEM identity for his long-term STEM career exploration.

  13. Optimization of fuel exchange machine operation for boiling water reactors using an artificial intelligence technique

    International Nuclear Information System (INIS)

    Sekimizu, K.; Araki, T.; Tatemichi, S.I.

    1987-01-01

    Optimization of fuel assembly exchange machine movements during periodic refueling outage is discussed. The fuel assembly movements during a fuel shuffling were examined, and it was found that the fuel assembly movements consist of two different movement sequences;one is the ''PATH,'' which begins at a discharged fuel assembly and terminates at a fresh fuel assembly, and the other is the ''LOOP,'' where fuel assemblies circulate in the core. It is also shown that fuel-loading patterns during the fuel shuffling can be expressed by the state of each PATH, which is the number of elements already accomplished in the PATH actions. Based on this fact, a scheme to determine a fuel assembly movement sequence within the constraint was formulated using the artificial intelligence language PROLOG. An additional merit to the scheme is that it can simultaneously evaluate fuel assembly movement, due to the control rods and local power range monitor exchange, in addition to normal fuel shuffling. Fuel assembly movements, for fuel shuffling in a 540-MW(electric) boiling water reactor power plant, were calculated by this scheme. It is also shown that the true optimization to minimize the fuel exchange machine movements would be costly to obtain due to the number of alternatives that would need to be evaluated. However, a method to obtain a quasi-optimum solution is suggested

  14. 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.

  15. Machine Learning Techniques for Characterizing IEEE 802.11b Encrypted Data Streams

    National Research Council Canada - National Science Library

    Henson, Michael

    2004-01-01

    .... Even though there have been major advancements in encryption technology, security protocols and packet header obfuscation techniques, other distinguishing characteristics do exist in wireless network traffic...

  16. 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.

  17. Prediction of Five Softwood Paper Properties from its Density using Support Vector Machine Regression Techniques

    Directory of Open Access Journals (Sweden)

    Esperanza García-Gonzalo

    2016-01-01

    Full Text Available Predicting paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kernel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris and cypress species (Cupressus lusitanica, C. sempervirens, and C. arizonica beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination with two variables: the numerical variable density and the categorical variable species.

  18. Classification of fMRI resting-state maps using machine learning techniques: A comparative study

    Science.gov (United States)

    Gallos, Ioannis; Siettos, Constantinos

    2017-11-01

    We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

  19. 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.

  20. Analysis and design of machine learning techniques evolutionary solutions for regression, prediction, and control problems

    CERN Document Server

    Stalph, Patrick

    2014-01-01

    Manipulating or grasping objects seems like a trivial task for humans, as these are motor skills of everyday life. Nevertheless, motor skills are not easy to learn for humans and this is also an active research topic in robotics. However, most solutions are optimized for industrial applications and, thus, few are plausible explanations for human learning. The fundamental challenge, that motivates Patrick Stalph, originates from the cognitive science: How do humans learn their motor skills? The author makes a connection between robotics and cognitive sciences by analyzing motor skill learning using implementations that could be found in the human brain – at least to some extent. Therefore three suitable machine learning algorithms are selected – algorithms that are plausible from a cognitive viewpoint and feasible for the roboticist. The power and scalability of those algorithms is evaluated in theoretical simulations and more realistic scenarios with the iCub humanoid robot. Convincing results confirm the...

  1. 3D Cloud Field Prediction using A-Train Data and Machine Learning Techniques

    Science.gov (United States)

    Johnson, C. L.

    2017-12-01

    Validation of cloud process parameterizations used in global climate models (GCMs) would greatly benefit from observed 3D cloud fields at the size comparable to that of a GCM grid cell. For the highest resolution simulations, surface grid cells are on the order of 100 km by 100 km. CloudSat/CALIPSO data provides 1 km width of detailed vertical cloud fraction profile (CFP) and liquid and ice water content (LWC/IWC). This work utilizes four machine learning algorithms to create nonlinear regressions of CFP, LWC, and IWC data using radiances, surface type and location of measurement as predictors and applies the regression equations to off-track locations generating 3D cloud fields for 100 km by 100 km domains. The CERES-CloudSat-CALIPSO-MODIS (C3M) merged data set for February 2007 is used. Support Vector Machines, Artificial Neural Networks, Gaussian Processes and Decision Trees are trained on 1000 km of continuous C3M data. Accuracy is computed using existing vertical profiles that are excluded from the training data and occur within 100 km of the training data. Accuracy of the four algorithms is compared. Average accuracy for one day of predicted data is 86% for the most successful algorithm. The methodology for training the algorithms, determining valid prediction regions and applying the equations off-track is discussed. Predicted 3D cloud fields are provided as inputs to the Ed4 NASA LaRC Fu-Liou radiative transfer code and resulting TOA radiances compared to observed CERES/MODIS radiances. Differences in computed radiances using predicted profiles and observed radiances are compared.

  2. Trace elements and naturally occurring radioactive materials in 'Non-traditional fertilizers' used in Ghana

    International Nuclear Information System (INIS)

    Assibey, E. O.

    2013-07-01

    Fertilizers have been implicated for being contaminated with toxic trace elements and naturally occurring radioactive materials (NORMs) even though they are an indispensable component of our agriculture. This phenomenon of contamination has been investigated and established world-wide in various forms of fertilizers (i.e., granular or 'traditional' type and liquid/powder or 'non-traditional type'). In Ghana, the crop sub-sector has seen a gradual rise in the importation and use of 'non-traditional fertilizers' which are applied to both the foliar parts and roots of plants. This notwithstanding, research on fertilizers has been largely skewed towards the 'traditional' types, focusing principally on the subjects of yield, effects of application and their quality. This study was, therefore, undertaken to bridge the knowledge gap by investigating the levels of trace elements and NORMs found in the 'non-traditional' fertilizers used in Ghana. The principal objective of the study was to investigate the suitability of the 'non-traditional fertilizers' for agricultural purposes with respect to trace elements and NORMs contamination. Atomic Absorption Spectrometry and instrumental Neutron Activation Analysis were employed to determine the trace elements (Cu, Zn, Fe, Na, Al, Br, Ni, Cd, As, Hg, Co, Pb, La, Mn, Si, Ca, Cl, S, K, Ba and V) and NORMs ( 238 U, 232 Th and 40 K) concentrations in thirty-nine (39) fertilizer samples taken from two major agro-input hubs in the country (Kumasi-Kejetia and Accra). Multivariate statistical analyses (cluster analysis, principal component analysis and pearson's correlation) were applied to the data obtained in order to identify possible sources of contamination, investigate sample/ parameter affinities and groupings and for fingerprinting. The toxic trace element concentrations determined in all samples were found to be in the order Fe>Cu>Co>Cd>Cr >Ni>Pb>As>Hg. The study found most of the trace elements determined to be within limits set

  3. Reverse engineering smart card malware using side channel analysis with machine learning techniques

    CSIR Research Space (South Africa)

    Djonon Tsague, Hippolyte

    2016-12-01

    Full Text Available as much variance of the original data as possible. Among feature extraction techniques, PCA and LDA are very common dimensionality reduction algorithms that have successfully been applied in many classification problems like face recognition, character...

  4. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

    Science.gov (United States)

    Yassin, Nisreen I R; Omran, Shaimaa; El Houby, Enas M F; Allam, Hemat

    2018-03-01

    The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer. The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests. This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Jonas eKaplan

    2015-03-01

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

  6. Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques.

    Science.gov (United States)

    Alejo, Luz; Atkinson, John; Guzmán-Fierro, Víctor; Roeckel, Marlene

    2018-05-16

    Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.

  7. Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

    Science.gov (United States)

    Bassier, M.; Vergauwen, M.; Van Genechten, B.

    2017-08-01

    Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

  8. submitter Studies of CMS data access patterns with machine learning techniques

    CERN Document Server

    De Luca, Silvia

    This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy ove...

  9. 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.

  10. Prediction of Driver's Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques.

    Science.gov (United States)

    Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

    2017-06-10

    Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver's intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver's intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver's intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver's intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

  11. TECHNOLOGICAL ASPECTS OF PRODUCTION OF THE CANDIED FRUITS FROM NON-TRADITIONAL RAW MATERIAL

    Directory of Open Access Journals (Sweden)

    I. R. Belenkaya

    2016-08-01

    Full Text Available The article analyses the candied fruit market in Ukraine and describes the main technological operations pertainingto processing of non-traditional candied products – celery and parsnip roots. Darkening of the roots surface caused bythe enzyme oxidation is one of the problems arising when processing white roots, which leads to worse marketable conditionof the product. To prevent darkening, the developed technology provides for soaking raw material in 1% citric acid solutionimmediately after peeling. To improve the diffusion and osmotic processes and to soften roots before boiling in sugar syrup,the steam blanching has been applied. The constructed Gantt diagram proves that the developed technology can shorten thecandied fruit cooking period. The biochemical indicators of the obtained new products have been studied. It was establishedthat the candied fruit possess the appropriate physical and chemical indicators and original organoleptic properties resulting ina demand by consumers. The results of the taste evaluation of the experimental specimen confirmed a high quality of the products.

  12. Reaching Non-Traditional and Under-Served Communities through Global Astronomy Month Programs

    Science.gov (United States)

    Simmons, Michael

    2013-01-01

    Global Astronomy Month (GAM), organized each year by Astronomers Without Borders (AWB), has become the world's largest annual celebration of astronomy. Launched as a follow-up to the unprecedented success of the 100 Hours of Astronomy Cornerstone Project of IYA2009, GAM quickly attracted not only traditional partners in astronomy and space science outreach, but also unusual partners from very different fields. GAM's third annual edition, GAM2012, included worldwide programs for the sight-impaired, astronomy in the arts, and other non-traditional programs. The special planetarium program, OPTICKS, combined elements such as Moonbounce (sending images to the Moon and back) and artistic elements in a unique presentation of the heavens. Programs were developed to present the heavens to the sight-impaired as well. The Cosmic Concert, in which a new musical piece is composed each year, combined with background images of celestial objects, and presented during GAM, has become an annual event. Several astronomy themed art video projects were presented online. AWB's Astropoetry Blog held a very successful contest during GAM2012 that attracted more than 70 entries from 17 countries. Students were engaged by participation in special GAM campaigns of the International Asteroid Search Campaign. AWB and GAM have both developed into platforms where innovative programs can develop, and interdisciplinary collaborations can flourish. As AWB's largest program, GAM brings the audience and resources that provide a boost for these new types of programs. Examples, lessons learned, new projects, and plans for the future of AWB and GAM will be presented.

  13. Evolving techniques of diagnosis. Toward establishment of new paradigm for human machine cooperation

    International Nuclear Information System (INIS)

    Kitamura, Masaharu; Takahashi, Makoto; Kanamoto, Shigeru; Saeki, Akira; Washio, Takashi; Ohga, Yukiharu; Furuta, Kazuo; Yoshikawa, Shinji

    1998-01-01

    By monitoring equipments of a plant and state of a process, the diagnostic technique to detect a sign of abnormality properly to identify its reason has often been advanced on a lot of researches in various industrial fields containing atomic force. Some fundamental studies expected for such diagnostic technique to play an important role to keep and improve operational safety of a nuclear plant have been conducted since early period of the nuclear reaction development, but their contents are evolved and changed rapidly, in recent. The technique on the diagnosis was related closely to a statistical analysis method on signal fluctuation component, so-called reactor noise analysis method in early 1980s, but technical innovation step of their recent advancement were remarkable by introduction of new techniques such as chaos theory, wavelet analysis, model base application of expert system, artificial intelligence, and so on at middle of 1980s. And, when diagnosing in the field of atomic force, owing to be required for much high ability, studies on a multi method integration system considered complementary application of a plurality of technical methods and a cooperative method between human and mechanical intelligences, are also forwarded actively faster than those in other industrial areas. In this paper, in each important item, its technical nature and present state of its application to diagnosis are described with their future technical view. (G.K.)

  14. Application of a support vector machine algorithm to the safety precaution technique of medium-low pressure gas regulators

    Science.gov (United States)

    Hao, Xuejun; An, Xaioran; Wu, Bo; He, Shaoping

    2018-02-01

    In the gas pipeline system, safe operation of a gas regulator determines the stability of the fuel gas supply, and the medium-low pressure gas regulator of the safety precaution system is not perfect at the present stage in the Beijing Gas Group; therefore, safety precaution technique optimization has important social and economic significance. In this paper, according to the running status of the medium-low pressure gas regulator in the SCADA system, a new method for gas regulator safety precaution based on the support vector machine (SVM) is presented. This method takes the gas regulator outlet pressure data as input variables of the SVM model, the fault categories and degree as output variables, which will effectively enhance the precaution accuracy as well as save significant manpower and material resources.

  15. 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.

  16. 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.

  17. A Geoscience Workforce Model for Non-Geoscience and Non-Traditional STEM Students

    Science.gov (United States)

    Liou-Mark, J.; Blake, R.; Norouzi, H.; Vladutescu, D. V.; Yuen-Lau, L.

    2016-12-01

    The Summit on the Future of Geoscience Undergraduate Education has recently identified key professional skills, competencies, and conceptual understanding necessary in the development of undergraduate geoscience students (American Geosciences Institute, 2015). Through a comprehensive study involving a diverse range of the geoscience academic and employer community, the following professional scientist skills were rated highly important: 1) critical thinking/problem solving skills; 2) effective communication; 3) ability to access and integrate information; 4) strong quantitative skills; and 5) ability to work in interdisciplinary/cross cultural teams. Based on the findings of the study above, the New York City College of Technology (City Tech) has created a one-year intensive training program that focusses on the development of technical and non-technical geoscience skills for non-geoscience, non-traditional STEM students. Although City Tech does not offer geoscience degrees, the primary goal of the program is to create an unconventional pathway for under-represented minority STEM students to enter, participate, and compete in the geoscience workforce. The selected cohort of STEM students engage in year-round activities that include a geoscience course, enrichment training workshops, networking sessions, leadership development, research experiences, and summer internships at federal, local, and private geoscience facilities. These carefully designed programmatic elements provide both the geoscience knowledge and the non-technical professional skills that are essential for the geoscience workforce. Moreover, by executing this alternate, robust geoscience workforce model that attracts and prepares underrepresented minorities for geoscience careers, this unique pathway opens another corridor that helps to ameliorate the dire plight of the geoscience workforce shortage. This project is supported by NSF IUSE GEOPATH Grant # 1540721.

  18. The C1q family of proteins: insights into the emerging non-traditional functions

    Directory of Open Access Journals (Sweden)

    Berhane eGhebrehiwet

    2012-04-01

    Full Text Available Research conducted over the past 20 years have helped us unravel not only the hidden structural and functional subtleties of human C1q, but also has catapulted the molecule from a mere recognition unit of the classical pathway to a well-recognized molecular sensor of damage modified self or non-self antigens. Thus, C1q is involved in a rapidly expanding list of pathological disorders—including autoimmunity, trophoblast migration, preeclampsia and cancer. The results of two recent reports are provided to underscore the critical role C1q plays in health and disease. First is the observation by Singh and colleagues showing that pregnant C1q-/- mice recapitulate the key features of human preeclampsia that correlate with increased fetal death. Treatment of the C1q-/- mice with pravastatin restored trophoblast invasiveness, placental blood flow, and angiogenic balance and, thus, prevented the onset of preeclampsia. Second is the report by Hong et al., which showed that C1q can induce apoptosis of prostate cancer cells by activating the tumor suppressor molecule WW-domain containing oxydoreductase (WWOX or WOX1 and destabilizing cell adhesion. Downregulation of C1q on the other hand enhanced prostate hyperplasia and cancer formation due to failure of WOX1 activation. Recent evidence also shows that C1q belongs to a family of structurally and functionally related TNFα-like family of proteins that may have arisen from a common ancestral gene. Therefore C1q not only shares the diverse functions with the TNF family of proteins, but also explains why C1q has retained some of its ancestral cytokine-like activities. This review is intended to highlight some of the structural and functional aspects of C1q by underscoring the growing list of its non-traditional functions.

  19. Mechanical properties of micro-sized copper bending beams machined by the focused ion beam technique

    International Nuclear Information System (INIS)

    Motz, C.; Schoeberl, T.; Pippan, R.

    2005-01-01

    Micro-sized bending beams with thicknesses, t, from 7.5 down to 1.0 μm were fabricated with the focused ion beam technique from a copper single crystal with an {1 1 1} orientation. The beams were loaded with a nano-indenter and the force vs. displacement curves were recorded. A strong size effect was found where the flow stress reaches almost 1 GPa for the thinnest beams. A common strain gradient plasticity approach was used to explain the size effect. However, the strong t -1.14 dependence of the flow stress could not be explained by this model. Additionally, the combination of two other dislocation mechanisms is discussed: the limitation of available dislocation sources and a dislocation pile-up at the beam centre. The contribution of the pile-up stress to the flow stress gives a t -1 dependence, which is in good agreement with the experimental results

  20. Tracer techniques for the investigation of wear mechanisms in coated or surface-treated machine parts

    International Nuclear Information System (INIS)

    Goedecke, T.; Grosch, J.

    1990-01-01

    Tracer techniques allow wear measurement down to rates of only some μg/h, and these measurements can be done continuously within an inspection test run, not requiring dismantling of the parts to be examined. The measurements revealed the materials pair of a chilled cast iron camshaft and a hard metal coated rocker arm to be superior in terms of wear behaviour over the materials pair of a malleable cast iron camshaft with induction hardening and a rocker arm with hard chromium plating. The total wear of a chilled cast iron camshaft was measured to be approx. 90% less than that of the malleable cast iron camshaft, under equal loading conditions. With the rocker arms, this ratio is approx. 1:3. Another disadvantage of the latter pair is the overall wear ratio of 19:1. The best wear resistance was measured with a TiN-coated rocker arm combined with a chilled cast iron camshaft. (orig./MM) [de

  1. A Phenomenological Study of the Lived Experiences of Non-Traditional Students in Higher Level Mathematics at a Midwest University

    Science.gov (United States)

    Wood, Brian B.

    2017-01-01

    The current literature suggests that the use of Husserl's and Heidegger's approaches to phenomenology is still practiced. However, a clear gap exists on how these approaches are viewed in the context of constructivism, particularly with non-traditional female students' study of mathematics. The dissertation attempts to clarify the constructivist…

  2. The Long and Winding Road: Grades, Psychological Disengagement and Motivation among Female Students in (Non-)Traditional Career Paths

    Science.gov (United States)

    Rinfret, Natalie; Tougas, Francine; Beaton, Ann M.; Laplante, Joelle; Ngo Manguelle, Christiane; Lagacé, Marie Claude

    2014-01-01

    The purpose of this study was to evaluate the links between grades, psychological disengagement mechanisms (discounting evaluative feedback and devaluing school), and motivation among female students in traditional and non-traditional career paths. We predicted that the association between grades and discounting is affected by the importance of…

  3. An exploration of on-line access by non-traditional students in higher education: a case study.

    Science.gov (United States)

    Dearnley, Chris; Dunn, Ginny; Watson, Sue

    2006-07-01

    The nature of Higher Education (HE) has seen many changes throughout the last decade. The agenda for widening participation in HE has led to an increase in the number of students with a broader range of educational backgrounds. At the same time there has been a surge in the development of digitalisation and the convergence of computing and telecommunications technologies available for use in education. This paper discusses the outcomes of a case study, conducted in a School of Health Studies within a northern English University, which identified the extent to which 'non-traditional' students access on-line learning facilities, such as virtual learning environments and library networks, and what factors enhanced or formed barriers to access. 'Non-traditional' students, for the purpose of this study, were defined as mature students who were returning to higher education after a considerable break. The outcomes indicated that skill deficit is a major obstacle for many 'non-traditional' students. The paper explores this issue in depth and suggests potential ways forward for the delivery of technology supported learning for 'non-traditional' students in Higher Education.

  4. The influence of out-of-institution environments on the university schooling project of non-traditional students in Uganda

    NARCIS (Netherlands)

    Tumuheki, Peace Buhwamatsiko; Zeelen, Jacobus; Openjuru, George L.

    2018-01-01

    Participation and integration of non-traditional students (NTS) in university education is influenced by factors within the institution and those external to the institution, including participants’ self-perceptions and dispositions. The objective of this qualitative study is to draw from the

  5. Assessing Changes in Medical Student Attitudes toward Non-Traditional Human Sexual Behaviors Using a Confidential Audience Response System

    Science.gov (United States)

    Tucker, Phebe; Candler, Chris; Hamm, Robert M.; Smith, E. Michael; Hudson, Joseph C.

    2010-01-01

    Medical students encountering patients with unfamiliar, unconventional sexual practices may have attitudes that can affect open communication during sexual history-taking. We measured changes in first-year US medical student attitudes toward 22 non-traditional sexual behaviors before and after exposure to human sexuality instruction. An…

  6. Student learning or the student experience: the shift from traditional to non-traditional faculty in higher education

    Directory of Open Access Journals (Sweden)

    Carlos Tasso Eira de Aquino

    2016-10-01

    Full Text Available Trends in higher education indicate transformations from teachers to facilitators, mentors, or coaches. New classroom management requires diverse teaching methods for a changing population. Non-traditional students require non-traditional faculty. Higher education operates similar to a traditional corporation, but competes for students, faculty, and funding to sustain daily operations and improve academic ranking among peers (Pak, 2013. This growing phenomenon suggests the need for faculty to transform the existing educational culture, ensuring the ability to attract and retain students. Transitions from student learning to the student experience and increasing student satisfaction scores are influencing facilitation in the classroom. On-line facilitation methods are transforming to include teamwork, interactive tutorials, media, and extending beyond group discussion. Faculty should be required to provide more facilitation, coaching, and mentoring with the shifting roles resulting in transitions from traditional faculty to faculty-coach and faculty mentor. The non-traditional adult student may require a more hands on guidance approach and may not be as self-directed as the adult learning theory proposes. This topic is important to individuals that support creation of new knowledge related to non-traditional adult learning models.

  7. Connecting Bourdieu, Winnicott, and Honneth: Understanding the Experiences of Non-Traditional Learners through an Interdisciplinary Lens

    Science.gov (United States)

    West, Linden; Fleming, Ted; Finnegan, Fergal

    2013-01-01

    This paper connects Bourdieu's concepts of habitus, dispositions and capital with a psychosocial analysis of how Winnicott's psychoanalysis and Honneth's recognition theory can be of importance in understanding how and why non-traditional students remain in higher education. Understanding power relations in an interdisciplinary way makes…

  8. Hair analysis by means of laser induced breakdown spectroscopy technique and support vector machine model for diagnosing addiction

    Directory of Open Access Journals (Sweden)

    M Vahid Dastjerdi

    2018-02-01

    Full Text Available Along with the development of laboratory methods for diagnosing addiction, concealment ways, either physically or chemically, for creating false results have been in progress. In this research based on the Laser Induced Breakdown Spectroscopy technique (LIBS and analyzing hair of addicted and normal people, we are proposing a new method to overcome problems in conventional methods and reduce possibility of cheating in the process of diagnosing addiction. For this purpose, at first we have sampled hair of 17 normal and addicted people and recorded 5 spectrums for each sample, overall 170 spectrums. After analyzing the recorded LIBS spectra and detecting the atomic and ionic lines as well as molecular bands, relative intensities of emission lines for Aluminum to Calcium (Al/Ca and Aluminum to Sodium (Al/Na were selected as the input variables for the Support Vector Machine model (SVM.The Radial Basis, Polynomial Kernel functions and a linear function were chosen for classifying the data in SVM model. The results of this research showed that by the combination of LIBS technique and SVM one can distinguish addicted person with precision of 100%. Because of several advantages of LIBS such as high speed analysis and being portable, this method can be used individually or together with available methods as an automatic method for diagnosing addiction through hair analysis.

  9. GAPscreener: An automatic tool for screening human genetic association literature in PubMed using the support vector machine technique

    Directory of Open Access Journals (Sweden)

    Khoury Muin J

    2008-04-01

    Full Text Available Abstract Background Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM, a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies. Results The data source for this research was the HuGE Navigator, formerly known as the HuGE Pub Lit database. Weighted SVM feature selection based on a keyword list obtained by the two-way z score method demonstrated the best screening performance, achieving 97.5% recall, 98.3% specificity and 31.9% precision in performance testing. Compared with the traditional screening process based on a complex PubMed query, the SVM tool reduced by about 90% the number of abstracts requiring individual review by the database curator. The tool also ascertained 47 articles that were missed by the traditional literature screening process during the 4-week test period. We examined the literature on genetic associations with preterm birth as an example. Compared with the traditional, manual process, the GAPscreener both reduced effort and improved accuracy. Conclusion GAPscreener is the first free SVM-based application available for screening the human genetic association literature in PubMed with high recall and specificity. The user-friendly graphical user interface makes this a practical, stand-alone application. The software can be downloaded at no charge.

  10. Techniques for optimizing human-machine information transfer related to real-time interactive display systems

    Science.gov (United States)

    Granaas, Michael M.; Rhea, Donald C.

    1989-01-01

    In recent years the needs of ground-based researcher-analysts to access real-time engineering data in the form of processed information has expanded rapidly. Fortunately, the capacity to deliver that information has also expanded. The development of advanced display systems is essential to the success of a research test activity. Those developed at the National Aeronautics and Space Administration (NASA), Western Aeronautical Test Range (WATR), range from simple alphanumerics to interactive mapping and graphics. These unique display systems are designed not only to meet basic information display requirements of the user, but also to take advantage of techniques for optimizing information display. Future ground-based display systems will rely heavily not only on new technologies, but also on interaction with the human user and the associated productivity with that interaction. The psychological abilities and limitations of the user will become even more important in defining the difference between a usable and a useful display system. This paper reviews the requirements for development of real-time displays; the psychological aspects of design such as the layout, color selection, real-time response rate, and interactivity of displays; and an analysis of some existing WATR displays.

  11. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Science.gov (United States)

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  12. Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.

    Science.gov (United States)

    Sakr, Sherif; Elshawi, Radwa; Ahmed, Amjad M; Qureshi, Waqas T; Brawner, Clinton A; Keteyian, Steven J; Blaha, Michael J; Al-Mallah, Mouaz H

    2017-12-19

    Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness

  13. A case study of non-traditional students re-entry into college physics and engineering

    Science.gov (United States)

    Langton, Stewart Gordon

    Two groups of students in introductory physics courses of an Access Program for engineering technologies were the subjects of this study. Students with a wide range of academic histories and abilities were enrolled in the program; many of the students were re-entry and academically unprepared for post-secondary education. Five years of historical data were evaluated to use as a benchmark for revised instruction. Data were gathered to describe the pre-course academic state of the students and their academic progress during two physics courses. Additional information was used to search for factors that might constrain academic success and as feedback for the instructional methods. The data were interpreted to regulate constructivist design features for the physics courses. The Engineering Technology Access Program was introduced to meet the demand from non-traditional students for admission to two-year engineering' technology programs, but who did not meet normal academic requirements. The duration of the Access Program was two terms for electronic and computer engineering students and three terms for civil and mechanical engineering students. The sequence of mathematics and physics courses was different for the two groups. The Civil/Mechanical students enrolled in their first mathematics course before undertaking their first physics course. The first mathematics and physics courses for the Electronics students were concurrent. Academic success in the two groups was affected by this difference. Over a five-year period the success rate of students graduating with a technology diploma was approximately twenty-five percent. Results from this study indicate that it was possible to reduce the very high attrition in the combined Access/Technology Programs. While the success rate for the Electronics students increased to 38% the rate for the Civil/Mechanical students increased dramatically to 77%. It is likely that several factors, related to the extra term in the Access

  14. Glycaemic indices and non-traditional biochemical cardiovascular disease markers in a diabetic population in Nigeria

    International Nuclear Information System (INIS)

    Okeoghene, O.A.; Azenabor, A.

    2011-01-01

    Objective: To determine the frequency of hyperfibrinogenaemia, elevated C-reactive protein, hyperuricaemia and elevated lipoprotein A in a clinic population of patients with type 2 Diabetes mellitus (DM) compared with healthy controls; and determine the interrelationship between fasting plasma glucose levels and indices of long-term glycaemic control (fructosamine and glycosylated haemoglobin) in DM. Study Design: Cross-sectional, analytical study. Place and Duration of Study: The study was conducted at the Lagos State University Teaching Hospital, Ikeja, from April to June 2009. Methodology: A total of 200 patients with type 2 DM and 100 age and gender matched healthy controls were recruited for the study. Glycaemic control was assessed using fasting blood glucose, fructosamine and glycosylated haemoglobin levels. The non-traditional risk factors studied included C-reactive protein (CRP), Lipoprotein a (Lpa), serum uric acid (SUA), microalbuminuria and fibrinogen. Mann-whitney, chi-square and Pearson's correlation tests were used for analysis as applicable. Results: Hyperfibrinoginaemia, elevated CRP, LPa, microalbuminuria and hyperuricaemia were present in 3.5%, 65%, 12%, 6% and 57% respectively in type 2 DM. The mean levels of these CV risk factors were significantly higher in subjects with type 2 DM than that of the control subject. There was a positive and significant correlation between HbA1c and FBS (r=0.46, p=0.0001) and HbA1c and fructosamine (r=0.49, p=0.0001). All studied CVS risk factors were related to indices of glycaemic control which were found to be interrelated. Fasting blood glucose significantly correlated with both HbA1c and fructosamine but HbA1c showed better correlation to FPG than fructosamine (r=0.51 vs. 0.32). Conclusion: Glycosylated haemoglobin and fasting plasma glucose but not fructosamine are significantly associated with microalbuminuria, fibrinogen SUA and CRP in type 2 DM. HbA1c was found to be better than fructosamine in

  15. Expedition Zenith: Experiences of eighth grade girls in a non-traditional math/science program

    Science.gov (United States)

    Ulm, Barbara Jean

    2004-11-01

    This qualitative study describes the experiences of a group of sixteen, eighth grade girls participating in a single-sex, math/science program based on gender equity research and constructivist theory. This phenomenological case study highlights the individual changes each girl perceives in herself as a result of her involvement in this program which was based at a suburban middle school just north of New York City. Described in narrative form is what took place during this single-sex program. At the start of the program the girls worked cooperatively in groups to build canoes. The canoes were then used to study a wetland during the final days of the program. To further immerse the participants into nature, the girls also camped during these final days. Data were collected from a number of sources to uncover, as fully as possible, the true essence of the program and the girls' experiences in it. The data collection methods included direct observation; in-depth, open-ended interviews; and written documentation. As a result of data collection, the girls' perceived outcomes and assessment of the program, as well as their recommendations for future math/science programs are revealed. The researcher in this study also acted as teacher, directing the program, and as participant to better understand the experiences of the girls involved in the program. Thus, unique insights could be made. The findings in this study provide insight into the learning of the participants, as well as into the relationships they formed both inside and outside of the program. Their perceived experiences and assessment of the program were then used to develop a greater understanding as to the effectiveness of this non-traditional program. Although this study echoed much of what research says about the needs of girls in learning situations, and therefore, reinforces previously accepted beliefs, it also reveals significant findings in areas previously unaddressed by gender studies. For example

  16. CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

    Energy Technology Data Exchange (ETDEWEB)

    Saz Parkinson, P. M. [Department of Physics, The University of Hong Kong, Pokfulam Road, Hong Kong (China); Xu, H.; Yu, P. L. H. [Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong (China); Salvetti, D.; Marelli, M. [INAF—Istituto di Astrofisica Spaziale e Fisica Cosmica Milano, via E. Bassini 15, I-20133, Milano (Italy); Falcone, A. D. [Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA 16802 (United States)

    2016-03-20

    We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.

  17. CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

    International Nuclear Information System (INIS)

    Saz Parkinson, P. M.; Xu, H.; Yu, P. L. H.; Salvetti, D.; Marelli, M.; Falcone, A. D.

    2016-01-01

    We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies

  18. Machine Learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  19. Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant.

    Science.gov (United States)

    Cilla, Myriam; Borgiani, Edoardo; Martínez, Javier; Duda, Georg N; Checa, Sara

    2017-01-01

    Today, different implant designs exist in the market; however, there is not a clear understanding of which are the best implant design parameters to achieve mechanical optimal conditions. Therefore, the aim of this project was to investigate if the geometry of a commercial short stem hip prosthesis can be further optimized to reduce stress shielding effects and achieve better short-stemmed implant performance. To reach this aim, the potential of machine learning techniques combined with parametric Finite Element analysis was used. The selected implant geometrical parameters were: total stem length (L), thickness in the lateral (R1) and medial (R2) and the distance between the implant neck and the central stem surface (D). The results show that the total stem length was not the only parameter playing a role in stress shielding. An optimized implant should aim for a decreased stem length and a reduced length of the surface in contact with the bone. The two radiuses that characterize the stem width at the distal cross-section in contact with the bone were less influential in the reduction of stress shielding compared with the other two parameters; but they also play a role where thinner stems present better results.

  20. Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

    Directory of Open Access Journals (Sweden)

    Ralph Olusola Aluko

    2016-12-01

    Full Text Available In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

  1. Rainfall Prediction of Indian Peninsula: Comparison of Time Series Based Approach and Predictor Based Approach using Machine Learning Techniques

    Science.gov (United States)

    Dash, Y.; Mishra, S. K.; Panigrahi, B. K.

    2017-12-01

    Prediction of northeast/post monsoon rainfall which occur during October, November and December (OND) over Indian peninsula is a challenging task due to the dynamic nature of uncertain chaotic climate. It is imperative to elucidate this issue by examining performance of different machine leaning (ML) approaches. The prime objective of this research is to compare between a) statistical prediction using historical rainfall observations and global atmosphere-ocean predictors like Sea Surface Temperature (SST) and Sea Level Pressure (SLP) and b) empirical prediction based on a time series analysis of past rainfall data without using any other predictors. Initially, ML techniques have been applied on SST and SLP data (1948-2014) obtained from NCEP/NCAR reanalysis monthly mean provided by the NOAA ESRL PSD. Later, this study investigated the applicability of ML methods using OND rainfall time series for 1948-2014 and forecasted up to 2018. The predicted values of aforementioned methods were verified using observed time series data collected from Indian Institute of Tropical Meteorology and the result revealed good performance of ML algorithms with minimal error scores. Thus, it is found that both statistical and empirical methods are useful for long range climatic projections.

  2. New Paradigms for the Study of Ocular Alphaherpesvirus Infections: Insights into the Use of Non-Traditional Host Model Systems

    Directory of Open Access Journals (Sweden)

    Matthew R. Pennington

    2017-11-01

    Full Text Available Ocular herpesviruses, most notably human alphaherpesvirus 1 (HSV-1, canid alphaherpesvirus 1 (CHV-1 and felid alphaherpesvirus 1 (FHV-1, infect and cause severe disease that may lead to blindness. CHV-1 and FHV-1 have a pathogenesis and induce clinical disease in their hosts that is similar to HSV-1 ocular infections in humans, suggesting that infection of dogs and cats with CHV-1 and FHV-1, respectively, can be used as a comparative natural host model of herpesvirus-induced ocular disease. In this review, we discuss both strengths and limitations of the various available model systems to study ocular herpesvirus infection, with a focus on the use of these non-traditional virus-natural host models. Recent work has demonstrated the robustness and reproducibility of experimental ocular herpesvirus infections in dogs and cats, and, therefore, these non-traditional models can provide additional insights into the pathogenesis of ocular herpesvirus infections.

  3. Traditional and non-traditional treatments for autism spectrum disorder with seizures: an on-line survey

    OpenAIRE

    Frye, Richard E; Sreenivasula, Swapna; Adams, James B

    2011-01-01

    Abstract Background Despite the high prevalence of seizure, epilepsy and abnormal electroencephalograms in individuals with autism spectrum disorder (ASD), there is little information regarding the relative effectiveness of treatments for seizures in the ASD population. In order to determine the effectiveness of traditional and non-traditional treatments for improving seizures and influencing other clinical factor relevant to ASD, we developed a comprehensive on-line seizure survey. Methods A...

  4. "Too big to fail" or "Too non-traditional to fail"?: The determinants of banks' systemic importance

    OpenAIRE

    Moore, Kyle; Zhou, Chen

    2013-01-01

    This paper empirically analyzes the determinants of banks' systemic importance. In constructing a measure on the systemic importance of financial institutions we find that size is a leading determinant. This confirms the usual "Too big to fail'' argument. Nevertheless, banks with size above a sufficiently high level have equal systemic importance. In addition to size, we find that the extent to which banks engage in non-traditional banking activities is also positively related to ...

  5. Coping with the energy crisis: Impact assessment and potentials of non-traditional renewable energy in rural Kyrgyzstan

    International Nuclear Information System (INIS)

    Liu, Melisande F.M.; Pistorius, Till

    2012-01-01

    The Kyrgyz energy sector is characterised by a dramatic energy crisis that has deprived a substantial part of the population from access to energy. Non-traditional renewable energy sources have emerged as a promising alternative in providing basic energy services to the rural poor. Based on qualitative interview data from local households and project planners, this study sets out to assess impacts, limitations and barriers of non-traditional renewable energy projects in rural areas in Kyrgyzstan. This study argues that recent renewable energy efforts from multilateral international agencies, the private sector, and nongovernmental organisations exhibit great potential in creating tangible benefits and improving basic energy services, but have so far been inefficient in establishing and replicating sustainable and long-term energy solutions. Existing practices need to be improved by attaching greater importance to the capacities and real needs of the rural poor. The guidance of integrated programmes and policies along with alternative financing schemes and awareness-raising are urgently needed to leverage local success stories and to facilitate a sustainable energy development in rural Kyrgyzstan. - Highlights: ► We examine 11 rural households and 5 project planners in rural Kyrgyzstan. ► We assess impacts of non-traditional renewable energies compared with conventional fuels. ► Renewable energies exhibit a range of tangible benefits for rural users. ► Limitations concern performance, durability, repair, acceptance, finance and policy. ► Renewable energy is a promising alternative for rural households in Kyrgyzstan.

  6. Estimating photometric redshifts for X-ray sources in the X-ATLAS field using machine-learning techniques

    Science.gov (United States)

    Mountrichas, G.; Corral, A.; Masoura, V. A.; Georgantopoulos, I.; Ruiz, A.; Georgakakis, A.; Carrera, F. J.; Fotopoulou, S.

    2017-12-01

    We present photometric redshifts for 1031 X-ray sources in the X-ATLAS field using the machine-learning technique TPZ. X-ATLAS covers 7.1 deg2 observed with XMM-Newton within the Science Demonstration Phase of the H-ATLAS field, making it one of the largest contiguous areas of the sky with both XMM-Newton and Herschel coverage. All of the sources have available SDSS photometry, while 810 additionally have mid-IR and/or near-IR photometry. A spectroscopic sample of 5157 sources primarily in the XMM/XXL field, but also from several X-ray surveys and the SDSS DR13 redshift catalogue, was used to train the algorithm. Our analysis reveals that the algorithm performs best when the sources are split, based on their optical morphology, into point-like and extended sources. Optical photometry alone is not enough to estimate accurate photometric redshifts, but the results greatly improve when at least mid-IR photometry is added in the training process. In particular, our measurements show that the estimated photometric redshifts for the X-ray sources of the training sample have a normalized absolute median deviation, nmad ≈ 0.06, and a percentage of outliers, η = 10-14%, depending upon whether the sources are extended or point like. Our final catalogue contains photometric redshifts for 933 out of the 1031 X-ray sources with a median redshift of 0.9. The table of the photometric redshifts is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/608/A39

  7. An investigation of penetrant techniques for detection of machining-induced surface-breaking cracks on monolithic ceramics

    Energy Technology Data Exchange (ETDEWEB)

    Forster, G.A.; Ellingson, W.A.

    1996-02-01

    The purpose of this effort was to evaluate penetrant methods for their ability to detect surface-breaking cracks in monolithic ceramic materials with an emphasis on detection of cracks generated by machining. There are two basic penetrant types, visible and fluorescent. The visible penetrant method is usually augmented by powder developers and cracks detected can be seen in visible light. Cracks detected by fluorescent penetrant are visible only under ultraviolet light used with or without a developer. The developer is basically a powder that wicks up penetrant from a crack to make it more observable. Although fluorescent penetrants were recommended in the literature survey conducted early in this effort, visible penetrants and two non-standard techniques, a capillary gaseous diffusion method under development at the institute of Chemical Physics in Moscow, and the {open_quotes}statiflux{close_quotes} method which involves use of electrically charged particles, were also investigated. SiAlON ring specimens (1 in. diameter, 3/4 in. wide) which had been subjected to different thermal-shock cycles were used for these tests. The capillary gaseous diffusion method is based on ammonia; the detector is a specially impregnated paper much like litmus paper. As expected, visible dye penetrants offered no detection sensitivity for tight, surface-breaking cracks in ceramics. Although the non-standard statiflux method showed promise on high-crack-density specimens, it was ineffective on limited-crack-density specimens. The fluorescent penetrant method was superior for surface-breaking crack detection, but successful application of this procedure depends greatly on the skill of the user. Two presently available high-sensitivity fluorescent penetrants were then evaluated for detection of microcracks on Si{sub 3}N{sub 4} and SiC from different suppliers. Although 50X optical magnification may be sufficient for many applications, 200X magnification provides excellent delectability.

  8. Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

    Science.gov (United States)

    Foulquier, Nathan; Redou, Pascal; Le Gal, Christophe; Rouvière, Bénédicte; Pers, Jacques-Olivier; Saraux, Alain

    2018-05-17

    Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

  9. Frame by frame stop motion non-traditional approaches to stop motion animation

    CERN Document Server

    Gasek, Tom

    2011-01-01

    In a world that is dominated by computer images, alternative stop motion techniques like pixilation, time-lapse photography and down-shooting techniques combined with new technologies offer a new, tangible and exciting approach to animation. With over 25 years professional experience, industry veteran, Tom Gasek presents a comprehensive guide to stop motion animation without the focus on puppetry or model animation. With tips, tricks and hands-on exercises, Frame by Frame will help both experienced and novice filmmakers get the most effective results from this underutilized branch of animation

  10. Historical and Epistemological Reflections on the Culture of Machines around the Renaissance: How Science and Technique Work?

    Directory of Open Access Journals (Sweden)

    Raffaele Pisano

    2014-10-01

    Full Text Available This paper is divided into two parts, this being the first one. The second is entitled ‘Historical and Epistemological Reflections on the Culture of Machines around Renaissance: Machines, Machineries and Perpetual Motion’ and will be published in Acta Baltica Historiae et Philosophiae Scientiarum in 2015. Based on our recent studies, we provide here a historical and epistemological feature on the role played by machines and machineries. Ours is an epistemological thesis based on a series of historical examples to show that the relations between theoretical science and the construction of machines cannot be taken for granted, a priori. Our analysis is mainly based on the culture of machines around 15th and 17th centuries, namely the epoch of Late Renaissance and Early Modern Age. For this is the period of scientific revolution and this age offers abundant interesting material for researches into the relations of theoretical science/construction of machines as well. However, to prove our epistemological thesis, we will also exploit examples of machines built in other historical periods. Particularly, a discussion concerning the relationship between science theory and the development of science art crafts produced by non-recognized scientists in a certain historical time is presented. The main questions are: when and why did the tension between science (physics, mathematics and geometry give rise to a new scientific approach to applied discipline such as studies on machines and machineries? What kind of science was used (if at all for projecting machines and machineries? Was science at the time a necessary precondition to build a machine? In the first part we will focus on the difference between Aristotelian-Euclidean and Archimedean approaches and we will outline the heritage of these two different approaches in late medieval and Renaissance science. In the second part, we will apply our reconstructions to some historical and epistemological

  11. What Makes a Student Non-Traditional? A Comparison of Students over and under Age 25 in Online, Accelerated Psychology Courses

    Science.gov (United States)

    Tilley, Brian P.

    2014-01-01

    The growing proportion of non-traditional students, very commonly defined as students over the age of 25 (though other features vary from study to study) necessitates more studies with this increasingly relevant group participating. Recently, the growth of non-traditional universities such as those offering predominantly online, accelerated…

  12. Application of machine learning techniques for solving real world business problems : the case study - target marketing of insurance policies

    OpenAIRE

    Juozenaite, Ineta

    2018-01-01

    The concept of machine learning has been around for decades, but now it is becoming more and more popular not only in the business, but everywhere else as well. It is because of increased amount of data, cheaper data storage, more powerful and affordable computational processing. The complexity of business environment leads companies to use data-driven decision making to work more efficiently. The most common machine learning methods, like Logistic Regression, Decision Tree, Artificial Neural...

  13. A study on ultra-precision machining technique for Al6061-T6 to fabricate space infrared optics

    Science.gov (United States)

    Ryu, Geun-man; Lee, Gil-jae; Hyun, Sang-won; Sung, Ha-yeong; Chung, Euisik; Kim, Geon-hee

    2014-08-01

    In this paper, analysis of variance on designed experiments with full factorial design was applied to determine the optimized machining parameters for ultra-precision fabrication of the secondary aspheric mirror, which is one of the key elements of the space cryogenic infrared optics. A single point diamond turning machine (SPDTM, Nanotech 4μpL Moore) was adopted to fabricate the material, AL6061-T6, and the three machining parameters of cutting speed, feed rate and depth of cut were selected. With several randomly assigned experimental conditions, surface roughness of each condition was measured by a non-contact optical profiler (NT2000; Vecco). As a result of analysis using Minitab, the optimum cutting condition was determined as following; cutting speed: 122 m/min, feed rate: 3 mm/min and depth of cut: 1 μm. Finally, a 120 mm diameter aspheric secondary mirror was attached to a particularly designed jig by using mixture of paraffin and wax and successfully fabricated under the optimum machining parameters. The profile of machined surface was measured by a high-accuracy 3-D profilometer(UA3P; Panasonic) and we obtained the geometrical errors of 30.6 nm(RMS) and 262.4 nm(PV), which satisfy the requirements of the space cryogenic infrared optics.

  14. Traditional and non-traditional treatments for autism spectrum disorder with seizures: an on-line survey.

    Science.gov (United States)

    Frye, Richard E; Sreenivasula, Swapna; Adams, James B

    2011-05-18

    Despite the high prevalence of seizure, epilepsy and abnormal electroencephalograms in individuals with autism spectrum disorder (ASD), there is little information regarding the relative effectiveness of treatments for seizures in the ASD population. In order to determine the effectiveness of traditional and non-traditional treatments for improving seizures and influencing other clinical factor relevant to ASD, we developed a comprehensive on-line seizure survey. Announcements (by email and websites) by ASD support groups asked parents of children with ASD to complete the on-line surveys. Survey responders choose one of two surveys to complete: a survey about treatments for individuals with ASD and clinical or subclinical seizures or abnormal electroencephalograms, or a control survey for individuals with ASD without clinical or subclinical seizures or abnormal electroencephalograms. Survey responders rated the perceived effect of traditional antiepileptic drug (AED), non-AED seizure treatments and non-traditional ASD treatments on seizures and other clinical factors (sleep, communication, behavior, attention and mood), and listed up to three treatment side effects. Responses were obtained concerning 733 children with seizures and 290 controls. In general, AEDs were perceived to improve seizures but worsened other clinical factors for children with clinical seizure. Valproic acid, lamotrigine, levetiracetam and ethosuximide were perceived to improve seizures the most and worsen other clinical factors the least out of all AEDs in children with clinical seizures. Traditional non-AED seizure and non-traditional treatments, as a group, were perceived to improve other clinical factors and seizures but the perceived improvement in seizures was significantly less than that reported for AEDs. Certain traditional non-AED treatments, particularly the ketogenic diet, were perceived to improve both seizures and other clinical factors.For ASD individuals with reported

  15. PROSPECTS OF INTRODUCTION OF NON-TRADITIONAL FRUIT BERRY AND VEGETABLE CROPS IN THE CONDITIONS OF DAGESTAN

    Directory of Open Access Journals (Sweden)

    M. S. Gins

    2014-01-01

    Full Text Available June 9-13, 2014 in Makhachkala hosted XI International scientific-methodical conference on the theme: «Introduction, conservation and use of biological diversity of cultivated plants», organized by FGBNU VNIISSOK, Dagestan Research Institute for Agriculture and GBS DSC RAS. The conference was attended by scientists from Russia, CIS and foreign countries. Due to the conference Dagestan turned out to be a prime location for the cultivation of both traditional and non-traditional plants with a high content of biologically active substances, as well as a training ground for resistance tests because of the combination of mountain and plain zones.

  16. Multidsciplinary Approaches to Coastal Adaptation - Aplying Machine Learning Techniques to assess coastal risk in Latin America and The Caribbean

    Science.gov (United States)

    Calil, J.

    2016-12-01

    The global population, currently at 7.3 billion, is increasing by nearly 230,000 people every day. As the world's population grows to an estimated 11.2 billion by 2100, the number of people living in low elevation areas, exposed to coastal hazards, is continuing to increase. In 2013, 22 million people were displaced by extreme weather events, with 37 events displacing at least 100,000 people each. Losses from natural disasters and disaster risk are determined by a complex interaction between physical hazards and the vulnerability of a society or social-ecological system, and its exposure to such hazards. Impacts from coastal hazards depend on the number of people, value of assets, and presence of critical resources in harm's way. Moreover, coastal risks are amplified by challenging socioeconomic dynamics, including ill-advised urban development, income inequality, and poverty level. Our results demonstrate that in Latin America and the Caribbean (LAC), more than half a million people live in areas where coastal hazards, exposure (of people, assets and ecosystems), and poverty converge, creating the ideal conditions for a perfect storm. In order to identify the population at greatest risk to coastal hazards in LAC, and in response to a growing demand for multidisciplinary coastal adaptation approaches, this study employs a combination of machine learning clustering techniques (K-Means and Self Organizing Maps), and a spatial index, to assess coastal risks on a comparative scale. Data for more than 13,000 coastal locations in LAC were collected and allocated into three categories: (1) Coastal Hazards (including storm surge, wave energy and El Niño); (2) Geographic Exposure (including population, agriculture, and ecosystems); and (3) Vulnerability (including income inequality, infant mortality rate and malnutrition). This study identified hotspots of coastal vulnerability, the key drivers of coastal risk at each geographic location. Our results provide important

  17. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

    Science.gov (United States)

    de Ávila, Maurício Boff; Xavier, Mariana Morrone; Pintro, Val Oliveira; de Azevedo, Walter Filgueira

    2017-12-09

    Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC 50 ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. LINGUISTIC ANALYSIS FOR THE BELARUSIAN CORPUS WITH THE APPLICATION OF NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Yu. S. Hetsevich

    2017-01-01

    Full Text Available The article focuses on the problems existing in text-to-speech synthesis. Different morphological, lexical and syntactical elements were localized with the help of the Belarusian unit of NooJ program. Those types of errors, which occur in Belarusian texts, were analyzed and corrected. Language model and part of speech tagging model were built. The natural language processing of Belarusian corpus with the help of developed algorithm using machine learning was carried out. The precision of developed models of machine learning has been 80–90 %. The dictionary was enriched with new words for the further using it in the systems of Belarusian speech synthesis.

  19. Monitoring changes in soil carbon resulting from intensive production, a non-traditional agricultural methodology.

    Energy Technology Data Exchange (ETDEWEB)

    Dwyer, Brian P.

    2013-03-01

    New Mexico State University and a group of New Mexico farmers are evaluating an innovative agricultural technique they call Intensive Production (IP). In contrast to conventional agricultural practice, IP uses intercropping, green fallowing, application of soil amendments and soil microbial inocula to sequester carbon as plant biomass, resulting in improved soil quality. Sandia National Laboratories role was to identify a non-invasive, cost effective technology to monitor soil carbon changes. A technological review indicated that Laser Induced Breakdown Spectroscopy (LIBS) best met the farmers objectives. Sandia partnered with Los Alamos National Laboratory (LANL) to analyze farmers test plots using a portable LIBS developed at LANL. Real-time LIBS field sample analysis was conducted and grab samples were collected for laboratory comparison. The field and laboratory results correlated well implying the strong potential for LIBS as an economical field scale analytical tool for analysis of elements such as carbon, nitrogen, and phosphate.

  20. PbS Thin Films for Photovoltaic Applications Obtained by Non-Traditional Chemical Bath Deposition

    Directory of Open Access Journals (Sweden)

    Pérez-García Claudia Elena

    2015-01-01

    Full Text Available To optimize cost-efficiency relation for thin film solar cells, we explore the recently developed versions of chemical deposition of semiconductor films, together with classic CBD (Chemical Bath Deposition: SILAR (Successive Ionic Layer Adsorption and Reaction and PCBD (Photo Chemical Bath Deposition, all of them ammonia-free and ecologically friendly. The films of CdS and PbS were made, and experimental solar cells with CdS window layer and PbS absorber elaborated. We found that band gap of PbS films can be monitored by deposition process due to porosity-induced quantum confinement which depends on the parameters of the process. We expect that the techniques employed can be successfully used for production of optoelectronic devices.

  1. Data set on the bioprecipitation of sulfate and trivalent arsenic by acidophilic non-traditional sulfur reducing bacteria.

    Science.gov (United States)

    de Matos, Letícia Paiva; Costa, Patrícia Freitas; Moreira, Mariana; Gomes, Paula Cristine Silva; de Queiroz Silva, Silvana; Gurgel, Leandro Vinícius Alves; Teixeira, Mônica Cristina

    2018-04-01

    Data presented here are related to the original paper "Simultaneous removal of sulfate and arsenic using immobilized non-traditional sulfate reducing bacteria (SRB) mixed culture and alternative low-cost carbon sources" published by same authors (Matos et al., 2018) [1]. The data set here presented aims to facilitate this paper comprehension by giving readers some additional information. Data set includes a brief description of experimental conditions and the results obtained during both batch and semi-continuous reactors experiments. Data confirmed arsenic and sulfate were simultaneously removed under acidic pH by using a biological treatment based on the activity of a non-traditional sulfur reducing bacteria consortium. This microbial consortium was able to utilize glycerol, powdered chicken feathers as carbon donors, and proved to be resistant to arsenite up to 8.0 mg L - 1 . Data related to sulfate and arsenic removal efficiencies, residual arsenite and sulfate contents, pH and Eh measurements obtained under different experimental conditions were depicted in graphical format. Refers to https://doi.org/10.1016/j.cej.2017.11.035.

  2. Automated Sample Preparation for Radiogenic and Non-Traditional Metal Isotopes: Removing an Analytical Barrier for High Sample Throughput

    Science.gov (United States)

    Field, M. Paul; Romaniello, Stephen; Gordon, Gwyneth W.; Anbar, Ariel D.; Herrmann, Achim; Martinez-Boti, Miguel A.; Anagnostou, Eleni; Foster, Gavin L.

    2014-05-01

    MC-ICP-MS has dramatically improved the analytical throughput for high-precision radiogenic and non-traditional isotope ratio measurements, compared to TIMS. The generation of large data sets, however, remains hampered by tedious manual drip chromatography required for sample purification. A new, automated chromatography system reduces the laboratory bottle neck and expands the utility of high-precision isotope analyses in applications where large data sets are required: geochemistry, forensic anthropology, nuclear forensics, medical research and food authentication. We have developed protocols to automate ion exchange purification for several isotopic systems (B, Ca, Fe, Cu, Zn, Sr, Cd, Pb and U) using the new prepFAST-MC™ (ESI, Nebraska, Omaha). The system is not only inert (all-flouropolymer flow paths), but is also very flexible and can easily facilitate different resins, samples, and reagent types. When programmed, precise and accurate user defined volumes and flow rates are implemented to automatically load samples, wash the column, condition the column and elute fractions. Unattended, the automated, low-pressure ion exchange chromatography system can process up to 60 samples overnight. Excellent reproducibility, reliability, recovery, with low blank and carry over for samples in a variety of different matrices, have been demonstrated to give accurate and precise isotopic ratios within analytical error for several isotopic systems (B, Ca, Fe, Cu, Zn, Sr, Cd, Pb and U). This illustrates the potential of the new prepFAST-MC™ (ESI, Nebraska, Omaha) as a powerful tool in radiogenic and non-traditional isotope research.

  3. Data set on the bioprecipitation of sulfate and trivalent arsenic by acidophilic non-traditional sulfur reducing bacteria

    Directory of Open Access Journals (Sweden)

    Letícia Paiva de Matos

    2018-04-01

    Full Text Available Data presented here are related to the original paper “Simultaneous removal of sulfate and arsenic using immobilized non-traditional sulfate reducing bacteria (SRB mixed culture and alternative low-cost carbon sources” published by same authors (Matos et al., 2018 [1]. The data set here presented aims to facilitate this paper comprehension by giving readers some additional information. Data set includes a brief description of experimental conditions and the results obtained during both batch and semi-continuous reactors experiments. Data confirmed arsenic and sulfate were simultaneously removed under acidic pH by using a biological treatment based on the activity of a non-traditional sulfur reducing bacteria consortium. This microbial consortium was able to utilize glycerol, powdered chicken feathers as carbon donors, and proved to be resistant to arsenite up to 8.0 mg L−1. Data related to sulfate and arsenic removal efficiencies, residual arsenite and sulfate contents, pH and Eh measurements obtained under different experimental conditions were depicted in graphical format.Refers to https://doi.org/10.1016/j.cej.2017.11.035 Keywords: Arsenite, Sulfate reduction, Bioremediation, Immobilized cells, Acid pH

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

    Science.gov (United States)

    Hussain, Lal

    2018-06-01

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

  5. A Declarative Design Approach to Modeling Traditional and Non-Traditional Space Systems

    Science.gov (United States)

    Hoag, Lucy M.

    The space system design process is known to be laborious, complex, and computationally demanding. It is highly multi-disciplinary, involving several interdependent subsystems that must be both highly optimized and reliable due to the high cost of launch. Satellites must also be capable of operating in harsh and unpredictable environments, so integrating high-fidelity analysis is important. To address each of these concerns, a holistic design approach is necessary. However, while the sophistication of space systems has evolved significantly in the last 60 years, improvements in the design process have been comparatively stagnant. Space systems continue to be designed using a procedural, subsystem-by-subsystem approach. This method is inadequate since it generally requires extensive iteration and limited or heuristic-based search, which can be slow, labor-intensive, and inaccurate. The use of a declarative design approach can potentially address these inadequacies. In the declarative programming style, the focus of a problem is placed on what the objective is, and not necessarily how it should be achieved. In the context of design, this entails knowledge expressed as a declaration of statements that are true about the desired artifact instead of explicit instructions on how to implement it. A well-known technique is through constraint-based reasoning, where a design problem is represented as a network of rules and constraints that are reasoned across by a solver to dynamically discover the optimal candidate(s). This enables implicit instantiation of the tradespace and allows for automatic generation of all feasible design candidates. As such, this approach also appears to be well-suited to modeling adaptable space systems, which generally have large tradespaces and possess configurations that are not well-known a priori. This research applied a declarative design approach to holistic satellite design and to tradespace exploration for adaptable space systems. The

  6. Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

    Directory of Open Access Journals (Sweden)

    Ole Marius Hoel Rindal

    2017-12-01

    Full Text Available The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.

  7. The Changing Face of the of Former Soviet Cities: Elucidated by Remote Sensing and Machine Learning Techniques

    Science.gov (United States)

    Poghosyan, Armen

    2017-04-01

    Despite remote sensing of urbanization emerged as a powerful tool to acquire critical knowledge about urban growth and its effects on global environmental change, human-environment interface as well as environmentally sustainable urban development, there is lack of studies utilizing remote sensing techniques to investigate urbanization trends in the Post-Soviet states. The unique challenges accompanying the urbanization in the Post-Soviet republics combined with the expected robust urban growth in developing countries over the next several decades highlight the critical need for a quantitative assessment of the urban dynamics in the former Soviet states as they navigate towards a free market democracy. This study uses total of 32 Level-1 precision terrain corrected (L1T) Landsat scenes with 30-m resolution as well as further auxiliary population and economic data for ten cities distributed in nine former Soviet republics to quantify the urbanization patterns in the Post-Soviet region. Land cover in each urban center of this study was classified by using Support Vector Machine (SVM) learning algorithm with overall accuracies ranging from 87 % to 97 % for 29 classification maps over three time steps during the past twenty-five years in order to estimate quantities, trends and drivers of urban growth in the study area. The results demonstrated several spatial and temporal urbanization patterns observed across the Post-Soviet states and based on urban expansion rates the cities can be divided into two groups, fast growing and slow growing urban centers. The relatively fast-growing urban centers have an average urban expansion rate of about 2.8 % per year, whereas the slow growing cities have an average urban expansion rate of about 1.0 % per year. The total area of new land converted to urban environment ranged from as low as 26 km2 to as high as 780 km2 for the ten cities over the 1990 - 2015 period, while the overall urban land increase ranged from 11.3 % to 96

  8. Non-Traditional Security Threats in the Border Areas: Terrorism, Piracy, Environmental Degradation in Southeast Asian Maritime Domain

    Science.gov (United States)

    Dabova, E. L.

    2013-11-01

    In addition to facilitating peaceful trade and economic development, sovereign territory, territorial waters and international waters are being used by various criminal groups that pose threats to governments, businesses and civilian population in Southeast Asia. Nonstate criminal maritime activities were not receiving appropriate attention as they were overshadowed by traditional military security challenges. Yet more and more frequently, the non-traditional actors challenge lines of communication, jeopardize access to strategic resources, complicate traditional defence tasks, and harm the environment. Understanding the nature of non-traditional threats, and the ways to combat them, requires international legal, historical and political science analysis within a united problem-oriented approach. A fair critique to pure interest, power and knowledge -based theories of regime formation was developed by E.K. Leonard's1, who explained the evolution of the international system from the global governance perspective. The present study is based on the premise that pure nation-state approaches are incapable of providing a theoretical ground for addressing the growing influence of international criminal networks in South East Asia. From an international relations theory perspective, the author of this study agrees with D.Snidal2 that the hegemonic stability theory has "limits" and is insufficient in describing modern challenges to sustainable international security regime, including non-traditional threats, where collective action is more efficient from an interest and capability standpoint. At the same time the author of this study does not share the viewpoint on "marginalization"3 of international law in current international order due to its fragmentation and regionalization4 and "global power shifts"5 . The United Nations, as a global institution at the top of the vertical hierarchy of international legal order, and the EU as an example of "self-contained" regime along

  9. Information session proceedings of the National First Nations and Inuit Working Group on the Non-Traditional Use of Tobacco for Medical Services Branch, Health Canada

    National Research Council Canada - National Science Library

    Dumont-Smith, Claudette

    1995-01-01

    The publication covers topics ranging from the impact on the non-traditional use of tobacco among First Nations and Inuit Communities, current trends, opportunities and challenges, to current efforts...

  10. An evaluation of machine processing techniques of ERTS-1 data for user applications. [urban land use and soil association mapping in Indiana

    Science.gov (United States)

    Landgrebe, D.

    1974-01-01

    A broad study is described to evaluate a set of machine analysis and processing techniques applied to ERTS-1 data. Based on the analysis results in urban land use analysis and soil association mapping together with previously reported results in general earth surface feature identification and crop species classification, a profile of general applicability of this procedure is beginning to emerge. Put in the hands of a user who knows well the information needed from the data and also is familiar with the region to be analyzed it appears that significantly useful information can be generated by these methods. When supported by preprocessing techniques such as the geometric correction and temporal registration capabilities, final products readily useable by user agencies appear possible. In parallel with application, through further research, there is much potential for further development of these techniques both with regard to providing higher performance and in new situations not yet studied.

  11. Crawling up the value chain: domestic institutions and non-traditional foreign direct investment in Brazil, 1990-2010

    Directory of Open Access Journals (Sweden)

    PATRICK J. W. EGAN

    2015-03-01

    Full Text Available Brazil attracted relatively little innovation-intensive and export-oriented foreign investment during the liberalization period of 1990 to 2010, especially compared with competitors such as China and India. Adopting an institutionalist perspective, I argue that multinational firm investment profiles can be partly explained by the characteristics of investment promotion policies and bureaucracies charged with their implementation. Brazil's FDI policies were passive and non-discriminating in the second half of the 1990s, but became more selective under Lula. Investment promotion efforts have often been undercut by weakly coordinated and inconsistent institutions. The paper highlights the need for active, discriminating investment promotion policies if benefits from non-traditional FDI are to be realized.

  12. Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision

    OpenAIRE

    Chao-Ching Ho; Dung-Sheng Wu

    2018-01-01

    Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was p...

  13. Customer Characteristics and Shopping Patterns Associated with Healthy and Unhealthy Purchases at Small and Non-traditional Food Stores.

    Science.gov (United States)

    Lenk, Kathleen M; Caspi, Caitlin E; Harnack, Lisa; Laska, Melissa N

    2018-02-01

    Small and non-traditional food stores (e.g., corner stores) are often the most accessible source of food for residents of lower income urban neighborhoods in the U.S. Although healthy options are often limited at these stores, little is known about customers who purchase healthy, versus less healthy, foods/beverages in these venues. We conducted 661 customer intercept interviews at 105 stores (corner stores, gas marts, pharmacies, dollar stores) in Minneapolis/St. Paul, Minnesota, assessing all food and beverage items purchased. We defined three categories of "healthy" and four categories of "unhealthy" purchases. Interviews assessed customer characteristics [e.g., demographics, body-mass index (BMI)]. We examined associations between healthy versus unhealthy purchases categories and customer characteristics. Overall, 11% of customers purchased ≥1 serving of healthy foods/beverages in one or more of the three categories: 8% purchased fruits/vegetables, 2% whole grains, and 1% non-/low-fat dairy. Seventy-one percent of customers purchased ≥1 serving of unhealthy foods/beverages in one or more of four categories: 46% purchased sugar-sweetened beverages, 17% savory snacks, 15% candy, and 13% sweet baked goods. Male (vs. female) customers, those with a lower education levels, and those who reported shopping at the store for convenience (vs. other reasons) were less likely to purchase fruits/vegetables. Unhealthy purchases were more common among customers with a BMI ≥30 kg/m 2 (vs. lower BMI). Results suggest intervention opportunities to increase healthy purchases at small and non-traditional food stores, particularly interventions aimed at male residents, those with lower education levels and residents living close to the store.

  14. Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms

    Directory of Open Access Journals (Sweden)

    Debkalpa Goswami

    2015-03-01

    Full Text Available Ultrasonic machining (USM is a mechanical material removal process used to erode holes and cavities in hard or brittle workpieces by using shaped tools, high-frequency mechanical motion and an abrasive slurry. Unlike other non-traditional machining processes, such as laser beam and electrical discharge machining, USM process does not thermally damage the workpiece or introduce significant levels of residual stress, which is important for survival of materials in service. For having enhanced machining performance and better machined job characteristics, it is often required to determine the optimal control parameter settings of an USM process. The earlier mathematical approaches for parametric optimization of USM processes have mostly yielded near optimal or sub-optimal solutions. In this paper, two almost unexplored non-conventional optimization techniques, i.e. gravitational search algorithm (GSA and fireworks algorithm (FWA are applied for parametric optimization of USM processes. The optimization performance of these two algorithms is compared with that of other popular population-based algorithms, and the effects of their algorithm parameters on the derived optimal solutions and computational speed are also investigated. It is observed that FWA provides the best optimal results for the considered USM processes.

  15. Quantum machine learning.

    Science.gov (United States)

    Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

    2017-09-13

    Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

  16. Comments on Frequency Swept Rotating Input Perturbation Techniques and Identification of the Fluid Force Models in Rotor/bearing/seal Systems and Fluid Handling Machines

    Science.gov (United States)

    Muszynska, Agnes; Bently, Donald E.

    1991-01-01

    Perturbation techniques used for identification of rotating system dynamic characteristics are described. A comparison between two periodic frequency-swept perturbation methods applied in identification of fluid forces of rotating machines is presented. The description of the fluid force model identified by inputting circular periodic frequency-swept force is given. This model is based on the existence and strength of the circumferential flow, most often generated by the shaft rotation. The application of the fluid force model in rotor dynamic analysis is presented. It is shown that the rotor stability is an entire rotating system property. Some areas for further research are discussed.

  17. Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor

    Science.gov (United States)

    Effati, Meysam; Thill, Jean-Claude; Shabani, Shahin

    2015-04-01

    The contention of this paper is that many social science research problems are too "wicked" to be suitably studied using conventional statistical and regression-based methods of data analysis. This paper argues that an integrated geospatial approach based on methods of machine learning is well suited to this purpose. Recognizing the intrinsic wickedness of traffic safety issues, such approach is used to unravel the complexity of traffic crash severity on highway corridors as an example of such problems. The support vector machine (SVM) and coactive neuro-fuzzy inference system (CANFIS) algorithms are tested as inferential engines to predict crash severity and uncover spatial and non-spatial factors that systematically relate to crash severity, while a sensitivity analysis is conducted to determine the relative influence of crash severity factors. Different specifications of the two methods are implemented, trained, and evaluated against crash events recorded over a 4-year period on a regional highway corridor in Northern Iran. Overall, the SVM model outperforms CANFIS by a notable margin. The combined use of spatial analysis and artificial intelligence is effective at identifying leading factors of crash severity, while explicitly accounting for spatial dependence and spatial heterogeneity effects. Thanks to the demonstrated effectiveness of a sensitivity analysis, this approach produces comprehensive results that are consistent with existing traffic safety theories and supports the prioritization of effective safety measures that are geographically targeted and behaviorally sound on regional highway corridors.

  18. Evaluation of bent-crystal x-ray backlighting and microscopy techniques for the Sandia Z machine.

    Science.gov (United States)

    Sinars, Daniel B; Bennett, Guy R; Wenger, David F; Cuneo, Michael E; Porter, John L

    2003-07-01

    X-ray backlighting and microscopy systems for the 1-10-keV range based on spherically or toroidally bent crystals are discussed. These systems are ideal for use on the Sandia Z machine, a megajoule-class x-ray facility. Near-normal-incidence crystal microscopy systems have been shown to be more efficient than pinhole cameras with the same spatial resolution and magnification [Appl. Opt. 37, 1784 (1998)]. We show that high-resolution (< or = 10 microm) x-ray backlighting systems using bent crystals can be more efficient than analogous point-projection imaging systems. Examples of bent-crystal-backlighting results that demonstrate 10-microm resolution over a 20-mm field of view are presented.

  19. Evaluation of bent-crystal x-ray backlighting and microscopy techniques for the Sandia Z machine

    International Nuclear Information System (INIS)

    Sinars, Daniel B.; Wenger, David F.; Cuneo, Michael E.; Porter, John L.; Bennett, Guy R.

    2003-01-01

    X-ray backlighting and microscopy systems for the 1-10-keV range based on spherically or toroidally bent crystals are discussed. These systems are ideal for use on the Sandia Z machine, a megajoule-class x-ray facility. Near-normal-incidence crystal microscopy systems have been shown to be more efficient than pinhole cameras with the same spatial resolution and magnification [Appl. Opt. 37, 1784 (1998)]. We show that high-resolution (≤10 μm) x-ray backlighting systems using bent crystals can be more efficient than analogous point-projection imaging systems. Examples of bent-crystal-backlighting results that demonstrate 10-μm resolution over a 20-mm field of view are presented

  20. Machinability of a Stainless Steel by Electrochemical Discharge Microdrilling

    International Nuclear Information System (INIS)

    Coteata, Margareta; Pop, Nicolae; Slatineanu, Laurentiu; Schulze, Hans-Peter; Besliu, Irina

    2011-01-01

    Due to the chemical elements included in their structure for ensuring an increased resistance to the environment action, the stainless steels are characterized by a low machinability when classical machining methods are applied. For this reason, sometimes non-traditional machining methods are applied, one of these being the electrochemical discharge machining. To obtain microholes and to evaluate the machinability by electrochemical discharge microdrilling, test pieces of stainless steel were used for experimental research. The electrolyte was an aqueous solution of sodium silicate with different densities. A complete factorial plan was designed to highlight the influence of some input variables on the sizes of the considered machinability indexes (electrode tool wear, material removal rate, depth of the machined hole). By mathematically processing of experimental data, empirical functions were established both for stainless steel and carbon steel. Graphical representations were used to obtain more suggestive vision concerning the influence exerted by the considered input variables on the size of the machinability indexes.

  1. Student Modeling and Machine Learning

    OpenAIRE

    Sison , Raymund; Shimura , Masamichi

    1998-01-01

    After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine le...

  2. Prevalence of chronic kidney disease of non-traditional causes in patients on hemodialysis in southwest Guatemala

    Directory of Open Access Journals (Sweden)

    Timothy S. Laux

    Full Text Available ABSTRACT Objective To document the prevalence of patients on hemodialysis in southwestern Guatemala who have chronic kidney disease (CKD of non-traditional causes (CKDnt. Methods This cross-sectional descriptive study interviewed patients on hemodialysis at the Instituto Guatemalteco de Seguridad Social on their health and occupational history. Laboratory serum, urine and vital sign data at the initiation of hemodialysis were obtained from chart reviews. Patients were classified according to whether they had hypertension or obesity or neither. The proportion of patients with and without these traditional CKD risk factors was recorded and the association between demographic and occupational factors and a lack of traditional CKD risk factors analyzed using multivariate logistic regression. Results Of 242 total patients (including 171 non-diabetics enrolled in hemodialysis in southwestern Guatemala, 45 (18.6% of total patients and 26.3% of non-diabetics lacked traditional CKD risk factors. While agricultural work history was common, only travel time greater than 30 minutes and age less than 50 years old were significantly associated with CKD in the absence of traditional risk factors. Individuals without such risk factors lived throughout southwestern Guatemala’s five departments. Conclusions The prevalence of CKDnT appears to be much lower in this sample of patients receiving hemodialysis in Southwestern Guatemala than in hospitalized patients in El Salvador. It has yet to be determined whether the prevalence is higher in the general population and in patients on peritoneal dialysis.

  3. Prevalence of chronic kidney disease of non-traditional causes in patients on hemodialysis in southwest Guatemala.

    Science.gov (United States)

    Laux, Timothy S; Barnoya, Joaquin; Cipriano, Ever; Herrera, Erick; Lopez, Noemi; Polo, Vicente Sanchez; Rothstein, Marcos

    2016-04-01

    Objective To document the prevalence of patients on hemodialysis in southwestern Guatemala who have chronic kidney disease (CKD) of non-traditional causes (CKDnt). Methods This cross-sectional descriptive study interviewed patients on hemodialysis at the Instituto Guatemalteco de Seguridad Social on their health and occupational history. Laboratory serum, urine and vital sign data at the initiation of hemodialysis were obtained from chart reviews. Patients were classified according to whether they had hypertension or obesity or neither. The proportion of patients with and without these traditional CKD risk factors was recorded and the association between demographic and occupational factors and a lack of traditional CKD risk factors analyzed using multivariate logistic regression. Results Of 242 total patients (including 171 non-diabetics) enrolled in hemodialysis in southwestern Guatemala, 45 (18.6% of total patients and 26.3% of non-diabetics) lacked traditional CKD risk factors. While agricultural work history was common, only travel time greater than 30 minutes and age less than 50 years old were significantly associated with CKD in the absence of traditional risk factors. Individuals without such risk factors lived throughout southwestern Guatemala's five departments. Conclusions The prevalence of CKDnT appears to be much lower in this sample of patients receiving hemodialysis in Southwestern Guatemala than in hospitalized patients in El Salvador. It has yet to be determined whether the prevalence is higher in the general population and in patients on peritoneal dialysis.

  4. Estimation of the Impacts of Non-Oil Traditional and NonTraditional Export Sectors on Non-Oil Export of Azerbaijan

    Directory of Open Access Journals (Sweden)

    Nicat Hagverdiyev

    2016-12-01

    Full Text Available The significant share of oil sector of the Azerbaijan export portfolio necessitates promotion of non-oil exports. This study analyzes weather the commodities which contain the main share (more than 70% in non-oil export are traditional or non-traditional areas, using the so-called Commodity-specific cumulative export experience function, for the 1995-2015 time frame. Then, the impact of traditional and non-traditional exports on non-oil GDP investigated employing econometric model. The results of the study based on 16 non-oil commodities show that cotton, tobacco, and production of mechanic devices are traditional sectors in non-oil export. The estimation results of the model indicate that both, traditional and non-traditional non-oil export sectors have economically and statistically significant impact on non-oil GDP.

  5. Machine Shop Grinding Machines.

    Science.gov (United States)

    Dunn, James

    This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…

  6. Machine-assisted verification of latent fingerprints: first results for nondestructive contact-less optical acquisition techniques with a CWL sensor

    Science.gov (United States)

    Hildebrandt, Mario; Kiltz, Stefan; Krapyvskyy, Dmytro; Dittmann, Jana; Vielhauer, Claus; Leich, Marcus

    2011-11-01

    A machine-assisted analysis of traces from crime scenes might be possible with the advent of new high-resolution non-destructive contact-less acquisition techniques for latent fingerprints. This requires reliable techniques for the automatic extraction of fingerprint features from latent and exemplar fingerprints for matching purposes using pattern recognition approaches. Therefore, we evaluate the NIST Biometric Image Software for the feature extraction and verification of contact-lessly acquired latent fingerprints to determine potential error rates. Our exemplary test setup includes 30 latent fingerprints from 5 people in two test sets that are acquired from different surfaces using a chromatic white light sensor. The first test set includes 20 fingerprints on two different surfaces. It is used to determine the feature extraction performance. The second test set includes one latent fingerprint on 10 different surfaces and an exemplar fingerprint to determine the verification performance. This utilized sensing technique does not require a physical or chemical visibility enhancement of the fingerprint residue, thus the original trace remains unaltered for further investigations. No particular feature extraction and verification techniques have been applied to such data, yet. Hence, we see the need for appropriate algorithms that are suitable to support forensic investigations.

  7. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    Directory of Open Access Journals (Sweden)

    Wei Luo

    Full Text Available For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD outcomes (four NCDs and two major clinical risk factors, based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88 and those excluded from the development for use as a completely separated validation sample (median correlation 0.85, demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

  8. A noninvasive technique for real-time detection of bruises in apple surface based on machine vision

    Science.gov (United States)

    Zhao, Juan; Peng, Yankun; Dhakal, Sagar; Zhang, Leilei; Sasao, Akira

    2013-05-01

    Apple is one of the highly consumed fruit item in daily life. However, due to its high damage potential and massive influence on taste and export, the quality of apple has to be detected before it reaches the consumer's hand. This study was aimed to develop a hardware and software unit for real-time detection of apple bruises based on machine vision technology. The hardware unit consisted of a light shield installed two monochrome cameras at different angles, LED light source to illuminate the sample, and sensors at the entrance of box to signal the positioning of sample. Graphical Users Interface (GUI) was developed in VS2010 platform to control the overall hardware and display the image processing result. The hardware-software system was developed to acquire the images of 3 samples from each camera and display the image processing result in real time basis. An image processing algorithm was developed in Opencv and C++ platform. The software is able to control the hardware system to classify the apple into two grades based on presence/absence of surface bruises with the size of 5mm. The experimental result is promising and the system with further modification can be applicable for industrial production in near future.

  9. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    Science.gov (United States)

    Luo, Wei; Nguyen, Thin; Nichols, Melanie; Tran, Truyen; Rana, Santu; Gupta, Sunil; Phung, Dinh; Venkatesh, Svetha; Allender, Steve

    2015-01-01

    For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

  10. Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

    Science.gov (United States)

    Roberts, J. Brent; Robertson, Franklin R.; Funk, Chris

    2014-01-01

    Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.

  11. A self-centering active probing technique for kinematic parameter identification and verification of articulated arm coordinate measuring machines

    International Nuclear Information System (INIS)

    Santolaria, J; Brau, A; Velázquez, J; Aguilar, J J

    2010-01-01

    A crucial task in the procedure of identifying the parameters of a kinematic model of an articulated arm coordinate measuring machine (AACMM) or robot arm is the process of capturing data. In this paper a capturing data method is analyzed using a self-centering active probe, which drastically reduces the capture time and the required number of positions of the gauge as compared to the usual standard and manufacturer methods. The mathematical models of the self-centering active probe and AACMM are explained, as well as the mathematical model that links the AACMM global reference system to the probe reference system. We present a self-calibration method that will allow us to determine a homogeneous transformation matrix that relates the probe's reference system to the AACMM last reference system from the probing of a single sphere. In addition, a comparison between a self-centering passive probe and self-centering active probe is carried out to show the advantages of the latter in the procedures of kinematic parameter identification and verification of the AACMM

  12. Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

    Science.gov (United States)

    Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

    2017-01-01

    Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. PMID:28604582

  13. Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Il-Hwan Kim

    2017-06-01

    Full Text Available Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN models, and the augmented information is fed to a support vector machine (SVM to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

  14. Machining of uranium and uranium alloys

    International Nuclear Information System (INIS)

    Morris, T.O.

    1981-01-01

    Uranium and uranium alloys can be readily machined by conventional methods in the standard machine shop when proper safety and operating techniques are used. Material properties that affect machining processes and recommended machining parameters are discussed. Safety procedures and precautions necessary in machining uranium and uranium alloys are also covered. 30 figures

  15. Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

    Science.gov (United States)

    Feng, Jingwen; Feng, Tong; Yang, Chengwen; Wang, Wei; Sa, Yu; Feng, Yuanming

    2018-06-01

    This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.

  16. Brazil’s fight against narcotraffic in the border with Colombia. An approach to the restrains of non-traditional threats over foreign policy

    Directory of Open Access Journals (Sweden)

    Emilse Calderón

    2014-05-01

    Full Text Available In the post-Cold War international scenario, the non-traditional nature of security threats conditions the states’ foreign policies. An example of the above is the policy employed by Brazil regarding the border shared with Colombia regarding the development that narcotraffic has been having since the end of the 20th century. Therefore, this article proposes a brief analysis around the influence exercised by the non-traditional nature of the drug traffic threat over the design of Brazilian foreign policy between 1999 and 2010.

  17. Meter-scale Urban Land Cover Mapping for EPA EnviroAtlas Using Machine Learning and OBIA Remote Sensing Techniques

    Science.gov (United States)

    Pilant, A. N.; Baynes, J.; Dannenberg, M.; Riegel, J.; Rudder, C.; Endres, K.

    2013-12-01

    US EPA EnviroAtlas is an online collection of tools and resources that provides geospatial data, maps, research, and analysis on the relationships between nature, people, health, and the economy (http://www.epa.gov/research/enviroatlas/index.htm). Using EnviroAtlas, you can see and explore information related to the benefits (e.g., ecosystem services) that humans receive from nature, including clean air, clean and plentiful water, natural hazard mitigation, biodiversity conservation, food, fuel, and materials, recreational opportunities, and cultural and aesthetic value. EPA developed several urban land cover maps at very high spatial resolution (one-meter pixel size) for a portion of EnviroAtlas devoted to urban studies. This urban mapping effort supported analysis of relations among land cover, human health and demographics at the US Census Block Group level. Supervised classification of 2010 USDA NAIP (National Agricultural Imagery Program) digital aerial photos produced eight-class land cover maps for several cities, including Durham, NC, Portland, ME, Tampa, FL, New Bedford, MA, Pittsburgh, PA, Portland, OR, and Milwaukee, WI. Semi-automated feature extraction methods were used to classify the NAIP imagery: genetic algorithms/machine learning, random forest, and object-based image analysis (OBIA). In this presentation we describe the image processing and fuzzy accuracy assessment methods used, and report on some sustainability and ecosystem service metrics computed using this land cover as input (e.g., carbon sequestration from USFS iTREE model; health and demographics in relation to road buffer forest width). We also discuss the land cover classification schema (a modified Anderson Level 1 after the National Land Cover Data (NLCD)), and offer some observations on lessons learned. Meter-scale urban land cover in Portland, OR overlaid on NAIP aerial photo. Streets, buildings and individual trees are identifiable.

  18. Mapping Urban Areas with Integration of DMSP/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Wenlong Jing

    2015-09-01

    Full Text Available Mapping urban areas at global and regional scales is an urgent and crucial task for detecting urbanization and human activities throughout the world and is useful for discerning the influence of urban expansion upon the ecosystem and the surrounding environment. DMSP-OLS stable nighttime lights have provided an effective way to monitor human activities on a global scale. Threshold-based algorithms have been widely used for extracting urban areas and estimating urban expansion, but the accuracy can decrease because of the empirical and subjective selection of threshold values. This paper proposes an approach for extracting urban areas with the integration of DMSP-OLS stable nighttime lights and MODIS data utilizing training sample datasets selected from DMSP-OLS and MODIS NDVI based on several simple strategies. Four classification algorithms were implemented for comparison: the classification and regression tree (CART, k-nearest-neighbors (k-NN, support vector machine (SVM, and random forests (RF. A case study was carried out on the eastern part of China, covering 99 cities and 1,027,700 km2. The classification results were validated using an independent land cover dataset, and then compared with an existing contextual classification method. The results showed that the new method can achieve results with comparable accuracies, and is easier to implement and less sensitive to the initial thresholds than the contextual method. Among the four classifiers implemented, RF achieved the most stable results and the highest average Kappa. Meanwhile CART produced highly overestimated results compared to the other three classifiers. Although k-NN and SVM tended to produce similar accuracy, less-bright areas around the urban cores seemed to be ignored when using SVM, which led to the underestimation of urban areas. Furthermore, quantity assessment showed that the results produced by k-NN, SVM, and RFs exhibited better agreement in larger cities and low

  19. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jeffrey De Fauw

    2017-06-01

    Full Text Available There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet” age-related macular degeneration (wet AMD and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves. Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  20. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jeffrey De Fauw

    2016-07-01

    Full Text Available There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases.   Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet” age-related macular degeneration (wet AMD and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves. Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges.   This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients.   Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, Google DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  1. Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field

    Science.gov (United States)

    Pérez-Ortiz, M. F.; García-Varela, A.; Quiroz, A. J.; Sabogal, B. E.; Hernández, J.

    2017-09-01

    Context. Optical and infrared variability surveys produce a large number of high quality light curves. Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to train an automatic classification system. Quantities related to the magnitude density of the light curves and their Fourier coefficients have been chosen as features in previous studies. However, some of these features are not robust to the presence of outliers and the calculation of Fourier coefficients is computationally expensive for large data sets. Aims: We propose and evaluate the performance of a new robust set of features using supervised classifiers in order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic pole field. Methods: We calculated the proposed set of features on six types of variable stars and also on a set of Be star candidates reported in the literature. We evaluated the performance of these features using classification trees and random forests along with the K-nearest neighbours, support vector machines, and gradient boosted trees methods. We tuned the classifiers with a 10-fold cross-validation and grid search. We then validated the performance of the best classifier on a set of OGLE-IV light curves and applied this to find new Be star candidates. Results: The random forest classifier outperformed the others. By using the random forest classifier and colours criteria we found 50 Be star candidates in the direction of the Gaia south ecliptic pole field, four of which have infrared colours that are consistent with Herbig Ae/Be stars. Conclusions: Supervised methods are very useful in order to obtain preliminary samples of variable stars extracted from large databases. As usual, the stars classified as Be stars candidates must be checked for the colours and spectroscopic characteristics

  2. Manufacturing of mortars and concretes non-traditionals, by Portland cement, metakaoline and gypsum (15.05%

    Directory of Open Access Journals (Sweden)

    Talero, R.

    1999-12-01

    Full Text Available In a thorough previous research (1, it appeared that creation, evolution and development of the values of compressive mechanical strength (CS and flexural strength (FS, measured in specimens 1x1x6cm of mortar type ASTM C 452-68 (2, manufactured by ordinary Portland cement P-1 (14.11% C3A or PY-6 (0.00% C3A, metakaolin and gypsum (CaSO4∙2H2O -or ternary cements, CT-, were similar to the ones commonly developed in mortars and concretes of OPC. This paper sets up the experimental results obtained from non-traditional mortars and concretes prepared with such ternary cements -TC-, being the portland cement/metakaolin mass ratio, as follows: 80/20, 70/30 and 60/40. Finally, the behaviour of these cements against gypsum attack, has been also determined, using the following parameters: increase in length (ΔL%, compressive, CS, and flexural, FS, strengths, and ultrasound energy, UE. Experimental results obtained from these non-traditional mortars and concretes, show an increase in length (ΔL, in CS and FS, and in UE values, when there is addition of metakaolin.

    En una exhaustiva investigación anterior (1, se pudo comprobar que la creación, evolución y desarrollo de los valores de resistencias mecánicas a compresión, RMC, y flexotracción, RMF, proporcionados por probetas de 1x1x6 cm, de mortero 1:2,75, selenitoso tipo ASTM C 452-68 (2 -que habían sido preparadas con arena de Ottawa, cemento portland, P-1 (14,11% C3A o PY- 6 (0,00% C3A, metacaolín y yeso (CaSO4∙2H2O-, fue semejante a la que, comúnmente, desarrollan los morteros y hormigones tradicionales de cemento portland. En el presente trabajo se exponen los resultados experimentales obtenidos de morteros y hormigones no tradicionales, preparados con dichos cementos ternarios, CT, siendo las proporciones porcentuales en masa ensayadas, cemento portland/metacaolín, las siguientes: 80/20, 70

  3. Clinical characteristics of chronic kidney disease of non-traditional causes in women of agricultural communities in El Salvador.

    Science.gov (United States)

    Herrera Valdés, Raúl; Orantes, Carlos M; Almaguer López, Miguel; López Marín, Laura; Arévalo, Pedro Alfonso; Smith González, Magaly J; Morales, Fabrizio E; Bacallao, Raymed; Bayarre, Héctor D; Vela Parada, Xavier F

    2015-01-01

    A chronic kidney disease of non-traditional causes (CKDu) has emerged in Central America and elsewhere, predominantly affecting male farmworkers. In El Salvador (2009), it was the second cause of death in men > 18 years old. Causality has not been determined. Most available research focused on men and there is scarce data on women. Describe the clinical and histopathologic characteristics of CKDu in women of agricultural communities in El Salvador. A descriptive study was carried out in 10 women with CKDu stages 2, 3a, and 3b. Researchers studied demographics, clinical examination; hematological and biochemical analyses, urine sediment, renal injury markers, and assessed renal, cardiac, and peripheral arteries, liver, pancreas, and lung anatomy and functions. Kidney biopsy was performed in all. Data was collected on the Lime Survey platform and exported to SPSS 19.0. Patient distribution by stages: 2 (70%), 3a (10%), 3b (20%). Occupation: agricultural 7; non-agricultural 3. agrochemical exposure 100%; farmworkers 70%; incidental malaria 50%, NSAIDs use 40%; hypertension 40%. nocturia 50%; dysuria 50%; arthralgia 70%; asthenia 50%; cramps 30%, profuse sweating 20%. Renal markers: albumin creatinine ratio (ACR) > 300 mg/g 90%; β microglobulin and neutrophil gelatinase- associated lipocalin (NGAL) presence in 40%. Kidney function: hypermagnesuria 100%; hyperphosphaturia 50%, hypercalciuria 40%; hypernatriuria 30%; hyponatremia 60%, hypocalcemia 50%. Doppler: tibial artery damage 40%. Neurological: reflex abnormalities 30%; Babinski and myoclonus 20%. Neurosensorial hypoacusis 70%. Histopathology: damage restricted mostly to the tubulo-interstitium, urine was essentially bland. CKDu in women is a chronic tubulointerstitial nephropathy with varied extrarenal symptoms.

  4. Dialysis enrollment patterns in Guatemala: evidence of the chronic kidney disease of non-traditional causes epidemic in Mesoamerica.

    Science.gov (United States)

    Laux, Timothy S; Barnoya, Joaquin; Guerrero, Douglas R; Rothstein, Marcos

    2015-04-14

    In western Nicaragua and El Salvador, chronic kidney disease (CKD) is highly prevalent and generally affects young, male, agricultural (usually sugar cane) workers without the established CKD risk factors. It is yet unknown if the prevalence of this CKD of Non-Traditional causes (CKDnT) extends to the northernmost Central American country, Guatemala. Therefore, we sought to compare dialysis enrollment rates by region, municipality, sex, daily temperature, and agricultural production in Guatemala and assess if there is a similar CKDnT distribution pattern as in Nicaragua and El Salvador. The National Center for Chronic Kidney Disease Treatment (Unidad Nacional de Atención al Enfermo Renal Crónico) is the largest provider of dialysis in Guatemala. We used population, Human Development Index, literacy, and agricultural databases to assess the geographic, economic, and educational correlations with the National Center for Chronic Kidney Disease Treatment's hemodialysis and peritoneal dialysis enrollment database. Enrollment rates (per 100 000) inhabitants were compared by region and mapped for comparison to regional agricultural and daytime temperature data. The distribution of men and women enrolled in dialysis were compared by region using Fisher's exact tests. Spearman's rank correlation coefficients were calculated. Dialysis enrollment is higher in the Southwest compared to the rest of the country where enrollees are more likely (p Guatemala. In Guatemala, CKDnT incidence may have a similar geographic distribution as Nicaragua and El Salvador (higher in the high temperature and sugar cane growing regions). Therefore, it is likely that the CKNnT epidemic extends throughout the Mesoamerican region.

  5. A Benchmark for Banks’ Strategy in Online Presence – An Innovative Approach Based on Elements of Search Engine Optimization (SEO and Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Camelia Elena CIOLAC

    2011-06-01

    Full Text Available This paper aims to offer a new decision tool to assist banks in evaluating their efficiency of Internet presence and in planning the IT investments towards gaining better Internet popularity. The methodology used in this paper goes beyond the simple website interface analysis and uses web crawling as a source for collecting website performance data and employed web technologies and servers. The paper complements this technical perspective with a proposed scorecard used to assess the efforts of banks in Internet presence that reflects the banks’ commitment to Internet as a distribution channel. An innovative approach based on Machine Learning Techniques, the K-Nearest Neighbor Algorithm, is proposed by the author to estimate the Internet Popularity that a bank is likely to achieve based on its size and efforts in Internet presence.

  6. Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

    Science.gov (United States)

    Rienow, A.; Menz, G.

    2015-12-01

    Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future

  7. Drifting Apart or Converging? Grades among Non-Traditional and Traditional Students over the Course of Their Studies: A Case Study from Germany

    Science.gov (United States)

    Brändle, Tobias; Lengfeld, Holger

    2017-01-01

    Since 2009, German universities were opened by law to freshmen who do not possess the traditional graduation certificate required for entry into University, but who are rather vocationally qualified. In this article, we track the grades of these so-called non-traditional students and compare them to those of traditional students using a…

  8. Re-Entry Women Students in Higher Education: A Model for Non-Traditional Support Programs in Counseling and Career Advisement.

    Science.gov (United States)

    Karr-Kidwell, PJ

    A model program of support for non-traditional women students has been developed at Texas Woman's University (TWU). Based on a pilot study, several steps were taken to assist these re-entry students at TWU. For example, in spring semester of 1983, a committee for re-entry students was established, with a student organization--Women in…

  9. Comparison of a traditional and non-traditional residential care facility for persons living with dementia and the impact of the environment on occupational engagement.

    Science.gov (United States)

    Richards, Kieva; D'Cruz, Rachel; Harman, Suzanne; Stagnitti, Karen

    2015-12-01

    Dementia residential facilities can be described as traditional or non-traditional facilities. Non-traditional facilities aim to utilise principles of environmental design to create a milieu that supports persons experiencing cognitive decline. This study aimed to compare these two environments in rural Australia, and their influence on residents' occupational engagement. The Residential Environment Impact Survey (REIS) was used and consists of: a walk-through of the facility; activity observation; interviews with residents and employees. Thirteen residents were observed and four employees interviewed. Resident interviews did not occur given the population diagnosis of moderate to severe dementia. Descriptive data from the walk-through and activity observation were analysed for potential opportunities of occupational engagement. Interviews were thematically analysed to discern perception of occupational engagement of residents within their facility. Both facilities provided opportunities for occupational engagement. However, the non-traditional facility provided additional opportunities through employee interactions and features of the physical environment. Interviews revealed six themes: Comfortable environment; roles and responsibilities; getting to know the resident; more stimulation can elicit increased engagement; the home-like experience and environmental layout. These themes coupled with the features of the environment provided insight into the complexity of occupational engagement within this population. This study emphasises the influence of the physical and social environment on occupational engagement opportunities. A non-traditional dementia facility maximises these opportunities and can support development of best-practice guidelines within this population. © 2015 Occupational Therapy Australia.

  10. Review of traditional and non-traditional medicinal genetic resources in the USDA, ARS, PGRCU collection evaluated for flavonoid concentrations and anthocyanin indexes

    Science.gov (United States)

    Non-traditional medicinal species include velvetleaf (Abutilon theophrasti Medik.), Desmodium species, Termanus labialis (L.f.) Spreng. and the traditional species consists of roselle (Hibiscus sabdariffa L.). There is a need to identify plant sources of flavonoids and anthocyanins since they have s...

  11. Supporting Online, Non-Traditional Students through the Introduction of Effective E-Learning Tools in a Pre-University Tertiary Enabling Programme

    Science.gov (United States)

    Lambrinidis, George

    2014-01-01

    The increasing number of external students enrolling at Charles Darwin University has led to the university investing in new technologies to provide better support for students studying online. Many students, however, come from non-traditional backgrounds and lack some of the skills and confidence to participate successfully in an e-learning…

  12. Design of rotating electrical machines

    CERN Document Server

    Pyrhonen , Juha; Hrabovcova , Valeria

    2013-01-01

    In one complete volume, this essential reference presents an in-depth overview of the theoretical principles and techniques of electrical machine design. This timely new edition offers up-to-date theory and guidelines for the design of electrical machines, taking into account recent advances in permanent magnet machines as well as synchronous reluctance machines. New coverage includes: Brand new material on the ecological impact of the motors, covering the eco-design principles of rotating electrical machinesAn expanded section on the design of permanent magnet synchronous machines, now repo

  13. Epidemiological characteristics of chronic kidney disease of non-traditional causes in women of agricultural communities of El Salvador.

    Science.gov (United States)

    Orantes Navarro, Carlos M; Herrera Valdés, Raúl; López, Miguel Almaguer; Calero, Denis J; Fuentes de Morales, Jackeline; Alvarado Ascencio, Nelly P; Vela Parada, Xavier F; Zelaya Quezada, Susana M; Granados Castro, Delmy V; Orellana de Figueroa, Patricia

    2015-01-01

    women of Salvadoran agricultural communities is associated with disadvantaged populations, traditional (DM, HT, obesity) and non-traditional causes (environmental and occupational exposure to toxic agents and inadequate working conditions). Our results reinforce the hypotheses emerging from other studies, suggesting a multifactorial etiopathology including environmental and occupational nephrotoxic exposure.

  14. Detecting Neolithic Burial Mounds from LiDAR-Derived Elevation Data Using a Multi-Scale Approach and Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Alexandre Guyot

    2018-02-01

    Full Text Available Airborne LiDAR technology is widely used in archaeology and over the past decade has emerged as an accurate tool to describe anthropomorphic landforms. Archaeological features are traditionally emphasised on a LiDAR-derived Digital Terrain Model (DTM using multiple Visualisation Techniques (VTs, and occasionally aided by automated feature detection or classification techniques. Such an approach offers limited results when applied to heterogeneous structures (different sizes, morphologies, which is often the case for archaeological remains that have been altered throughout the ages. This study proposes to overcome these limitations by developing a multi-scale analysis of topographic position combined with supervised machine learning algorithms (Random Forest. Rather than highlighting individual topographic anomalies, the multi-scalar approach allows archaeological features to be examined not only as individual objects, but within their broader spatial context. This innovative and straightforward method provides two levels of results: a composite image of topographic surface structure and a probability map of the presence of archaeological structures. The method was developed to detect and characterise megalithic funeral structures in the region of Carnac, the Bay of Quiberon, and the Gulf of Morbihan (France, which is currently considered for inclusion on the UNESCO World Heritage List. As a result, known archaeological sites have successfully been geo-referenced with a greater accuracy than before (even when located under dense vegetation and a ground-check confirmed the identification of a previously unknown Neolithic burial mound in the commune of Carnac.

  15. Selection of Levels of Dressing Process Parameters by Using TOPSIS Technique for Surface Roughness of En-31 Work piece in CNC Cylindrical Grinding Machine

    Science.gov (United States)

    Patil, Sanjay S.; Bhalerao, Yogesh J.

    2017-02-01

    Grinding is metal cutting process used for mainly finishing the automobile components. The grinding wheel performance becomes dull by using it most of times. So it should be reshaping for consistent performance. It is necessary to remove dull grains of grinding wheel which is known as dressing process. The surface finish produced on the work piece is dependent on the dressing parameters in sub-sequent grinding operation. Multi-point diamond dresser has four important parameters such as the dressing cross feed rate, dressing depth of cut, width of the diamond dresser and drag angle of the dresser. The range of cross feed rate level is from 80-100 mm/min, depth of cut varies from 10 - 30 micron, width of diamond dresser is from 0.8 - 1.10mm and drag angle is from 40o - 500, The relative closeness to ideal levels of dressing parameters are found for surface finish produced on the En-31 work piece during sub-sequent grinding operation by using Technique of Order Preference by Similarity to Ideal Solution (TOPSIS).In the present work, closeness to ideal solution i.e. levels of dressing parameters are found for Computer Numerical Control (CNC) cylindrical angular grinding machine. After the TOPSIS technique, it is found that the value of Level I is 0.9738 which gives better surface finish on the En-31 work piece in sub-sequent grinding operation which helps the user to select the correct levels (combinations) of dressing parameters.

  16. A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

    Science.gov (United States)

    Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

    2017-02-15

    The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

    Directory of Open Access Journals (Sweden)

    Kamran Siddique

    2017-09-01

    Full Text Available Anomaly detection systems, also known as intrusion detection systems (IDSs, continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii employs logistic regression and extreme gradient boosting techniques for classification; (iii introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

  18. Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.

    Science.gov (United States)

    Nilsson, M; Herlin, A H; Ardö, H; Guzhva, O; Åström, K; Bergsten, C

    2015-11-01

    In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640 × 480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness.

  19. Patient-related quality assurance with different combinations of treatment planning systems, techniques, and machines. A multi-institutional survey

    Energy Technology Data Exchange (ETDEWEB)

    Steiniger, Beatrice; Schwedas, Michael; Weibert, Kirsten; Wiezorek, Tilo [University Hospital Jena, Department of Radiation Oncology, Jena (Germany); Berger, Rene [SRH Hospital Gera, Department of Radiation Oncology, Gera (Germany); Eilzer, Sabine [Martin-Luther-Hospital, Radiation Therapy, Berlin (Germany); Kornhuber, Christine [University Hospital Halle, Department of Radiation Oncology, Halle (Saale) (Germany); Lorenz, Kathleen [Hospital of Chemnitz, Department for Radiation Oncology, Chemnitz (Germany); Peil, Torsten [MVZ Center for Radiation Oncology Halle GmbH, Halle (Saale) (Germany); Reiffenstuhl, Carsten [University Hospital Carl Gustav Carus, Department of Radiation Oncology, Dresden (Germany); Schilz, Johannes [Helios Hospital Erfurt, Department of Radiation Oncology, Erfurt (Germany); Schroeder, Dirk [SRH Central Hospital Suhl, Department of Radiation Oncology, Suhl (Germany); Pensold, Stephanie [Community Hospital Dresden-Friedrichstadt, Department of Radiation Oncology, Dresden (Germany); Walke, Mathias [Otto-von-Guericke University Magdeburg, Department of Radiation Oncology, Magdeburg (Germany); Wolf, Ulrich [University Hospital Leipzig, Department of Radiation Oncology, Leipzig (Germany)

    2017-01-15

    This project compares the different patient-related quality assurance systems for intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) techniques currently used in the central Germany area with an independent measuring system. The participating institutions generated 21 treatment plans with different combinations of treatment planning systems (TPS) and linear accelerators (LINAC) for the QUASIMODO (Quality ASsurance of Intensity MODulated radiation Oncology) patient model. The plans were exposed to the ArcCHECK measuring system (Sun Nuclear Corporation, Melbourne, FL, USA). The dose distributions were analyzed using the corresponding software and a point dose measured at the isocenter with an ionization chamber. According to the generally used criteria of a 10 % threshold, 3 % difference, and 3 mm distance, the majority of plans investigated showed a gamma index exceeding 95 %. Only one plan did not fulfill the criteria and three of the plans did not comply with the commonly accepted tolerance level of ±3 % in point dose measurement. Using only one of the two examined methods for patient-related quality assurance is not sufficiently significant in all cases. (orig.) [German] Im Rahmen des Projekts sollten die verschiedenen derzeit im mitteldeutschen Raum eingesetzten patientenbezogenen Qualitaetssicherungssysteme zur intensitaetsmodulierten Radiotherapie (IMRT) und volumenmodulierten Arc-Radiotherapie (VMAT) mit einem unabhaengigen Messsystem verglichen werden. Die teilnehmenden Einrichtungen berechneten insgesamt 21 Bestrahlungsplaene mit verschiedenen Planungssystemen (TPS) und Linearbeschleunigern (LINAC) fuer das Patientenmodell QUASIMODO (Quality ASsurance of Intensity MODulated radiation Oncology), die dann auf das ArcCHECK-Phantom (Sun Nuclear Corporation, Melbourne, FL, USA) uebertragen und abgestrahlt wurden. Zur Auswertung wurde sowohl eine Punktmessung im Isozentrum als auch die Dosisverteilung in der Diodenebene des

  20. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

    Science.gov (United States)

    Kassahun, Yohannes; Yu, Bingbin; Tibebu, Abraham Temesgen; Stoyanov, Danail; Giannarou, Stamatia; Metzen, Jan Hendrik; Vander Poorten, Emmanuel

    2016-04-01

    Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical

  1. Sustainable machining

    CERN Document Server

    2017-01-01

    This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

  2. Machine learning in healthcare informatics

    CERN Document Server

    Acharya, U; Dua, Prerna

    2014-01-01

    The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.

  3. Massively collaborative machine learning

    NARCIS (Netherlands)

    Rijn, van J.N.

    2016-01-01

    Many scientists are focussed on building models. We nearly process all information we perceive to a model. There are many techniques that enable computers to build models as well. The field of research that develops such techniques is called Machine Learning. Many research is devoted to develop

  4. Association between proximity to and coverage of traditional fast-food restaurants and non-traditional fast-food outlets and fast-food consumption among rural adults

    OpenAIRE

    Sharkey, Joseph R; Johnson, Cassandra M; Dean, Wesley R; Horel, Scott A

    2011-01-01

    Abstract Objective The objective of this study is to examine the relationship between residential exposure to fast-food entrées, using two measures of potential spatial access: proximity (distance to the nearest location) and coverage (number of different locations), and weekly consumption of fast-food meals. Methods Traditional fast-food restaurants and non-traditional fast-food outlets, such as convenience stores, supermarkets, and grocery stores, from the 2006 Brazos Valley Food Environmen...

  5. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

    Science.gov (United States)

    Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

    2017-11-01

    The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

  6. A Novel Flavour Tagging Algorithm using Machine Learning Techniques and a Precision Measurement of the $B^0 - \\overline{B^0}$ Oscillation Frequency at the LHCb Experiment

    CERN Document Server

    Kreplin, Katharina

    This thesis presents a novel flavour tagging algorithm using machine learning techniques and a precision measurement of the $B^0 -\\overline{B^0}$ oscillation frequency $\\Delta m_d$ using semileptonic $B^0$ decays. The LHC Run I data set is used which corresponds to $3 \\textrm{fb}^{-1}$ of data taken by the LHCb experiment at a center-of-mass energy of 7 TeV and 8 TeV. The performance of flavour tagging algorithms, exploiting the $b\\bar{b}$ pair production and the $b$ quark hadronization, is relatively low at the LHC due to the large amount of soft QCD background in inelastic proton-proton collisions. The standard approach is a cut-based selection of particles, whose charges are correlated to the production flavour of the $B$ meson. The novel tagging algorithm classifies the particles using an artificial neural network (ANN). It assigns higher weights to particles, which are likely to be correlated to the $b$ flavour. A second ANN combines the particles with the highest weights to derive the tagging decision. ...

  7. Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

    Directory of Open Access Journals (Sweden)

    Shirin Enshaeifar

    Full Text Available The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM. TIHM is a technology assisted monitoring system that uses Internet of Things (IoT enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

  8. Creativity in Machine Learning

    OpenAIRE

    Thoma, Martin

    2016-01-01

    Recent machine learning techniques can be modified to produce creative results. Those results did not exist before; it is not a trivial combination of the data which was fed into the machine learning system. The obtained results come in multiple forms: As images, as text and as audio. This paper gives a high level overview of how they are created and gives some examples. It is meant to be a summary of the current work and give people who are new to machine learning some starting points.

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

    Science.gov (United States)

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

    2016-01-01

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

  10. The development of damage identification methods for buildings with image recognition and machine learning techniques utilizing aerial photographs of the 2016 Kumamoto earthquake

    Science.gov (United States)

    Shohei, N.; Nakamura, H.; Fujiwara, H.; Naoichi, M.; Hiromitsu, T.

    2017-12-01

    It is important to get schematic information of the damage situation immediately after the earthquake utilizing photographs shot from an airplane in terms of the investigation and the decision-making for authorities. In case of the 2016 Kumamoto earthquake, we have acquired more than 1,800 orthographic projection photographs adjacent to damaged areas. These photos have taken between April 16th and 19th by airplanes, then we have distinguished damages of all buildings with 4 levels, and organized as approximately 296,000 GIS data corresponding to the fundamental Geospatial data published by Geospatial Information Authority of Japan. These data have organized by effort of hundreds of engineers. However, it is not considered practical for more extensive disasters like the Nankai Trough earthquake by only human powers. So, we have been developing the automatic damage identification method utilizing image recognition and machine learning techniques. First, we have extracted training data of more than 10,000 buildings which have equally damage levels divided in 4 grades. With these training data, we have been raster scanning in each scanning ranges of entire images, then clipping patch images which represents damage levels each. By utilizing these patch images, we have been developing discriminant models by two ways. One is a model using the Support Vector Machine (SVM). First, extract a feature quantity of each patch images. Then, with these vector values, calculate the histogram density as a method of Bag of Visual Words (BoVW), then classify borders with each damage grades by SVM. The other one is a model using the multi-layered Neural Network. First, design a multi-layered Neural Network. Second, input patch images and damage levels based on a visual judgement, and then, optimize learning parameters with error backpropagation method. By use of both discriminant models, we are going to discriminate damage levels in each patches, then create the image that shows

  11. The perfect storm of information: combining traditional and non-traditional data sources for public health situational awareness during hurricane response.

    Science.gov (United States)

    Bennett, Kelly J; Olsen, Jennifer M; Harris, Sara; Mekaru, Sumiko; Livinski, Alicia A; Brownstein, John S

    2013-12-16

    Hurricane Isaac made landfall in southeastern Louisiana in late August 2012, resulting in extensive storm surge and inland flooding. As the lead federal agency responsible for medical and public health response and recovery coordination, the Department of Health and Human Services (HHS) must have situational awareness to prepare for and address state and local requests for assistance following hurricanes. Both traditional and non-traditional data have been used to improve situational awareness in fields like disease surveillance and seismology. This study investigated whether non-traditional data (i.e., tweets and news reports) fill a void in traditional data reporting during hurricane response, as well as whether non-traditional data improve the timeliness for reporting identified HHS Essential Elements of Information (EEI). HHS EEIs provided the information collection guidance, and when the information indicated there was a potential public health threat, an event was identified and categorized within the larger scope of overall Hurricane Issac situational awareness. Tweets, news reports, press releases, and federal situation reports during Hurricane Isaac response were analyzed for information about EEIs. Data that pertained to the same EEI were linked together and given a unique event identification number to enable more detailed analysis of source content. Reports of sixteen unique events were examined for types of data sources reporting on the event and timeliness of the reports. Of these sixteen unique events identified, six were reported by only a single data source, four were reported by two data sources, four were reported by three data sources, and two were reported by four or more data sources. For five of the events where news tweets were one of multiple sources of information about an event, the tweet occurred prior to the news report, press release, local government\\emergency management tweet, and federal situation report. In all circumstances where

  12. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

    2014-08-15

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

  13. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    International Nuclear Information System (INIS)

    Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

    2014-01-01

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

  14. Simple machines

    CERN Document Server

    Graybill, George

    2007-01-01

    Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s

  15. Machine Learning for Security

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Applied statistics, aka ‘Machine Learning’, offers a wealth of techniques for answering security questions. It’s a much hyped topic in the big data world, with many companies now providing machine learning as a service. This talk will demystify these techniques, explain the math, and demonstrate their application to security problems. The presentation will include how-to’s on classifying malware, looking into encrypted tunnels, and finding botnets in DNS data. About the speaker Josiah is a security researcher with HP TippingPoint DVLabs Research Group. He has over 15 years of professional software development experience. Josiah used to do AI, with work focused on graph theory, search, and deductive inference on large knowledge bases. As rules only get you so far, he moved from AI to using machine learning techniques identifying failure modes in email traffic. There followed digressions into clustered data storage and later integrated control systems. Current ...

  16. Machine musicianship

    Science.gov (United States)

    Rowe, Robert

    2002-05-01

    The training of musicians begins by teaching basic musical concepts, a collection of knowledge commonly known as musicianship. Computer programs designed to implement musical skills (e.g., to make sense of what they hear, perform music expressively, or compose convincing pieces) can similarly benefit from access to a fundamental level of musicianship. Recent research in music cognition, artificial intelligence, and music theory has produced a repertoire of techniques that can make the behavior of computer programs more musical. Many of these were presented in a recently published book/CD-ROM entitled Machine Musicianship. For use in interactive music systems, we are interested in those which are fast enough to run in real time and that need only make reference to the material as it appears in sequence. This talk will review several applications that are able to identify the tonal center of musical material during performance. Beyond this specific task, the design of real-time algorithmic listening through the concurrent operation of several connected analyzers is examined. The presentation includes discussion of a library of C++ objects that can be combined to perform interactive listening and a demonstration of their capability.

  17. Machine Learning and Radiology

    Science.gov (United States)

    Wang, Shijun; Summers, Ronald M.

    2012-01-01

    In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

  18. Machine learning and radiology.

    Science.gov (United States)

    Wang, Shijun; Summers, Ronald M

    2012-07-01

    In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

  19. Status On Multi-microsecond Prepulse Technique On Sphinx Machine Going From Nested To Single Wire Array For 800 ns Implosion Time Z-pinch

    Science.gov (United States)

    Maury, P.; Calamy, H.; Grunenwald, J.; Lassalle, F.; Zucchini, F.; Loyen, A.; Georges, A.; Morell, A.; Bedoch, J. P.

    2009-01-01

    The Sphinx machine[1] is a 6 MA, 1 μS driver based on the LTD technology, used for Z-pinch experiments. Important improvements of Sphinx radiation output were recently obtained using a multi-microsecond current prepulse[2]. Total power per unit of length is multiplied by a factor of 6 and FWHM divided by a factor of 2.5. Early breakdown of the wires during the prepulse phase dramatically changes the ablation phase leading to an improvement of axial homogeneity of both the implosion and the final radiating column. As a consequence, the cathode bubble observed on classical shots is definitively removed. The implosion is then centered and zippering effect is reduced, leading to simultaneous x-ray emission of the whole length. A great reproducibility is obtained. Nested arrays were used before to mitigate the Rayleigh-Taylor instabilities during the implosion phase. Further experiments with pre-pulse technique are described here were inner array was removed. The goal of these experiments was to see if long prepulse could give stable enough implosion with single array and at the same time increase the η parameter by reducing the mass of the load. Experimental results of single wire array loads of typical dimension 5 cm in height with implosion time between 700 and 900 ns and diameter varying between 80 and 140 mm are given. Parameters of the loads were varying in term of radius and number of wires. Comparisons with nested wire array loads are done and trends are proposed. Characteristics of both the implosion and the final radiating column are shown. 2D MHD numerical simulations of single wire array become easier as there is no interaction between outer and inner array anymore. A systematic study was done using injection mass model to benchmark simulation with experiments.

  20. Status On Multi-microsecond Prepulse Technique On Sphinx Machine Going From Nested To Single Wire Array For 800 ns Implosion Time Z-pinch

    International Nuclear Information System (INIS)

    Maury, P.; Calamy, H.; Grunenwald, J.; Lassalle, F.; Zucchini, F.; Loyen, A.; Georges, A.; Morell, A.; Bedoch, J. P.

    2009-01-01

    The Sphinx machine [1] is a 6 MA, 1 μS driver based on the LTD technology, used for Z-pinch experiments. Important improvements of Sphinx radiation output were recently obtained using a multi-microsecond current prepulse [2] . Total power per unit of length is multiplied by a factor of 6 and FWHM divided by a factor of 2.5. Early breakdown of the wires during the prepulse phase dramatically changes the ablation phase leading to an improvement of axial homogeneity of both the implosion and the final radiating column. As a consequence, the cathode bubble observed on classical shots is definitively removed. The implosion is then centered and zippering effect is reduced, leading to simultaneous x-ray emission of the whole length. A great reproducibility is obtained. Nested arrays were used before to mitigate the Rayleigh-Taylor instabilities during the implosion phase. Further experiments with pre-pulse technique are described here were inner array was removed. The goal of these experiments was to see if long prepulse could give stable enough implosion with single array and at the same time increase the η parameter by reducing the mass of the load. Experimental results of single wire array loads of typical dimension 5 cm in height with implosion time between 700 and 900 ns and diameter varying between 80 and 140 mm are given. Parameters of the loads were varying in term of radius and number of wires. Comparisons with nested wire array loads are done and trends are proposed. Characteristics of both the implosion and the final radiating column are shown. 2D MHD numerical simulations of single wire array become easier as there is no interaction between outer and inner array anymore. A systematic study was done using injection mass model to benchmark simulation with experiments.

  1. Face machines

    Energy Technology Data Exchange (ETDEWEB)

    Hindle, D.

    1999-06-01

    The article surveys latest equipment available from the world`s manufacturers of a range of machines for tunnelling. These are grouped under headings: excavators; impact hammers; road headers; and shields and tunnel boring machines. Products of thirty manufacturers are referred to. Addresses and fax numbers of companies are supplied. 5 tabs., 13 photos.

  2. Electric machine

    Science.gov (United States)

    El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI

    2012-07-17

    An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.

  3. Machine Learning.

    Science.gov (United States)

    Kirrane, Diane E.

    1990-01-01

    As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

  4. Nonplanar machines

    International Nuclear Information System (INIS)

    Ritson, D.

    1989-05-01

    This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs

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

    Science.gov (United States)

    Dipnall, Joanna F.

    2016-01-01

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

  6. Contribution of non-traditional lipid profiles to reduced glomerular filtration rate in H-type hypertension population of rural China.

    Science.gov (United States)

    Wang, Haoyu; Li, Zhao; Guo, Xiaofan; Chen, Yintao; Chen, Shuang; Tian, Yichen; Sun, Yingxian

    2018-05-01

    Despite current interest in the unfavourable impact of non-traditional lipid profiles on cardiovascular disease, information regarding its relations to reduced glomerular filtration rate (GFR) in H-type hypertension population has not been systemically elucidated. Analyses were based upon a cross-sectional study of 3259 participants with H-type hypertension who underwent assessment of biochemical, anthropometric and blood pressure values. Reduced GFR was considered if meeting estimated GFR <60 ml/min/1.73 m 2 . A stepwise multivariate regression analysis indicated that non-traditional lipid parameters remained as independent determinants of estimated GFR (all p < .001). In multivariable models, we observed a 50%, 51%, 31%, and 24% higher risk for decreased GFR with each SD increment in TC/HDL-C, TG/HDL-C, LDL-C/HDL-C ratios and non-HDL-C levels, respectively. The highest quartile of TC/HDL-C, TG/HDL-C and LDL-C/HDL-C ratios carried reduced GFR odds (confidence intervals) of 5.50 (2.50 to 12.09), 6.63 (2.58 to 17.05) and 2.22 (1.15 to 4.29), respectively. The relative independent contribution of non-traditional lipid profiles, as indexed by TC/HDL-C, TG/HDL-C, LDL-C/HDL-C ratios and non-HDL-C, towards reduced GFR putting research evidence at the very heart of lipoprotein-mediated renal injury set a vital example for applying a clinical and public health recommendation for reducing the burden of chronic kidney disease. KEY MESSAGES Non-traditional lipid profiles has been linked with the occurrence of cardiovascular disease, but none of the studies that address the effect of non-traditional lipid profiles on reduced GFR risk in H-type hypertension population has been specifically established. A greater emphasis of this study resided in the intrinsic value of TC/HDL-C, TG/HDL-C, LDL-C/HDL-C ratios and non-HDL-C that integrate atherogenic and anti-atherogenic lipid molecules to predict the risk of reduced GFR among H-type hypertension population and provide

  7. The quest for knowledge transfer efficacy: blended teaching, online and in-class, with consideration of learning typologies for non-traditional and traditional students

    Science.gov (United States)

    Van Doorn, Judy R.; Van Doorn, John D.

    2014-01-01

    The pedagogical paradigm shift in higher education to 24-h learning environments composed of teaching delivery methods of online courses, blended/hybrid formats, and face-to-face (f2f) classes is increasing access to global, lifelong learning. Online degrees have been offered at 62.4% of 2800 colleges and universities. Students can now design flexible, life-balanced course schedules. Higher knowledge transfer rates may exist with blended course formats with online quizzes and valuable class time set for Socratic, quality discussions and creative team presentations. Research indicates that younger, traditional students exhibit heightened performance goal orientations and prefer entertaining professors who are funny, whereas non-traditional students exhibit mastery profiles and prefer courses taught by flexible, yet organized, professors. A 5-year study found that amongst 51,000 students taking both f2f and online courses, higher online failure rates occurred. Competing life roles for non-traditional students and reading and writing needs for at-risk students suggest that performance may be better if programs are started in f2f courses. Models on effective knowledge transfer consider the planning process, delivery methods, and workplace application, but a gap exists for identifying the diversity of learner needs. Higher education enrollments are being compromised with lower online retention rates. Therefore, the main purpose of this review is to delineate disparate learning styles and present a typology for the learning needs of traditional and non-traditional students. Secondly, psychology as a science may need more rigorous curriculum markers like mapping APA guidelines to knowledge objectives, critical assignments, and student learning outcomes (SLOs) (e.g., online rubric assessments for scoring APA style critical thinking essays on selected New York Times books). Efficacious knowledge transfer to diverse, 21st century students should be the Academy's focus. PMID

  8. The quest for knowledge transfer efficacy: blended teaching, online and in-class, with consideration of learning typologies for non-traditional and traditional students

    Directory of Open Access Journals (Sweden)

    Judy Rouse Van Doorn

    2014-04-01

    Full Text Available The pedagogical paradigm shift in higher education to 24-hour learning environments composed of teaching delivery methods of online courses, blended/hybrid formats, and face-to-face (f2f classes is increasing access to global, lifelong learning. Online degrees have been offered at 62.4% of 2,800 colleges and universities. Students can now design flexible, life-balanced course schedules. Higher knowledge transfer rates may exist with blended course formats with online quizzes and valuable class time set for Socratic, quality discussions and creative team presentations. Research indicates that younger, traditional students exhibit heightened performance goal orientations and prefer entertaining professors who are funny, whereas non-traditional students exhibit mastery profiles and prefer courses taught by flexible, yet organized, professors. A 5-year study found that amongst 51,000 students taking both f2f and online courses, higher online failure rates occurred. Competing life roles for non-traditional students and reading and writing needs for at-risk students suggest that performance may be better if programs are started in f2f courses. Models on effective knowledge transfer consider the planning process, delivery methods, and workplace application, but a gap exists for identifying the diversity of learner needs. Higher education enrollments are being compromised with lower online retention rates. Therefore, the main purpose of this review is to delineate disparate learning styles and present a typology for the learning needs of traditional and non-traditional students. Secondly, psychology as a science may need more rigorous curriculum markers like mapping APA guidelines to knowledge objectives, critical assignments, and student learning outcomes (SLOs (e.g. online rubric assessments for scoring APA style critical thinking essays on selected New York Times books. Efficacious knowledge transfer to diverse, 21st century students should be the

  9. The quest for knowledge transfer efficacy: blended teaching, online and in-class, with consideration of learning typologies for non-traditional and traditional students.

    Science.gov (United States)

    Van Doorn, Judy R; Van Doorn, John D

    2014-01-01

    The pedagogical paradigm shift in higher education to 24-h learning environments composed of teaching delivery methods of online courses, blended/hybrid formats, and face-to-face (f2f) classes is increasing access to global, lifelong learning. Online degrees have been offered at 62.4% of 2800 colleges and universities. Students can now design flexible, life-balanced course schedules. Higher knowledge transfer rates may exist with blended course formats with online quizzes and valuable class time set for Socratic, quality discussions and creative team presentations. Research indicates that younger, traditional students exhibit heightened performance goal orientations and prefer entertaining professors who are funny, whereas non-traditional students exhibit mastery profiles and prefer courses taught by flexible, yet organized, professors. A 5-year study found that amongst 51,000 students taking both f2f and online courses, higher online failure rates occurred. Competing life roles for non-traditional students and reading and writing needs for at-risk students suggest that performance may be better if programs are started in f2f courses. Models on effective knowledge transfer consider the planning process, delivery methods, and workplace application, but a gap exists for identifying the diversity of learner needs. Higher education enrollments are being compromised with lower online retention rates. Therefore, the main purpose of this review is to delineate disparate learning styles and present a typology for the learning needs of traditional and non-traditional students. Secondly, psychology as a science may need more rigorous curriculum markers like mapping APA guidelines to knowledge objectives, critical assignments, and student learning outcomes (SLOs) (e.g., online rubric assessments for scoring APA style critical thinking essays on selected New York Times books). Efficacious knowledge transfer to diverse, 21st century students should be the Academy's focus.

  10. Practical recommendations for the implementation of health technologies to enhance physical fitness of students in extracurricular classes during non-traditional gymnastics

    Directory of Open Access Journals (Sweden)

    E.V. Fomenko

    2014-07-01

    Full Text Available Purpose : to develop practical recommendations for extracurricular classes nontraditional kinds of gymnastics to improve the organization of physical education teachers in schools. Material : in the experiment involved 358 students. Analyzed the available literature data. Results : a comparative analysis of physical fitness of students and practical recommendations for the non-traditional occupations gymnastics. Been a significant interest in physical education classes. Found that the main ways of improving physical education students may be the formation of the need for strengthening health facilities fitness aerobics, shaping, pilates. Conclusions : highlights the need to structure the problems they need and develop appropriate solutions.

  11. Transductive and matched-pair machine learning for difficult target detection problems

    Science.gov (United States)

    Theiler, James

    2014-06-01

    This paper will describe the application of two non-traditional kinds of machine learning (transductive machine learning and the more recently proposed matched-pair machine learning) to the target detection problem. The approach combines explicit domain knowledge to model the target signal with a more agnostic machine-learning approach to characterize the background. The concept is illustrated with simulated data from an elliptically-contoured background distribution, on which a subpixel target of known spectral signature but unknown spatial extent has been implanted.

  12. Machine learning with R cookbook

    CERN Document Server

    Chiu, Yu-Wei

    2015-01-01

    If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.

  13. An improved excitation control technique of three-phase induction machine operating as dual winding generator for micro-wind domestic application

    International Nuclear Information System (INIS)

    Chatterjee, Arunava; Chatterjee, Debashis

    2015-01-01

    Highlights: • A three-phase induction machine working as single phase generator is studied. • The generator is assisted by an inverter and photovoltaic panel for excitation. • Proposed control involves operating the machine as balanced two-phase generator. • Torque pulsations associated with unbalanced phase currents are minimized. • The generator can be used for grid-isolated micro-wind power generation. - Abstract: Single-phase generation schemes are widely utilized for harnessing wind power in remote and grid secluded applications. This paper presents a novel control methodology for a three-phase induction machine working as a single-phase dual winding induction generator. Three-phase induction machines providing single-phase output with proper control strategy can be beneficial in grid secluded micro-wind energy conversion systems compared to single-phase induction generators. Three-phase induction machines operating in single-phase mode are mostly excited asymmetrically to provide single-phase power leading to unbalanced current flow in the stator windings causing heating and insulation breakdown. The asymmetrical excitation also initiates torque pulsations which results in additional stress and vibration at the machine shaft and bearings degrading the machine performance. The proposed control is chiefly aimed to minimize this unbalance. The variable excitation required for the proposed generator is provided through a single-phase inverter with photovoltaic panels. The suitability for such a generator along with its control is tested with appropriate simulations and experimental results. The induction generator with the proposed control strategy is expected to be useful in remote and grid isolated households as a standalone source of single-phase electrical power

  14. The Machine within the Machine

    CERN Multimedia

    Katarina Anthony

    2014-01-01

    Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

  15. FOREIGN DIRECT INVESTMENTS AND THEIR NON-TRADITIONAL QUALITY FACTORS. A VAR ANALYSIS IN ROMANIA AND BULGARIA

    Directory of Open Access Journals (Sweden)

    Magdalena RADULESCU

    2016-05-01

    Full Text Available The aim of this paper is to present an econometric analysis using VAR techniques for emphasizing the political institutional factors, economic freedom factors and the quality of labor force factors impacting on FDIs attracted in Bulgaria and Romania. We used yearly data series between 2000 and 2014, provided by the World Bank. These two countries display a very friendly climate (law income corporate tax, but they attracted large amounts of FDIs only for a short period of time at mid-2000s’. The foreign investments sharply dropped during the crisis, and the perspectives are not so good. The foreign investors claim that high corruption and bureaucracy greatly diminish the advantages of an attractive fiscal environment in these two specific countries.

  16. Machine translation

    Energy Technology Data Exchange (ETDEWEB)

    Nagao, M

    1982-04-01

    Each language has its own structure. In translating one language into another one, language attributes and grammatical interpretation must be defined in an unambiguous form. In order to parse a sentence, it is necessary to recognize its structure. A so-called context-free grammar can help in this respect for machine translation and machine-aided translation. Problems to be solved in studying machine translation are taken up in the paper, which discusses subjects for semantics and for syntactic analysis and translation software. 14 references.

  17. A novel flavour tagging algorithm using machine learning techniques and a precision measurement of the B0- anti B0 oscillation frequency at the LHCb experiment

    International Nuclear Information System (INIS)

    Kreplin, Katharina

    2015-01-01

    This thesis presents a novel flavour tagging algorithm using machine learning techniques and a precision measurement of the B 0 - anti B 0 oscillation frequency Δm d using semileptonic B 0 decays. The LHC Run I data set is used which corresponds to 3 fb -1 of data taken by the LHCb experiment at a center-of-mass energy of 7 TeV and 8 TeV. The performance of flavour tagging algorithms, exploiting the b anti b pair production and the b quark hadronization, is relatively low at the LHC due to the large amount of soft QCD background in inelastic proton-proton collisions. The standard approach is a cut-based selection of particles, whose charges are correlated to the production flavour of the B meson. The novel tagging algorithm classifies the particles using an artificial neural network (ANN). It assigns higher weights to particles, which are likely to be correlated to the b flavour. A second ANN combines the particles with the highest weights to derive the tagging decision. An increase of the opposite side kaon tagging performance of 50% and 30% is achieved on B + → J/ψK + data. The second number corresponds to a readjustment of the algorithm to the B 0 s production topology. This algorithm is employed in the precision measurement of Δm d . A data set of 3.2 x 10 6 semileptonic B 0 decays is analysed, where the B 0 decays into a D - (K + π - π - ) or D *- (π - anti D 0 (K + π - )) and a μ + ν μ pair. The ν μ is not reconstructed, therefore, the B 0 momentum needs to be statistically corrected for the missing momentum of the neutrino to compute the correct B 0 decay time. A result of Δm d =0.503±0.002(stat.)±0.001(syst.) ps -1 is obtained. This is the world's best measurement of this quantity.

  18. Perceptions of medical students and their mentors in a specialised programme designed to provide insight into non-traditional career paths

    Science.gov (United States)

    Josephson, Anna; Stenfors-Hayes, Terese

    2011-01-01

    Objectives This pilot study explores the perceptions of medical students and their individual mentors who advised them in a specialised programme where students gained insight into non-tradition career paths. Methods Twelve medical students in years 3-6 at Karolinska Institutet, Sweden were recruited to the Prominentia mentor programme where they were individually paired with mentors who met with them to discuss and advise them on non-traditional career paths. Application letters of students to join the programme as well as electronically distributed questionnaires and semi-structured interviews were used to assess the perceptions of mentors and students to the programme. Both the questionnaire and the interview transcripts were thematised using content analysis. Results In terms of expectations and requests, the application letters showed that all students specified their career goals and the type of mentor they desired. Whereas mentors in general had fewer requests and some had no specific demands. In light of perceived effects, all mentors felt they discussed future careers with their students and the majority of students responded the same way, with some interesting deviations. Most discussed topics during meetings were: future career, medical education, combinations of private life and work, and work environment. Conclusions This pilot study revealed that students appreciated receiving inspiration and seeing career path opportunities outside academic medicine as well as receiving support in personal and professional development and guidance about the students’ role as a doctor. However, discrepancies were found regarding how mentors and students respectively perceived the mentor programme.

  19. A meta-analysis of the effects of non-traditional teaching methods on the critical thinking abilities of nursing students.

    Science.gov (United States)

    Lee, JuHee; Lee, Yoonju; Gong, SaeLom; Bae, Juyeon; Choi, Moonki

    2016-09-15

    Scientific framework is important in designing curricula and evaluating students in the field of education and clinical practice. The purpose of this study was to examine the effectiveness of non-traditional educational methods on critical thinking skills. A systematic review approach was applied. Studies published in peer-reviewed journals from January 2001 to December 2014 were searched using electronic databases and major education journals. A meta-analysis was performed using Review Manager 5.2. Reviewing the included studies, the California Critical Thinking Dispositions Inventory (CCTDI) and California Critical Thinking Skills Test (CCTST) were used to assess the effectiveness of critical thinking in the meta-analysis. The eight CCTDI datasets showed that non- traditional teaching methods (i.e., no lectures) were more effective compared to control groups (standardized mean difference [SMD]: 0.42, 95 % confidence interval [CI]: 0.26-0.57, p teaching and learning methods in these studies were also had significantly more effects when compared to the control groups (SMD: 0.29, 95 % CI: 0.10-0.48, p = 0.003). This research showed that new teaching and learning methods designed to improve critical thinking were generally effective at enhancing critical thinking dispositions.

  20. ARSIS AND THESIS: A REVIEW OF TWO ELEMENTS OF RHYTHM IN NON-TRADITIONAL MUSIC WRITTEN BY F. H. SMITH VAN WAESBERGHE D.J

    Directory of Open Access Journals (Sweden)

    Sunarto

    2014-06-01

    Full Text Available This paper aims to discuss the comparative terms of arsis and thesis in the study of Western music. The purpose of the study is to study the forms of music from the terms of language and its application, because there are many elements of music that are not understandable. Method of this study uses classical literature and musicology approach in which the great phrase rhythm of Gregorian music was more appropriately take a literary term; arsis and thesis. The focus of this study is to discuss the terms of Arsis and Thesis used in the section of rhythm elements of non-traditional music. This study reveals several musical terms in which there are similarities and differences between the rhythm and bars of music. The similarities and differences in the analysis are based on the history of Western music from Gregorian music. Gregorian was monophonic music that still existed in Europe until the 19th century. There were only two phrases in Gregorian music; when the melody moved up and when it moved down. In this case, there were two main elements in Gregorian music; they were different in rhythmic and they were in one rhythm of music. Arsis is a hard melody while thesis is a soft melody. It could be said that arsis and thesis are also parts of the dynamics form of music work. Keywords: arsis; Thesis; music rhythm; non-traditional music.

  1. Temporal stability of growth and yield among Hevea genotypes introduced to a non-traditional rubber growing region of peninsular India

    Directory of Open Access Journals (Sweden)

    K.K. Vinod

    2013-12-01

    Full Text Available Extensive cultivation of Hevea brasiliensis in India now focus on non-traditional regions for rubber cultivation. As a prelude for selection of genotypes for commercial cultivation, many introduced genotypes are being tested in genotype adaptation experiments in these regions. Present study, reports for the first time, growth and yield adaptation of 28 genotypes in a non-traditional rubber growing region of peninsular India viz., the coastal Karnataka region. Agroclimate of this region was found favoring growth and establishment of all the genotypes evaluated. However, not all the genotypes grew and yielded well. Only four genotypes, RRII 203, KRS 25, PB 260 and PB 235 showed good growth and yield. On grouping, the genotypes fell into categories of moderate high yielders, moderate low yielders and low yielders. The most popular variety of the traditional region, RRII 105 did not perform well in this region. Biological stability in growth and yield of RRII 203 and PB 260 was identified as stable and these genotypes were the best adapted. KRS 25 and PB 235 had unstable yielding pattern. The best identified genotypes can be considered for extensive culture as single clone plantations or as major constituent of clone blends as well as parents in future breeding programmes. Other moderate stable yielders may be used for clone blending in smaller proportions and may be subjected to yield improvement.

  2. Synthesis, Structure, and Magnetism of Tris(amide) {Ln[N(SiMe3)2]3}1- Complexes of the Non-Traditional +2 Lanthanide Ions.

    Science.gov (United States)

    Ryan, Austin Jack; Darago, Lucy E; Balasubramini, Sree Ganesh; Chen, Guo P; Ziller, Joseph W; Furche, Filipp; Long, Jeffrey R; Evans, William J

    2018-02-28

    A new series of Ln2+ complexes has been synthesized that overturns two previous generalizations in rare-earth metal reduction chemistry: that amide ligands do not form isolable complexes of the highly-reducing non-traditional Ln2+ ions and that yttrium is a good model for the late lanthanides in these reductive reactions. Reduction of Ln(NR2)3 (R = SiMe3) complexes in THF under Ar with M = K or Rb in the presence of 2.2.2-cryptand (crypt) forms crystallographically-characterizable [M(crypt)][Ln(NR2)3] complexes not only for the traditional Tm2+ ion and the configurational crossover ions, Nd2+ and Dy2+, but also for the non-traditional Gd2+, Tb2+, Ho2+, and Er2+ ions. Crystallographic data as well as UV-visible, magnetic susceptibility, and density functional theory studies are consistent with the accessibility of 4fn5d1 configurations for Ln2+ ions in this tris(silylamide) ligand environment. The Dy2+ complex, [K(crypt)][Dy(NR2)3], has a higher magnetic moment than previously observed for any monometallic complex: 11.67 µB. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. The effect of non traditional teaching methods in entrepreneurship education on students entrepreneurial interest and business startups: A data article.

    Science.gov (United States)

    Olokundun, Maxwell; Moses, Chinonye Love; Iyiola, Oluwole; Ibidunni, Stephen; Ogbari, Mercy; Peter, Fred; Borishade, Taiye

    2018-08-01

    Traditional methods of teaching entrepreneurship in universities involves more theoretical approaches which are less effective in motivating considerations for an entrepreneurship career. This owes to the fact that such techniques essentially make students develop a dormant attitude rather than active participation. Expert views suggest that experiential entrepreneurship teaching methods in universities which involve practical activities and active participation can be considered salient to students' development of entrepreneurial interest an business startup potentials. This present study presents data on the extent to which experiential teaching methods in entrepreneurship adopted by Nigerian universities stimulate students' entrepreneurial interest and business startups. Data have been gathered following a descriptive cross-sectional quantitative survey conducted among university students ( N = 600) of four selected institutions in Nigeria offering a degree programme in entrepreneurship. Hierarchical Multiple Regression Analysis was used in confirming the hypothesis proposed in the study using the Statistical Package for Social Sciences (SPSS) version 22.The findings from the analysis showed that the adoption of experiential practical activities considered as best practices in entrepreneurship teaching in Nigerian universities can stimulate students' interest and drive for engaging in business start-up activities even as undergraduates. The field data set is made extensively available to allow for critical investigation.

  4. Machine Translation

    Indian Academy of Sciences (India)

    Research Mt System Example: The 'Janus' Translating Phone Project. The Janus ... based on laptops, and simultaneous translation of two speakers in a dialogue. For more ..... The current focus in MT research is on using machine learning.

  5. Machine learning techniques for the verification of refueling activities in CANDU-type nuclear power plants (NPPs) with direct applications in nuclear safeguards

    International Nuclear Information System (INIS)

    Budzinski, J.

    2006-06-01

    This dissertation deals with the problem of automated classification of the signals obtained from certain radiation monitoring systems, specifically from the Core Discharge Monitor (CDM) systems, that are successfully operated by the International Atomic Energy Agency (IAEA) at various CANDU-type nuclear power plants around the world. In order to significantly reduce the costly and error-prone manual evaluation of the large amounts of the collected CDM signals, a reliable and efficient algorithm for the automated data evaluation is necessary, which might ensure real-time performance with maximum of 0.01 % misclassification ratio. This thesis describes the research behind finding a successful prototype implementation of such automated analysis software. The finally adopted methodology assumes a nonstationary data-generating process that has a finite number of states or basic fueling activities, each of which can emit observable data patterns having particular stationary characteristics. To find out the underlying state sequences, a unified probabilistic approach known as the hidden Markov model (HMM) is used. Each possible fueling sequence is modeled by a distinct HMM having a left-right profile topology with explicit insert and delete states. Given an unknown fueling sequence, a dynamic programming algorithm akin to the Viterbi search is used to find the maximum likelihood state path through each model and eventually the overall best-scoring path is picked up as the recognition hypothesis. Machine learning techniques are applied to estimate the observation densities of the states, because the densities are not simply parameterizable. Unlike most present applications of continuous monitoring systems that rely on heuristic approaches to the recognition of possibly risky events, this research focuses on finding techniques that make optimal use of prior knowledge and computer simulation in the recognition task. Thus, a suitably modified, approximate n-best variant of

  6. Computer vision and machine learning for archaeology

    NARCIS (Netherlands)

    van der Maaten, L.J.P.; Boon, P.; Lange, G.; Paijmans, J.J.; Postma, E.

    2006-01-01

    Until now, computer vision and machine learning techniques barely contributed to the archaeological domain. The use of these techniques can support archaeologists in their assessment and classification of archaeological finds. The paper illustrates the use of computer vision techniques for

  7. Electrical machines diagnosis

    CERN Document Server

    Trigeassou, Jean-Claude

    2013-01-01

    Monitoring and diagnosis of electrical machine faults is a scientific and economic issue which is motivated by objectives for reliability and serviceability in electrical drives.This book provides a survey of the techniques used to detect the faults occurring in electrical drives: electrical, thermal and mechanical faults of the electrical machine, faults of the static converter and faults of the energy storage unit.Diagnosis of faults occurring in electrical drives is an essential part of a global monitoring system used to improve reliability and serviceability. This diagnosis is perf

  8. Policy environments matters: Access to higher education of non-traditional students in Denmark. Paper presented at the 56th CIES conference, San Juan, Puerto Rico, 22-27 April

    DEFF Research Database (Denmark)

    Milana, Marcella

    2012-01-01

    Despite the massification of higher education that has brought about an increase in the enrollment rates of non-traditional students, and the internationalization of higher education, which has led towards cross-national homogenization when it comes to the typology of educational programs run...... by universities, access of non-traditional students is still a much debated issue. The scope of this paper is to critically examine the policy environment, and related practice, which supports (or hampers) access to higher education of non-traditional students, with a special attention to adult and mature...... from a common ideal that results from cross-national cooperation implemented through the Bologna process. The data source includes relevant scientific literature, policy documents as well as interviews with policy makers, representatives of higher education institutions and non-traditional students...

  9. Machine learning in virtual screening.

    Science.gov (United States)

    Melville, James L; Burke, Edmund K; Hirst, Jonathan D

    2009-05-01

    In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.

  10. NICeSim: an open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making.

    Science.gov (United States)

    Cerqueira, Fabio Ribeiro; Ferreira, Tiago Geraldo; de Paiva Oliveira, Alcione; Augusto, Douglas Adriano; Krempser, Eduardo; Corrêa Barbosa, Helio José; do Carmo Castro Franceschini, Sylvia; de Freitas, Brunnella Alcantara Chagas; Gomes, Andreia Patricia; Siqueira-Batista, Rodrigo

    2014-11-01

    This paper describes NICeSim, an open-source simulator that uses machine learning (ML) techniques to aid health professionals to better understand the treatment and prognosis of premature newborns. The application was developed and tested using data collected in a Brazilian hospital. The available data were used to feed an ML pipeline that was designed to create a simulator capable of predicting the outcome (death probability) for newborns admitted to neonatal intensive care units. However, unlike previous scoring systems, our computational tool is not intended to be used at the patients bedside, although it is possible. Our primary goal is to deliver a computational system to aid medical research in understanding the correlation of key variables with the studied outcome so that new standards can be established for future clinical decisions. In the implemented simulation environment, the values of key attributes can be changed using a user-friendly interface, where the impact of each change on the outcome is immediately reported, allowing a quantitative analysis, in addition to a qualitative investigation, and delivering a totally interactive computational tool that facilitates hypothesis construction and testing. Our statistical experiments showed that the resulting model for death prediction could achieve an accuracy of 86.7% and an area under the receiver operating characteristic curve of 0.84 for the positive class. Using this model, three physicians and a neonatal nutritionist performed simulations with key variables correlated with chance of death. The results indicated important tendencies for the effect of each variable and the combination of variables on prognosis. We could also observe values of gestational age and birth weight for which a low Apgar score and the occurrence of respiratory distress syndrome (RDS) could be less or more severe. For instance, we have noticed that for a newborn with 2000 g or more the occurrence of RDS is far less problematic

  11. Machine Protection

    International Nuclear Information System (INIS)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012

  12. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

    The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

  13. Machine Protection

    CERN Document Server

    Zerlauth, Markus; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  14. Machine Protection

    Energy Technology Data Exchange (ETDEWEB)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg [European Organization for Nuclear Research, Geneva (Switzerland)

    2012-07-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  15. Machines and Metaphors

    Directory of Open Access Journals (Sweden)

    Ángel Martínez García-Posada

    2016-10-01

    Full Text Available The edition La ley del reloj. Arquitectura, máquinas y cultura moderna (Cátedra, Madrid, 2016 registers the useful paradox of the analogy between architecture and technique. Its author, the architect Eduardo Prieto, also a philosopher, professor and writer, acknowledges the obvious distance from machines to buildings, so great that it can only be solved using strange comparisons, since architecture does not move nor are the machines habitable, however throughout the book, from the origin of the metaphor of the machine, with clarity in his essay and enlightening erudition, he points out with certainty some concomitances of high interest, drawing throughout history a beautiful cartography of the fruitful encounter between organics and mechanics.

  16. New small molecule inhibitors of histone methyl transferase DOT1L with a nitrile as a non-traditional replacement for heavy halogen atoms.

    Science.gov (United States)

    Spurr, Sophie S; Bayle, Elliott D; Yu, Wenyu; Li, Fengling; Tempel, Wolfram; Vedadi, Masoud; Schapira, Matthieu; Fish, Paul V

    2016-09-15

    A number of new nucleoside derivatives are disclosed as inhibitors of DOT1L activity. SARs established that DOT1L inhibition could be achieved through incorporation of polar groups and small heterocycles at the 5-position (5, 6, 12) or by the application of alternative nitrogenous bases (18). Based on these results, CN-SAH (19) was identified as a potent and selective inhibitor of DOT1L activity where the polar 5-nitrile group was shown by crystallography to bind in the hydrophobic pocket of DOT1L. In addition, we show that a polar nitrile group can be used as a non-traditional replacement for heavy halogen atoms. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Tuition fees and funding – barriers for non-traditional students? First results from the international research project Opening Universities for Lifelong Learning (OPULL)

    DEFF Research Database (Denmark)

    Moissidis, Sonja; Schwarz, Jochen; Yndigegn, Carsten

    2011-01-01

    Project OPULL – Opening Universities for Lifelong Learning – is undertaking research into ways of opening up higher education to vocationally qualified and experienced target groups in four European countries. Open university models in Germany, Denmark, Finland and the United Kingdom are being...... investigated in three research phases between 2009 and 2012 with the aim of identifying critical success factors for building open universities for Europe. This paper presents the first phase, in which educational systems in the participant countries have been mapped and interviews with lifelong learning...... experts undertaken. The current situation and perspectives in each country together with critical issues on how fees and funding influence higher education access for non-traditional students in these countries are discussed and explored through the interview evidence. The initial findings of the first...

  18. Association between proximity to and coverage of traditional fast-food restaurants and non-traditional fast-food outlets and fast-food consumption among rural adults

    Directory of Open Access Journals (Sweden)

    Horel Scott A

    2011-05-01

    Full Text Available Abstract Objective The objective of this study is to examine the relationship between residential exposure to fast-food entrées, using two measures of potential spatial access: proximity (distance to the nearest location and coverage (number of different locations, and weekly consumption of fast-food meals. Methods Traditional fast-food restaurants and non-traditional fast-food outlets, such as convenience stores, supermarkets, and grocery stores, from the 2006 Brazos Valley Food Environment Project were linked with individual participants (n = 1409 who completed the nutrition module in the 2006 Brazos Valley Community Health Assessment. Results Increased age, poverty, increased distance to the nearest fast food, and increased number of different traditional fast-food restaurants, non-traditional fast-food outlets, or fast-food opportunities were associated with less frequent weekly consumption of fast-food meals. The interaction of gender and proximity (distance or coverage (number indicated that the association of proximity to or coverage of fast-food locations on fast-food consumption was greater among women and opposite of independent effects. Conclusions Results provide impetus for identifying and understanding the complex relationship between access to all fast-food opportunities, rather than to traditional fast-food restaurants alone, and fast-food consumption. The results indicate the importance of further examining the complex interaction of gender and distance in rural areas and particularly in fast-food consumption. Furthermore, this study emphasizes the need for health promotion and policy efforts to consider all sources of fast-food as part of promoting healthful food choices.

  19. Opportunities for development of non-traditional hydrocarbon resources in the Timan-North Ural region, taking into account ecosystem services

    Directory of Open Access Journals (Sweden)

    I. G. Burtseva

    2017-12-01

    Full Text Available The authors formulate the definition of non-traditional resources from geological-genetic, technological and economic viewpoints. The authors present a detailed assessment of the resource potential of non-traditional hydrocarbon raw material in the Timan-Severouralsk region, including hydrocarbons in the deposits of the domanic type, methane of coal seams, liquid and gaseous hydrocarbons potentially extracted from black, brown coal and combustible shales. The authors also show the main directions of industrial use of coal and oil shales. The assessment of the resource potential of hydrocarbon raw materials in the deposits of the domanic type varies widely; the recoverable resources may amount to about 1 billion tons. Bituminous coals with a high volatile yield have the highest degree of conversion to liquid hydrocarbons, and brown and black coals of with a low degree of metamorphism usually serve for the production of combustible gas and primary resin. The paper describes the option of developing oil shale deposits as a possible investment project. The determined components and overall values of the economic effect from the implementation of the projects under consideration allow us to estimate that the payback period of investments does not exceed seven years. There is also a social effect: the creation of an additional 550 jobs in the operation of the quarry and about 700 jobs – in the enrichment and processing of oil shales. The estimated annual volume of output is 25–30 billion rubles, and the volume of tax revenues – up to 100 billion rubles. The authors evaluated ecosystem services in the territories of potential industrial development of coal and oil shale deposits; identified the beneficiaries of the benefits from the use of environmental services and the possibility of calculating payments.

  20. Postgraduates' perceptions of preparedness for work as a doctor and making future career decisions: support for rural, non-traditional medical schools.

    Science.gov (United States)

    Eley, D S

    2010-08-01

    The intern year is a critical time for making career decisions and gaining confidence in clinical skills, communication and teamwork practices; this justifies an interest in junior doctors' perceptions of their level of preparedness for hospital work. This study explored Australian junior doctors' perspectives regarding the transition from student to doctor roles, their preparation as medical undergraduates within either traditional metropolitan schools or smaller, outer metropolitan-based (rural) programs such as Rural Clinical Schools (RCS), and the educational environment they experienced in their internship. A qualitative cross-sectional design used semi-structured interviews with postgraduate year one and two junior doctors (9 females and 11 males) within teaching hospitals in Queensland Australia. Interview questions focussed on four major content areas: preparedness for hospital work, undergraduate training, building confidence and career advice. Data were analyzed using a framework method to identify and explore major themes. Junior doctors who spent undergraduate years training at smaller, non-traditional medical schools felt more confident and better prepared at internship. More hands-on experience as students, more patient contact and a better grounding in basic sciences were felt by interns to be ideal for building confidence. Junior doctors perceived a general lack of career guidance in both undergraduate and postgraduate teaching environments to help them with the transition from the student to junior doctor roles. Findings are congruent with studies that have confirmed student opinion on the higher quality of undergraduate medical training outside a traditional metropolitan-based program, such as a RCS. The serious shortage of doctors in rural and remote Australia makes these findings particularly relevant. It will be important to gain a better understanding of how smaller non-traditional medical programs build confidence and feelings of work

  1. Association between proximity to and coverage of traditional fast-food restaurants and non-traditional fast-food outlets and fast-food consumption among rural adults

    Science.gov (United States)

    2011-01-01

    Objective The objective of this study is to examine the relationship between residential exposure to fast-food entrées, using two measures of potential spatial access: proximity (distance to the nearest location) and coverage (number of different locations), and weekly consumption of fast-food meals. Methods Traditional fast-food restaurants and non-traditional fast-food outlets, such as convenience stores, supermarkets, and grocery stores, from the 2006 Brazos Valley Food Environment Project were linked with individual participants (n = 1409) who completed the nutrition module in the 2006 Brazos Valley Community Health Assessment. Results Increased age, poverty, increased distance to the nearest fast food, and increased number of different traditional fast-food restaurants, non-traditional fast-food outlets, or fast-food opportunities were associated with less frequent weekly consumption of fast-food meals. The interaction of gender and proximity (distance) or coverage (number) indicated that the association of proximity to or coverage of fast-food locations on fast-food consumption was greater among women and opposite of independent effects. Conclusions Results provide impetus for identifying and understanding the complex relationship between access to all fast-food opportunities, rather than to traditional fast-food restaurants alone, and fast-food consumption. The results indicate the importance of further examining the complex interaction of gender and distance in rural areas and particularly in fast-food consumption. Furthermore, this study emphasizes the need for health promotion and policy efforts to consider all sources of fast-food as part of promoting healthful food choices. PMID:21599955

  2. Teletherapy machine

    International Nuclear Information System (INIS)

    Panyam, Vinatha S.; Rakshit, Sougata; Kulkarni, M.S.; Pradeepkumar, K.S.

    2017-01-01

    Radiation Standards Section (RSS), RSSD, BARC is the national metrology institute for ionizing radiation. RSS develops and maintains radiation standards for X-ray, beta, gamma and neutron radiations. In radiation dosimetry, traceability, accuracy and consistency of radiation measurements is very important especially in radiotherapy where the success of patient treatment is dependent on the accuracy of the dose delivered to the tumour. Cobalt teletherapy machines have been used in the treatment of cancer since the early 1950s and India had its first cobalt teletherapy machine installed at the Cancer Institute, Chennai in 1956

  3. 1- to 10-keV x-ray backlighting of annular wire arrays on the Sandia Z-machine using bent-crystal imaging techniques

    International Nuclear Information System (INIS)

    Rambo, Patrick K.; Wenger, David Franklin; Bennett, Guy R.; Sinars, Daniel Brian; Smith, Ian Craig; Porter, John Larry Jr.; Cuneo, Michael Edward; Rovang, Dean Curtis; Anderson, Jessica E.

    2003-01-01

    Annular wire array implosions on the Sandia Z-machine can produce >200 TW and 1-2 MJ of soft x rays in the 0.1-10 keV range. The x-ray flux and debris in this environment present significant challenges for radiographic diagnostics. X-ray backlighting diagnostics at 1865 and 6181 eV using spherically-bent crystals have been fielded on the Z-machine, each with a ∼0.6 eVspectral bandpass, 10 (micro)m spatial resolution, and a 4 mm by 20mm field of view. The Z-Beamlet laser, a 2-TW, 2-kJ Nd:glass laser(λ = 527 nm), is used to produce 0.1-1 J x-ray sources for radiography. The design, calibration, and performance of these diagnostics is presented.

  4. Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis.

    Science.gov (United States)

    Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

    2014-01-01

    Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

  5. Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

    Directory of Open Access Journals (Sweden)

    Cíntia Matsuda Toledo

    Full Text Available Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario.OBJECTIVE: The aims were to describe how to: (i develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii automatically identify the features that best distinguish the groups.METHODS: The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age. In this study, the descriptions by 144 of the subjects studied in Toledo18 were used, which included 200 healthy Brazilians of both genders.RESULTS AND CONCLUSION:A Support Vector Machine (SVM with a radial basis function (RBF kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS is a strong candidate to replace manual feature selection methods.

  6. Non-traditional Infrasound Deployment

    Science.gov (United States)

    McKenna, M. H.; McComas, S.; Simpson, C. P.; Diaz-Alvarez, H.; Costley, R. D.; Hayward, C.; Golden, P.; Endress, A.

    2017-12-01

    Historically, infrasound arrays have been deployed in rural environments where anthropological noise sources are limited. As interest in monitoring low energy sources at local distances grows in the infrasound community, it will be vital to understand how to monitor infrasound sources in an urban environment. Arrays deployed in urban centers have to overcome the decreased signal-to-noise ratio and reduced amount of real estate available to deploy an array. To advance the understanding of monitoring infrasound sources in urban environments, local and regional infrasound arrays were deployed on building rooftops on the campus at Southern Methodist University (SMU), and data were collected for one seasonal cycle. The data were evaluated for structural source signals (continuous-wave packets), and when a signal was identified, the back azimuth to the source was determined through frequency-wavenumber analysis. This information was used to identify hypothesized structural sources; these sources were verified through direct measurement and dynamic structural analysis modeling. In addition to the rooftop arrays, a camouflaged infrasound sensor was installed on the SMU campus and evaluated to determine its effectiveness for wind noise reduction. Permission to publish was granted by Director, Geotechnical and Structures Laboratory.

  7. Machine testning

    DEFF Research Database (Denmark)

    De Chiffre, Leonardo

    This document is used in connection with a laboratory exercise of 3 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercise includes a series of tests carried out by the student on a conventional and a numerically controled lathe, respectively. This document...

  8. Machine rates for selected forest harvesting machines

    Science.gov (United States)

    R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

    2002-01-01

    Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

  9. Machine performance assessment and enhancement for a hexapod machine

    Energy Technology Data Exchange (ETDEWEB)

    Mou, J.I. [Arizona State Univ., Tempe, AZ (United States); King, C. [Sandia National Labs., Livermore, CA (United States). Integrated Manufacturing Systems Center

    1998-03-19

    The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess the status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.

  10. Nontraditional machining processes research advances

    CERN Document Server

    2013-01-01

    Nontraditional machining employs processes that remove material by various methods involving thermal, electrical, chemical and mechanical energy or even combinations of these. Nontraditional Machining Processes covers recent research and development in techniques and processes which focus on achieving high accuracies and good surface finishes, parts machined without burrs or residual stresses especially with materials that cannot be machined by conventional methods. With applications to the automotive, aircraft and mould and die industries, Nontraditional Machining Processes explores different aspects and processes through dedicated chapters. The seven chapters explore recent research into a range of topics including laser assisted manufacturing, abrasive water jet milling and hybrid processes. Students and researchers will find the practical examples and new processes useful for both reference and for developing further processes. Industry professionals and materials engineers will also find Nontraditional M...

  11. Electric machines

    CERN Document Server

    Gross, Charles A

    2006-01-01

    BASIC ELECTROMAGNETIC CONCEPTSBasic Magnetic ConceptsMagnetically Linear Systems: Magnetic CircuitsVoltage, Current, and Magnetic Field InteractionsMagnetic Properties of MaterialsNonlinear Magnetic Circuit AnalysisPermanent MagnetsSuperconducting MagnetsThe Fundamental Translational EM MachineThe Fundamental Rotational EM MachineMultiwinding EM SystemsLeakage FluxThe Concept of Ratings in EM SystemsSummaryProblemsTRANSFORMERSThe Ideal n-Winding TransformerTransformer Ratings and Per-Unit ScalingThe Nonideal Three-Winding TransformerThe Nonideal Two-Winding TransformerTransformer Efficiency and Voltage RegulationPractical ConsiderationsThe AutotransformerOperation of Transformers in Three-Phase EnvironmentsSequence Circuit Models for Three-Phase Transformer AnalysisHarmonics in TransformersSummaryProblemsBASIC MECHANICAL CONSIDERATIONSSome General PerspectivesEfficiencyLoad Torque-Speed CharacteristicsMass Polar Moment of InertiaGearingOperating ModesTranslational SystemsA Comprehensive Example: The ElevatorP...

  12. Charging machine

    International Nuclear Information System (INIS)

    Medlin, J.B.

    1976-01-01

    A charging machine for loading fuel slugs into the process tubes of a nuclear reactor includes a tubular housing connected to the process tube, a charging trough connected to the other end of the tubular housing, a device for loading the charging trough with a group of fuel slugs, means for equalizing the coolant pressure in the charging trough with the pressure in the process tubes, means for pushing the group of fuel slugs into the process tube and a latch and a seal engaging the last object in the group of fuel slugs to prevent the fuel slugs from being ejected from the process tube when the pusher is removed and to prevent pressure liquid from entering the charging machine. 3 claims, 11 drawing figures

  13. Genesis machines

    CERN Document Server

    Amos, Martyn

    2014-01-01

    Silicon chips are out. Today's scientists are using real, wet, squishy, living biology to build the next generation of computers. Cells, gels and DNA strands are the 'wetware' of the twenty-first century. Much smaller and more intelligent, these organic computers open up revolutionary possibilities. Tracing the history of computing and revealing a brave new world to come, Genesis Machines describes how this new technology will change the way we think not just about computers - but about life itself.

  14. Non-traditional CD4+CD25-CD69+ regulatory T cells are correlated to leukemia relapse after allogeneic hematopoietic stem cell transplantation.

    Science.gov (United States)

    Zhao, Xiao-su; Wang, Xu-hua; Zhao, Xiang-yu; Chang, Ying-jun; Xu, Lan-ping; Zhang, Xiao-hui; Huang, Xiao-jun

    2014-07-01

    Non-traditional CD4+CD25-CD69+ T cells were found to be involved in disease progression in tumor-bearing mouse models and cancer patients recently. We attempted to define whether this subset of T cells were related to leukemia relapse after allogeneic hematopoietic cell transplantation (allo-HSCT). The frequency of CD4+CD25-CD69+ T cells among the CD4+ T cell population from the bone marrow of relapsed patients, patients with positive minimal residual disease (MRD+) and healthy donors was examined by flow cytometry. The CD4+CD25-CD69+ T cells were also stained with the intracellular markers to determine the cytokine (TGF-β, IL-2 and IL-10) secretion. The results showed that the frequency of CD4+CD25-CD69 + T cells was markedly increased in patients in the relapsed group and the MRD + group compared to the healthy donor group. The percentage of this subset of T cells was significantly decreased after effective intervention treatment. We also analyzed the reconstitution of CD4+CD25-CD69+ T cells at various time points after allo-HSCT, and the results showed that this subset of T cells reconstituted rapidly and reached a relatively higher level at +60 d in patients compared to controls. The incidence of either MRD+ or relapse in patients with a high frequency of CD4+CD25-CD69+ T cells (>7%) was significantly higher than that of patients with a low frequency of CD4+CD25-CD69+ T cells at +60 d, +90 d and +270 d after transplant. However, our preliminary data indicated that CD4+CD25-CD69+ T cells may not exert immunoregulatory function via cytokine secretion. This study provides the first clinical evidence of a correlation between non-traditional CD4+CD25-CD69+ Tregs and leukemia relapse after allo-HSCT and suggests that exploration of new methods of adoptive immunotherapy may be beneficial. Further research related to regulatory mechanism behind this phenomenon would be necessary.

  15. Considerations upon the Machine Learning Technologies

    OpenAIRE

    Alin Munteanu; Cristina Ofelia Sofran

    2006-01-01

    Artificial intelligence offers superior techniques and methods by which problems from diverse domains may find an optimal solution. The Machine Learning technologies refer to the domain of artificial intelligence aiming to develop the techniques allowing the computers to “learn”. Some systems based on Machine Learning technologies tend to eliminate the necessity of the human intelligence while the others adopt a man-machine collaborative approach.

  16. Considerations upon the Machine Learning Technologies

    Directory of Open Access Journals (Sweden)

    Alin Munteanu

    2006-01-01

    Full Text Available Artificial intelligence offers superior techniques and methods by which problems from diverse domains may find an optimal solution. The Machine Learning technologies refer to the domain of artificial intelligence aiming to develop the techniques allowing the computers to “learn”. Some systems based on Machine Learning technologies tend to eliminate the necessity of the human intelligence while the others adopt a man-machine collaborative approach.

  17. Children's adjustment in non-traditional families in Israel: the effect of parental sexual orientation and the number of parents on children's development.

    Science.gov (United States)

    Shechner, T; Slone, M; Lobel, T E; Shechter, R

    2013-03-01

    This study examined differences in children's psychological and social indicators in non-traditional families in Israel, focusing on fatherless families headed by lesbian mothers and single mothers by choice. Although Israel is considered an industrialized westernized country, centrality of the traditional nuclear family predominates this country. This factorial design study included four family types: lesbian and heterosexual mothers, each in both single and coupled parenthood. Children's measures included the Child Behavior Checklist, perception of peer relations and perceived self-competence. Children from single parent as opposed to two-parent families exhibited more externalizing behaviour problems and aggressiveness. Children of lesbian mothers reported more prosocial behaviours and less loneliness than children from heterosexual families. No differences emerged for perceived self-competence across family types. Mother's sexual orientation did not affect children's adjustment negatively, whereas single parenthood placed children at greater risk for some difficulties. Implications include the need for apprising health professionals of effects of family types on children's development. © 2011 Blackwell Publishing Ltd.

  18. Temporal stability of growth and yield among Hevea genotypes introduced to a non-traditional rubber growing region of peninsular India

    Directory of Open Access Journals (Sweden)

    K.K. Vinod

    2010-09-01

    Full Text Available Extensive cultivation of Hevea brasiliensis in India now focus onnon-traditional regions for rubber cultivation. As a prelude for selection of genotypes for commercial cultivation, many introduced genotypes are being tested in genotype adaptation experiments in these regions. Present study,reports for the first time, growth and yield adaptation of 28 genotypes in a non-traditional rubber growing region of peninsular India viz., the coastal Karnataka region. Agroclimate of this region was found favoring growth andestablishment of all the genotypes evaluated. However, not all the genotypes grew and yielded well. Only four genotypes, RRII 203, KRS 25, PB 260 and PB 235 showed good growth and yield. On grouping, the genotypes fell into categories of moderate high yielders, moderate low yielders and low yielders. The most popular variety of the traditional region, RRII 105 did not perform well in this region. Biological stability in growth and yield of RRII 203 and PB 260 was identified as stable and these genotypes were the best adapted. KRS 25 and PB 235 had unstable yielding pattern. The best identifiedgenotypes can be considered for extensive culture as single clone plantations or as major constituent of clone blends as well as parents in future breeding programmes. Other moderate stable yielders may be used for clone blending in smaller proportions and may be subjected to yield improvement.

  19. Five years of lesson modification to implement non-traditional learning sessions in a traditional-delivery curriculum: A retrospective assessment using applied implementation variables.

    Science.gov (United States)

    Gleason, Shaun E; McNair, Bryan; Kiser, Tyree H; Franson, Kari L

    Non-traditional learning (NTL), including aspects of self-directed learning (SDL), may address self-awareness development needs. Many factors can impact successful implementation of NTL. To share our multi-year experience with modifications that aim to improve NTL sessions in a traditional curriculum. To improve understanding of applied implementation variables (some of which were based on successful SDL implementation components) that impact NTL. We delivered a single lesson in a traditional-delivery curriculum once annually for five years, varying delivery annually in response to student learning and reaction-to-learning results. At year 5, we compared student learning and reaction-to-learning to applied implementation factors using logistic regression. Higher instructor involvement and overall NTL levels predicted correct exam responses (p=0.0007 and ptraditional and highest overall NTL deliveries. Students rated instructor presentation skills and teaching methods higher when greater instructor involvement (pmethods were most effective when lower student involvement and higher technology levels (ptraditional-delivery curriculum, instructor involvement appears essential, while the impact of student involvement and educational technology levels varies. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Lot quality assurance sampling for monitoring coverage and quality of a targeted condom social marketing programme in traditional and non-traditional outlets in India.

    Science.gov (United States)

    Piot, Bram; Mukherjee, Amajit; Navin, Deepa; Krishnan, Nattu; Bhardwaj, Ashish; Sharma, Vivek; Marjara, Pritpal

    2010-02-01

    This study reports on the results of a large-scale targeted condom social marketing campaign in and around areas where female sex workers are present. The paper also describes the method that was used for the routine monitoring of condom availability in these sites. The lot quality assurance sampling (LQAS) method was used for the assessment of the geographical coverage and quality of coverage of condoms in target areas in four states and along selected national highways in India, as part of Avahan, the India AIDS initiative. A significant general increase in condom availability was observed in the intervention area between 2005 and 2008. High coverage rates were gradually achieved through an extensive network of pharmacies and particularly of non-traditional outlets, whereas traditional outlets were instrumental in providing large volumes of condoms. LQAS is seen as a valuable tool for the routine monitoring of the geographical coverage and of the quality of delivery systems of condoms and of health products and services in general. With a relatively small sample size, easy data collection procedures and simple analytical methods, it was possible to inform decision-makers regularly on progress towards coverage targets.

  1. Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.

    Science.gov (United States)

    Vock, David M; Wolfson, Julian; Bandyopadhyay, Sunayan; Adomavicius, Gediminas; Johnson, Paul E; Vazquez-Benitez, Gabriela; O'Connor, Patrick J

    2016-06-01

    Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5years) based on individual patient characteristics are important tools for managing patient care. Electronic health data (EHD) are appealing sources of training data because they provide access to large amounts of rich individual-level data from present-day patient populations. However, because EHD are derived by extracting information from administrative and clinical databases, some fraction of subjects will not be under observation for the entire time frame over which one wants to make predictions; this loss to follow-up is often due to disenrollment from the health system. For subjects without complete follow-up, whether or not they experienced the adverse event is unknown, and in statistical terms the event time is said to be right-censored. Most machine learning approaches to the problem have been relatively ad hoc; for example, common approaches for handling observations in which the event status is unknown include (1) discarding those observations, (2) treating them as non-events, (3) splitting those observations into two observations: one where the event occurs and one where the event does not. In this paper, we present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models. We then show that our approach leads to better calibrated predictions than the three ad hoc approaches when applied to predicting the 5-year risk of experiencing a cardiovascular adverse event, using EHD from a large U.S. Midwestern healthcare system. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis and machine learning techniques

    Science.gov (United States)

    Rathore, Saima; Bakas, Spyridon; Akbari, Hamed; Shukla, Gaurav; Rozycki, Martin; Davatzikos, Christos

    2018-02-01

    There is mounting evidence that assessment of multi-parametric magnetic resonance imaging (mpMRI) profiles can noninvasively predict survival in many cancers, including glioblastoma. The clinical adoption of mpMRI as a prognostic biomarker, however, depends on its applicability in a multicenter setting, which is hampered by inter-scanner variations. This concept has not been addressed in existing studies. We developed a comprehensive set of within-patient normalized tumor features such as intensity profile, shape, volume, and tumor location, extracted from multicenter mpMRI of two large (npatients=353) cohorts, comprising the Hospital of the University of Pennsylvania (HUP, npatients=252, nscanners=3) and The Cancer Imaging Archive (TCIA, npatients=101, nscanners=8). Inter-scanner harmonization was conducted by normalizing the tumor intensity profile, with that of the contralateral healthy tissue. The extracted features were integrated by support vector machines to derive survival predictors. The predictors' generalizability was evaluated within each cohort, by two cross-validation configurations: i) pooled/scanner-agnostic, and ii) across scanners (training in multiple scanners and testing in one). The median survival in each configuration was used as a cut-off to divide patients in long- and short-survivors. Accuracy (ACC) for predicting long- versus short-survivors, for these configurations was ACCpooled=79.06% and ACCpooled=84.7%, ACCacross=73.55% and ACCacross=74.76%, in HUP and TCIA datasets, respectively. The hazard ratio at 95% confidence interval was 3.87 (2.87-5.20, P<0.001) and 6.65 (3.57-12.36, P<0.001) for HUP and TCIA datasets, respectively. Our findings suggest that adequate data normalization coupled with machine learning classification allows robust prediction of survival estimates on mpMRI acquired by multiple scanners.

  3. Sneaky Submarine Landslides, and how to Quantify them: A Case Study from the Mississippi River Delta Front Contrasting Geophysical and Machine Learning Techniques

    Science.gov (United States)

    Obelcz, J.; Xu, K.; Bentley, S. J.; Wood, W. T.; Georgiou, I. Y.; Maloney, J. M.; Miner, M. D.

    2017-12-01

    The highly publicized subsidence and decline of the Mississippi River Delta Front's (MRDF) subaerial section has recently precipitated studies of the subaqueous MRDF to assess whether it too is subsiding and regressing landward. These studies have largely focused on the area offshore the most active current distributary of the Mississippi River, Southwest Pass, during a decade (post-Hurricane Rita 2005-2014) of relatively quiescent Gulf of Mexico hurricane activity. Utilizing repeat swath bathymetric surveys, it was determined that submarine landslides not associated with major (category ≥ 3) passage are important drivers of downslope sediment transport on the MRDF. Volumetrically, sediment flux downslope without major hurricane influence is approximately half that during a given hurricane-influenced year (5.5 x 105 and 1.1 x 106 m3, respectively). This finding is notable and warrants comparison with other settings to assess the global impact on the source-to-sink budget of small but frequent landslides, but the resource-intensive repeat geophysical surveys required make it a prohibitive option at the margin and global scale. One option to quantify small-scale submarine slope failures while reducing required data acquisition is to utilize machine learning algorithms (MLAs) to intelligently estimate the occurrence and magnitude of submarine landslides based on correlated physical and geological parameters. Here, the MRDF volumetric changes described above are parsed into training and validation data, and physical and geological parameters associated with slope failure (such as porosity, steep slopes, high rates of sedimentation, and presence of gas in pore water) known from prior coring and seafloor mapping expeditions serve as potential predictive variables. The resulting submarine landslide spatial distribution and magnitude maps output by the MLAs are compared to those obtained through geophysical surveys, providing a proof of concept that machine learning can

  4. Machine Learning of Musical Gestures

    OpenAIRE

    Caramiaux, Baptiste; Tanaka, Atau

    2013-01-01

    We present an overview of machine learning (ML) techniques and theirapplication in interactive music and new digital instruments design. We firstgive to the non-specialist reader an introduction to two ML tasks,classification and regression, that are particularly relevant for gesturalinteraction. We then present a review of the literature in current NIMEresearch that uses ML in musical gesture analysis and gestural sound control.We describe the ways in which machine learning is useful for cre...

  5. Representational Machines

    DEFF Research Database (Denmark)

    Photography not only represents space. Space is produced photographically. Since its inception in the 19th century, photography has brought to light a vast array of represented subjects. Always situated in some spatial order, photographic representations have been operatively underpinned by social...... to the enterprises of the medium. This is the subject of Representational Machines: How photography enlists the workings of institutional technologies in search of establishing new iconic and social spaces. Together, the contributions to this edited volume span historical epochs, social environments, technological...... possibilities, and genre distinctions. Presenting several distinct ways of producing space photographically, this book opens a new and important field of inquiry for photography research....

  6. Shear machines

    International Nuclear Information System (INIS)

    Astill, M.; Sunderland, A.; Waine, M.G.

    1980-01-01

    A shear machine for irradiated nuclear fuel elements has a replaceable shear assembly comprising a fuel element support block, a shear blade support and a clamp assembly which hold the fuel element to be sheared in contact with the support block. A first clamp member contacts the fuel element remote from the shear blade and a second clamp member contacts the fuel element adjacent the shear blade and is advanced towards the support block during shearing to compensate for any compression of the fuel element caused by the shear blade (U.K.)

  7. Teachers' views of using e-learning for non-traditional students in higher education across three disciplines [nursing, chemistry and management] at a time of massification and increased diversity in higher education.

    Science.gov (United States)

    Allan, Helen T; O'Driscoll, Mike; Simpson, Vikki; Shawe, Jill

    2013-09-01

    The expansion of the higher educational sector in the United Kingdom over the last two decades to meet political aspirations of the successive governments and popular demand for participation in the sector (the Widening Participation Agenda) has overlapped with the introduction of e-learning. This paper describes teachers' views of using e-learning for non-traditional students in higher education across three disciplines [nursing, chemistry and management] at a time of massification and increased diversity in higher education. A three phase, mixed methods study; this paper reports findings from phase two of the study. One university in England. Higher education teachers teaching on the nursing, chemistry and management programmes. Focus groups with these teachers. Findings from these data show that teachers across the programmes have limited knowledge of whether students are non-traditional or what category of non-traditional status they might be in. Such knowledge as they have does not seem to influence the tailoring of teaching and learning for non-traditional students. Teachers in chemistry and nursing want more support from the university to improve their use of e-learning, as did teachers in management but to a lesser extent. Our conclusions confirm other studies in the field outside nursing which suggest that non-traditional students' learning needs have not been considered meaningfully in the development of e-learning strategies in universities. We suggest that this may be because teachers have been required to develop e-learning at the same time as they cope with the massification of, and widening participation in, higher education. The findings are of particular importance to nurse educators given the high number of non-traditional students on nursing programmes. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. 5-axes modular CNC machining center

    Directory of Open Access Journals (Sweden)

    Breaz Radu-Eugen

    2017-01-01

    Full Text Available The paper presents the development of a 5-axes CNC machining center. The main goal of the machine was to provide the students a practical layout for training in advanced CAM techniques. The mechanical structure of the machine was built in a modular way by a specialized company, which also implemented the CNC controller. The authors of this paper developed the geometric and kinematic model of the CNC machining center and the post-processor, in order to use the machine in a CAM environment.

  9. Electricity of machine tool

    International Nuclear Information System (INIS)

    Gijeon media editorial department

    1977-10-01

    This book is divided into three parts. The first part deals with electricity machine, which can taints from generator to motor, motor a power source of machine tool, electricity machine for machine tool such as switch in main circuit, automatic machine, a knife switch and pushing button, snap switch, protection device, timer, solenoid, and rectifier. The second part handles wiring diagram. This concludes basic electricity circuit of machine tool, electricity wiring diagram in your machine like milling machine, planer and grinding machine. The third part introduces fault diagnosis of machine, which gives the practical solution according to fault diagnosis and the diagnostic method with voltage and resistance measurement by tester.

  10. Environmentally Friendly Machining

    CERN Document Server

    Dixit, U S; Davim, J Paulo

    2012-01-01

    Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

  11. Machine Protection

    CERN Document Server

    Schmidt, R

    2014-01-01

    The protection of accelerator equipment is as old as accelerator technology and was for many years related to high-power equipment. Examples are the protection of powering equipment from overheating (magnets, power converters, high-current cables), of superconducting magnets from damage after a quench and of klystrons. The protection of equipment from beam accidents is more recent. It is related to the increasing beam power of high-power proton accelerators such as ISIS, SNS, ESS and the PSI cyclotron, to the emission of synchrotron light by electron–positron accelerators and FELs, and to the increase of energy stored in the beam (in particular for hadron colliders such as LHC). Designing a machine protection system requires an excellent understanding of accelerator physics and operation to anticipate possible failures that could lead to damage. Machine protection includes beam and equipment monitoring, a system to safely stop beam operation (e.g. dumping the beam or stopping the beam at low energy) and an ...

  12. Predictive analysis of the influence of the chemical composition and pre-processing regimen on structural properties of steel alloys using machine learning techniques

    Science.gov (United States)

    Krishnamurthy, Narayanan; Maddali, Siddharth; Romanov, Vyacheslav; Hawk, Jeffrey

    We present some structural properties of multi-component steel alloys as predicted by a random forest machine-learning model. These non-parametric models are trained on high-dimensional data sets defined by features such as chemical composition, pre-processing temperatures and environmental influences, the latter of which are based upon standardized testing procedures for tensile, creep and rupture properties as defined by the American Society of Testing and Materials (ASTM). We quantify the goodness of fit of these models as well as the inferred relative importance of each of these features, all with a conveniently defined metric and scale. The models are tested with synthetic data points, generated subject to the appropriate mathematical constraints for the various features. By this we highlight possible trends in the increase or degradation of the structural properties with perturbations in the features of importance. This work is presented as part of the Data Science Initiative at the National Energy Technology Laboratory, directed specifically towards the computational design of steel alloys.

  13. Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a low-cost point-of-care digital colposcope

    Science.gov (United States)

    Asiedu, Mercy Nyamewaa; Simhal, Anish; Lam, Christopher T.; Mueller, Jenna; Chaudhary, Usamah; Schmitt, John W.; Sapiro, Guillermo; Ramanujam, Nimmi

    2018-02-01

    The world health organization recommends visual inspection with acetic acid (VIA) and/or Lugol's Iodine (VILI) for cervical cancer screening in low-resource settings. Human interpretation of diagnostic indicators for visual inspection is qualitative, subjective, and has high inter-observer discordance, which could lead both to adverse outcomes for the patient and unnecessary follow-ups. In this work, we a simple method for automatic feature extraction and classification for Lugol's Iodine cervigrams acquired with a low-cost, miniature, digital colposcope. Algorithms to preprocess expert physician-labelled cervigrams and to extract simple but powerful color-based features are introduced. The features are used to train a support vector machine model to classify cervigrams based on expert physician labels. The selected framework achieved a sensitivity, specificity, and accuracy of 89.2%, 66.7% and 80.6% with majority diagnosis of the expert physicians in discriminating cervical intraepithelial neoplasia (CIN +) relative to normal tissues. The proposed classifier also achieved an area under the curve of 84 when trained with majority diagnosis of the expert physicians. The results suggest that utilizing simple color-based features may enable unbiased automation of VILI cervigrams, opening the door to a full system of low-cost data acquisition complemented with automatic interpretation.

  14. Comparative study of coated and uncoated tool inserts with dry machining of EN47 steel using Taguchi L9 optimization technique

    Science.gov (United States)

    Vasu, M.; Shivananda, Nayaka H.

    2018-04-01

    EN47 steel samples are machined on a self-centered lathe using Chemical Vapor Deposition of coated TiCN/Al2O3/TiN and uncoated tungsten carbide tool inserts, with nose radius 0.8mm. Results are compared with each other and optimized using statistical tool. Input (cutting) parameters that are considered in this work are feed rate (f), cutting speed (Vc), and depth of cut (ap), the optimization criteria are based on the Taguchi (L9) orthogonal array. ANOVA method is adopted to evaluate the statistical significance and also percentage contribution for each model. Multiple response characteristics namely cutting force (Fz), tool tip temperature (T) and surface roughness (Ra) are evaluated. The results discovered that coated tool insert (TiCN/Al2O3/TiN) exhibits 1.27 and 1.29 times better than the uncoated tool insert for tool tip temperature and surface roughness respectively. A slight increase in cutting force was observed for coated tools.

  15. Machine learning techniques in searches for t t-bar h in the h  →  b b-bar decay channel

    International Nuclear Information System (INIS)

    Santos, R.; Nguyen, M.; Zhou, J.; Webster, J.; Ryu, S.; Chekanov, S.; Adelman, J.

    2017-01-01

    Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely large mass of the top quark plays a special role in electroweak symmetry breaking. Higgs bosons decay predominantly to b b-bar , yielding signatures for the signal that are similar to t t-bar  + jets with heavy flavor. Though particularly challenging to study due to the similar kinematics between signal and background events, such final states ( t t-bar   b b-bar ) are an important channel for studying the top quark Yukawa coupling. This paper presents a systematic study of machine learning (ML) methods for detecting t t-bar h in the h  →  b b-bar decay channel. Among the eight ML methods tested, we show that two models, extreme gradient boosted trees and neural network models, outperform alternative methods. We further study the effectiveness of ML algorithms by investigating the impact of feature set and data size, as well as the structure of the models. While extended feature set and larger training sets expectedly lead to improvement of performance, shallow models deliver comparable or better performance than their deeper counterparts. Our study suggests that ensembles of trees and neurons, not necessarily deep, work effectively for the problem of t t-bar h detection.

  16. Eating habits of a population undergoing a rapid dietary transition: portion sizes of traditional and non-traditional foods and beverages consumed by Inuit adults in Nunavut, Canada

    Science.gov (United States)

    2013-01-01

    Background To determine the portion sizes of traditional and non-traditional foods being consumed by Inuit adults in three remote communities in Nunavut, Canada. Methods A cross-sectional study was carried out between June and October, 2008. Trained field workers collected dietary data using a culturally appropriate, validated quantitative food frequency questionnaire (QFFQ) developed specifically for the study population. Results Caribou, muktuk (whale blubber and skin) and Arctic char (salmon family), were the most commonly consumed traditional foods; mean portion sizes for traditional foods ranged from 10 g for fermented seal fat to 424 g for fried caribou. Fried bannock and white bread were consumed by >85% of participants; mean portion sizes for these foods were 189 g and 70 g, respectively. Sugar-sweetened beverages and energy-dense, nutrient-poor foods were also widely consumed. Mean portion sizes for regular pop and sweetened juices with added sugar were 663 g and 572 g, respectively. Mean portion sizes for potato chips, pilot biscuits, cakes, chocolate and cookies were 59 g, 59 g, 106 g, 59 g, and 46 g, respectively. Conclusions The present study provides further evidence of the nutrition transition that is occurring among Inuit in the Canadian Arctic. It also highlights a number of foods and beverages that could be targeted in future nutritional intervention programs aimed at obesity and diet-related chronic disease prevention in these and other Inuit communities. PMID:23724920

  17. Machine Learning for Medical Imaging.

    Science.gov (United States)

    Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

    2017-01-01

    Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

  18. Machine Protection

    International Nuclear Information System (INIS)

    Schmidt, R

    2014-01-01

    The protection of accelerator equipment is as old as accelerator technology and was for many years related to high-power equipment. Examples are the protection of powering equipment from overheating (magnets, power converters, high-current cables), of superconducting magnets from damage after a quench and of klystrons. The protection of equipment from beam accidents is more recent. It is related to the increasing beam power of high-power proton accelerators such as ISIS, SNS, ESS and the PSI cyclotron, to the emission of synchrotron light by electron–positron accelerators and FELs, and to the increase of energy stored in the beam (in particular for hadron colliders such as LHC). Designing a machine protection system requires an excellent understanding of accelerator physics and operation to anticipate possible failures that could lead to damage. Machine protection includes beam and equipment monitoring, a system to safely stop beam operation (e.g. dumping the beam or stopping the beam at low energy) and an interlock system providing the glue between these systems. The most recent accelerator, the LHC, will operate with about 3 × 10 14 protons per beam, corresponding to an energy stored in each beam of 360 MJ. This energy can cause massive damage to accelerator equipment in case of uncontrolled beam loss, and a single accident damaging vital parts of the accelerator could interrupt operation for years. This article provides an overview of the requirements for protection of accelerator equipment and introduces the various protection systems. Examples are mainly from LHC, SNS and ESS

  19. Machine terms dictionary

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1979-04-15

    This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.

  20. Classroom Assessment Techniques: A Literature Review

    Science.gov (United States)

    DiCarlo, Kristen; Cooper, Lori

    2014-01-01

    Effective classroom assessment techniques are directly linked to course objectives and proposed outcomes. Results within formative and summative assessments have been studied in the online learning environment as educators seek to meet objectives with respect to student success in the non-traditional setting. Online classroom assessment techniques…

  1. Analysis of aerosol emission and hazard evaluation of electrical discharge machining (EDM) process.

    Science.gov (United States)

    Jose, Mathew; Sivapirakasam, S P; Surianarayanan, M

    2010-01-01

    The safety and environmental aspects of a manufacturing process are important due to increased environmental regulations and life quality. In this paper, the concentration of aerosols in the breathing zone of the operator of Electrical Discharge Machining (EDM), a commonly used non traditional manufacturing process is presented. The pattern of aerosol emissions from this process with varying process parameters such as peak current, pulse duration, dielectric flushing pressure and the level of dielectric was evaluated. Further, the HAZOP technique was employed to identify the inherent safety aspects and fire risk of the EDM process under different working conditions. The analysis of aerosol exposure showed that the concentration of aerosol was increased with increase in the peak current, pulse duration and dielectric level and was decreased with increase in the flushing pressure. It was also found that at higher values of peak current (7A) and pulse duration (520 micros), the concentration of aerosols at breathing zone of the operator was above the permissible exposure limit value for respirable particulates (5 mg/m(3)). HAZOP study of the EDM process showed that this process is vulnerable to fire and explosion hazards. A detailed discussion on preventing the fire and explosion hazard is presented in this paper. The emission and risk of fire of the EDM process can be minimized by selecting proper process parameters and employing appropriate control strategy.

  2. Differences in traditional and non-traditional risk factors with special reference to nutritional factors in patients with coronary artery disease with or without diabetes mellitus

    Directory of Open Access Journals (Sweden)

    Namita P Mahalle

    2013-01-01

    Full Text Available Introduction: There is an increase in awareness about the role of nutritional factors in chronic non-communicable diseases. We therefore conducted this study with an aim to assess the relationship between nutritional factor (vitamin B12 and homocysteine [Hcy] and its association with insulin resistance and inflammatory markers, and differences in traditional and non-traditional risk factors among diabetics and non-diabetics in known cases of coronary artery disease (CAD. Materials and Methods: Three hundred consecutive patients with known coronary disease on coronary angiography, who were >25 years old were included in this study. All cases were interviewed using a questionnaire. Blood samples were analyzed for insulin, vitamin B12, Hcy and inflammatory markers (highly sensitive C-reactive protein [hsCRP], interleukin-6 [IL-6], Tumor necrosis factor-alfa [TNF-α]. Insulin resistance was calculated with homeostasis model assessment of insulin resistance (HOMA-IR. Results: Mean age of the patients was 60.95 ± 12.3 years. Body mass index and waist hip ratio were comparable in both groups. Triglyceride, very low-density lipoprotein and HbA1C were significantly higher and high-density lipoprotein (HDL was significantly lower in patients with diabetes. Patients with diabetes had significantly high levels of IL-6, hsCRP and TNF-α compared with non-diabetic patients. Insulin resistance was twofold higher in diabetic patients. Serum vitamin B12 levels were significantly lower and Hcy was significantly higher in the diabetic group compared with the non-diabetic patients. HbA1C, HOMA-IR and Hcy levels were positively correlated with inflammatory markers in the total study population and in the non-diabetic patients; but, in diabetic patients, HbA1C and Hcy showed this relation. Conclusions: Vitamin B12 deficiency is common in the diabetic population. Hcy levels were higher in diabetics compared with non-diabetics, and were related to glycemic level and

  3. Heavy metals, arsenic, and pesticide contamination in an area with high incidence of chronic kidney disease of non-traditional causes in El Salvador

    Science.gov (United States)

    Lopez, D. A.; Ribó, A.; Quinteros, E.; Mejia, R.; Jovel, R.; VanDervort, D.; Orantes, C. M.

    2013-12-01

    Chronic kidney disease of non-traditional causes is epidemic in Central America, Southern Mexico and other regions of the world such as Sri Lanka, where the origin of the illness is attributed to exposure to agrochemicals and arsenic in soils and groundwater. In Central America, several causes have been suggested for this illness including: high ambient temperatures and chronic dehydration, and toxic effects of agrochemicals. Previous research using step-wise multivariate regression in El Salvador found statistically significant correlation between the spatial distribution of the number of sick people per thousand inhabitants and the percent area cultivated with sugar cane, cotton, and beans, and maximum ambient temperature, with sugar cane cultivation as the most significant factor. This study aims to investigate the possible effects of agricultural activities in the occurrence of this illness looking at heavy metal, arsenic and pesticide contamination in soil, water and sediments of a community located in Bajo Lempa region (Ciudad Romero, El Salvador) and heavily affected by this illness. The Bajo Lempa region is close to Lempa River delta, in the Pacific coast. Ground and surface water, sediment and soil samples were collected in the village where the patients live and in the agricultural areas where they work. With respect to the heavy metals, lead and cadmium where detected in the soils but below the standards for cultivated soils, however, they were not detected in the majority of surface and groundwater. Of the inorganic contaminants, arsenic was present in most soil, sediments, and water samples with some concentrations considerable higher than the standards for cultivated lands and drinking water. Statistically different concentrations in soils were found for the village soils and the cultivated soils, with arsenic higher in the cultivated soils. For the pesticides, results show a significant pollution of soil and groundwater of organochlorine pesticides

  4. Research on the proficient machine system. Theoretical part; Jukutatsu machine system no chosa kenkyu. Rironhen

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-03-01

    The basic theory of the proficient machine system to be developed was studied. Important proficient techniques in manufacturing industries are becoming extinct because of insufficient succession to next generation. The proficient machine system was proposed to cope with such situation. This machine system includes the mechanism for progress and evolution of techniques and sensibilities to be adaptable to environmental changes by learning and recognizing various motions such as work and process. Consequently, the basic research fields are composed of thought, learning, perception and action. This machine requires not only deigned fixed functions but also introduction of the same proficient concept as human being to be adaptable to changes in situation, purpose, time and machine`s complexity. This report explains in detail the basic concept, system principle, approaching procedure and practical elemental technologies of the proficient machine system, and also describes the future prospect. 133 refs., 110 figs., 7 tabs.

  5. Using Machine Learning to Predict Student Performance

    OpenAIRE

    Pojon, Murat

    2017-01-01

    This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. Three different machine learning methods were used in this thesis. They are linear regression, decision trees, and naïve Bayes classification. Feature engineering, the process of modification ...

  6. Addiction Machines

    Directory of Open Access Journals (Sweden)

    James Godley

    2011-10-01

    Full Text Available Entry into the crypt William Burroughs shared with his mother opened and shut around a failed re-enactment of William Tell’s shot through the prop placed upon a loved one’s head. The accidental killing of his wife Joan completed the installation of the addictation machine that spun melancholia as manic dissemination. An early encryptment to which was added the audio portion of abuse deposited an undeliverable message in WB. Wil- liam could never tell, although his corpus bears the in- scription of this impossibility as another form of pos- sibility. James Godley is currently a doctoral candidate in Eng- lish at SUNY Buffalo, where he studies psychoanalysis, Continental philosophy, and nineteenth-century litera- ture and poetry (British and American. His work on the concept of mourning and “the dead” in Freudian and Lacanian approaches to psychoanalytic thought and in Gothic literature has also spawned an essay on zombie porn. Since entering the Academy of Fine Arts Karlsruhe in 2007, Valentin Hennig has studied in the classes of Sil- via Bächli, Claudio Moser, and Corinne Wasmuht. In 2010 he spent a semester at the Dresden Academy of Fine Arts. His work has been shown in group exhibi- tions in Freiburg and Karlsruhe.

  7. MLnet report: training in Europe on machine learning

    OpenAIRE

    Ellebrecht, Mario; Morik, Katharina

    1999-01-01

    Machine learning techniques offer opportunities for a variety of applications and the theory of machine learning investigates problems that are of interest for other fields of computer science (e.g., complexity theory, logic programming, pattern recognition). However, the impacts of machine learning can only be recognized by those who know the techniques and are able to apply them. Hence, teaching machine learning is necessary before this field can diversify computer science. In order ...

  8. Higgs Machine Learning Challenge 2014

    CERN Document Server

    Olivier, A-P; Bourdarios, C ; LAL / Orsay; Goldfarb, S ; University of Michigan

    2014-01-01

    High Energy Physics (HEP) has been using Machine Learning (ML) techniques such as boosted decision trees (paper) and neural nets since the 90s. These techniques are now routinely used for difficult tasks such as the Higgs boson search. Nevertheless, formal connections between the two research fields are rather scarce, with some exceptions such as the AppStat group at LAL, founded in 2006. In collaboration with INRIA, AppStat promotes interdisciplinary research on machine learning, computational statistics, and high-energy particle and astroparticle physics. We are now exploring new ways to improve the cross-fertilization of the two fields by setting up a data challenge, following the footsteps of, among others, the astrophysics community (dark matter and galaxy zoo challenges) and neurobiology (connectomics and decoding the human brain). The organization committee consists of ATLAS physicists and machine learning researchers. The Challenge will run from Monday 12th to September 2014.

  9. The effectiveness of using non-traditional teaching methods to prepare student health care professionals for the delivery of mental state examination: a systematic review.

    Science.gov (United States)

    Xie, Huiting; Liu, Lei; Wang, Jia; Joon, Kum Eng; Parasuram, Rajni; Gunasekaran, Jamuna; Poh, Chee Lien

    2015-08-14

    With the evolution of education, there has been a shift from the use of traditional teaching methods, such as didactic or rote teaching, towards non-traditional teaching methods, such as viewing of role plays, simulation, live interviews and the use of virtual environments. Mental state examination is an essential competency for all student healthcare professionals. If mental state examination is not taught in the most effective manner so learners can comprehend its concepts and interpret the findings correctly, it could lead to serious repercussions and subsequently impact on clinical care provided for patients with mental health conditions, such as incorrect assessment of suicidal ideation. However, the methods for teaching mental state examination vary widely between countries, academic institutions and clinical settings. This systematic review aimed to identify and synthesize the best available evidence of effective teaching methods used to prepare student health care professionals for the delivery of mental state examination. This review considered evidence from primary quantitative studies which address the effectiveness of a chosen method used for the teaching of mental state examination published in English, including studies that measure learner outcomes, i.e. improved knowledge and skills, self-confidence and learners' satisfaction. A three-step search strategy was undertaken in this review to search for articles published in English from the inception of the database to December 2014. An initial search of MEDLINE and CINAHL was undertaken to identify keywords. Secondly, the keywords identified were used to search electronic databases, namely, CINAHL, Medline, Cochrane Central Register of Controlled Trials, Ovid, PsycINFO and, ProQuest Dissertations & Theses. Thirdly, reference lists of the articles identified in the second stage were searched for other relevant studies. Studies selected were assessed by two independent reviewers for methodological

  10. Effect of machining fluid on the process performance of wire electrical discharge machining of nanocomposite ceramic

    Directory of Open Access Journals (Sweden)

    Zhang Chengmao

    2015-01-01

    Full Text Available Wire electric discharge machining (WEDM promise to be effective and economical techniques for the production of tools and parts from conducting ceramic blanks. However, the manufacturing of nanocomposite ceramics blanks with these processes is a long and costly process. This paper presents a new process of machining nanocomposite ceramics using WEDM. WEDM uses water based emulsion, polyvinyl alcohol and distilled water as the machining fluid. Machining fluid is a primary factor that affects the material removal rate and surface quality of WEDM. The effects of emulsion concentration, polyvinyl alcohol concentration and distilled water of the machining fluid on the process performance have been investigated.

  11. VIRTUAL MODELING OF A NUMERICAL CONTROL MACHINE TOOL USED FOR COMPLEX MACHINING OPERATIONS

    Directory of Open Access Journals (Sweden)

    POPESCU Adrian

    2015-11-01

    Full Text Available This paper presents the 3D virtual model of the numerical control machine Modustar 100, in terms of machine elements. This is a CNC machine of modular construction, all components allowing the assembly in various configurations. The paper focused on the design of the subassemblies specific to the axes numerically controlled by means of CATIA v5, which contained different drive kinematic chains of different translation modules that ensures translation on X, Y and Z axis. Machine tool development for high speed and highly precise cutting demands employment of advanced simulation techniques witch it reflect on cost of total development of the machine.

  12. Machine Translation in Post-Contemporary Era

    Science.gov (United States)

    Lin, Grace Hui Chin

    2010-01-01

    This article focusing on translating techniques via personal computer or laptop reports updated artificial intelligence progresses before 2010. Based on interpretations and information for field of MT [Machine Translation] by Yorick Wilks' book, "Machine Translation, Its scope and limits," this paper displays understandable theoretical frameworks…

  13. Metallizing of machinable glass ceramic

    International Nuclear Information System (INIS)

    Seigal, P.K.

    1976-02-01

    A satisfactory technique has been developed for metallizing Corning (Code 9658) machinable glass ceramic for brazing. Analyses of several bonding materials suitable for metallizing were made using microprobe analysis, optical metallography, and tensile strength tests. The effect of different cleaning techniques on the microstructure and the effect of various firing temperatures on the bonding interface were also investigated. A nickel paste, used for thick-film application, has been applied to obtain braze joints with strength in excess of 2000 psi

  14. Machine technology: a survey

    International Nuclear Information System (INIS)

    Barbier, M.M.

    1981-01-01

    An attempt was made to find existing machines that have been upgraded and that could be used for large-scale decontamination operations outdoors. Such machines are in the building industry, the mining industry, and the road construction industry. The road construction industry has yielded the machines in this presentation. A review is given of operations that can be done with the machines available

  15. Machine Shop Lathes.

    Science.gov (United States)

    Dunn, James

    This guide, the second in a series of five machine shop curriculum manuals, was designed for use in machine shop courses in Oklahoma. The purpose of the manual is to equip students with basic knowledge and skills that will enable them to enter the machine trade at the machine-operator level. The curriculum is designed so that it can be used in…

  16. Superconducting rotating machines

    International Nuclear Information System (INIS)

    Smith, J.L. Jr.; Kirtley, J.L. Jr.; Thullen, P.

    1975-01-01

    The opportunities and limitations of the applications of superconductors in rotating electric machines are given. The relevant properties of superconductors and the fundamental requirements for rotating electric machines are discussed. The current state-of-the-art of superconducting machines is reviewed. Key problems, future developments and the long range potential of superconducting machines are assessed

  17. Gradient Boosting Machines, A Tutorial

    Directory of Open Access Journals (Sweden)

    Alexey eNatekin

    2013-12-01

    Full Text Available Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications. They are highly customizable to the particular needs of the application, like being learned with respect to different loss functions. This article gives a tutorial introduction into the methodology of gradient boosting methods. A theoretical information is complemented with many descriptive examples and illustrations which cover all the stages of the gradient boosting model design. Considerations on handling the model complexity are discussed. A set of practical examples of gradient boosting applications are presented and comprehensively analyzed.

  18. Robotic refueling machine

    International Nuclear Information System (INIS)

    Challberg, R.C.; Jones, C.R.

    1996-01-01

    One of the longest critical path operations performed during the outage is removing and replacing the fuel. A design is currently under development for a refueling machine which would allow faster, fully automated operation and would also allow the handling of two fuel assemblies at the same time. This design is different from current designs, (a) because of its lighter weight, making increased acceleration and speed possible, (b) because of its control system which makes locating the fuel assembly more dependable and faster, and (c) because of its dual handling system allowing simultaneous fuel movements. The new design uses two robotic arms to span a designated area of the vessel and the fuel storage area. Attached to the end of each robotic arm is a lightweight telescoping mast with a pendant attached to the end of each mast. The pendant acts as the base unit, allowing attachment of any number of end effectors depending on the servicing or inspection operation. Housed within the pendant are two television cameras used for the positioning control system. The control system is adapted from the robotics field using the technology known as machine vision, which provides both object and character recognition techniques to enable relative position control rather than absolute position control as in past designs. The pendant also contains thrusters that are used for fast, short distance, precise positioning. The new refueling machine system design is capable of a complete off load and reload of an 872 element core in about 5.3 days compared to 13 days for a conventional system

  19. Machine tool structures

    CERN Document Server

    Koenigsberger, F

    1970-01-01

    Machine Tool Structures, Volume 1 deals with fundamental theories and calculation methods for machine tool structures. Experimental investigations into stiffness are discussed, along with the application of the results to the design of machine tool structures. Topics covered range from static and dynamic stiffness to chatter in metal cutting, stability in machine tools, and deformations of machine tool structures. This volume is divided into three sections and opens with a discussion on stiffness specifications and the effect of stiffness on the behavior of the machine under forced vibration c

  20. 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.