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  1. RANDOM FORESTS-BASED FEATURE SELECTION FOR LAND-USE CLASSIFICATION USING LIDAR DATA AND ORTHOIMAGERY

    Directory of Open Access Journals (Sweden)

    H. Guan

    2012-07-01

    Full Text Available The development of lidar system, especially incorporated with high-resolution camera components, has shown great potential for urban classification. However, how to automatically select the best features for land-use classification is challenging. Random Forests, a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Especially, it can provide the measure of variable importance. Thus, in this study the performance of the Random Forests-based feature selection for urban areas was explored. First, we extract features from lidar data, including height-based, intensity-based GLCM measures; other spectral features can be obtained from imagery, such as Red, Blue and Green three bands, and GLCM-based measures. Finally, Random Forests is used to automatically select the optimal and uncorrelated features for landuse classification. 0.5-meter resolution lidar data and aerial imagery are used to assess the feature selection performance of Random Forests in the study area located in Mannheim, Germany. The results clearly demonstrate that the use of Random Forests-based feature selection can improve the classification performance by the selected features.

  2. An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration

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    Yun, Yong-Huan; Li, Hong-Dong; Wood, Leslie R. E.; Fan, Wei; Wang, Jia-Jun; Cao, Dong-Sheng; Xu, Qing-Song; Liang, Yi-Zeng

    2013-07-01

    Wavelength selection is a critical step for producing better prediction performance when applied to spectral data. Considering the fact that the vibrational and rotational spectra have continuous features of spectral bands, we propose a novel method of wavelength interval selection based on random frog, called interval random frog (iRF). To obtain all the possible continuous intervals, spectra are first divided into intervals by moving window of a fix width over the whole spectra. These overlapping intervals are ranked applying random frog coupled with PLS and the optimal ones are chosen. This method has been applied to two near-infrared spectral datasets displaying higher efficiency in wavelength interval selection than others. The source code of iRF can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.

  3. Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

    Science.gov (United States)

    Shamsoddini, A.; Aboodi, M. R.; Karami, J.

    2017-09-01

    Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

  4. TEHRAN AIR POLLUTANTS PREDICTION BASED ON RANDOM FOREST FEATURE SELECTION METHOD

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    A. Shamsoddini

    2017-09-01

    Full Text Available Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

  5. Classification of epileptic EEG signals based on simple random sampling and sequential feature selection.

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    Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiangkui

    2016-06-01

    Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.

  6. Classification of epileptic EEG signals based on simple random sampling and sequential feature selection

    OpenAIRE

    Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiangkui

    2016-01-01

    Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential fea...

  7. Blocked Randomization with Randomly Selected Block Sizes

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    Jimmy Efird

    2010-12-01

    Full Text Available When planning a randomized clinical trial, careful consideration must be given to how participants are selected for various arms of a study. Selection and accidental bias may occur when participants are not assigned to study groups with equal probability. A simple random allocation scheme is a process by which each participant has equal likelihood of being assigned to treatment versus referent groups. However, by chance an unequal number of individuals may be assigned to each arm of the study and thus decrease the power to detect statistically significant differences between groups. Block randomization is a commonly used technique in clinical trial design to reduce bias and achieve balance in the allocation of participants to treatment arms, especially when the sample size is small. This method increases the probability that each arm will contain an equal number of individuals by sequencing participant assignments by block. Yet still, the allocation process may be predictable, for example, when the investigator is not blind and the block size is fixed. This paper provides an overview of blocked randomization and illustrates how to avoid selection bias by using random block sizes.

  8. Blocked randomization with randomly selected block sizes.

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    Efird, Jimmy

    2011-01-01

    When planning a randomized clinical trial, careful consideration must be given to how participants are selected for various arms of a study. Selection and accidental bias may occur when participants are not assigned to study groups with equal probability. A simple random allocation scheme is a process by which each participant has equal likelihood of being assigned to treatment versus referent groups. However, by chance an unequal number of individuals may be assigned to each arm of the study and thus decrease the power to detect statistically significant differences between groups. Block randomization is a commonly used technique in clinical trial design to reduce bias and achieve balance in the allocation of participants to treatment arms, especially when the sample size is small. This method increases the probability that each arm will contain an equal number of individuals by sequencing participant assignments by block. Yet still, the allocation process may be predictable, for example, when the investigator is not blind and the block size is fixed. This paper provides an overview of blocked randomization and illustrates how to avoid selection bias by using random block sizes.

  9. Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

    Science.gov (United States)

    Le, Trang T; Simmons, W Kyle; Misaki, Masaya; Bodurka, Jerzy; White, Bill C; Savitz, Jonathan; McKinney, Brett A

    2017-09-15

    Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. Code

  10. Random selection of Borel sets

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    Bernd Günther

    2010-10-01

    Full Text Available A theory of random Borel sets is presented, based on dyadic resolutions of compact metric spaces. The conditional expectation of the intersection of two independent random Borel sets is investigated. An example based on an embedding of Sierpinski’s universal curve into the space of Borel sets is given.

  11. A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

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    Nantian Huang

    2016-09-01

    Full Text Available The prediction accuracy of short-term load forecast (STLF depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.

  12. Randomized selection on the GPU

    Energy Technology Data Exchange (ETDEWEB)

    Monroe, Laura Marie [Los Alamos National Laboratory; Wendelberger, Joanne R [Los Alamos National Laboratory; Michalak, Sarah E [Los Alamos National Laboratory

    2011-01-13

    We implement here a fast and memory-sparing probabilistic top N selection algorithm on the GPU. To our knowledge, this is the first direct selection in the literature for the GPU. The algorithm proceeds via a probabilistic-guess-and-chcck process searching for the Nth element. It always gives a correct result and always terminates. The use of randomization reduces the amount of data that needs heavy processing, and so reduces the average time required for the algorithm. Probabilistic Las Vegas algorithms of this kind are a form of stochastic optimization and can be well suited to more general parallel processors with limited amounts of fast memory.

  13. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests.

    Science.gov (United States)

    Nguyen, Thanh-Tung; Huang, Joshua; Wu, Qingyao; Nguyen, Thuy; Li, Mark

    2015-01-01

    Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the disease. Advanced machine learning methods have been successfully used in Genome-wide association studies (GWAS) for identification of genetic variants that have relatively big effects in some common, complex diseases. Among them, the most successful one is Random Forests (RF). Despite of performing well in terms of prediction accuracy in some data sets with moderate size, RF still suffers from working in GWAS for selecting informative SNPs and building accurate prediction models. In this paper, we propose to use a new two-stage quality-based sampling method in random forests, named ts-RF, for SNP subspace selection for GWAS. The method first applies p-value assessment to find a cut-off point that separates informative and irrelevant SNPs in two groups. The informative SNPs group is further divided into two sub-groups: highly informative and weak informative SNPs. When sampling the SNP subspace for building trees for the forest, only those SNPs from the two sub-groups are taken into account. The feature subspaces always contain highly informative SNPs when used to split a node at a tree. This approach enables one to generate more accurate trees with a lower prediction error, meanwhile possibly avoiding overfitting. It allows one to detect interactions of multiple SNPs with the diseases, and to reduce the dimensionality and the amount of Genome-wide association data needed for learning the RF model. Extensive experiments on two genome-wide SNP data sets (Parkinson case-control data comprised of 408,803 SNPs and Alzheimer case-control data comprised of 380,157 SNPs) and 10 gene data sets have demonstrated that the proposed model significantly reduced prediction errors and outperformed

  14. A brief, web-based personalized feedback selective intervention for college student marijuana use: a randomized clinical trial.

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    Lee, Christine M; Neighbors, Clayton; Kilmer, Jason R; Larimer, Mary E

    2010-06-01

    Despite clear need, brief web-based interventions for marijuana-using college students have not been evaluated in the literature. The current study was designed to evaluate a brief, web-based personalized feedback intervention for at-risk marijuana users transitioning to college. All entering first-year students were invited to complete a brief questionnaire. Participants meeting criteria completed a baseline assessment (N = 341) and were randomly assigned to web-based personalized feedback or assessment-only control conditions. Participants completed 3-month (95.0%) and 6-month (94.4%) follow-up assessments. Results indicated that although there was no overall intervention effect, moderator analyses found promising effects for those with a family history of drug problems and, to a smaller extent, students who were higher in contemplation of changing marijuana use at baseline. Implications of these findings for selective intervention of college marijuana use and web-based interventions in general are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

  15. CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

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    Ma, Li; Fan, Suohai

    2017-03-14

    The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

  16. A Brief, Web-based Personalized Feedback Selective Intervention for College Student Marijuana Use: A Randomized Clinical Trial

    OpenAIRE

    Lee, Christine M.; Neighbors, Clayton; Kilmer, Jason R; Larimer, Mary E.

    2010-01-01

    Despite clear need, brief web-based interventions for marijuana using college students have not been evaluated in the literature. The current study was designed to evaluate a brief, web-based personalized feedback intervention for at-risk marijuana users transitioning to college. All entering first-year students were invited to complete a brief questionnaire. Participants meeting criteria completed a baseline assessment (N = 341) and were randomly assigned to web-based personalized feedback o...

  17. Improving randomness characterization through Bayesian model selection.

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    Díaz Hernández Rojas, Rafael; Solís, Aldo; Angulo Martínez, Alí M; U'Ren, Alfred B; Hirsch, Jorge G; Marsili, Matteo; Pérez Castillo, Isaac

    2017-06-08

    Random number generation plays an essential role in technology with important applications in areas ranging from cryptography to Monte Carlo methods, and other probabilistic algorithms. All such applications require high-quality sources of random numbers, yet effective methods for assessing whether a source produce truly random sequences are still missing. Current methods either do not rely on a formal description of randomness (NIST test suite) on the one hand, or are inapplicable in principle (the characterization derived from the Algorithmic Theory of Information), on the other, for they require testing all the possible computer programs that could produce the sequence to be analysed. Here we present a rigorous method that overcomes these problems based on Bayesian model selection. We derive analytic expressions for a model's likelihood which is then used to compute its posterior distribution. Our method proves to be more rigorous than NIST's suite and Borel-Normality criterion and its implementation is straightforward. We applied our method to an experimental device based on the process of spontaneous parametric downconversion to confirm it behaves as a genuine quantum random number generator. As our approach relies on Bayesian inference our scheme transcends individual sequence analysis, leading to a characterization of the source itself.

  18. Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

    Directory of Open Access Journals (Sweden)

    Xin Ma

    2015-01-01

    Full Text Available The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR method, followed by incremental feature selection (IFS. We incorporated features of conjoint triad features and three novel features: binding propensity (BP, nonbinding propensity (NBP, and evolutionary information combined with physicochemical properties (EIPP. The results showed that these novel features have important roles in improving the performance of the predictor. Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient. High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.

  19. Randomized Controlled Trial of Acupuncture for Women with Fibromyalgia: Group Acupuncture with Traditional Chinese Medicine Diagnosis-Based Point Selection.

    Science.gov (United States)

    Mist, Scott D; Jones, Kim Dupree

    2018-02-13

    Group acupuncture is a growing and cost-effective method for delivering acupuncture in the United States and is the practice model in China. However, group acupuncture has not been tested in a research setting. To test the treatment effect of group acupuncture vs group education in persons with fibromyalgia. Random allocation two-group study with repeated measures. Group clinic in an academic health center in Portland, Oregon. Women with confirmed diagnosis of fibromyalgia (American College of Radiology 1990 criteria) and moderate to severe pain levels. Twenty treatments of a manualized acupuncture treatment based on Traditional Chinese Medicine diagnosis or group education over 10 weeks (both 900 minutes total). Weekly Revised Fibromyalgia Impact Questionnaire (FIQR) and Global Fatigue Index at baseline, five weeks, and 10 weeks and a four-week follow-up were assessed. Thirty women were recruited, with 78% reporting symptoms for longer than 10 years. The mean attendance was 810 minutes for acupuncture and 861 minutes for education. FIQR total, FIQR pain, and Global Fatigue Index all had clinically and statistically significant improvement in the group receiving acupuncture at end of treatment and four weeks post-treatment but not in participants receiving group education between groups. Compared with education, group acupuncture improved global symptom impact, pain, and fatigue. Furthermore, it was a safe and well-tolerated treatment option, improving a broader proportion of patients than current pharmaceutical options.

  20. Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.

    Directory of Open Access Journals (Sweden)

    Wangchao Lou

    Full Text Available Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that

  1. Species selection and random drift in macroevolution.

    Science.gov (United States)

    Chevin, Luis-Miguel

    2016-03-01

    Species selection resulting from trait-dependent speciation and extinction is increasingly recognized as an important mechanism of phenotypic macroevolution. However, the recent bloom in statistical methods quantifying this process faces a scarcity of dynamical theory for their interpretation, notably regarding the relative contributions of deterministic versus stochastic evolutionary forces. I use simple diffusion approximations of birth-death processes to investigate how the expected and random components of macroevolutionary change depend on phenotype-dependent speciation and extinction rates, as can be estimated empirically. I show that the species selection coefficient for a binary trait, and selection differential for a quantitative trait, depend not only on differences in net diversification rates (speciation minus extinction), but also on differences in species turnover rates (speciation plus extinction), especially in small clades. The randomness in speciation and extinction events also produces a species-level equivalent to random genetic drift, which is stronger for higher turnover rates. I then show how microevolutionary processes including mutation, organismic selection, and random genetic drift cause state transitions at the species level, allowing comparison of evolutionary forces across levels. A key parameter that would be needed to apply this theory is the distribution and rate of origination of new optimum phenotypes along a phylogeny. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

  2. H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection.

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    Ebina, Teppei; Suzuki, Ryosuke; Tsuji, Ryotaro; Kuroda, Yutaka

    2014-08-01

    Domain linker prediction is attracting much interest as it can help identifying novel domains suitable for high throughput proteomics analysis. Here, we report H-DROP, an SVM-based Helical Domain linker pRediction using OPtimal features. H-DROP is, to the best of our knowledge, the first predictor for specifically and effectively identifying helical linkers. This was made possible first because a large training dataset became available from IS-Dom, and second because we selected a small number of optimal features from a huge number of potential ones. The training helical linker dataset, which included 261 helical linkers, was constructed by detecting helical residues at the boundary regions of two independent structural domains listed in our previously reported IS-Dom dataset. 45 optimal feature candidates were selected from 3,000 features by random forest, which were further reduced to 26 optimal features by stepwise selection. The prediction sensitivity and precision of H-DROP were 35.2 and 38.8%, respectively. These values were over 10.7% higher than those of control methods including our previously developed DROP, which is a coil linker predictor, and PPRODO, which is trained with un-differentiated domain boundary sequences. Overall, these results indicated that helical linkers can be predicted from sequence information alone by using a strictly curated training data set for helical linkers and carefully selected set of optimal features. H-DROP is available at http://domserv.lab.tuat.ac.jp.

  3. EcmPred: Prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar Umar

    2013-01-01

    The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases. In this work, we report a random forest approach, EcmPred, for the prediction of ECM proteins from protein sequences. EcmPred was trained on a dataset containing 300 ECM and 300 non-ECM and tested on a dataset containing 145 ECM and 4187 non-ECM proteins. EcmPred achieved 83% accuracy on the training and 77% on the test dataset. EcmPred predicted 15 out of 20 experimentally verified ECM proteins. By scanning the entire human proteome, we predicted novel ECM proteins validated with gene ontology and InterPro. The dataset and standalone version of the EcmPred software is available at http://www.inb.uni-luebeck.de/tools-demos/Extracellular_matrix_proteins/EcmPred. © 2012 Elsevier Ltd.

  4. 32 CFR 1624.1 - Random selection procedures for induction.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 6 2010-07-01 2010-07-01 false Random selection procedures for induction. 1624... SYSTEM INDUCTIONS § 1624.1 Random selection procedures for induction. (a) The Director of Selective Service shall from time to time establish a random selection sequence for induction by a drawing to be...

  5. Radiofrequency catheter selection based on cavotricuspid angiography compared with a control group with an externally cooled-tip catheter: a randomized pilot study.

    Science.gov (United States)

    Da Costa, Antoine; Romeyer-Bouchard, Cécile; Jamon, Yann; Bisch, Laurence; Isaaz, Karl

    2009-05-01

    Radiofrequency ablation (RFA) of cavotricuspid isthmus (CTI)-dependent atrial flutter (AFL) can be performed using either externally cooled-tip RFA catheters or large-tip (8 mm) catheters. However, experimental and clinical studies suggest that the efficacy of both catheters may vary with CTI anatomy and catheters orientation. The aim of this prospective study was to evaluate: a RFA catheter selection based on CTI angiography compared with a control group with an externally cooled-tip catheter together with the risk of an expensive crossover catheter in both groups. Over a period of 16 months, 119 patients were included and randomized. When comparing the angiographic group (n = 56) and the externally cooled-tip RFA catheter group (n = 63), the duration of application time with a median of 7 min (interquartile range 4.5-11) versus a median of 10 min (interquartile range 6-20; P = 0.008) and the duration of X-ray exposure with a median of 7 min (interquartile range 4-10) versus a median of 10 min (interquartile range 5-15; P = 0.025) were significantly lower in the angiographic group versus externally cooled-tip catheter group. Furthermore, the number of catheters crossover was significantly higher in the angiographic group versus externally cooled-tip catheter group I (27% vs 7%; P = 0.007). This study shows that a strategy with a catheter selection based on a CTI angiographic evaluation is superior to an empirical use of an externally cooled-tip catheter during CTI RFA. Thus, angiographic isthmus evaluation predicts the effectiveness of a RFA catheter and the risk of an expensive catheter crossover.

  6. Local randomization in neighbor selection improves PRM roadmap quality

    KAUST Repository

    McMahon, Troy

    2012-10-01

    Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K\\'), that first computes the K\\' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K\\'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.

  7. Selection for altruism through random drift in variable size populations.

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    Houchmandzadeh, Bahram; Vallade, Marcel

    2012-05-10

    Altruistic behavior is defined as helping others at a cost to oneself and a lowered fitness. The lower fitness implies that altruists should be selected against, which is in contradiction with their widespread presence is nature. Present models of selection for altruism (kin or multilevel) show that altruistic behaviors can have 'hidden' advantages if the 'common good' produced by altruists is restricted to some related or unrelated groups. These models are mostly deterministic, or assume a frequency dependent fitness. Evolutionary dynamics is a competition between deterministic selection pressure and stochastic events due to random sampling from one generation to the next. We show here that an altruistic allele extending the carrying capacity of the habitat can win by increasing the random drift of "selfish" alleles. In other terms, the fixation probability of altruistic genes can be higher than those of a selfish ones, even though altruists have a smaller fitness. Moreover when populations are geographically structured, the altruists advantage can be highly amplified and the fixation probability of selfish genes can tend toward zero. The above results are obtained both by numerical and analytical calculations. Analytical results are obtained in the limit of large populations. The theory we present does not involve kin or multilevel selection, but is based on the existence of random drift in variable size populations. The model is a generalization of the original Fisher-Wright and Moran models where the carrying capacity depends on the number of altruists.

  8. Selection for altruism through random drift in variable size populations

    Directory of Open Access Journals (Sweden)

    Houchmandzadeh Bahram

    2012-05-01

    Full Text Available Abstract Background Altruistic behavior is defined as helping others at a cost to oneself and a lowered fitness. The lower fitness implies that altruists should be selected against, which is in contradiction with their widespread presence is nature. Present models of selection for altruism (kin or multilevel show that altruistic behaviors can have ‘hidden’ advantages if the ‘common good’ produced by altruists is restricted to some related or unrelated groups. These models are mostly deterministic, or assume a frequency dependent fitness. Results Evolutionary dynamics is a competition between deterministic selection pressure and stochastic events due to random sampling from one generation to the next. We show here that an altruistic allele extending the carrying capacity of the habitat can win by increasing the random drift of “selfish” alleles. In other terms, the fixation probability of altruistic genes can be higher than those of a selfish ones, even though altruists have a smaller fitness. Moreover when populations are geographically structured, the altruists advantage can be highly amplified and the fixation probability of selfish genes can tend toward zero. The above results are obtained both by numerical and analytical calculations. Analytical results are obtained in the limit of large populations. Conclusions The theory we present does not involve kin or multilevel selection, but is based on the existence of random drift in variable size populations. The model is a generalization of the original Fisher-Wright and Moran models where the carrying capacity depends on the number of altruists.

  9. In-Place Randomized Slope Selection

    DEFF Research Database (Denmark)

    Blunck, Henrik; Vahrenhold, Jan

    2006-01-01

    Slope selection is a well-known algorithmic tool used in the context of computing robust estimators for fitting a line to a collection P of n points in the plane. We demonstrate that it is possible to perform slope selection in expected O(nlogn) time using only constant extra space in addition to...

  10. Random effect selection in generalised linear models

    DEFF Research Database (Denmark)

    Denwood, Matt; Houe, Hans; Forkman, Björn

    We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest ...

  11. Sequential selection of random vectors under a sum constraint

    OpenAIRE

    Stanke, Mario

    2004-01-01

    We observe a sequence X1,X2,...,Xn of independent and identically distributed coordinatewise nonnegative d-dimensional random vectors. When a vector is observed it can either be selected or rejected but once made this decision is final. In each coordinate the sum of the selected vectors must not exceed a given constant. The problem is to find a selection policy that maximizes the expected number of selected vectors. For a general absolutely continuous distribution of t...

  12. Selectivity and sparseness in randomly connected balanced networks.

    Directory of Open Access Journals (Sweden)

    Cengiz Pehlevan

    Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

  13. Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information

    Directory of Open Access Journals (Sweden)

    Hyontai Sug

    2012-01-01

    Full Text Available Random forests are known to be good for data mining of classification tasks, because random forests are robust for datasets having insufficient information possibly with some errors. But applying random forests blindly may not produce good results, and a dataset in the domain of rotogravure printing is one of such datasets. Hence, in this paper, some best classification accuracy based on clever application of random forests to predict the occurrence of cylinder bands in rotogravure printing is investigated. Since random forests could generate good results with an appropriate combination of parameters like the number of randomly selected attributes for each split and the number of trees in the forests, an effective data mining procedure considering the property of the target dataset by way of trial random forests is investigated. The effectiveness of the suggested procedure is shown by experiments with very good results.

  14. Pseudo-random number generator based on asymptotic deterministic randomness

    Energy Technology Data Exchange (ETDEWEB)

    Wang Kai [Department of Radio Engineering, Southeast University, Nanjing (China)], E-mail: kaiwang@seu.edu.cn; Pei Wenjiang; Xia Haishan [Department of Radio Engineering, Southeast University, Nanjing (China); Cheung Yiuming [Department of Computer Science, Hong Kong Baptist University, Hong Kong (China)

    2008-06-09

    A novel approach to generate the pseudorandom-bit sequence from the asymptotic deterministic randomness system is proposed in this Letter. We study the characteristic of multi-value correspondence of the asymptotic deterministic randomness constructed by the piecewise linear map and the noninvertible nonlinearity transform, and then give the discretized systems in the finite digitized state space. The statistic characteristics of the asymptotic deterministic randomness are investigated numerically, such as stationary probability density function and random-like behavior. Furthermore, we analyze the dynamics of the symbolic sequence. Both theoretical and experimental results show that the symbolic sequence of the asymptotic deterministic randomness possesses very good cryptographic properties, which improve the security of chaos based PRBGs and increase the resistance against entropy attacks and symbolic dynamics attacks.

  15. Fast, Randomized Join-Order Selection - Why Use Transformations?

    NARCIS (Netherlands)

    C.A. Galindo-Legaria; A.J. Pellenkoft (Jan); M.L. Kersten (Martin)

    1994-01-01

    textabstractWe study the effectiveness of probabilistic selection of join-query evaluation plans, without reliance on tree transformation rules. Instead, each candidate plan is chosen uniformly at random from the space of valid evaluation orders. This leads to a transformation-free strategy where a

  16. The reliability of randomly selected final year pharmacy students in ...

    African Journals Online (AJOL)

    Employing ANOVA, factorial experimental analysis, and the theory of error, reliability studies were conducted on the assessment of the drug product chloroquine phosphate tablets. The G–Study employed equal numbers of the factors for uniform control, and involved three analysts (randomly selected final year Pharmacy ...

  17. Selecting a phoneme-to-grapheme mapping: Random or weighted selection?

    Directory of Open Access Journals (Sweden)

    Binna Lee

    2015-05-01

    Our findings demonstrate that random selection underestimates MOA’s PG correspondences whereas weighted selection predicts higher PG correspondences than he produces. To explain his intermediate spelling performance on PPEs, we will test additional approaches to weighing the relative probability of PG mappings, including using log frequencies, separating consonant and vowel status, and considering the number of grapheme options in each phoneme.

  18. Impact of retreatment with an artemisinin-based combination on malaria incidence and its potential selection of resistant strains: study protocol for a randomized controlled clinical trial

    NARCIS (Netherlands)

    Muhindo Mavoko, Hypolite; Nabasumba, Carolyn; Tinto, Halidou; d'Alessandro, Umberto; Grobusch, Martin Peter; Lutumba, Pascal; van Geertruyden, Jean-Pierre

    2013-01-01

    Artemisinin-based combination therapy is currently recommended by the World Health Organization as first-line treatment of uncomplicated malaria. Recommendations were adapted in 2010 regarding rescue treatment in case of treatment failure. Instead of quinine monotherapy, it should be combined with

  19. Interference-aware random beam selection for spectrum sharing systems

    KAUST Repository

    Abdallah, Mohamed M.

    2012-09-01

    Spectrum sharing systems have been introduced to alleviate the problem of spectrum scarcity by allowing secondary unlicensed networks to share the spectrum with primary licensed networks under acceptable interference levels to the primary users. In this paper, we develop interference-aware random beam selection schemes that provide enhanced throughput for the secondary link under the condition that the interference observed at the primary link is within a predetermined acceptable value. For a secondary transmitter equipped with multiple antennas, our schemes select a random beam, among a set of power- optimized orthogonal random beams, that maximizes the capacity of the secondary link while satisfying the interference constraint at the primary receiver for different levels of feedback information describing the interference level at the primary receiver. For the proposed schemes, we develop a statistical analysis for the signal-to-noise and interference ratio (SINR) statistics as well as the capacity of the secondary link. Finally, we present numerical results that study the effect of system parameters including number of beams and the maximum transmission power on the capacity of the secondary link attained using the proposed schemes. © 2012 IEEE.

  20. Random Group Problem-Based Learning in Engineering Dynamics

    CERN Document Server

    Fleischfresser, Luciano

    2014-01-01

    Dynamics problem solving is highly specific to the problem at hand and to develop the general mind framework to become an effective problem solver requires ingenuity and creativity on top of a solid grounding on theoretical and conceptual knowledge. A blended approach with prototype demo, problem-based learning, and an opinion questionnaire was used during first semester of 2013. Students working in randomly selected teams had to interact with classmates while solving a randomly selected problem. The approach helps improve awareness of what is important to learn in this class while reducing grading load. It also provides a more rewarding contact time for both pupils and instructor.

  1. Unbiased split variable selection for random survival forests using maximally selected rank statistics.

    Science.gov (United States)

    Wright, Marvin N; Dankowski, Theresa; Ziegler, Andreas

    2017-04-15

    The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. An alternative is to use maximally selected rank statistics for the split point selection. As in conditional inference forests, splitting variables are compared on the p-value scale. However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split variable selection is possible. However, there is a trade-off between unbiased split variable selection and runtime. In benchmark studies of prediction performance on simulated and real datasets, the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison, the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  2. The signature of positive selection at randomly chosen loci.

    Science.gov (United States)

    Przeworski, Molly

    2002-03-01

    In Drosophila and humans, there are accumulating examples of loci with a significant excess of high-frequency-derived alleles or high levels of linkage disequilibrium, relative to a neutral model of a random-mating population of constant size. These are features expected after a recent selective sweep. Their prevalence suggests that positive directional selection may be widespread in both species. However, as I show here, these features do not persist long after the sweep ends: The high-frequency alleles drift to fixation and no longer contribute to polymorphism, while linkage disequilibrium is broken down by recombination. As a result, loci chosen without independent evidence of recent selection are not expected to exhibit either of these features, even if they have been affected by numerous sweeps in their genealogical history. How then can we explain the patterns in the data? One possibility is population structure, with unequal sampling from different subpopulations. Alternatively, positive selection may not operate as is commonly modeled. In particular, the rate of fixation of advantageous mutations may have increased in the recent past.

  3. Random forest ensemble classification based fuzzy logic

    Science.gov (United States)

    Ben Ayed, Abdelkarim; Benhammouda, Marwa; Ben Halima, Mohamed; Alimi, Adel M.

    2017-03-01

    In this paper, we treat the supervised data classification, while using the fuzzy random forests that combine the hardiness of the decision trees, the power of the random selection that increases the diversity of the trees in the forest as well as the flexibility of the fuzzy logic for noise. We will be interested in the construction of a forest of fuzzy decision trees. Our system is validated on nine standard classification benchmarks from UCI repository and have the specificity to control some data, to reduce the rate of mistakes and to put in evidence more of hardiness and more of interoperability.

  4. Blind Measurement Selection: A Random Matrix Theory Approach

    KAUST Repository

    Elkhalil, Khalil

    2016-12-14

    This paper considers the problem of selecting a set of $k$ measurements from $n$ available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain $m$ dimensional parameter vector. The exhaustive search inspecting each of the $n\\\\choose k$ possible choices would require a very high computational complexity and as such is not practical for large $n$ and $k$. Alternative methods with low complexity have recently been investigated but their main drawbacks are that 1) they require perfect knowledge of the measurement matrix and 2) they need to be applied at the pace of change of the measurement matrix. To overcome these issues, we consider the asymptotic regime in which $k$, $n$ and $m$ grow large at the same pace. Tools from random matrix theory are then used to approximate in closed-form the most important error measures that are commonly used. The asymptotic approximations are then leveraged to select properly $k$ measurements exhibiting low values for the asymptotic error measures. Two heuristic algorithms are proposed: the first one merely consists in applying the convex optimization artifice to the asymptotic error measure. The second algorithm is a low-complexity greedy algorithm that attempts to look for a sufficiently good solution for the original minimization problem. The greedy algorithm can be applied to both the exact and the asymptotic error measures and can be thus implemented in blind and channel-aware fashions. We present two potential applications where the proposed algorithms can be used, namely antenna selection for uplink transmissions in large scale multi-user systems and sensor selection for wireless sensor networks. Numerical results are also presented and sustain the efficiency of the proposed blind methods in reaching the performances of channel-aware algorithms.

  5. Pediatric selective mutism therapy: a randomized controlled trial.

    Science.gov (United States)

    Esposito, Maria; Gimigliano, Francesca; Barillari, Maria R; Precenzano, Francesco; Ruberto, Maria; Sepe, Joseph; Barillari, Umberto; Gimigliano, Raffaele; Militerni, Roberto; Messina, Giovanni; Carotenuto, Marco

    2017-10-01

    Selective mutism (SM) is a rare disease in children coded by DSM-5 as an anxiety disorder. Despite the disabling nature of the disease, there is still no specific treatment. The aims of this study were to verify the efficacy of six-month standard psychomotor treatment and the positive changes in lifestyle, in a population of children affected by SM. Randomized controlled trial registered in the European Clinical Trials Registry (EuDract 2015-001161-36). University third level Centre (Child and Adolescent Neuropsychiatry Clinic). Study population was composed by 67 children in group A (psychomotricity treatment) (35 M, mean age 7.84±1.15) and 71 children in group B (behavioral and educational counseling) (37 M, mean age 7.75±1.36). Psychomotor treatment was administered by trained child therapists in residential settings three times per week. Each child was treated for the whole period by the same therapist and all the therapists shared the same protocol. The standard psychomotor session length is of 45 minutes. At T0 and after 6 months (T1) of treatments, patients underwent a behavioral and SM severity assessment. To verify the effects of the psychomotor management, the Child Behavior Checklist questionnaire (CBCL) and Selective Mutism Questionnaire (SMQ) were administered to the parents. After 6 months of psychomotor treatment SM children showed a significant reduction among CBCL scores such as in social relations, anxious/depressed, social problems and total problems (Ppsychomotricity a safe and efficacy therapy for pediatric selective mutism.

  6. Optimizing Event Selection with the Random Grid Search

    Energy Technology Data Exchange (ETDEWEB)

    Bhat, Pushpalatha C. [Fermilab; Prosper, Harrison B. [Florida State U.; Sekmen, Sezen [Kyungpook Natl. U.; Stewart, Chip [Broad Inst., Cambridge

    2017-06-29

    The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.

  7. Towards a Faster Randomized Parcellation Based Inference

    OpenAIRE

    Hoyos-Idrobo, Andrés; Varoquaux, Gaël; Thirion, Bertrand

    2017-01-01

    International audience; In neuroimaging, multi-subject statistical analysis is an essential step, as it makes it possible to draw conclusions for the population under study. However, the lack of power in neuroimaging studies combined with the lack of stability and sensitivity of voxel-based methods may lead to non-reproducible results. A method designed to tackle this problem is Randomized Parcellation-Based Inference (RPBI), which has shown good empirical performance. Nevertheless, the use o...

  8. SDE based regression for random PDEs

    KAUST Repository

    Bayer, Christian

    2016-01-06

    A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.

  9. Event selection with a Random Forest in IceCube

    Energy Technology Data Exchange (ETDEWEB)

    Ruhe, Tim [TU, Dortmund (Germany); Collaboration: IceCube-Collaboration

    2011-07-01

    The Random Forest method is a multivariate algorithm that can be used for classification and regression respectively. The Random Forest implemented in the RapidMiner learning environment has been used for training and validation on data and Monte Carlo simulations of the IceCube neutrino telescope. Latest results are presented.

  10. Performance of variable selection methods using stability-based selection.

    Science.gov (United States)

    Lu, Danny; Weljie, Aalim; de Leon, Alexander R; McConnell, Yarrow; Bathe, Oliver F; Kopciuk, Karen

    2017-04-04

    Variable selection is frequently carried out during the analysis of many types of high-dimensional data, including those in metabolomics. This study compared the predictive performance of four variable selection methods using stability-based selection, a new secondary selection method that is implemented in the R package BioMark. Two of these methods were evaluated using the more well-known false discovery rate (FDR) as well. Simulation studies varied factors relevant to biological data studies, with results based on the median values of 200 partial area under the receiver operating characteristic curve. There was no single top performing method across all factor settings, but the student t test based on stability selection or with FDR adjustment and the variable importance in projection (VIP) scores from partial least squares regression models obtained using a stability-based approach tended to perform well in most settings. Similar results were found with a real spiked-in metabolomics dataset. Group sample size, group effect size, number of significant variables and correlation structure were the most important factors whereas the percentage of significant variables was the least important. Researchers can improve prediction scores for their study data by choosing VIP scores based on stability variable selection over the other approaches when the number of variables is small to modest and by increasing the number of samples even moderately. When the number of variables is high and there is block correlation amongst the significant variables (i.e., true biomarkers), the FDR-adjusted student t test performed best. The R package BioMark is an easy-to-use open-source program for variable selection that had excellent performance characteristics for the purposes of this study.

  11. Feature Selection Based on Confidence Machine

    OpenAIRE

    Liu, Chang; Xu, Yi

    2014-01-01

    In machine learning and pattern recognition, feature selection has been a hot topic in the literature. Unsupervised feature selection is challenging due to the loss of labels which would supply the related information.How to define an appropriate metric is the key for feature selection. We propose a filter method for unsupervised feature selection which is based on the Confidence Machine. Confidence Machine offers an estimation of confidence on a feature'reliability. In this paper, we provide...

  12. Study on MAX-MIN Ant System with Random Selection in Quadratic Assignment Problem

    Science.gov (United States)

    Iimura, Ichiro; Yoshida, Kenji; Ishibashi, Ken; Nakayama, Shigeru

    Ant Colony Optimization (ACO), which is a type of swarm intelligence inspired by ants' foraging behavior, has been studied extensively and its effectiveness has been shown by many researchers. The previous studies have reported that MAX-MIN Ant System (MMAS) is one of effective ACO algorithms. The MMAS maintains the balance of intensification and diversification concerning pheromone by limiting the quantity of pheromone to the range of minimum and maximum values. In this paper, we propose MAX-MIN Ant System with Random Selection (MMASRS) for improving the search performance even further. The MMASRS is a new ACO algorithm that is MMAS into which random selection was newly introduced. The random selection is one of the edgechoosing methods by agents (ants). In our experimental evaluation using ten quadratic assignment problems, we have proved that the proposed MMASRS with the random selection is superior to the conventional MMAS without the random selection in the viewpoint of the search performance.

  13. Feature selection based classifier combination approach for ...

    Indian Academy of Sciences (India)

    Conditional mutual information based feature selection when driving the ensemble of classifier produces improved recognition results for most of the benchmarking datasets. The improve- ment is also observed with maximum relevance minimum redundancy based feature selection when used in combination with ensemble ...

  14. Polystyrene Based Silver Selective Electrodes

    Directory of Open Access Journals (Sweden)

    Shiva Agarwal

    2002-06-01

    Full Text Available Silver(I selective sensors have been fabricated from polystyrene matrix membranes containing macrocycle, Me6(14 diene.2HClO4 as ionophore. Best performance was exhibited by the membrane having a composition macrocycle : Polystyrene in the ratio 15:1. This membrane worked well over a wide concentration range 5.0×10-6–1.0×10-1M of Ag+ with a near-Nernstian slope of 53.0 ± 1.0 mV per decade of Ag+ activity. The response time of the sensor is <15 s and the membrane can be used over a period of four months with good reproducibility. The proposed electrode works well in a wide pH range 2.5-9.0 and demonstrates good discriminating power over a number of mono-, di-, and trivalent cations. The sensor has also been used as an indicator electrode in the potentiometric titration of silver(II ions against NaCl solution. The sensor can also be used in non-aqueous medium with no significant change in the value of slope or working concentration range for the estimation of Ag+ in solution having up to 25% (v/v nonaqueous fraction.

  15. Classifier ensemble selection based on affinity propagation clustering.

    Science.gov (United States)

    Meng, Jun; Hao, Han; Luan, Yushi

    2016-04-01

    A small number of features are significantly correlated with classification in high-dimensional data. An ensemble feature selection method based on cluster grouping is proposed in this paper. Classification-related features are chosen using a ranking aggregation technique. These features are divided into unrelated groups by an affinity propagation clustering algorithm with a bicor correlation coefficient. Some diversity and distinguishing feature subsets are constructed by randomly selecting a feature from each group and are used to train base classifiers. Finally, some base classifiers that have better classification performance are selected using a kappa coefficient and integrated using a majority voting strategy. The experimental results based on five gene expression datasets show that the proposed method has low classification error rates, stable classification performance and strong scalability in terms of sensitivity, specificity, accuracy and G-Mean criteria. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Assessment of dual selection in grid based selectivity systems

    DEFF Research Database (Denmark)

    Sistiaga, Manu; Herrmann, Bent; Grimaldo, Eduardo

    2010-01-01

    Herein we propose a method to assess dual selection in grid based selectivity systems. This method takes into account the parameter “grid contact likelihood” (Cgrid), which can be interpreted as the proportion of fish that actually makes an attempt to escape through the grid. In a case study...... of the Barents Sea cod and haddock trawl fishery, we demonstrate that our model describes the experimental data better than the models previously used to fit similar data. For both cod and haddock, Cgrid was significantly smaller than 1.0, which demonstrated the relevance of the proposed model. Cgrid was higher......-compartment setup to avoid imprecise estimates of Cgrid, L50grid, SRgrid, L50codend, and SRcodend. In general, only the combined selectivity of the grid and the codend could be estimated with acceptable precision using a standard two-compartment sampling approach....

  17. The prevalence of symptoms associated with pulmonary tuberculosis in randomly selected children from a high burden community

    OpenAIRE

    Marais, B.; Obihara, C; Gie, R.; Schaaf, H; Hesseling, A.; Lombard, C.; Enarson, D; Bateman, E; Beyers, N

    2005-01-01

    Background: Diagnosis of childhood tuberculosis is problematic and symptom based diagnostic approaches are often promoted in high burden settings. This study aimed (i) to document the prevalence of symptoms associated with tuberculosis among randomly selected children living in a high burden community, and (ii) to compare the prevalence of these symptoms in children without tuberculosis to those in children with newly diagnosed tuberculosis.

  18. A Secure LFSR Based Random Measurement Matrix for Compressive Sensing

    Science.gov (United States)

    George, Sudhish N.; Pattathil, Deepthi P.

    2014-11-01

    In this paper, a novel approach for generating the secure measurement matrix for compressive sensing (CS) based on linear feedback shift register (LFSR) is presented. The basic idea is to select the different states of LFSR as the random entries of the measurement matrix and normalize these values to get independent and identically distributed (i.i.d.) random variables with zero mean and variance , where N is the number of input samples. The initial seed for the LFSR system act as the key to the user to provide security. Since the measurement matrix is generated from the LFSR system, and memory overload to store the measurement matrix is avoided in the proposed system. Moreover, the proposed system can provide security maintaining the robustness to noise of the CS system. The proposed system is validated through different block-based CS techniques of images. To enhance security, the different blocks of images are measured with different measurement matrices so that the proposed encryption system can withstand known plaintext attack. A modulo division circuit is used to reseed the LFSR system to generate multiple random measurement matrices, whereby after each fundamental period of LFSR, the feedback polynomial of the modulo circuit is modified in terms of a chaotic value. The proposed secure robust CS paradigm for images is subjected to several forms of attacks and is proven to be resistant against the same. From experimental analysis, it is proven that the proposed system provides better performance than its counterparts.

  19. In vivo selection of randomly mutated retroviral genomes

    NARCIS (Netherlands)

    Berkhout, B.; Klaver, B.

    1993-01-01

    Darwinian evolution, that is the outgrowth of the fittest variants in a population, usually applies to living organisms over long periods of time. Recently, in vitro selection/amplification techniques have been developed that allow for the rapid evolution of functionally active nucleic acids from a

  20. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

    Science.gov (United States)

    Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...

  1. The frequency of drugs in randomly selected drivers in Denmark

    DEFF Research Database (Denmark)

    Simonsen, Kirsten Wiese; Steentoft, Anni; Hels, Tove

    Introduction Driving under the influence of alcohol and drugs is a global problem. In Denmark as well as in other countries there is an increasing focus on impaired driving. Little is known about the occurrence of psychoactive drugs in the general traffic. Therefore the European commission...... initiated the DRUID project. This roadside study is the Danish part of the EU-project DRUID (Driving under the Influence of Drugs, Alcohol, and Medicines) and included three representative regions in Denmark. Methods Oral fluid samples (n = 3002) were collected randomly from drivers using a sampling scheme...... stratified by time, season, and road type. The oral fluid samples were screened for 29 illegal and legal psychoactive substances and metabolites as well as ethanol. Results Fourteen (0.5%) drivers were positive for ethanol (alone or in combination with drugs) at concentrations above 0.53 g/l, which...

  2. Sample Selection in Randomized Experiments: A New Method Using Propensity Score Stratified Sampling

    Science.gov (United States)

    Tipton, Elizabeth; Hedges, Larry; Vaden-Kiernan, Michael; Borman, Geoffrey; Sullivan, Kate; Caverly, Sarah

    2014-01-01

    Randomized experiments are often seen as the "gold standard" for causal research. Despite the fact that experiments use random assignment to treatment conditions, units are seldom selected into the experiment using probability sampling. Very little research on experimental design has focused on how to make generalizations to well-defined…

  3. Pseudo cluster randomization dealt with selection bias and contamination in clinical trials

    NARCIS (Netherlands)

    Teerenstra, S.; Melis, R.J.F.; Peer, P.G.M.; Borm, G.F.

    2006-01-01

    BACKGROUND AND OBJECTIVES: When contamination is present, randomization on a patient level leads to dilution of the treatment effect. The usual solution is to randomize on a cluster level, but at the cost of efficiency and more importantly, this may introduce selection bias. Furthermore, it may slow

  4. Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa.

    Science.gov (United States)

    Kapwata, Thandi; Gebreslasie, Michael T

    2016-11-16

    Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF) statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI)], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

  5. Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

    Directory of Open Access Journals (Sweden)

    Thandi Kapwata

    2016-11-01

    Full Text Available Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

  6. Active classifier selection for RGB-D object categorization using a Markov random field ensemble method

    Science.gov (United States)

    Durner, Maximilian; Márton, Zoltán.; Hillenbrand, Ulrich; Ali, Haider; Kleinsteuber, Martin

    2017-03-01

    In this work, a new ensemble method for the task of category recognition in different environments is presented. The focus is on service robotic perception in an open environment, where the robot's task is to recognize previously unseen objects of predefined categories, based on training on a public dataset. We propose an ensemble learning approach to be able to flexibly combine complementary sources of information (different state-of-the-art descriptors computed on color and depth images), based on a Markov Random Field (MRF). By exploiting its specific characteristics, the MRF ensemble method can also be executed as a Dynamic Classifier Selection (DCS) system. In the experiments, the committee- and topology-dependent performance boost of our ensemble is shown. Despite reduced computational costs and using less information, our strategy performs on the same level as common ensemble approaches. Finally, the impact of large differences between datasets is analyzed.

  7. Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier.

    Science.gov (United States)

    Paul, Desbordes; Su, Ruan; Romain, Modzelewski; Sébastien, Vauclin; Pierre, Vera; Isabelle, Gardin

    2017-09-01

    The outcome prediction of patients can greatly help to personalize cancer treatment. A large amount of quantitative features (clinical exams, imaging, …) are potentially useful to assess the patient outcome. The challenge is to choose the most predictive subset of features. In this paper, we propose a new feature selection strategy called GARF (genetic algorithm based on random forest) extracted from positron emission tomography (PET) images and clinical data. The most relevant features, predictive of the therapeutic response or which are prognoses of the patient survival 3 years after the end of treatment, were selected using GARF on a cohort of 65 patients with a local advanced oesophageal cancer eligible for chemo-radiation therapy. The most relevant predictive results were obtained with a subset of 9 features leading to a random forest misclassification rate of 18±4% and an areas under the of receiver operating characteristic (ROC) curves (AUC) of 0.823±0.032. The most relevant prognostic results were obtained with 8 features leading to an error rate of 20±7% and an AUC of 0.750±0.108. Both predictive and prognostic results show better performances using GARF than using 4 other studied methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Acceptance sampling using judgmental and randomly selected samples

    Energy Technology Data Exchange (ETDEWEB)

    Sego, Landon H.; Shulman, Stanley A.; Anderson, Kevin K.; Wilson, John E.; Pulsipher, Brent A.; Sieber, W. Karl

    2010-09-01

    We present a Bayesian model for acceptance sampling where the population consists of two groups, each with different levels of risk of containing unacceptable items. Expert opinion, or judgment, may be required to distinguish between the high and low-risk groups. Hence, high-risk items are likely to be identifed (and sampled) using expert judgment, while the remaining low-risk items are sampled randomly. We focus on the situation where all observed samples must be acceptable. Consequently, the objective of the statistical inference is to quantify the probability that a large percentage of the unsampled items in the population are also acceptable. We demonstrate that traditional (frequentist) acceptance sampling and simpler Bayesian formulations of the problem are essentially special cases of the proposed model. We explore the properties of the model in detail, and discuss the conditions necessary to ensure that required samples sizes are non-decreasing function of the population size. The method is applicable to a variety of acceptance sampling problems, and, in particular, to environmental sampling where the objective is to demonstrate the safety of reoccupying a remediated facility that has been contaminated with a lethal agent.

  9. An explicit semantic relatedness measure based on random walk

    Directory of Open Access Journals (Sweden)

    HU Sihui

    2016-10-01

    Full Text Available The semantic relatedness calculation of open domain knowledge network is a significant issue.In this paper,pheromone strategy is drawn from the thought of ant colony algorithm and is integrated into the random walk which is taken as the basic framework of calculating the semantic relatedness degree.The pheromone distribution is taken as a criterion of determining the tightness degree of semantic relatedness.A method of calculating semantic relatedness degree based on random walk is proposed and the exploration process of calculating the semantic relatedness degree is presented in a dominant way.The method mainly contains Path Select Model(PSM and Semantic Relatedness Computing Model(SRCM.PSM is used to simulate the path selection of ants and pheromone release.SRCM is used to calculate the semantic relatedness by utilizing the information returned by ants.The result indicates that the method could complete semantic relatedness calculation in linear complexity and extend the feasible strategy of semantic relatedness calculation.

  10. Supplier selection an MCDA-based approach

    CERN Document Server

    Mukherjee, Krishnendu

    2017-01-01

    The purpose of this book is to present a comprehensive review of the latest research and development trends at the international level for modeling and optimization of the supplier selection process for different industrial sectors. It is targeted to serve two audiences: the MBA and PhD student interested in procurement, and the practitioner who wishes to gain a deeper understanding of procurement analysis with multi-criteria based decision tools to avoid upstream risks to get better supply chain visibility. The book is expected to serve as a ready reference for supplier selection criteria and various multi-criteria based supplier’s evaluation methods for forward, reverse and mass customized supply chain. This book encompasses several criteria, methods for supplier selection in a systematic way based on extensive literature review from 1998 to 2012. It provides several case studies and some useful links which can serve as a starting point for interested researchers. In the appendix several computer code wri...

  11. Randomized clinical trial: group counseling based on tinnitus retraining therapy

    National Research Council Canada - National Science Library

    Henry, James A; Loovis, Carl; Montero, Melissa; Kaelin, Christine; Anselmi, Kathryn-Anne; Coombs, Rebecca; Hensley, June; James, Kenneth E

    2007-01-01

    .... We conducted a randomized clinical trial to test the hypothesis that group educational counseling based on TRT principles would effectively treat veterans who have clinically significant tinnitus...

  12. Norm based Threshold Selection for Fault Detectors

    DEFF Research Database (Denmark)

    Rank, Mike Lind; Niemann, Henrik

    1998-01-01

    The design of fault detectors for fault detection and isolation (FDI) in dynamic systems is considered from a norm based point of view. An analysis of norm based threshold selection is given based on different formulations of FDI problems. Both the nominal FDI problem as well as the uncertain FDI...... problem are considered. Based on this analysis, a performance index based on norms of the involved transfer functions is given. The performance index allows us also to optimize the structure of the fault detection filter directly...

  13. Personnel Selection Based on Fuzzy Methods

    Directory of Open Access Journals (Sweden)

    Lourdes Cañós

    2011-03-01

    Full Text Available The decisions of managers regarding the selection of staff strongly determine the success of the company. A correct choice of employees is a source of competitive advantage. We propose a fuzzy method for staff selection, based on competence management and the comparison with the valuation that the company considers the best in each competence (ideal candidate. Our method is based on the Hamming distance and a Matching Level Index. The algorithms, implemented in the software StaffDesigner, allow us to rank the candidates, even when the competences of the ideal candidate have been evaluated only in part. Our approach is applied in a numerical example.

  14. Access Network Selection Based on Fuzzy Logic and Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Mohammed Alkhawlani

    2008-01-01

    Full Text Available In the next generation of heterogeneous wireless networks (HWNs, a large number of different radio access technologies (RATs will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS provisioning. This paper proposes a general scheme to solve the access network selection (ANS problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL and genetic algorithms (GAs have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.

  15. Case Based Heuristic Selection for Timetabling Problems

    OpenAIRE

    Burke, Edmund; Petrovic, Sanja; Qu, Rong

    2006-01-01

    This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than the current state of the art. Heuristics that worked well in previous similar situations are memorized in a case base and are retrieved for solving the problem in hand. Knowledge discovery techniques are employed in two distinct scenarios. Firstl...

  16. Automatic learning-based beam angle selection for thoracic IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Amit, Guy; Marshall, Andrea [Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9 (Canada); Purdie, Thomas G., E-mail: tom.purdie@rmp.uhn.ca; Jaffray, David A. [Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9 (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2 (Canada); Techna Institute, University Health Network, Toronto, Ontario M5G 1P5 (Canada); Levinshtein, Alex [Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4 (Canada); Hope, Andrew J.; Lindsay, Patricia [Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada and Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2 (Canada); Pekar, Vladimir [Philips Healthcare, Markham, Ontario L6C 2S3 (Canada)

    2015-04-15

    Purpose: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose–volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner’s clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload. Methods: The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth. Results: The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8° ± 10°, correlation 0.75 ± 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume

  17. Classification of hyperspectral images based on conditional random fields

    Science.gov (United States)

    Hu, Yang; Saber, Eli; Monteiro, Sildomar T.; Cahill, Nathan D.; Messinger, David W.

    2015-02-01

    A significant increase in the availability of high resolution hyperspectral images has led to the need for developing pertinent techniques in image analysis, such as classification. Hyperspectral images that are correlated spatially and spectrally provide ample information across the bands to benefit this purpose. Conditional Random Fields (CRFs) are discriminative models that carry several advantages over conventional techniques: no requirement of the independence assumption for observations, flexibility in defining local and pairwise potentials, and an independence between the modules of feature selection and parameter leaning. In this paper we present a framework for classifying remotely sensed imagery based on CRFs. We apply a Support Vector Machine (SVM) classifier to raw remotely sensed imagery data in order to generate more meaningful feature potentials to the CRFs model. This approach produces promising results when tested with publicly available AVIRIS Indian Pine imagery.

  18. Additional benefit of using a risk-based selection for prostate biopsy: an analysis of biopsy complications in the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer.

    Science.gov (United States)

    Chiu, Peter K; Alberts, Arnout R; Venderbos, Lionne D F; Bangma, Chris H; Roobol, Monique J

    2017-09-01

    To investigate biopsy complications and hospital admissions that could be reduced by the use of European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculators. All biopsies performed in the Rotterdam section of the ERSPC between 1993 and 2015 were included. Biopsy complications and hospital admission data were prospectively recorded in questionnaires that were completed 2 weeks after biopsy. The ERSPC risk calculators 3 (RC3) and 4 (RC4) were applied to men attending the first and subsequent rounds of screening, respectively. Applying the predefined RC3/4 probability thresholds for prostate cancer (PCa) risk of ≥12.5% and high-grade PCa risk ≥3%, we assessed the number of complications, admissions and costs that could be reduced by avoiding biopsies in men below these thresholds. A total of 10 747 biopsies with complete questionnaires were included. For these biopsies a complication rate of 67.9% (7294/10 747), a post-biopsy fever rate of 3.9% (424/10747) and a hospital admission rate of 0.9% (92/10747) were recorded. The fever rate was found to be static over the years, but the hospital admission rate tripled from 0.6% (1993-1996) to 2.1% (2009-2015). Among 7704 biopsies which fit the criteria for RC3 or RC4, 35.8% of biopsies (2757/7704), 37.4% of complications (1972/5268), 39.4% of fever events (128/325) and 42.3% of admissions (30/71) could have been avoided by using one of the risk calculators. More complications could have been avoided if RC4 had been used and for more recent biopsies (2009-2015). Our findings show that 35.9% of the total cost of biopsies and complication treatment could have been avoided. A significant proportion of biopsy complications, hospital admissions and costs could be reduced if biopsy decisions were based on ERSPC risk calculators instead of PSA only. This effect was most prominent in more recent biopsies and in men with repeated biopsies or screening. © 2017 The Authors BJU International © 2017 BJU

  19. Orientation selectivity based structure for texture classification

    Science.gov (United States)

    Wu, Jinjian; Lin, Weisi; Shi, Guangming; Zhang, Yazhong; Lu, Liu

    2014-10-01

    Local structure, e.g., local binary pattern (LBP), is widely used in texture classification. However, LBP is too sensitive to disturbance. In this paper, we introduce a novel structure for texture classification. Researches on cognitive neuroscience indicate that the primary visual cortex presents remarkable orientation selectivity for visual information extraction. Inspired by this, we investigate the orientation similarities among neighbor pixels, and propose an orientation selectivity based pattern for local structure description. Experimental results on texture classification demonstrate that the proposed structure descriptor is quite robust to disturbance.

  20. SNP selection and classification of genome-wide SNP data using stratified sampling random forests.

    Science.gov (United States)

    Wu, Qingyao; Ye, Yunming; Liu, Yang; Ng, Michael K

    2012-09-01

    For high dimensional genome-wide association (GWA) case-control data of complex disease, there are usually a large portion of single-nucleotide polymorphisms (SNPs) that are irrelevant with the disease. A simple random sampling method in random forest using default mtry parameter to choose feature subspace, will select too many subspaces without informative SNPs. Exhaustive searching an optimal mtry is often required in order to include useful and relevant SNPs and get rid of vast of non-informative SNPs. However, it is too time-consuming and not favorable in GWA for high-dimensional data. The main aim of this paper is to propose a stratified sampling method for feature subspace selection to generate decision trees in a random forest for GWA high-dimensional data. Our idea is to design an equal-width discretization scheme for informativeness to divide SNPs into multiple groups. In feature subspace selection, we randomly select the same number of SNPs from each group and combine them to form a subspace to generate a decision tree. The advantage of this stratified sampling procedure can make sure each subspace contains enough useful SNPs, but can avoid a very high computational cost of exhaustive search of an optimal mtry, and maintain the randomness of a random forest. We employ two genome-wide SNP data sets (Parkinson case-control data comprised of 408 803 SNPs and Alzheimer case-control data comprised of 380 157 SNPs) to demonstrate that the proposed stratified sampling method is effective, and it can generate better random forest with higher accuracy and lower error bound than those by Breiman's random forest generation method. For Parkinson data, we also show some interesting genes identified by the method, which may be associated with neurological disorders for further biological investigations.

  1. Clinical outcome of intracytoplasmic injection of spermatozoa morphologically selected under high magnification: a prospective randomized study.

    Science.gov (United States)

    Balaban, Basak; Yakin, Kayhan; Alatas, Cengiz; Oktem, Ozgur; Isiklar, Aycan; Urman, Bulent

    2011-05-01

    Recent evidence shows that the selection of spermatozoa based on the analysis of morphology under high magnification (×6000) may have a positive impact on embryo development in cases with severe male factor infertility and/or previous implantation failures. The objective of this prospective randomized study was to compare the clinical outcome of 87 intracytoplasmic morphologically selected sperm injection (IMSI) cycles with 81 conventional intracytoplasmic sperm injection (ICSI) cycles in an unselected infertile population. IMSI did not provide a significant improvement in the clinical outcome compared with ICSI although there were trends for higher implantation (28.9% versus 19.5%), clinical pregnancy (54.0% versus 44.4%) and live birth rates (43.7% versus 38.3%) in the IMSI group. However, severe male factor patients benefited from the IMSI procedure as shown by significantly higher implantation rates compared with their counterparts in the ICSI group (29.6% versus 15.2%, P=0.01). These results suggest that IMSI may improve IVF success rates in a selected group of patients with male factor infertility. New technological developments enable the real time examination of motile spermatozoa with an inverted light microscope equipped with high-power differential interference contrast optics, enhanced by digital imaging. High magnification (over ×6000) provides the identification of spermatozoa with a normal nucleus and nuclear content. Intracytoplasmic injection of spermatozoa selected according to fine nuclear morphology under high magnification may improve the clinical outcome in cases with severe male factor infertility. Copyright © 2010 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

  2. Radiographic methods used before removal of mandibular third molars among randomly selected general dental clinics.

    Science.gov (United States)

    Matzen, Louise H; Petersen, Lars B; Wenzel, Ann

    2016-01-01

    To assess radiographic methods and diagnostically sufficient images used before removal of mandibular third molars among randomly selected general dental clinics. Furthermore, to assess factors predisposing for an additional radiographic examination. 2 observers visited 18 randomly selected clinics in Denmark and studied patient files, including radiographs of patients who had their mandibular third molar(s) removed. The radiographic unit and type of receptor were registered. A diagnostically sufficient image was defined as the whole tooth and mandibular canal were displayed in the radiograph (yes/no). Overprojection between the tooth and mandibular canal (yes/no) and patient-reported inferior alveolar nerve sensory disturbances (yes/no) were recorded. Regression analyses tested if overprojection between the third molar and the mandibular canal and an insufficient intraoral image predisposed for additional radiographic examination(s). 1500 mandibular third molars had been removed; 1090 had intraoral, 468 had panoramic and 67 had CBCT examination. 1000 teeth were removed after an intraoral examination alone, 433 after panoramic examination and 67 after CBCT examination. 90 teeth had an additional examination after intraoral. Overprojection between the tooth and mandibular canal was a significant factor (p < 0.001, odds ratio = 3.56) for an additional examination. 63.7% of the intraoral images were sufficient and 36.3% were insufficient, with no significant difference between images performed with phosphor plates and solid-state sensors (p = 0.6). An insufficient image predisposed for an additional examination (p = 0.008, odds ratio = 1.8) but was only performed in 11% of the cases. Most mandibular third molars were removed based on an intraoral examination although 36.3% were insufficient.

  3. Delay line length selection in generating fast random numbers with a chaotic laser.

    Science.gov (United States)

    Zhang, Jianzhong; Wang, Yuncai; Xue, Lugang; Hou, Jiayin; Zhang, Beibei; Wang, Anbang; Zhang, Mingjiang

    2012-04-10

    The chaotic light signals generated by an external cavity semiconductor laser have been experimentally demonstrated to extract fast random numbers. However, the photon round-trip time in the external cavity can cause the occurrence of the periodicity in random sequences. To overcome it, the exclusive-or operation on corresponding random bits in samples of the chaotic signal and its time-delay signal from a chaotic laser is required. In this scheme, the proper selection of delay length is a key issue. By doing a large number of experiments and theoretically analyzing the interplay between the Runs test and the threshold value of the autocorrelation function, we find when the corresponding delay time of autocorrelation trace with the correlation coefficient of less than 0.007 is considered as the delay time between the chaotic signal and its time-delay signal, streams of random numbers can be generated with verified randomness.

  4. Generation of Aptamers from A Primer-Free Randomized ssDNA Library Using Magnetic-Assisted Rapid Aptamer Selection

    Science.gov (United States)

    Tsao, Shih-Ming; Lai, Ji-Ching; Horng, Horng-Er; Liu, Tu-Chen; Hong, Chin-Yih

    2017-04-01

    Aptamers are oligonucleotides that can bind to specific target molecules. Most aptamers are generated using random libraries in the standard systematic evolution of ligands by exponential enrichment (SELEX). Each random library contains oligonucleotides with a randomized central region and two fixed primer regions at both ends. The fixed primer regions are necessary for amplifying target-bound sequences by PCR. However, these extra-sequences may cause non-specific bindings, which potentially interfere with good binding for random sequences. The Magnetic-Assisted Rapid Aptamer Selection (MARAS) is a newly developed protocol for generating single-strand DNA aptamers. No repeat selection cycle is required in the protocol. This study proposes and demonstrates a method to isolate aptamers for C-reactive proteins (CRP) from a randomized ssDNA library containing no fixed sequences at 5‧ and 3‧ termini using the MARAS platform. Furthermore, the isolated primer-free aptamer was sequenced and binding affinity for CRP was analyzed. The specificity of the obtained aptamer was validated using blind serum samples. The result was consistent with monoclonal antibody-based nephelometry analysis, which indicated that a primer-free aptamer has high specificity toward targets. MARAS is a feasible platform for efficiently generating primer-free aptamers for clinical diagnoses.

  5. Ion selective electrodes based on chalcogenide glasses

    OpenAIRE

    Conde Garrido, Juan Manuel; Ureña, Maria Andrea; Arcondo, Bibiana Graciela

    2017-01-01

    The properties of chalcogenide glasses as sensitive membranes in ion selective electrodes (ISEs) have been investigated. It is shown that ISEs based on the AgGeSe system show sensitivity to the presence of Ag+ and Cu2+ ions in aqueous solutions and in both cases they exhibit super-nernstian responses. The analytical properties such as reproducibility, linear range, sensitivity and detection limit were studied. The response of the electrodes is apparently conditioned by the amount of Ag in the...

  6. Using ArcMap, Google Earth, and Global Positioning Systems to select and locate random households in rural Haiti

    Directory of Open Access Journals (Sweden)

    Wampler Peter J

    2013-01-01

    Full Text Available Abstract Background A remote sensing technique was developed which combines a Geographic Information System (GIS; Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost-effective manner. Methods The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. Results A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. Conclusions The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only

  7. Using ArcMap, Google Earth, and Global Positioning Systems to select and locate random households in rural Haiti.

    Science.gov (United States)

    Wampler, Peter J; Rediske, Richard R; Molla, Azizur R

    2013-01-18

    A remote sensing technique was developed which combines a Geographic Information System (GIS); Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost-effective manner. The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only rarely was local knowledge required to identify and locate households. This

  8. Two-year Randomized Clinical Trial Of Self-etching Adhesives And Selective Enamel Etching

    OpenAIRE

    Pena, MR; Rodrigues CE; JA; Ely; Giannini, C.; Reis, M; AF

    2016-01-01

    Objective: The aim of this randomized, controlled prospective clinical trial was to evaluate the clinical effectiveness of restoring noncarious cervical lesions with two self-etching adhesive systems applied with or without selective enamel etching. Methods: A one-step self-etching adhesive (Xeno V+) and a two-step self-etching system (Clearfil SE Bond) were used. The effectiveness of phosphoric acid selective etching of enamel margins was also evaluated. Fifty-six cavities were restored with...

  9. Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex.

    Science.gov (United States)

    Lindsay, Grace W; Rigotti, Mattia; Warden, Melissa R; Miller, Earl K; Fusi, Stefano

    2017-11-08

    Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear "mixed" selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli-and in particular, to combinations of stimuli ("mixed

  10. Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Heide, Janus; Zhang, Qi; Fitzek, Frank

    2013-01-01

    This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...

  11. Characterization of Pharmacologic and Pharmacokinetic Properties of CCX168, a Potent and Selective Orally Administered Complement 5a Receptor Inhibitor, Based on Preclinical Evaluation and Randomized Phase 1 Clinical Study.

    Science.gov (United States)

    Bekker, Pirow; Dairaghi, Daniel; Seitz, Lisa; Leleti, Manmohan; Wang, Yu; Ertl, Linda; Baumgart, Trageen; Shugarts, Sarah; Lohr, Lisa; Dang, Ton; Miao, Shichang; Zeng, Yibin; Fan, Pingchen; Zhang, Penglie; Johnson, Daniel; Powers, Jay; Jaen, Juan; Charo, Israel; Schall, Thomas J

    2016-01-01

    The complement 5a receptor has been an attractive therapeutic target for many autoimmune and inflammatory disorders. However, development of a selective and potent C5aR antagonist has been challenging. Here we describe the characterization of CCX168 (avacopan), an orally administered selective and potent C5aR inhibitor. CCX168 blocked the C5a binding, C5a-mediated migration, calcium mobilization, and CD11b upregulation in U937 cells as well as in freshly isolated human neutrophils. CCX168 retains high potency when present in human blood. A transgenic human C5aR knock-in mouse model allowed comparison of the in vitro and in vivo efficacy of the molecule. CCX168 effectively blocked migration in in vitro and ex vivo chemotaxis assays, and it blocked the C5a-mediated neutrophil vascular endothelial margination. CCX168 was effective in migration and neutrophil margination assays in cynomolgus monkeys. This thorough in vitro and preclinical characterization enabled progression of CCX168 into the clinic and testing of its safety, tolerability, pharmacokinetic, and pharmacodynamic profiles in a Phase 1 clinical trial in 48 healthy volunteers. CCX168 was shown to be well tolerated across a broad dose range (1 to 100 mg) and it showed dose-dependent pharmacokinetics. An oral dose of 30 mg CCX168 given twice daily blocked the C5a-induced upregulation of CD11b in circulating neutrophils by 94% or greater throughout the entire day, demonstrating essentially complete target coverage. This dose regimen is being tested in clinical trials in patients with anti-neutrophil cytoplasmic antibody-associated vasculitis. Trial Registration ISRCTN registry with trial ID ISRCTN13564773.

  12. Personal name in Igbo Culture: A dataset on randomly selected personal names and their statistical analysis.

    Science.gov (United States)

    Okagbue, Hilary I; Opanuga, Abiodun A; Adamu, Muminu O; Ugwoke, Paulinus O; Obasi, Emmanuela C M; Eze, Grace A

    2017-12-01

    This data article contains the statistical analysis of Igbo personal names and a sample of randomly selected of such names. This was presented as the following: 1). A simple random sampling of some Igbo personal names and their respective gender associated with each name. 2). The distribution of the vowels, consonants and letters of alphabets of the personal names. 3). The distribution of name length. 4). The distribution of initial and terminal letters of Igbo personal names. The significance of the data was discussed.

  13. Modeling Slotted Aloha as a Stochastic Game with Random Discrete Power Selection Algorithms

    Directory of Open Access Journals (Sweden)

    Rachid El-Azouzi

    2009-01-01

    Full Text Available We consider the uplink case of a cellular system where bufferless mobiles transmit over a common channel to a base station, using the slotted aloha medium access protocol. We study the performance of this system under several power differentiation schemes. Indeed, we consider a random set of selectable transmission powers and further study the impact of priorities given either to new arrival packets or to the backlogged ones. Later, we address a general capture model where a mobile transmits successfully a packet if its instantaneous SINR (signal to interferences plus noise ratio is lager than some fixed threshold. Under this capture model, we analyze both the cooperative team in which a common goal is jointly optimized as well as the noncooperative game problem where mobiles reach to optimize their own objectives. Furthermore, we derive the throughput and the expected delay and use them as the objectives to optimize and provide a stability analysis as alternative study. Exhaustive performance evaluations were carried out, we show that schemes with power differentiation improve significantly the individual as well as global performances, and could eliminate in some cases the bi-stable nature of slotted aloha.

  14. Ternary jitter-based true random number generator

    Science.gov (United States)

    Latypov, Rustam; Stolov, Evgeni

    2017-01-01

    In this paper a novel family of generators producing true uniform random numbers in ternary logic is presented. The generator consists of a number of identical ternary logic combinational units connected into a ring. All the units are provided to have a random delay time, and this time is supposed to be distributed in accordance with an exponential distribution. All delays are supposed to be independent events. The theory of the generator is based on Erlang equations. The generator can be used for test production in various systems. Features of multidimensional random vectors, produced by the generator, are discussed.

  15. Simulated Performance Evaluation of a Selective Tracker Through Random Scenario Generation

    DEFF Research Database (Denmark)

    Hussain, Dil Muhammad Akbar

    2006-01-01

      The paper presents a simulation study on the performance of a target tracker using selective track splitting filter algorithm through a random scenario implemented on a digital signal processor.  In a typical track splitting filter all the observation which fall inside a likelihood ellipse...... are used for update, however, in our proposed selective track splitting filter less number of observations are used for track update.  Much of the previous performance work [1] has been done on specific (deterministic) scenarios. One of the reasons for considering the specific scenarios, which were...

  16. The Reliability of Randomly Generated Math Curriculum-Based Measurements

    Science.gov (United States)

    Strait, Gerald G.; Smith, Bradley H.; Pender, Carolyn; Malone, Patrick S.; Roberts, Jarod; Hall, John D.

    2015-01-01

    "Curriculum-Based Measurement" (CBM) is a direct method of academic assessment used to screen and evaluate students' skills and monitor their responses to academic instruction and intervention. Interventioncentral.org offers a math worksheet generator at no cost that creates randomly generated "math curriculum-based measures"…

  17. State-based surveillance for selected hemoglobinopathies.

    Science.gov (United States)

    Hulihan, Mary M; Feuchtbaum, Lisa; Jordan, Lanetta; Kirby, Russell S; Snyder, Angela; Young, William; Greene, Yvonne; Telfair, Joseph; Wang, Ying; Cramer, William; Werner, Ellen M; Kenney, Kristy; Creary, Melissa; Grant, Althea M

    2015-02-01

    The lack of an ongoing surveillance system for hemoglobinopathies in the United States impedes the ability of public health organizations to identify individuals with these conditions, monitor their health-care utilization and clinical outcomes, and understand the effect these conditions have on the health-care system. This article describes the results of a pilot program that supported the development of the infrastructure and data collection methods for a state-based surveillance system for selected hemoglobinopathies. The system was designed to identify and gather information on all people living with a hemoglobinopathy diagnosis (sickle cell diseases or thalassemias) in the participating states during 2004-2008. Novel, three-level case definitions were developed, and multiple data sets were used to collect information. In total, 31,144 individuals who had a hemoglobinopathy diagnosis during the study period were identified in California; 39,633 in Florida; 20,815 in Georgia; 12,680 in Michigan; 34,853 in New York, and 8,696 in North Carolina. This approach provides a possible model for the development of state-based hemoglobinopathy surveillance systems.

  18. Selective electromembrane extraction based on isoelectric point

    DEFF Research Database (Denmark)

    Huang, Chuixiu; Gjelstad, Astrid; Pedersen-Bjergaard, Stig

    2015-01-01

    above the pI value (pH 5.13) was found to be optimal. Under the optimal conditions, 73% of AT2 AP (RSD 13%) and 48% of L-Enke (RSD 5%) were found in the solution after this two-step EME process, whereas the other three positively charged peptides were not detected. The observations above indicated......For the first time, selective isolation of a target peptide based on the isoelectric point (pI) was achieved using a two-step electromembrane extraction (EME) approach with a thin flat membrane-based EME device. In this approach, step #1 was an extraction process, where both the target peptide...... angiotensin II antipeptide (AT2 AP, pI=5.13) and the matrix peptides (pI>5.13) angiotensin II (AT2), neurotensin (NT), angiotensin I (AT1) and leu-enkephalin (L-Enke) were all extracted as net positive species from the sample (pH 3.50), through a supported liquid membrane (SLM) of 1-nonanol diluted with 2...

  19. MIS-based sensors with hydrogen selectivity

    Science.gov (United States)

    Li,; Dongmei, [Boulder, CO; Medlin, J William [Boulder, CO; McDaniel, Anthony H [Livermore, CA; Bastasz, Robert J [Livermore, CA

    2008-03-11

    The invention provides hydrogen selective metal-insulator-semiconductor sensors which include a layer of hydrogen selective material. The hydrogen selective material can be polyimide layer having a thickness between 200 and 800 nm. Suitable polyimide materials include reaction products of benzophenone tetracarboxylic dianhydride 4,4-oxydianiline m-phenylene diamine and other structurally similar materials.

  20. Statistical inference of selection and divergence from a time-dependent Poisson random field model.

    Directory of Open Access Journals (Sweden)

    Amei Amei

    Full Text Available We apply a recently developed time-dependent Poisson random field model to aligned DNA sequences from two related biological species to estimate selection coefficients and divergence time. We use Markov chain Monte Carlo methods to estimate species divergence time and selection coefficients for each locus. The model assumes that the selective effects of non-synonymous mutations are normally distributed across genetic loci but constant within loci, and synonymous mutations are selectively neutral. In contrast with previous models, we do not assume that the individual species are at population equilibrium after divergence. Using a data set of 91 genes in two Drosophila species, D. melanogaster and D. simulans, we estimate the species divergence time t(div = 2.16 N(e (or 1.68 million years, assuming the haploid effective population size N(e = 6.45 x 10(5 years and a mean selection coefficient per generation μ(γ = 1.98/N(e. Although the average selection coefficient is positive, the magnitude of the selection is quite small. Results from numerical simulations are also presented as an accuracy check for the time-dependent model.

  1. Implementing traceability using particle randomness-based textile printed tags

    Science.gov (United States)

    Agrawal, T. K.; Koehl, L.; Campagne, C.

    2017-10-01

    This article introduces a random particle-based traceability tag for textiles. The proposed tag not only act as a unique signature for the corresponding textile product but also possess the features such as easy to manufacture and hard to copy. It seeks applications in brand authentication and traceability in textile and clothing (T&C) supply chain. A prototype has been developed by screen printing process, in which micron-scale particles were mixed with the printing paste and printed on cotton fabrics to attain required randomness. To encode the randomness, the image of the developed tag was taken and analyzed using image processing. The randomness of the particles acts as a product key or unique signature which is required to decode the tag. Finally, washing and abrasion resistance tests were conducted to check the durability of the printed tag.

  2. Selection bias and subject refusal in a cluster-randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Rochelle Yang

    2017-07-01

    Full Text Available Abstract Background Selection bias and non-participation bias are major methodological concerns which impact external validity. Cluster-randomized controlled trials are especially prone to selection bias as it is impractical to blind clusters to their allocation into intervention or control. This study assessed the impact of selection bias in a large cluster-randomized controlled trial. Methods The Improved Cardiovascular Risk Reduction to Enhance Rural Primary Care (ICARE study examined the impact of a remote pharmacist-led intervention in twelve medical offices. To assess eligibility, a standardized form containing patient demographics and medical information was completed for each screened patient. Eligible patients were approached by the study coordinator for recruitment. Both the study coordinator and the patient were aware of the site’s allocation prior to consent. Patients who consented or declined to participate were compared across control and intervention arms for differing characteristics. Statistical significance was determined using a two-tailed, equal variance t-test and a chi-square test with adjusted Bonferroni p-values. Results were adjusted for random cluster variation. Results There were 2749 completed screening forms returned to research staff with 461 subjects who had either consented or declined participation. Patients with poorly controlled diabetes were found to be significantly more likely to decline participation in intervention sites compared to those in control sites. A higher mean diastolic blood pressure was seen in patients with uncontrolled hypertension who declined in the control sites compared to those who declined in the intervention sites. However, these findings were no longer significant after adjustment for random variation among the sites. After this adjustment, females were now found to be significantly more likely to consent than males (odds ratio = 1.41; 95% confidence interval = 1.03, 1

  3. N-state random switching based on quantum tunnelling

    Science.gov (United States)

    Bernardo Gavito, Ramón; Jiménez Urbanos, Fernando; Roberts, Jonathan; Sexton, James; Astbury, Benjamin; Shokeir, Hamzah; McGrath, Thomas; Noori, Yasir J.; Woodhead, Christopher S.; Missous, Mohamed; Roedig, Utz; Young, Robert J.

    2017-08-01

    In this work, we show how the hysteretic behaviour of resonant tunnelling diodes (RTDs) can be exploited for new functionalities. In particular, the RTDs exhibit a stochastic 2-state switching mechanism that could be useful for random number generation and cryptographic applications. This behaviour can be scaled to N-bit switching, by connecting various RTDs in series. The InGaAs/AlAs RTDs used in our experiments display very sharp negative differential resistance (NDR) peaks at room temperature which show hysteresis cycles that, rather than having a fixed switching threshold, show a probability distribution about a central value. We propose to use this intrinsic uncertainty emerging from the quantum nature of the RTDs as a source of randomness. We show that a combination of two RTDs in series results in devices with three-state outputs and discuss the possibility of scaling to N-state devices by subsequent series connections of RTDs, which we demonstrate for the up to the 4-state case. In this work, we suggest using that the intrinsic uncertainty in the conduction paths of resonant tunnelling diodes can behave as a source of randomness that can be integrated into current electronics to produce on-chip true random number generators. The N-shaped I-V characteristic of RTDs results in a two-level random voltage output when driven with current pulse trains. Electrical characterisation and randomness testing of the devices was conducted in order to determine the validity of the true randomness assumption. Based on the results obtained for the single RTD case, we suggest the possibility of using multi-well devices to generate N-state random switching devices for their use in random number generation or multi-valued logic devices.

  4. Association-rule based information source selection

    OpenAIRE

    Yang, Hui; Zhang, Minjie; Shi, Zhongzhi

    2004-01-01

    The proliferation of information sources available on the Wide World Web has resulted in a need for database selection tools to locate the potential useful information sources with respect to the user's information need. Current database selection tools always treat each database independently, ignoring the implicit, useful associations between distributed databases. To overcome this shortcoming, in this paper, we introduce a data-mining approach to assist the process of database selection by...

  5. Effect of non-random mating on genomic and BLUP selection schemes

    Directory of Open Access Journals (Sweden)

    Nirea Kahsay G

    2012-04-01

    Full Text Available Abstract Background The risk of long-term unequal contribution of mating pairs to the gene pool is that deleterious recessive genes can be expressed. Such consequences could be alleviated by appropriately designing and optimizing breeding schemes i.e. by improving selection and mating procedures. Methods We studied the effect of mating designs, random, minimum coancestry and minimum covariance of ancestral contributions on rate of inbreeding and genetic gain for schemes with different information sources, i.e. sib test or own performance records, different genetic evaluation methods, i.e. BLUP or genomic selection, and different family structures, i.e. factorial or pair-wise. Results Results showed that substantial differences in rates of inbreeding due to mating design were present under schemes with a pair-wise family structure, for which minimum coancestry turned out to be more effective to generate lower rates of inbreeding. Specifically, substantial reductions in rates of inbreeding were observed in schemes using sib test records and BLUP evaluation. However, with a factorial family structure, differences in rates of inbreeding due mating designs were minor. Moreover, non-random mating had only a small effect in breeding schemes that used genomic evaluation, regardless of the information source. Conclusions It was concluded that minimum coancestry remains an efficient mating design when BLUP is used for genetic evaluation or when the size of the population is small, whereas the effect of non-random mating is smaller in schemes using genomic evaluation.

  6. Fine mapping quantitative trait loci under selective phenotyping strategies based on linkage and linkage disequilibrium criteria

    DEFF Research Database (Denmark)

    Ansari-Mahyari, S; Berg, P; Lund, M S

    2009-01-01

    disequilibrium-based sampling criteria (LDC) for selecting individuals to phenotype are compared to random phenotyping in a quantitative trait loci (QTL) verification experiment using stochastic simulation. Several strategies based on LAC and LDC for selecting the most informative 30%, 40% or 50% of individuals......, in order to estimate unbiased QTL parameters based on LAC in a large half-sib family, prior information on QTL position was required. The LDC improved the accuracy to estimate the QTL position but not significantly compared to random phenotyping with the same sample size. When applying LDC (all phenotyping......In fine mapping of a large-scale experimental population where collection of phenotypes are very expensive, difficult to record or time-demanding, selective phenotyping could be used to phenotype the most informative individuals. Linkage analyses based sampling criteria (LAC) and linkage...

  7. Emulsion PCR: a high efficient way of PCR amplification of random DNA libraries in aptamer selection.

    Directory of Open Access Journals (Sweden)

    Keke Shao

    Full Text Available Aptamers are short RNA or DNA oligonucleotides which can bind with different targets. Typically, they are selected from a large number of random DNA sequence libraries. The main strategy to obtain aptamers is systematic evolution of ligands by exponential enrichment (SELEX. Low efficiency is one of the limitations for conventional PCR amplification of random DNA sequence library in aptamer selection because of relative low products and high by-products formation efficiency. Here, we developed emulsion PCR for aptamer selection. With this method, the by-products formation decreased tremendously to an undetectable level, while the products formation increased significantly. Our results indicated that by-products in conventional PCR amplification were from primer-product and product-product hybridization. In emulsion PCR, we can completely avoid the product-product hybridization and avoid the most of primer-product hybridization if the conditions were optimized. In addition, it also showed that the molecule ratio of template to compartment was crucial to by-product formation efficiency in emulsion PCR amplification. Furthermore, the concentration of the Taq DNA polymerase in the emulsion PCR mixture had a significant impact on product formation efficiency. So, the results of our study indicated that emulsion PCR could improve the efficiency of SELEX.

  8. Local search methods based on variable focusing for random K -satisfiability

    Science.gov (United States)

    Lemoy, Rémi; Alava, Mikko; Aurell, Erik

    2015-01-01

    We introduce variable focused local search algorithms for satisfiabiliity problems. Usual approaches focus uniformly on unsatisfied clauses. The methods described here work by focusing on random variables in unsatisfied clauses. Variants are considered where variables are selected uniformly and randomly or by introducing a bias towards picking variables participating in several unsatistified clauses. These are studied in the case of the random 3-SAT problem, together with an alternative energy definition, the number of variables in unsatisfied constraints. The variable-based focused Metropolis search (V-FMS) is found to be quite close in performance to the standard clause-based FMS at optimal noise. At infinite noise, instead, the threshold for the linearity of solution times with instance size is improved by picking preferably variables in several UNSAT clauses. Consequences for algorithmic design are discussed.

  9. Novel Zn2+-chelating peptides selected from a fimbria-displayed random peptide library

    DEFF Research Database (Denmark)

    Kjærgaard, Kristian; Schembri, Mark; Klemm, Per

    2001-01-01

    H adhesin. FimH is a component of the fimbrial organelle that can accommodate and display a diverse range of peptide sequences on the E. coli cell surface. In this study we have constructed a random peptide library in FimH. The library, consisting of similar to 40 million individual clones, was screened...... for peptide sequences that conferred on recombinant cells the ability to bind Zn2+. By serial selection, sequences that exhibited various degrees of binding affinity and specificity toward Zn2+ were enriched. None of the isolated sequences showed similarity to known Zn2+-binding proteins, indicating...

  10. Self-healing organic-dye-based random lasers

    CERN Document Server

    Anderson, Benjamin R; Eilers, Hergen

    2015-01-01

    One of the primary difficulties in the implementation of organic-dye-based random lasers is the tendency of organic dyes to irreversibly photodecay. In this letter we report the observation of "reversible" photodegradation in a Rhodamine 6G and ZrO$_2$ nanoparticle doped polyurethane random laser. We find that during degradation the emission broadens, redshifts, and decreases in intensity. After degradation the system is observed to self-heal leading to the emission returning to its pristine intensity, giving a recovery efficiency of 100%. While the peak intensity fully recovers, the process is not strictly "reversible" as the emission after recovery is still found to be broadened and redshifted. The combination of the peak emission fully recovering and the broadening of the emission leads to a remarkable result: the random laser cycled through degradation and recovery has a greater integrated emission intensity than the pristine system.

  11. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

    Science.gov (United States)

    Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

    2017-07-01

    Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in

  12. PReFerSim: fast simulation of demography and selection under the Poisson Random Field model.

    Science.gov (United States)

    Ortega-Del Vecchyo, Diego; Marsden, Clare D; Lohmueller, Kirk E

    2016-11-15

    The Poisson Random Field (PRF) model has become an important tool in population genetics to study weakly deleterious genetic variation under complicated demographic scenarios. Currently, there are no freely available software applications that allow simulation of genetic variation data under this model. Here we present PReFerSim, an ANSI C program that performs forward simulations under the PRF model. PReFerSim models changes in population size, arbitrary amounts of inbreeding, dominance and distributions of selective effects. Users can track summaries of genetic variation over time and output trajectories of selected alleles. PReFerSim is freely available at: https://github.com/LohmuellerLab/PReFerSim CONTACT: klohmueller@ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  13. Selective oropharyngeal decontamination versus selective digestive decontamination in critically ill patients: a meta-analysis of randomized controlled trials

    Directory of Open Access Journals (Sweden)

    Zhao D

    2015-07-01

    Full Text Available Di Zhao,1,* Jian Song,2,* Xuan Gao,3 Fei Gao,4 Yupeng Wu,2 Yingying Lu,5 Kai Hou1 1Department of Neurosurgery, The First Hospital of Hebei Medical University, 2Department of Neurosurgery, 3Department of Neurology, The Second Hospital of Hebei Medical University, 4Hebei Provincial Procurement Centers for Medical Drugs and Devices, 5Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang People’s Republic of China *These authors contributed equally to this work Background: Selective digestive decontamination (SDD and selective oropharyngeal decontamination (SOD are associated with reduced mortality and infection rates among patients in intensive care units (ICUs; however, whether SOD has a superior effect than SDD remains uncertain. Hence, we conducted a meta-analysis of randomized controlled trials (RCTs to compare SOD with SDD in terms of clinical outcomes and antimicrobial resistance rates in patients who were critically ill. Methods: RCTs published in PubMed, Embase, and Web of Science were systematically reviewed to compare the effects of SOD and SDD in patients who were critically ill. Outcomes included day-28 mortality, length of ICU stay, length of hospital stay, duration of mechanical ventilation, ICU-acquired bacteremia, and prevalence of antibiotic-resistant Gram-negative bacteria. Results were expressed as risk ratio (RR with 95% confidence intervals (CIs, and weighted mean differences (WMDs with 95% CIs. Pooled estimates were performed using a fixed-effects model or random-effects model, depending on the heterogeneity among studies. Results: A total of four RCTs involving 23,822 patients met the inclusion criteria and were included in this meta-analysis. Among patients whose admitting specialty was surgery, cardiothoracic surgery (57.3% and neurosurgery (29.7% were the two main types of surgery being performed. Pooled results showed that SOD had similar effects as SDD in day-28 mortality (RR =1

  14. Ethnopharmacological versus random plant selection methods for the evaluation of the antimycobacterial activity

    Directory of Open Access Journals (Sweden)

    Danilo R. Oliveira

    2011-05-01

    Full Text Available The municipality of Oriximiná, Brazil, has 33 quilombola communities in remote areas, endowed with wide experience in the use of medicinal plants. An ethnobotanical survey was carried out in five of these communities. A free-listing method directed for the survey of species locally indicated against Tuberculosis and lung problems was also applied. Data were analyzed by quantitative techniques: saliency index and major use agreement. Thirty four informants related 254 ethnospecies. Among these, 43 were surveyed for possible antimycobacterial activity. As a result of those informations, ten species obtained from the ethnodirected approach (ETHNO and eighteen species obtained from the random approach (RANDOM were assayed against Mycobacterium tuberculosis by the microdilution method, using resazurin as an indicator of cell viability. The best results for antimycobacterial activity were obtained of some plants selected by the ethnopharmacological approach (50% ETHNO x 16,7% RANDOM. These results can be even more significant if we consider that the therapeutic success obtained among the quilombola practice is complex, being the use of some plants acting as fortifying agents, depurative, vomitory, purgative and bitter remedy, especially to infectious diseases, of great importance to the communities in the curing or recovering of health as a whole.

  15. Selecting the appropriate pacing mode for patients with sick sinus syndrome: evidence from randomized clinical trials.

    Science.gov (United States)

    Albertsen, A E; Nielsen, J C

    2003-12-01

    Several observational studies have indicated that selection of pacing mode may be important for the clinical outcome in patients with symptomatic bradycardia, affecting the development of atrial fibrillation (AF), thromboembolism, congestive heart failure, mortality and quality of life. In this paper we present and discuss the most recent data from six randomized trials on mode selection in patients with sick sinus syndrome (SSS). In pacing mode selection, VVI(R) pacing is the least attractive solution, increasing the incidence of AF and-as compared with AAI(R) pacing, also the incidence of heart failure, thromboembolism and death. VVI(R) pacing should not be used as the primary pacing mode in patients with SSS, who haven't chronic AF. AAIR pacing is superior to DDDR pacing, reducing AF and preserving left ventricular function. Single site right ventricular pacing-VVI(R) or DDD(R) mode-causes an abnormal ventricular activation and contraction (called ventricular desynchronization), which results in a reduced left ventricular function. Despite the risk of AV block, we consider AAIR pacing to be the optimal pacing mode for isolated SSS today and an algorithm to select patients for AAIR pacing is suggested. Trials on new pacemaker algorithms minimizing right ventricular pacing as well as trials testing alternative pacing sites and multisite pacing to reduce ventricular desynchronization can be expected within the next years.

  16. Graphene based widely-tunable and singly-polarized pulse generation with random fiber lasers

    Science.gov (United States)

    Yao, B. C.; Rao, Y. J.; Wang, Z. N.; Wu, Y.; Zhou, J. H.; Wu, H.; Fan, M. Q.; Cao, X. L.; Zhang, W. L.; Chen, Y. F.; Li, Y. R.; Churkin, D.; Turitsyn, S.; Wong, C. W.

    2015-12-01

    Pulse generation often requires a stabilized cavity and its corresponding mode structure for initial phase-locking. Contrastingly, modeless cavity-free random lasers provide new possibilities for high quantum efficiency lasing that could potentially be widely tunable spectrally and temporally. Pulse generation in random lasers, however, has remained elusive since the discovery of modeless gain lasing. Here we report coherent pulse generation with modeless random lasers based on the unique polarization selectivity and broadband saturable absorption of monolayer graphene. Simultaneous temporal compression of cavity-free pulses are observed with such a polarization modulation, along with a broadly-tunable pulsewidth across two orders of magnitude down to 900 ps, a broadly-tunable repetition rate across three orders of magnitude up to 3 MHz, and a singly-polarized pulse train at 41 dB extinction ratio, about an order of magnitude larger than conventional pulsed fiber lasers. Moreover, our graphene-based pulse formation also demonstrates robust pulse-to-pulse stability and wide-wavelength operation due to the cavity-less feature. Such a graphene-based architecture not only provides a tunable pulsed random laser for fiber-optic sensing, speckle-free imaging, and laser-material processing, but also a new way for the non-random CW fiber lasers to generate widely tunable and singly-polarized pulses.

  17. Geography and genography: prediction of continental origin using randomly selected single nucleotide polymorphisms

    Directory of Open Access Journals (Sweden)

    Ramoni Marco F

    2007-03-01

    Full Text Available Abstract Background Recent studies have shown that when individuals are grouped on the basis of genetic similarity, group membership corresponds closely to continental origin. There has been considerable debate about the implications of these findings in the context of larger debates about race and the extent of genetic variation between groups. Some have argued that clustering according to continental origin demonstrates the existence of significant genetic differences between groups and that these differences may have important implications for differences in health and disease. Others argue that clustering according to continental origin requires the use of large amounts of genetic data or specifically chosen markers and is indicative only of very subtle genetic differences that are unlikely to have biomedical significance. Results We used small numbers of randomly selected single nucleotide polymorphisms (SNPs from the International HapMap Project to train naïve Bayes classifiers for prediction of ancestral continent of origin. Predictive accuracy was tested on two independent data sets. Genetically similar groups should be difficult to distinguish, especially if only a small number of genetic markers are used. The genetic differences between continentally defined groups are sufficiently large that one can accurately predict ancestral continent of origin using only a minute, randomly selected fraction of the genetic variation present in the human genome. Genotype data from only 50 random SNPs was sufficient to predict ancestral continent of origin in our primary test data set with an average accuracy of 95%. Genetic variations informative about ancestry were common and widely distributed throughout the genome. Conclusion Accurate characterization of ancestry is possible using small numbers of randomly selected SNPs. The results presented here show how investigators conducting genetic association studies can use small numbers of arbitrarily

  18. Joint random beam and spectrum selection for spectrum sharing systems with partial channel state information

    KAUST Repository

    Abdallah, Mohamed M.

    2013-11-01

    In this work, we develop joint interference-aware random beam and spectrum selection scheme that provide enhanced performance for the secondary network under the condition that the interference observed at the primary receiver is below a predetermined acceptable value. We consider a secondary link composed of a transmitter equipped with multiple antennas and a single-antenna receiver sharing the same spectrum with a set of primary links composed of a single-antenna transmitter and a single-antenna receiver. The proposed schemes jointly select a beam, among a set of power-optimized random beams, as well as the primary spectrum that maximizes the signal-to-interference-plus-noise ratio (SINR) of the secondary link while satisfying the primary interference constraint. In particular, we consider the case where the interference level is described by a q-bit description of its magnitude, whereby we propose a technique to find the optimal quantizer thresholds in a mean square error (MSE) sense. © 2013 IEEE.

  19. Interference-aware random beam selection schemes for spectrum sharing systems

    KAUST Repository

    Abdallah, Mohamed

    2012-10-19

    Spectrum sharing systems have been recently introduced to alleviate the problem of spectrum scarcity by allowing secondary unlicensed networks to share the spectrum with primary licensed networks under acceptable interference levels to the primary users. In this work, we develop interference-aware random beam selection schemes that provide enhanced performance for the secondary network under the condition that the interference observed by the receivers of the primary network is below a predetermined/acceptable value. We consider a secondary link composed of a transmitter equipped with multiple antennas and a single-antenna receiver sharing the same spectrum with a primary link composed of a single-antenna transmitter and a single-antenna receiver. The proposed schemes select a beam, among a set of power-optimized random beams, that maximizes the signal-to-interference-plus-noise ratio (SINR) of the secondary link while satisfying the primary interference constraint for different levels of feedback information describing the interference level at the primary receiver. For the proposed schemes, we develop a statistical analysis for the SINR statistics as well as the capacity and bit error rate (BER) of the secondary link.

  20. Identity-Based Verifiably Encrypted Signatures without Random Oracles

    Science.gov (United States)

    Zhang, Lei; Wu, Qianhong; Qin, Bo

    Fair exchange protocol plays an important role in electronic commerce in the case of exchanging digital contracts. Verifiably encrypted signatures provide an optimistic solution to these scenarios with an off-line trusted third party. In this paper, we propose an identity-based verifiably encrypted signature scheme. The scheme is non-interactive to generate verifiably encrypted signatures and the resulting encrypted signature consists of only four group elements. Based on the computational Diffie-Hellman assumption, our scheme is proven secure without using random oracles. To the best of our knowledge, this is the first identity-based verifiably encrypted signature scheme provably secure in the standard model.

  1. Structuring AHP-based maintenance policy selection

    NARCIS (Netherlands)

    Goossens, Adriaan; Basten, Robertus Johannes Ida; Hummel, J. Marjan; van der Wegen, Leonardus L.M.

    2015-01-01

    We aim to structure the maintenance policy selection process for ships, using the Analytic Hierarchy Process (AHP). Maintenance is an important contributor to reach the intended life-time of capital technical assets, and it is gaining increasing interest and relevance. A maintenance policy is a

  2. Gender based disruptive selection maintains body size ...

    Indian Academy of Sciences (India)

    2014-07-04

    Jul 4, 2014 ... Darwinian fitness in holometabolous insects like the fruit fly Drosophila melanogaster is reported to be positively correlated with body size. If large individuals in a population have higher fitness, then one would expect directional selection to operate leading to uniformly large individuals. However, size ...

  3. Factors Associated with High Use of a Workplace Web-Based Stress Management Program in a Randomized Controlled Intervention Study

    Science.gov (United States)

    Hasson, H.; Brown, C.; Hasson, D.

    2010-01-01

    In web-based health promotion programs, large variations in participant engagement are common. The aim was to investigate determinants of high use of a worksite self-help web-based program for stress management. Two versions of the program were offered to randomly selected departments in IT and media companies. A static version of the program…

  4. Performance Evaluation of Random Set Based Pedestrian Tracking Algorithms

    OpenAIRE

    Ristic, Branko; Sherrah, Jamie; García-Fernández, Ángel F.

    2012-01-01

    The paper evaluates the error performance of three random finite set based multi-object trackers in the context of pedestrian video tracking. The evaluation is carried out using a publicly available video dataset of 4500 frames (town centre street) for which the ground truth is available. The input to all pedestrian tracking algorithms is an identical set of head and body detections, obtained using the Histogram of Oriented Gradients (HOG) detector. The tracking error is measured using the re...

  5. DNA based Frequency Selective Electromagnetic Interference Shielding (Preprint)

    Science.gov (United States)

    2017-11-03

    AFRL-RX-WP-JA-2017-0495 DNA -BASED FREQUENCY SELECTIVE ELECTROMAGNETIC INTERFERENCE SHIELDING (PREPRINT) Fahima Ouchen, Eric Kreit...To) 31 October 2017 Interim 24 January 2014 – 30 September 2017 4. TITLE AND SUBTITLE DNA -BASED FREQUENCY SELECTIVE ELECTROMAGNETIC INTERFERENCE...92008 Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std. Z39-18 DNA -based frequency selective electromagnetic interference shielding

  6. Prediction of plant promoters based on hexamers and random triplet pair analysis

    Directory of Open Access Journals (Sweden)

    Noman Nasimul

    2011-06-01

    Full Text Available Abstract Background With an increasing number of plant genome sequences, it has become important to develop a robust computational method for detecting plant promoters. Although a wide variety of programs are currently available, prediction accuracy of these still requires further improvement. The limitations of these methods can be addressed by selecting appropriate features for distinguishing promoters and non-promoters. Methods In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA. In FDAFSA, adjacent triplet-pairs (hexamer sequences were selected based on the difference in the frequency of hexamers between promoters and non-promoters. In RTPFSGA, random triplet-pairs (RTPs were selected by exploiting a genetic algorithm that distinguishes frequencies of non-adjacent triplet pairs between promoters and non-promoters. Then, a support vector machine (SVM, a nonlinear machine-learning algorithm, was used to classify promoters and non-promoters by combining these two feature selection approaches. We referred to this novel algorithm as PromoBot. Results Promoter sequences were collected from the PlantProm database. Non-promoter sequences were collected from plant mRNA, rRNA, and tRNA of PlantGDB and plant miRNA of miRBase. Then, in order to validate the proposed algorithm, we applied a 5-fold cross validation test. Training data sets were used to select features based on FDAFSA and RTPFSGA, and these features were used to train the SVM. We achieved 89% sensitivity and 86% specificity. Conclusions We compared our PromoBot algorithm to five other algorithms. It was found that the sensitivity and specificity of PromoBot performed well (or even better with the algorithms tested. These results show that the two proposed feature selection methods based on hexamer frequencies

  7. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

    Science.gov (United States)

    Li, Jin; Tran, Maggie; Siwabessy, Justy

    2016-01-01

    Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  8. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness.

    Directory of Open Access Journals (Sweden)

    Jin Li

    Full Text Available Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70. We developed optimal predictive models to predict seabed hardness using random forest (RF based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS methods that are variable importance (VI, averaged variable importance (AVI, knowledge informed AVI (KIAVI, Boruta and regularized RF (RRF were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1 hard90 and hard70 are effective seabed hardness classification schemes; 2 seabed hardness of four classes can be predicted with a high degree of accuracy; 3 the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4 the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5 FS methods select the most accurate predictive model(s instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6 RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to 'small p and large n' problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  9. Nitrite-selective ISE based on uranyl salophen derivatives

    NARCIS (Netherlands)

    Wroblewski, Wojciech; Brzozka, Zbigniew; Rudkevich, Dmitry M.; Rudkevich, D.M.; Reinhoudt, David

    1996-01-01

    Anion selectivities of membranes based on uranyl salophen derivatives with substituents at the 4-position are presented. Derivative 2 (with 4-nitro substituent) has been applied to design a nitrite-selective ion-selective electrode (ISE) that shows linear response in the range 1¿3 of pNO2¿ with a

  10. Feature selection and classification of mechanical fault of an induction motor using random forest classifier

    Directory of Open Access Journals (Sweden)

    Raj Kumar Patel

    2016-09-01

    Full Text Available Fault detection and diagnosis is the most important technology in condition-based maintenance (CBM system for rotating machinery. This paper experimentally explores the development of a random forest (RF classifier, a recently emerged machine learning technique, for multi-class mechanical fault diagnosis in bearing of an induction motor. Firstly, the vibration signals are collected from the bearing using accelerometer sensor. Parameters from the vibration signal are extracted in the form of statistical features and used as input feature for the classification problem. These features are classified through RF classifiers for four class problems. The prime objective of this paper is to evaluate effectiveness of random forest classifier on bearing fault diagnosis. The obtained results compared with the existing artificial intelligence techniques, neural network. The analysis of results shows the better performance and higher accuracy than the well existing techniques.

  11. Does the Use of a Decision Aid Improve Decision Making in Prosthetic Heart Valve Selection? A Multicenter Randomized Trial

    NARCIS (Netherlands)

    Korteland, Nelleke M.; Ahmed, Yunus; Koolbergen, David R.; Brouwer, Marjan; de Heer, Frederiek; Kluin, Jolanda; Bruggemans, Eline F.; Klautz, Robert J. M.; Stiggelbout, Anne M.; Bucx, Jeroen J. J.; Roos-Hesselink, Jolien W.; Polak, Peter; Markou, Thanasie; van den Broek, Inge; Ligthart, Rene; Bogers, Ad J. J. C.; Takkenberg, Johanna J. M.

    2017-01-01

    A Dutch online patient decision aid to support prosthetic heart valve selection was recently developed. A multicenter randomized controlled trial was conducted to assess whether use of the patient decision aid results in optimization of shared decision making in prosthetic heart valve selection. In

  12. development development of base transceiver station selection

    African Journals Online (AJOL)

    eobe

    save cost and reduce the number of people who are at risk of radiation in BTSs located places as compared to each ... Keywords: Keywords: absolute radio frequency channel number; base transceiver station; collocation; radiation; spectral ..... [5] Singh R.K., “Assessment of Electromagnetic Radiation from Base Station ...

  13. Selective outcome reporting and sponsorship in randomized controlled trials in IVF and ICSI.

    Science.gov (United States)

    Braakhekke, M; Scholten, I; Mol, F; Limpens, J; Mol, B W; van der Veen, F

    2017-10-01

    Are randomized controlled trials (RCTs) on IVF and ICSI subject to selective outcome reporting and is this related to sponsorship? There are inconsistencies, independent from sponsorship, in the reporting of primary outcome measures in the majority of IVF and ICSI trials, indicating selective outcome reporting. RCTs are subject to bias at various levels. Of these biases, selective outcome reporting is particularly relevant to IVF and ICSI trials since there is a wide variety of outcome measures to choose from. An established cause of reporting bias is sponsorship. It is, at present, unknown whether RCTs in IVF/ICSI are subject to selective outcome reporting and whether this is related with sponsorship. We systematically searched RCTs on IVF and ICSI published between January 2009 and March 2016 in MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials and the publisher subset of PubMed. We analysed 415 RCTs. Per included RCT, we extracted data on impact factor of the journal, sample size, power calculation, and trial registry and thereafter data on primary outcome measure, the direction of trial results and sponsorship. Of the 415 identified RCTs, 235 were excluded for our primary analysis, because the sponsorship was not reported. Of the 180 RCTs included in our analysis, 7 trials did not report on any primary outcome measure and 107 of the remaining 173 trials (62%) reported on surrogate primary outcome measures. Of the 114 registered trials, 21 trials (18%) provided primary outcomes in their manuscript that were different from those in the trial registry. This indicates selective outcome reporting. We found no association between selective outcome reporting and sponsorship. We ran additional analyses to include the trials that had not reported sponsorship and found no outcomes that differed from our primary analysis. Since the majority of the trials did not report on sponsorship, there is a risk on sampling bias. IVF and ICSI trials are subject, to

  14. Cellular Automata-Based Parallel Random Number Generators Using FPGAs

    Directory of Open Access Journals (Sweden)

    David H. K. Hoe

    2012-01-01

    Full Text Available Cellular computing represents a new paradigm for implementing high-speed massively parallel machines. Cellular automata (CA, which consist of an array of locally connected processing elements, are a basic form of a cellular-based architecture. The use of field programmable gate arrays (FPGAs for implementing CA accelerators has shown promising results. This paper investigates the design of CA-based pseudo-random number generators (PRNGs using an FPGA platform. To improve the quality of the random numbers that are generated, the basic CA structure is enhanced in two ways. First, the addition of a superrule to each CA cell is considered. The resulting self-programmable CA (SPCA uses the superrule to determine when to make a dynamic rule change in each CA cell. The superrule takes its inputs from neighboring cells and can be considered itself a second CA working in parallel with the main CA. When implemented on an FPGA, the use of lookup tables in each logic cell removes any restrictions on how the super-rules should be defined. Second, a hybrid configuration is formed by combining a CA with a linear feedback shift register (LFSR. This is advantageous for FPGA designs due to the compactness of the LFSR implementations. A standard software package for statistically evaluating the quality of random number sequences known as Diehard is used to validate the results. Both the SPCA and the hybrid CA/LFSR were found to pass all the Diehard tests.

  15. Chaos-based Pseudo-random Number Generation

    KAUST Repository

    Barakat, Mohamed L.

    2014-04-10

    Various methods and systems related to chaos-based pseudo-random number generation are presented. In one example, among others, a system includes a pseudo-random number generator (PRNG) to generate a series of digital outputs and a nonlinear post processing circuit to perform an exclusive OR (XOR) operation on a first portion of a current digital output of the PRNG and a permutated version of a corresponding first portion of a previous post processed output to generate a corresponding first portion of a current post processed output. In another example, a method includes receiving at least a first portion of a current output from a PRNG and performing an XOR operation on the first portion of the current PRNG output with a permutated version of a corresponding first portion of a previous post processed output to generate a corresponding first portion of a current post processed output.

  16. Doppler Ambiguity Resolution Based on Random Sparse Probing Pulses

    Directory of Open Access Journals (Sweden)

    Yunjian Zhang

    2015-01-01

    Full Text Available A novel method for solving Doppler ambiguous problem based on compressed sensing (CS theory is proposed in this paper. A pulse train with the random and sparse transmitting time is transmitted. The received signals after matched filtering can be viewed as randomly sparse sampling from the traditional fixed-pulse repetition frequency (PRF echo signals. The whole target echo could be reconstructed via CS recovery algorithms. Through refining the sensing matrix, which is equivalent to increase the sampling frequency of target characteristic, the Doppler unambiguous range is enlarged. In particular, Complex Approximate Message Passing (CAMP algorithm is developed to estimate the unambiguity Doppler frequency. Cramer-Rao lower bound expressions are derived for the frequency. Numerical simulations validate the effectiveness of the proposed method. Finally, compared with traditional methods, the proposed method only requires transmitting a few sparse probing pulses to achieve a larger Doppler frequency unambiguous range and can also reduce the consumption of the radar time resources.

  17. Implications of structural genomics target selection strategies: Pfam5000, whole genome, and random approaches

    Energy Technology Data Exchange (ETDEWEB)

    Chandonia, John-Marc; Brenner, Steven E.

    2004-07-14

    The structural genomics project is an international effort to determine the three-dimensional shapes of all important biological macromolecules, with a primary focus on proteins. Target proteins should be selected according to a strategy which is medically and biologically relevant, of good value, and tractable. As an option to consider, we present the Pfam5000 strategy, which involves selecting the 5000 most important families from the Pfam database as sources for targets. We compare the Pfam5000 strategy to several other proposed strategies that would require similar numbers of targets. These include including complete solution of several small to moderately sized bacterial proteomes, partial coverage of the human proteome, and random selection of approximately 5000 targets from sequenced genomes. We measure the impact that successful implementation of these strategies would have upon structural interpretation of the proteins in Swiss-Prot, TrEMBL, and 131 complete proteomes (including 10 of eukaryotes) from the Proteome Analysis database at EBI. Solving the structures of proteins from the 5000 largest Pfam families would allow accurate fold assignment for approximately 68 percent of all prokaryotic proteins (covering 59 percent of residues) and 61 percent of eukaryotic proteins (40 percent of residues). More fine-grained coverage which would allow accurate modeling of these proteins would require an order of magnitude more targets. The Pfam5000 strategy may be modified in several ways, for example to focus on larger families, bacterial sequences, or eukaryotic sequences; as long as secondary consideration is given to large families within Pfam, coverage results vary only slightly. In contrast, focusing structural genomics on a single tractable genome would have only a limited impact in structural knowledge of other proteomes: a significant fraction (about 30-40 percent of the proteins, and 40-60 percent of the residues) of each proteome is classified in small

  18. EEG feature selection method based on decision tree.

    Science.gov (United States)

    Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

    2015-01-01

    This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

  19. A Comparison of Dietary Habits between Recreational Runners and a Randomly Selected Adult Population in Slovenia.

    Science.gov (United States)

    Škof, Branko; Rotovnik Kozjek, Nada

    2015-09-01

    The aim of the study was to compare the dietary habits of recreational runners with those of a random sample of the general population. We also wanted to determine the influence of gender, age and sports performance of recreational runners on their basic diet and compliance with recommendations in sports nutrition. The study population consisted of 1,212 adult Slovenian recreational runners and 774 randomly selected residents of Slovenia between the ages of 18 and 65 years. The data on the dietary habits of our subjects was gathered by means of two questionnaires. The following parameters were evaluated: the type of diet, a food pattern, and the frequency of consumption of individual food groups, the use of dietary supplements, fluid intake, and alcohol consumption. Recreational runners had better compliance with recommendations for healthy nutrition than the general population. This pattern increased with the runner's age and performance level. Compared to male runners, female runners ate more regularly and had a more frequent consumption of food groups associated with a healthy diet (fruit, vegetables, whole grain foods, and low-fat dairy products). The consumption of simple sugars and use of nutritional supplements by well-trained runners was inadequate with values recommended for physically active individuals. Recreational runners are an exemplary population group that actively seeks to adopt a healthier lifestyle.

  20. 44 CFR 321.2 - Selection of the mobilization base.

    Science.gov (United States)

    2010-10-01

    ... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Selection of the mobilization..., DEPARTMENT OF HOMELAND SECURITY PREPAREDNESS MAINTENANCE OF THE MOBILIZATION BASE (DEPARTMENT OF DEFENSE, DEPARTMENT OF ENERGY, MARITIME ADMINISTRATION) § 321.2 Selection of the mobilization base. (a) The Department...

  1. Content-Based Image Retrieval: Color-selection exploited

    NARCIS (Netherlands)

    Moens, Marie-Francine; van den Broek, Egon; Vuurpijl, L.G.; de Brusser, Rik; Kisters, P.M.F.; Hiemstra, Djoerd; Kraaij, Wessel; von Schmid, J.C.M.

    2002-01-01

    This research presents a new color selection interface that facilitates query-by-color in Content-Based Image Retrieval (CBIR). Existing CBIR color selection interfaces, are being judged as non-intuitive and difficult to use. Our interface copes with these problems of usability. It is based on 11

  2. The Prediction Model of Dam Uplift Pressure Based on Random Forest

    Science.gov (United States)

    Li, Xing; Su, Huaizhi; Hu, Jiang

    2017-09-01

    The prediction of the dam uplift pressure is of great significance in the dam safety monitoring. Based on the comprehensive consideration of various factors, 18 parameters are selected as the main factors affecting the prediction of uplift pressure, use the actual monitoring data of uplift pressure as the evaluation factors for the prediction model, based on the random forest algorithm and support vector machine to build the dam uplift pressure prediction model to predict the uplift pressure of the dam, and the predict performance of the two models were compared and analyzed. At the same time, based on the established random forest prediction model, the significance of each factor is analyzed, and the importance of each factor of the prediction model is calculated by the importance function. Results showed that: (1) RF prediction model can quickly and accurately predict the uplift pressure value according to the influence factors, the average prediction accuracy is above 96%, compared with the support vector machine (SVM) model, random forest model has better robustness, better prediction precision and faster convergence speed, and the random forest model is more robust to missing data and unbalanced data. (2) The effect of water level on uplift pressure is the largest, and the influence of rainfall on the uplift pressure is the smallest compared with other factors.

  3. Tungsten based catalysts for selective deoxygenation

    NARCIS (Netherlands)

    Gosselink, R.W.|info:eu-repo/dai/nl/326164081; Stellwagen, D.R.; Bitter, J.H.|info:eu-repo/dai/nl/160581435

    2013-01-01

    Over the past decades, impending oil shortages combined with petroleum market instability have prompted a search for a new source of both transportation fuels and bulk chemicals. Renewable bio-based feedstocks such as sugars, grains, and seeds are assumed to be capable of contributing to a

  4. DNA based random key generation and management for OTP encryption.

    Science.gov (United States)

    Zhang, Yunpeng; Liu, Xin; Sun, Manhui

    2017-09-01

    One-time pad (OTP) is a principle of key generation applied to the stream ciphering method which offers total privacy. The OTP encryption scheme has proved to be unbreakable in theory, but difficult to realize in practical applications. Because OTP encryption specially requires the absolute randomness of the key, its development has suffered from dense constraints. DNA cryptography is a new and promising technology in the field of information security. DNA chromosomes storing capabilities can be used as one-time pad structures with pseudo-random number generation and indexing in order to encrypt the plaintext messages. In this paper, we present a feasible solution to the OTP symmetric key generation and transmission problem with DNA at the molecular level. Through recombinant DNA technology, by using only sender-receiver known restriction enzymes to combine the secure key represented by DNA sequence and the T vector, we generate the DNA bio-hiding secure key and then place the recombinant plasmid in implanted bacteria for secure key transmission. The designed bio experiments and simulation results show that the security of the transmission of the key is further improved and the environmental requirements of key transmission are reduced. Analysis has demonstrated that the proposed DNA-based random key generation and management solutions are marked by high security and usability. Published by Elsevier B.V.

  5. Controlling Random Waves with Digital Building Blocks Based on Supersymmetry

    Science.gov (United States)

    Yu, Sunkyu; Piao, Xianji; Park, Namkyoo

    2017-11-01

    Harnessing multimode waves allows high information capacity through modal expansions. Although passive multimode devices for broadband responses have been demonstrated in momentum or frequency domains, the difficulty in achieving collective manipulation of all eigenmodes has hindered the implementation of digital multimode devices such as switching. Here we propose building blocks for digital switching of spatially random waves based on parity-converted supersymmetric pairs of multimode potentials. We reveal that unbroken supersymmetric transformations of any parity-symmetric potential derive the parity reversal of all eigenmodes, which allows the complete isolation of random waves in the "off" state. With two representative solvable potentials, building blocks for binary and many-valued logics are then demonstrated for random waves: a harmonic pair for binary switching of arbitrary wave fronts and a Pöschl-Teller pair for multilevel switching which implements fuzzy membership functions. Our results realizing the transfer of arbitrary wave fronts between wave elements will lay the foundation of high-bandwidth data processing.

  6. The Random Walk Model Based on Bipartite Network

    Directory of Open Access Journals (Sweden)

    Zhang Man-Dun

    2016-01-01

    Full Text Available With the continuing development of the electronic commerce and growth of network information, there is a growing possibility for citizens to be confused by the information. Though the traditional technology of information retrieval have the ability to relieve the overload of information in some extent, it can not offer a targeted personality service based on user’s interests and activities. In this context, the recommendation algorithm arose. In this paper, on the basis of conventional recommendation, we studied the scheme of random walk based on bipartite network and the application of it. We put forward a similarity measurement based on implicit feedback. In this method, a uneven character vector is imported(the weight of item in the system. We put forward a improved random walk pattern which make use of partial or incomplete neighbor information to create recommendation information. In the end, there is an experiment in the real data set, the recommendation accuracy and practicality are improved. We promise the reality of the result of the experiment

  7. Random-Profiles-Based 3D Face Recognition System

    Directory of Open Access Journals (Sweden)

    Joongrock Kim

    2014-03-01

    Full Text Available In this paper, a noble nonintrusive three-dimensional (3D face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation.

  8. Homogeneous 1-based structures and interpretability in random structures

    OpenAIRE

    Koponen, Vera

    2014-01-01

    Let $V$ be a finite relational vocabulary in which no symbol has arity greater than 2. Let $M$ be countable $V$-structure which is homogeneous, simple and 1-based. The first main result says that if $M$ is, in addition, primitive, then it is strongly interpretable in a random structure. The second main result, which generalizes the first, implies (without the assumption on primitivity) that if $M$ is "coordinatized" by a set with SU-rank 1 and there is no definable (without parameters) nontri...

  9. Statistical Downscaling Based on Spartan Spatial Random Fields

    Science.gov (United States)

    Hristopulos, Dionissios

    2010-05-01

    Stochastic methods of space-time interpolation and conditional simulation have been used in statistical downscaling approaches to increase the resolution of measured fields. One of the popular interpolation methods in geostatistics is kriging, also known as optimal interpolation in data assimilation. Kriging is a stochastic, linear interpolator which incorporates time/space variability by means of the variogram function. However, estimation of the variogram from data involves various assumptions and simplifications. At the same time, the high numerical complexity of kriging makes it difficult to use for very large data sets. We present a different approach based on the so-called Spartan Spatial Random Fields (SSRFs). SSRFs were motivated from classical field theories of statistical physics [1]. The SSRFs provide a different approach of parametrizing spatial dependence based on 'effective interactions,' which can be formulated based on general statistical principles or even incorporate physical constraints. This framework leads to a broad family of covariance functions [2], and it provides new perspectives in covariance parameter estimation and interpolation [3]. A significant advantage offered by SSRFs is reduced numerical complexity, which can lead to much faster codes for spatial interpolation and conditional simulation. In addition, on grids composed of rectangular cells, the SSRF representation leads to an explicit expression for the precision matrix (the inverse covariance). Therefore SSRFs could provide useful models of error covariance for data assimilation methods. We use simulated and real data to demonstrate SSRF properties and downscaled fields. keywords: interpolation, conditional simulation, precision matrix References [1] Hristopulos, D.T., 2003. Spartan Gibbs random field models for geostatistical applications, SIAM Journal in Scientific Computation, 24, 2125-2162. [2] Hristopulos, D.T., Elogne, S. N. 2007. Analytic properties and covariance

  10. Random number generation based on digital differential chaos

    KAUST Repository

    Zidan, Mohammed A.

    2012-07-29

    In this paper, we present a fully digital differential chaos based random number generator. The output of the digital circuit is proved to be chaotic by calculating the output time series maximum Lyapunov exponent. We introduce a new post processing technique to improve the distribution and statistical properties of the generated data. The post-processed output passes the NIST Sp. 800-22 statistical tests. The system is written in Verilog VHDL and realized on Xilinx Virtex® FPGA. The generator can fit into a very small area and have a maximum throughput of 2.1 Gb/s.

  11. Randomized clinical trial of Appendicitis Inflammatory Response score-based management of patients with suspected appendicitis.

    Science.gov (United States)

    Andersson, M; Kolodziej, B; Andersson, R E

    2017-10-01

    The role of imaging in the diagnosis of appendicitis is controversial. This prospective interventional study and nested randomized trial analysed the impact of implementing a risk stratification algorithm based on the Appendicitis Inflammatory Response (AIR) score, and compared routine imaging with selective imaging after clinical reassessment. Patients presenting with suspicion of appendicitis between September 2009 and January 2012 from age 10 years were included at 21 emergency surgical centres and from age 5 years at three university paediatric centres. Registration of clinical characteristics, treatments and outcomes started during the baseline period. The AIR score-based algorithm was implemented during the intervention period. Intermediate-risk patients were randomized to routine imaging or selective imaging after clinical reassessment. The baseline period included 1152 patients, and the intervention period 2639, of whom 1068 intermediate-risk patients were randomized. In low-risk patients, use of the AIR score-based algorithm resulted in less imaging (19·2 versus 34·5 per cent; P appendicitis (6·8 versus 9·7 per cent; P = 0·034). Intermediate-risk patients randomized to the imaging and observation groups had the same proportion of negative appendicectomies (6·4 versus 6·7 per cent respectively; P = 0·884), number of admissions, number of perforations and length of hospital stay, but routine imaging was associated with an increased proportion of patients treated for appendicitis (53·4 versus 46·3 per cent; P = 0·020). AIR score-based risk classification can safely reduce the use of diagnostic imaging and hospital admissions in patients with suspicion of appendicitis. Registration number: NCT00971438 ( http://www.clinicaltrials.gov). © 2017 BJS Society Ltd Published by John Wiley & Sons Ltd.

  12. Selecting supplier combination based on fuzzy multicriteria analysis

    Science.gov (United States)

    Han, Zhi-Qiu; Luo, Xin-Xing; Chen, Xiao-Hong; Yang, Wu-E.

    2015-07-01

    Existing multicriteria analysis (MCA) methods are probably ineffective in selecting a supplier combination. Thus, an MCA-based fuzzy 0-1 programming method is introduced. The programming relates to a simple MCA matrix that is used to select a single supplier. By solving the programming, the most feasible combination of suppliers is selected. Importantly, this result differs from selecting suppliers one by one according to a single-selection order, which is used to rank sole suppliers in existing MCA methods. An example highlights such difference and illustrates the proposed method.

  13. Control group selection in critical care randomized controlled trials evaluating interventional strategies: An ethical assessment.

    Science.gov (United States)

    Silverman, Henry J; Miller, Franklin G

    2004-03-01

    Ethical concern has been raised with critical care randomized controlled trials in which the standard of care reflects a broad range of clinical practices. Commentators have argued that trials without an unrestricted control group, in which standard practices are implemented at the discretion of the attending physician, lack the ability to redefine the standard of care and might expose subjects to excessive harms due to an inability to stop early. To develop a framework for analyzing control group selection for critical care trials. Ethical analysis. A key ethical variable in trial design is the extent with which the control group adequately reflects standard care practices. Such a control group might incorporate either the "unrestricted" practices of physicians or a protocol that specifies and restricts the parameters of standard practices. Control group selection should be determined with respect to the following ethical objectives of trial design: 1) clinical value, 2) scientific validity, 3) efficiency and feasibility, and 4) protection of human subjects. Because these objectives may conflict, control group selection will involve trade-offs and compromises. Trials using a protocolized rather than an unrestricted standard care control group will likely have enhanced validity. However, if the protocolized control group lacks representativeness to standard care practices, then trials that use such groups will offer less clinical value and could provide less assurance of protecting subjects compared with trials that use unrestricted control groups. For trials evaluating contrasting strategies that do not adequately represent standard practices, use of a third group that is more representative of standard practices will enhance clinical value and increase the ability to stop early if needed to protect subjects. These advantages might come at the expense of efficiency and feasibility. Weighing and balancing the competing ethical objectives of trial design should be

  14. Effectiveness of a selective, personality-targeted prevention program for adolescent alcohol use and misuse: a cluster randomized controlled trial.

    Science.gov (United States)

    Conrod, Patricia J; O'Leary-Barrett, Maeve; Newton, Nicola; Topper, Lauren; Castellanos-Ryan, Natalie; Mackie, Clare; Girard, Alain

    2013-03-01

    Selective school-based alcohol prevention programs targeting youth with personality risk factors for addiction and mental health problems have been found to reduce substance use and misuse in those with elevated personality profiles. To report 24-month outcomes of the Teacher-Delivered Personality-Targeted Interventions for Substance Misuse Trial (Adventure trial) in which school staff were trained to provide interventions to students with 1 of 4 high-risk (HR) profiles: anxiety sensitivity, hopelessness, impulsivity, and sensation seeking and to examine the indirect herd effects of this program on the broader low-risk (LR) population of students who were not selected for intervention. Cluster randomized controlled trial. Secondary schools in London, United Kingdom. A total of 1210 HR and 1433 LR students in the ninth grade (mean [SD] age, 13.7 [0.33] years). Schools were randomized to provide brief personality-targeted interventions to HR youth or treatment as usual (statutory drug education in class). Participants were assessed for drinking, binge drinking, and problem drinking before randomization and at 6-monthly intervals for 2 years. Two-part latent growth models indicated long-term effects of the intervention on drinking rates (β = -0.320, SE = 0.145, P = .03) and binge drinking rates (β = -0.400, SE = 0.179, P = .03) and growth in binge drinking (β = -0.716, SE = 0.274, P = .009) and problem drinking (β = -0.452, SE = 0.193, P = .02) for HR youth. The HR youth were also found to benefit from the interventions during the 24-month follow-up on drinking quantity (β = -0.098, SE = 0.047, P = .04), growth in drinking quantity (β = -0.176, SE = 0.073, P = .02), and growth in binge drinking frequency (β = -0.183, SE = 0.092, P = .047). Some herd effects in LR youth were observed, specifically on drinking rates (β = -0.259, SE = 0.132, P = .049) and growth of binge drinking (β = -0.244, SE = 0.073, P = .001), during the 24-month follow-up. Findings further

  15. An improved label propagation algorithm based on node importance and random walk for community detection

    Science.gov (United States)

    Ma, Tianren; Xia, Zhengyou

    2017-05-01

    Currently, with the rapid development of information technology, the electronic media for social communication is becoming more and more popular. Discovery of communities is a very effective way to understand the properties of complex networks. However, traditional community detection algorithms consider the structural characteristics of a social organization only, with more information about nodes and edges wasted. In the meanwhile, these algorithms do not consider each node on its merits. Label propagation algorithm (LPA) is a near linear time algorithm which aims to find the community in the network. It attracts many scholars owing to its high efficiency. In recent years, there are more improved algorithms that were put forward based on LPA. In this paper, an improved LPA based on random walk and node importance (NILPA) is proposed. Firstly, a list of node importance is obtained through calculation. The nodes in the network are sorted in descending order of importance. On the basis of random walk, a matrix is constructed to measure the similarity of nodes and it avoids the random choice in the LPA. Secondly, a new metric IAS (importance and similarity) is calculated by node importance and similarity matrix, which we can use to avoid the random selection in the original LPA and improve the algorithm stability. Finally, a test in real-world and synthetic networks is given. The result shows that this algorithm has better performance than existing methods in finding community structure.

  16. A Table-Based Random Sampling Simulation for Bioluminescence Tomography

    Directory of Open Access Journals (Sweden)

    Xiaomeng Zhang

    2006-01-01

    Full Text Available As a popular simulation of photon propagation in turbid media, the main problem of Monte Carlo (MC method is its cumbersome computation. In this work a table-based random sampling simulation (TBRS is proposed. The key idea of TBRS is to simplify multisteps of scattering to a single-step process, through randomly table querying, thus greatly reducing the computing complexity of the conventional MC algorithm and expediting the computation. The TBRS simulation is a fast algorithm of the conventional MC simulation of photon propagation. It retained the merits of flexibility and accuracy of conventional MC method and adapted well to complex geometric media and various source shapes. Both MC simulations were conducted in a homogeneous medium in our work. Also, we present a reconstructing approach to estimate the position of the fluorescent source based on the trial-and-error theory as a validation of the TBRS algorithm. Good agreement is found between the conventional MC simulation and the TBRS simulation.

  17. The adverse effect of selective cyclooxygenase-2 inhibitor on random skin flap survival in rats.

    Directory of Open Access Journals (Sweden)

    Haiyong Ren

    Full Text Available BACKGROUND: Cyclooxygenase-2(COX-2 inhibitors provide desired analgesic effects after injury or surgery, but evidences suggested they also attenuate wound healing. The study is to investigate the effect of COX-2 inhibitor on random skin flap survival. METHODS: The McFarlane flap model was established in 40 rats and evaluated within two groups, each group gave the same volume of Parecoxib and saline injection for 7 days. The necrotic area of the flap was measured, the specimens of the flap were stained with haematoxylin-eosin(HE for histologic analysis. Immunohistochemical staining was performed to analyse the level of VEGF and COX-2 . RESULTS: 7 days after operation, the flap necrotic area ratio in study group (66.65 ± 2.81% was significantly enlarged than that of the control group(48.81 ± 2.33%(P <0.01. Histological analysis demonstrated angiogenesis with mean vessel density per mm(2 being lower in study group (15.4 ± 4.4 than in control group (27.2 ± 4.1 (P <0.05. To evaluate the expression of COX-2 and VEGF protein in the intermediate area II in the two groups by immunohistochemistry test .The expression of COX-2 in study group was (1022.45 ± 153.1, and in control group was (2638.05 ± 132.2 (P <0.01. The expression of VEGF in the study and control groups were (2779.45 ± 472.0 vs (4938.05 ± 123.6(P <0.01.In the COX-2 inhibitor group, the expressions of COX-2 and VEGF protein were remarkably down-regulated as compared with the control group. CONCLUSION: Selective COX-2 inhibitor had adverse effect on random skin flap survival. Suppression of neovascularization induced by low level of VEGF was supposed to be the biological mechanism.

  18. Information Gain Based Dimensionality Selection for Classifying Text Documents

    Energy Technology Data Exchange (ETDEWEB)

    Dumidu Wijayasekara; Milos Manic; Miles McQueen

    2013-06-01

    Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexity is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.

  19. Rapid selection of accessible and cleavable sites in RNA by Escherichia coli RNase P and random external guide sequences

    OpenAIRE

    Lundblad, Eirik W.; Xiao, Gaoping; Ko, Jae-hyeong; Altman, Sidney

    2008-01-01

    A method of inhibiting the expression of particular genes by using external guide sequences (EGSs) has been improved in its rapidity and specificity. Random EGSs that have 14-nt random sequences are used in the selection procedure for an EGS that attacks the mRNA for a gene in a particular location. A mixture of the random EGSs, the particular target RNA, and RNase P is used in the diagnostic procedure, which, after completion, is analyzed in a gel with suitable control lanes. Within a few ho...

  20. Multispectral iris recognition based on group selection and game theory

    Science.gov (United States)

    Ahmad, Foysal; Roy, Kaushik

    2017-05-01

    A commercially available iris recognition system uses only a narrow band of the near infrared spectrum (700-900 nm) while iris images captured in the wide range of 405 nm to 1550 nm offer potential benefits to enhance recognition performance of an iris biometric system. The novelty of this research is that a group selection algorithm based on coalition game theory is explored to select the best patch subsets. In this algorithm, patches are divided into several groups based on their maximum contribution in different groups. Shapley values are used to evaluate the contribution of patches in different groups. Results show that this group selection based iris recognition

  1. A random-permutations-based approach to fast read alignment.

    Science.gov (United States)

    Lederman, Roy

    2013-01-01

    Read alignment is a computational bottleneck in some sequencing projects. Most of the existing software packages for read alignment are based on two algorithmic approaches: prefix-trees and hash-tables. We propose a new approach to read alignment using random permutations of strings. We present a prototype implementation and experiments performed with simulated and real reads of human DNA. Our experiments indicate that this permutations-based prototype is several times faster than comparable programs for fast read alignment and that it aligns more reads correctly. This approach may lead to improved speed, sensitivity, and accuracy in read alignment. The algorithm can also be used for specialized alignment applications and it can be extended to other related problems, such as assembly.More information: http://alignment.commons.yale.edu.

  2. A Rule-Based Industrial Boiler Selection System

    Science.gov (United States)

    Tan, C. F.; Khalil, S. N.; Karjanto, J.; Tee, B. T.; Wahidin, L. S.; Chen, W.; Rauterberg, G. W. M.; Sivarao, S.; Lim, T. L.

    2015-09-01

    Boiler is a device used for generating the steam for power generation, process use or heating, and hot water for heating purposes. Steam boiler consists of the containing vessel and convection heating surfaces only, whereas a steam generator covers the whole unit, encompassing water wall tubes, super heaters, air heaters and economizers. The selection of the boiler is very important to the industry for conducting the operation system successfully. The selection criteria are based on rule based expert system and multi-criteria weighted average method. The developed system consists of Knowledge Acquisition Module, Boiler Selection Module, User Interface Module and Help Module. The system capable of selecting the suitable boiler based on criteria weighted. The main benefits from using the system is to reduce the complexity in the decision making for selecting the most appropriate boiler to palm oil process plant.

  3. Recurrent selection of popcorn composites UEMCO1 and UEM-CO2 based on selection indices

    Directory of Open Access Journals (Sweden)

    Rafael Augusto Vieira

    2017-06-01

    Full Text Available Selection indices were applied to data sets of 169 half-sib families of the popcorn composites UEM-Co1 and UEM-Co2 in four cycles of recurrent selection. From 2005 to 2008, the experiments were arranged in a 13 by 13 lattice square design, with two replications per cycle and composite. Genetic gains for popping expansion (PE and grain yield (GY were estimated based on several selection indices and truncation selection. The magnitude and balance of gains estimated for each trait by the indices were compared by an auxiliary statistical value (Ci. This value Ci consists of an arbitrary value, resulting from differences between the gains estimated for n traits by truncation selection and by index i. The indices of Subandi and Mulamba and Mock were the most promising to estimate high and balanced genetic gains for PE and GY in recurrent selection of half-sib popcorn families.

  4. Selection of locations of knots for linear splines in random regression test-day models.

    Science.gov (United States)

    Jamrozik, J; Bohmanova, J; Schaeffer, L R

    2010-04-01

    Using spline functions (segmented polynomials) in regression models requires the knowledge of the location of the knots. Knots are the points at which independent linear segments are connected. Optimal positions of knots for linear splines of different orders were determined in this study for different scenarios, using existing estimates of covariance functions and an optimization algorithm. The traits considered were test-day milk, fat and protein yields, and somatic cell score (SCS) in the first three lactations of Canadian Holsteins. Two ranges of days in milk (from 5 to 305 and from 5 to 365) were taken into account. In addition, four different populations of Holstein cows, from Australia, Canada, Italy and New Zealand, were examined with respect to first lactation (305 days) milk only. The estimates of genetic and permanent environmental covariance functions were based on single- and multiple-trait test-day models, with Legendre polynomials of order 4 as random regressions. A differential evolution algorithm was applied to find the best location of knots for splines of orders 4 to 7 and the criterion for optimization was the goodness-of-fit of the spline covariance function. Results indicated that the optimal position of knots for linear splines differed between genetic and permanent environmental effects, as well as between traits and lactations. Different populations also exhibited different patterns of optimal knot locations. With linear splines, different positions of knots should therefore be used for different effects and traits in random regression test-day models when analysing milk production traits.

  5. Fluorescent naphthalene-based benzene tripod for selective ...

    Indian Academy of Sciences (India)

    Aluminium complex of a naphthalene-based benzene tripod ligand system has been reported for the selective recognition of fluoride in aqueous medium in physiological condition. The ligand can selectively recognize Al3+ through enhancement in the fluorescence intensity and this in situ formed aluminium complex ...

  6. Random genetic drift, natural selection, and noise in human cranial evolution.

    Science.gov (United States)

    Roseman, Charles C

    2016-08-01

    This study assesses the extent to which relationships among groups complicate comparative studies of adaptation in recent human cranial variation and the extent to which departures from neutral additive models of evolution hinder the reconstruction of population relationships among groups using cranial morphology. Using a maximum likelihood evolutionary model fitting approach and a mixed population genomic and cranial data set, I evaluate the relative fits of several widely used models of human cranial evolution. Moreover, I compare the goodness of fit of models of cranial evolution constrained by genomic variation to test hypotheses about population specific departures from neutrality. Models from population genomics are much better fits to cranial variation than are traditional models from comparative human biology. There is not enough evolutionary information in the cranium to reconstruct much of recent human evolution but the influence of population history on cranial variation is strong enough to cause comparative studies of adaptation serious difficulties. Deviations from a model of random genetic drift along a tree-like population history show the importance of environmental effects, gene flow, and/or natural selection on human cranial variation. Moreover, there is a strong signal of the effect of natural selection or an environmental factor on a group of humans from Siberia. The evolution of the human cranium is complex and no one evolutionary process has prevailed at the expense of all others. A holistic unification of phenome, genome, and environmental context, gives us a strong point of purchase on these problems, which is unavailable to any one traditional approach alone. Am J Phys Anthropol 160:582-592, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  7. Specific and selective probes for Staphylococcus aureus from phage-displayed random peptide libraries.

    Science.gov (United States)

    De Plano, Laura M; Carnazza, Santina; Messina, Grazia M L; Rizzo, Maria Giovanna; Marletta, Giovanni; Guglielmino, Salvatore P P

    2017-09-01

    Staphylococcus aureus is a major human pathogen causing health care-associated and community-associated infections. Early diagnosis is essential to prevent disease progression and to reduce complications that can be serious. In this study, we selected, from a 9-mer phage peptide library, a phage clone displaying peptide capable of specific binding to S. aureus cell surface, namely St.au9IVS5 (sequence peptide RVRSAPSSS).The ability of the isolated phage clone to interact specifically with S. aureus and the efficacy of its bacteria-binding properties were established by using enzyme linked immune-sorbent assay (ELISA). We also demonstrated by Western blot analysis that the most reactive and selective phage peptide binds a 78KDa protein on the bacterial cell surface. Furthermore, we observed selectivity of phage-bacteria-binding allowing to identify clinical isolates of S. aureus in comparison with a panel of other bacterial species. In order to explore the possibility of realizing a selective bacteria biosensor device, based on immobilization of affinity-selected phage, we have studied the physisorbed phage deposition onto a mica surface. Atomic Force Microscopy (AFM) was used to determine the organization of phage on mica surface and then the binding performance of mica-physisorbed phage to bacterial target was evaluated during the time by fluorescent microscopy. The system is able to bind specifically about 50% of S. aureus cells after 15' and 90% after one hour. Due to specificity and rapidness, this biosensing strategy paves the way to the further development of new cheap biosensors to be used in developing countries, as lab-on-chip (LOC) to detect bacterial agents in clinical diagnostics applications. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Nonvolatile transtance change random access memory based on magnetoelectric P(VDF-TrFE)/Metglas heterostructures

    Science.gov (United States)

    Lu, Peipei; Shang, Dashan; Shen, Jianxin; Chai, Yisheng; Yang, Chuansen; Zhai, Kun; Cong, Junzhuang; Shen, Shipeng; Sun, Young

    2016-12-01

    Transtance change random access memory (TCRAM) is a type of nonvolatile memory based on the nonlinear magnetoelectric coupling effects of multiferroics. In this work, ferroelectric P(VDF-TrFE) thin films were prepared on Metglas foil substrates by the sol-gel technique to form multiferroic heterostructures. The magnetoelectric voltage coefficient of the heterostructure can be switched reproducibly to different levels between positive and negative values by applying selective electric-field pulses. Compared with bulk multiferroic heterostructures, the polarization switching voltage was reduced to 7 V. Our facile technological approach enables this organic magnetoelectric heterostructure as a promising candidate for the applications in multilevel TCRAM devices.

  9. Solar Thermal AIR Collector Based on New Type Selective Coating

    National Research Council Canada - National Science Library

    Musiy, R.Y; Midyana, G.G; Makitra, R.G; Vasyutin, J.M; Khovanets, G.I; Zaborowskiy, A.B

    2014-01-01

    Based on the best for optical performance and selective coating solar thermal air collector, which operates by solar power on the principle of simultaneous ventilation and heating facilities, is designed...

  10. A learner support model based on peer tutor selection.

    NARCIS (Netherlands)

    Van Rosmalen, Peter; Sloep, Peter; Kester, Liesbeth; Brouns, Francis; De Croock, Marcel; Pannekeet, Kees; Koper, Rob

    2006-01-01

    Van Rosmalen, P., Sloep, P., Kester, L., Brouns, F., De Croock, M., Pannekeet, K., et al. (2008). A learner support model based on peer tutor selection. Journal of Computer Assisted Learning, 24(1), 74-86.

  11. Noise-induced hearing loss in randomly selected New York dairy farmers.

    Science.gov (United States)

    May, J J; Marvel, M; Regan, M; Marvel, L H; Pratt, D S

    1990-01-01

    To understand better the effects of noise levels associated with dairy farming, we randomly selected 49 full-time dairy farmers from an established cohort. Medical and occupational histories were taken and standard audiometric testing was done. Forty-six males (94%) and three females (6%) with a mean age of 43.5 (+/- 13) years and an average of 29.4 (+/- 14) years in farming were tested. Pure Tone Average thresholds (PTA4) at 0.5, 1.0, 2.0, and 3.0 kHz plus High Frequency Average thresholds (HFA3) at 3.0, 4.0, and 6.0 kHz were calculated. Subjects with a loss of greater than or equal to 20 db in either ear were considered abnormal. Eighteen subjects (37%) had abnormal PTA4S and 32 (65%) abnormal HFA3S. The left ear was more severely affected in both groups (p less than or equal to .05, t-test). Significant associations were found between hearing loss and years worked (odds ratio 4.1, r = .53) and age (odds ratio 4.1, r = .59). No association could be found between hearing loss and measles; mumps; previous ear infections; or use of power tools, guns, motorcycles, snowmobiles, or stereo headphones. Our data suggest that among farmers, substantial hearing loss occurs especially in the high-frequency ranges. Presbycusis is an important confounding variable.

  12. Secure Minutiae-Based Fingerprint Templates Using Random Triangle Hashing

    Science.gov (United States)

    Jin, Zhe; Jin Teoh, Andrew Beng; Ong, Thian Song; Tee, Connie

    Due to privacy concern on the widespread use of biometric authentication systems, biometric template protection has gained great attention in the biometric research recently. It is a challenging task to design a biometric template protection scheme which is anonymous, revocable and noninvertible while maintaining acceptable performance. Many methods have been proposed to resolve this problem, and cancelable biometrics is one of them. In this paper, we propose a scheme coined as Random Triangle Hashing which follows the concept of cancelable biometrics in the fingerprint domain. In this method, re-alignment of fingerprints is not required as all the minutiae are translated into a pre-defined 2 dimensional space based on a reference minutia. After that, the proposed Random Triangle hashing method is used to enforce the one-way property (non-invertibility) of the biometric template. The proposed method is resistant to minor translation error and rotation distortion. Finally, the hash vectors are converted into bit-strings to be stored in the database. The proposed method is evaluated using the public database FVC2004 DB1. An EER of less than 1% is achieved by using the proposed method.

  13. Improving Cluster Analysis with Automatic Variable Selection Based on Trees

    Science.gov (United States)

    2014-12-01

    DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE IMPROVING CLUSTER ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES 5. FUNDING NUMBERS 6...BLANK iii Approved for public release; distribution is unlimited IMPROVING CLUSTER ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES Anton D...Cluster: Cluster analysis basics and extensions. (R Package Version, 1.15.2) R Core Team. (2014). R: A language and environment for statistical

  14. CBFS: high performance feature selection algorithm based on feature clearness.

    Directory of Open Access Journals (Sweden)

    Minseok Seo

    Full Text Available BACKGROUND: The goal of feature selection is to select useful features and simultaneously exclude garbage features from a given dataset for classification purposes. This is expected to bring reduction of processing time and improvement of classification accuracy. METHODOLOGY: In this study, we devised a new feature selection algorithm (CBFS based on clearness of features. Feature clearness expresses separability among classes in a feature. Highly clear features contribute towards obtaining high classification accuracy. CScore is a measure to score clearness of each feature and is based on clustered samples to centroid of classes in a feature. We also suggest combining CBFS and other algorithms to improve classification accuracy. CONCLUSIONS/SIGNIFICANCE: From the experiment we confirm that CBFS is more excellent than up-to-date feature selection algorithms including FeaLect. CBFS can be applied to microarray gene selection, text categorization, and image classification.

  15. Jxta-Overlay : an interface for Efficient Peer Selection in P2P JXTA-based Systems

    OpenAIRE

    Xhafa, Fatos; Barolli, Leonard; Daradoumis Haralabus, Atanasi; Fernández Correa, Raúl; Caballé Llobet, Santi

    2008-01-01

    In this paper we address the problem of the efficient peer selection in P2P distributed platforms. To this end, we have developed a P2P distributed platform using Sun's JXTA technology, which is endowed with resource brokerage strategies to efficiently select peers using four selection models: (a) economic scheduling model; (b) priced-based model; (c) peer-priority selection model; and, (d) random selection model. Next, we have deployed the P2P platform in a real network using nodes of the Pl...

  16. Generative Learning Objects Instantiated with Random Numbers Based Expressions

    Directory of Open Access Journals (Sweden)

    Ciprian Bogdan Chirila

    2015-12-01

    Full Text Available The development of interactive e-learning content requires special skills like programming techniques, web integration, graphic design etc. Generally, online educators do not possess such skills and their e-learning products tend to be static like presentation slides and textbooks. In this paper we propose a new interactive model of generative learning objects as a compromise betweenstatic, dull materials and dynamic, complex software e-learning materials developed by specialized teams. We find that random numbers based automatic initialization learning objects increases content diversity, interactivity thus enabling learners’ engagement. The resulted learning object model is at a limited level of complexity related to special e-learning software, intuitive and capable of increasing learners’ interactivity, engagement and motivation through dynamic content. The approach was applied successfully on several computer programing disciplines.

  17. Rich: Region-based Intelligent Cluster-Head Selection and Node Deployment Strategy in Concentric-based WSNs

    Directory of Open Access Journals (Sweden)

    FAN, C.-S.

    2013-11-01

    Full Text Available In a random deployment, sensor nodes are scattered randomly in the sensing field. Hence, the coverage can not be guaranteed. In contrast, the coverage of uniformly deployment is in general larger than the random deployment. However, uniformly deployment strategy may cause unbalanced traffic pattern in wireless sensor networks (WSNs. In this situation, larger load may be imposed to CHs (cluster heads around the sink. Therefore, CHs close to the sink use up their energy earlier than those farther away from the sink. To overcome this problem, we propose a novel node deployment strategy in the concentric model, namely, Region-based Intelligent Cluster-Head selection and node deployment strategy (called Rich. The coverage, energy consumption and data routing issues are well investigated and taken into consideration in the proposed Rich scheme. The simulation results show that the proposed Rich alleviates the unbalanced traffic pattern significantly, prolongs network lifetime and achieves satisfactory coverage ratio.

  18. Rapid selection of accessible and cleavable sites in RNA by Escherichia coli RNase P and random external guide sequences.

    Science.gov (United States)

    Lundblad, Eirik W; Xiao, Gaoping; Ko, Jae-Hyeong; Altman, Sidney

    2008-02-19

    A method of inhibiting the expression of particular genes by using external guide sequences (EGSs) has been improved in its rapidity and specificity. Random EGSs that have 14-nt random sequences are used in the selection procedure for an EGS that attacks the mRNA for a gene in a particular location. A mixture of the random EGSs, the particular target RNA, and RNase P is used in the diagnostic procedure, which, after completion, is analyzed in a gel with suitable control lanes. Within a few hours, the procedure is complete. The action of EGSs designed by an older method is compared with EGSs designed by the random EGS method on mRNAs from two bacterial pathogens.

  19. A Randomized Controlled Trial of Cognitive Debiasing Improves Assessment and Treatment Selection for Pediatric Bipolar Disorder

    Science.gov (United States)

    Jenkins, Melissa M.; Youngstrom, Eric A.

    2015-01-01

    Objective This study examined the efficacy of a new cognitive debiasing intervention in reducing decision-making errors in the assessment of pediatric bipolar disorder (PBD). Method The study was a randomized controlled trial using case vignette methodology. Participants were 137 mental health professionals working in different regions of the US (M=8.6±7.5 years of experience). Participants were randomly assigned to a (1) brief overview of PBD (control condition), or (2) the same brief overview plus a cognitive debiasing intervention (treatment condition) that educated participants about common cognitive pitfalls (e.g., base-rate neglect; search satisficing) and taught corrective strategies (e.g., mnemonics, Bayesian tools). Both groups evaluated four identical case vignettes. Primary outcome measures were clinicians’ diagnoses and treatment decisions. The vignette characters’ race/ethnicity was experimentally manipulated. Results Participants in the treatment group showed better overall judgment accuracy, p < .001, and committed significantly fewer decision-making errors, p < .001. Inaccurate and somewhat accurate diagnostic decisions were significantly associated with different treatment and clinical recommendations, particularly in cases where participants missed comorbid conditions, failed to detect the possibility of hypomania or mania in depressed youths, and misdiagnosed classic manic symptoms. In contrast, effects of patient race were negligible. Conclusions The cognitive debiasing intervention outperformed the control condition. Examining specific heuristics in cases of PBD may identify especially problematic mismatches between typical habits of thought and characteristics of the disorder. The debiasing intervention was brief and delivered via the Web; it has the potential to generalize and extend to other diagnoses as well as to various practice and training settings. PMID:26727411

  20. Effect of tailoring in an internet-based intervention for smoking cessation: randomized controlled trial.

    Science.gov (United States)

    Wangberg, Silje C; Nilsen, Olav; Antypas, Konstantinos; Gram, Inger Torhild

    2011-12-15

    Studies suggest that tailored materials are superior to nontailored materials in supporting health behavioral change. Several trials on tailored Internet-based interventions for smoking cessation have shown good effects. There have, however, been few attempts to isolate the effect of the tailoring component of an Internet-based intervention for smoking cessation and to compare it with the effectiveness of the other components. The study aim was to isolate the effect of tailored emails in an Internet-based intervention for smoking cessation by comparing two versions of the intervention, with and without tailored content. We conducted a two-arm, randomized controlled trial of the open and free Norwegian 12-month follow-up, fully automated Internet-based intervention for smoking cessation, slutta.no. We collected information online on demographics, smoking, self-efficacy, use of the website, and participant evaluation at enrollment and subsequently at 1, 3, and 12 months. Altogether, 2298 self-selected participants aged 16 years or older registered at the website between August 15, 2006 and December 7, 2007 and were randomly assigned to either a multicomponent, nontailored Internet-based intervention for smoking cessation (control) or a version of the same Internet-based intervention with tailored content delivered on the website and via email. Of the randomly assigned participants, 116 (of 419, response rate = 27.7%) in the intervention group and 128 (of 428, response rate = 29.9%) in the control group had participated over the 12 months and responded at the end of follow-up. The 7-day intention-to-treat abstinence rate at 1 month was 15.2% (149/982) among those receiving the tailored intervention, compared with 9.4% (94/999) among those who received the nontailored intervention (P Internet-based intervention for smoking cessation seems to increase the success rates in the short term, but not in the long term.

  1. Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

    Science.gov (United States)

    Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

    2017-07-01

    All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

  2. Content analysis of a stratified random selection of JVME articles: 1974-2004.

    Science.gov (United States)

    Olson, Lynne E

    2011-01-01

    A content analysis was performed on a random sample (N = 168) of 25% of the articles published in the Journal of Veterinary Medical Education (JVME) per year from 1974 through 2004. Over time, there were increased numbers of authors per paper, more cross-institutional collaborations, greater prevalence of references or endnotes, and lengthier articles, which could indicate a trend toward publications describing more complex or complete work. The number of first authors that could be identified as female was greatest for the most recent time period studied (2000-2004). Two different categorization schemes were created to assess the content of the publications. The first categorization scheme identified the most frequently published topics as admissions, descriptions of courses, the effect of changing teaching methods, issues facing the profession, and examples of uses of technology. The second categorization scheme identified the subset of articles that described medical education research on the basis of the purpose of the research, which represented only 14% of the sample articles (24 of 168). Of that group, only three of 24, or 12%, represented studies based on a firm conceptual framework that could be confirmed or refuted by the study's results. The results indicate that JVME is meeting its broadly based mission and that publications in the veterinary medical education literature have features common to publications in medicine and medical education.

  3. Bayesian dose selection design for a binary outcome using restricted response adaptive randomization.

    Science.gov (United States)

    Meinzer, Caitlyn; Martin, Renee; Suarez, Jose I

    2017-09-08

    In phase II trials, the most efficacious dose is usually not known. Moreover, given limited resources, it is difficult to robustly identify a dose while also testing for a signal of efficacy that would support a phase III trial. Recent designs have sought to be more efficient by exploring multiple doses through the use of adaptive strategies. However, the added flexibility may potentially increase the risk of making incorrect assumptions and reduce the total amount of information available across the dose range as a function of imbalanced sample size. To balance these challenges, a novel placebo-controlled design is presented in which a restricted Bayesian response adaptive randomization (RAR) is used to allocate a majority of subjects to the optimal dose of active drug, defined as the dose with the lowest probability of poor outcome. However, the allocation between subjects who receive active drug or placebo is held constant to retain the maximum possible power for a hypothesis test of overall efficacy comparing the optimal dose to placebo. The design properties and optimization of the design are presented in the context of a phase II trial for subarachnoid hemorrhage. For a fixed total sample size, a trade-off exists between the ability to select the optimal dose and the probability of rejecting the null hypothesis. This relationship is modified by the allocation ratio between active and control subjects, the choice of RAR algorithm, and the number of subjects allocated to an initial fixed allocation period. While a responsive RAR algorithm improves the ability to select the correct dose, there is an increased risk of assigning more subjects to a worse arm as a function of ephemeral trends in the data. A subarachnoid treatment trial is used to illustrate how this design can be customized for specific objectives and available data. Bayesian adaptive designs are a flexible approach to addressing multiple questions surrounding the optimal dose for treatment efficacy

  4. A data mining approach to selecting herbs with similar efficacy: Targeted selection methods based on medical subject headings (MeSH).

    Science.gov (United States)

    Yea, Sang-Jun; Seong, BoSeok; Jang, Yunji; Kim, Chul

    2016-04-22

    Natural products have long been the most important source of ingredients in the discovery of new drugs. Moreover, since the Nagoya Protocol, finding alternative herbs with similar efficacy in traditional medicine has become a very important issue. Although random selection is a common method of finding ethno-medicinal herbs of similar efficacy, it proved to be less effective; therefore, this paper proposes a novel targeted selection method using data mining approaches in the MEDLINE database in order to identify and select herbs with a similar degree of efficacy. From among sixteen categories of medical subject headings (MeSH) descriptors, three categories containing terms related to herbal compounds, efficacy, toxicity, and the metabolic process were selected. In order to select herbs of similar efficacy in a targeted way, we adopted the similarity measurement method based on MeSH. In order to evaluate the proposed algorithm, we built up three different validation datasets which contain lists of original herbs and corresponding medicinal herbs of similar efficacy. The average area under curve (AUC) of the proposed algorithm was found to be about 500% larger than the random selection method. We found that the proposed algorithm puts more hits at the front of the top-10 list than the random selection method, and precisely discerns the efficacy of the herbs. It was also found that the AUC of the experiments either remained the same or increased slightly in all three validation datasets as the search range was increased. This study reveals and proves that the proposed algorithm is significantly more accurate and efficient in finding alternative herbs of similar efficacy than the random selection method. As such, it is hoped that this approach will be used in diverse applications in the ethno-pharmacology field. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  5. Structure-based design of cyclooxygenase-2 selectivity into ketoprofen.

    Science.gov (United States)

    Palomer, Albert; Pascual, Jaume; Cabré, Marta; Borràs, Liset; González, Gracia; Aparici, Mònica; Carabaza, Assumpta; Cabré, Francesc; García, M Luisa; Mauleón, David

    2002-02-25

    We have recently described how to achieve COX-2 selectivity from the non-selective inhibitor indomethacin (1) using a combination of a pharmacophore and computer 3-D models based on the known X-ray crystal structures of cyclooxygenases. In the present study we have focused on the design of COX-2 selective analogues of the NSAID ketoprofen (2). The design is similarly based on the combined use of the previous pharmacophore together with traditional medicinal chemistry techniques motivated by the comparative modeling of the 3-D structures of 2 docked into the COX active sites. The analysis includes use of the program GRID to detect isoenzyme differences near the active site region and is aimed at suggesting modifications of the basic benzophenone frame of the lead compound 2. The resulting series of compounds bearing this central framework is exemplified by the potent and selective COX-2 inhibitor 17 (LM-1669).

  6. A random walk-based segmentation framework for 3D ultrasound images of the prostate.

    Science.gov (United States)

    Ma, Ling; Guo, Rongrong; Tian, Zhiqiang; Fei, Baowei

    2017-10-01

    Accurate segmentation of the prostate on ultrasound images has many applications in prostate cancer diagnosis and therapy. Transrectal ultrasound (TRUS) has been routinely used to guide prostate biopsy. This manuscript proposes a semiautomatic segmentation method for the prostate on three-dimensional (3D) TRUS images. The proposed segmentation method uses a context-classification-based random walk algorithm. Because context information reflects patient-specific characteristics and prostate changes in the adjacent slices, and classification information reflects population-based prior knowledge, we combine the context and classification information at the same time in order to define the applicable population and patient-specific knowledge so as to more accurately determine the seed points for the random walk algorithm. The method is initialized with the user drawing the prostate and non-prostate circles on the mid-gland slice and then automatically segments the prostate on other slices. To achieve reliable classification, we use a new adaptive k-means algorithm to cluster the training data and train multiple decision-tree classifiers. According to the patient-specific characteristics, the most suitable classifier is selected and combined with the context information in order to locate the seed points. By providing accuracy locations of the seed points, the random walk algorithm improves segmentation performance. We evaluate the proposed segmentation approach on a set of 3D TRUS volumes of prostate patients. The experimental results show that our method achieved a Dice similarity coefficient of 91.0% ± 1.6% as compared to manual segmentation by clinically experienced radiologist. The random walk-based segmentation framework, which combines patient-specific characteristics and population information, is effective for segmenting the prostate on ultrasound images. The segmentation method can have various applications in ultrasound-guided prostate procedures. © 2017

  7. Cooperative Technique Based on Sensor Selection in Wireless Sensor Network

    OpenAIRE

    ISLAM, M. R.; KIM, J.

    2009-01-01

    An energy efficient cooperative technique is proposed for the IEEE 1451 based Wireless Sensor Networks. Selected numbers of Wireless Transducer Interface Modules (WTIMs) are used to form a Multiple Input Single Output (MISO) structure wirelessly connected with a Network Capable Application Processor (NCAP). Energy efficiency and delay of the proposed architecture are derived for different combination of cluster size and selected number of WTIMs. Optimized constellation parameters are used for...

  8. Supplier selection based on multi-criterial AHP method

    Directory of Open Access Journals (Sweden)

    Jana Pócsová

    2010-03-01

    Full Text Available This paper describes a case-study of supplier selection based on multi-criterial Analytic Hierarchy Process (AHP method.It is demonstrated that using adequate mathematical method can bring us “unprejudiced” conclusion, even if the alternatives (suppliercompanies are very similar in given selection-criteria. The result is the best possible supplier company from the viewpoint of chosen criteriaand the price of the product.

  9. Construction Tender Subcontract Selection using Case-based Reasoning

    Directory of Open Access Journals (Sweden)

    Due Luu

    2012-11-01

    Full Text Available Obtaining competitive quotations from suitably qualified subcontractors at tender tim n significantly increase the chance of w1nmng a construction project. Amidst an increasingly growing trend to subcontracting in Australia, selecting appropriate subcontractors for a construction project can be a daunting task requiring the analysis of complex and dynamic criteria such as past performance, suitable experience, track record of competitive pricing, financial stability and so on. Subcontractor selection is plagued with uncertainty and vagueness and these conditions are difficul_t o represent in generalised sets of rules. DeciSIOns pertaining to the selection of subcontr:act?s tender time are usually based on the mtu1t1onand past experience of construction estimators. Case-based reasoning (CBR may be an appropriate method of addressing the chal_lenges of selecting subcontractors because CBR 1s able to harness the experiential knowledge of practitioners. This paper reviews the practicality and suitability of a CBR approach for subcontractor tender selection through the development of a prototype CBR procurement advisory system. In this system, subcontractor selection cases are represented by a set of attributes elicited from experienced construction estimators. The results indicate that CBR can enhance the appropriateness of the selection of subcontractors for construction projects.

  10. Conflicts of Interest, Selective Inertia, and Research Malpractice in Randomized Clinical Trials: An Unholy Trinity.

    Science.gov (United States)

    Berger, Vance W

    2015-08-01

    Recently a great deal of attention has been paid to conflicts of interest in medical research, and the Institute of Medicine has called for more research into this important area. One research question that has not received sufficient attention concerns the mechanisms of action by which conflicts of interest can result in biased and/or flawed research. What discretion do conflicted researchers have to sway the results one way or the other? We address this issue from the perspective of selective inertia, or an unnatural selection of research methods based on which are most likely to establish the preferred conclusions, rather than on which are most valid. In many cases it is abundantly clear that a method that is not being used in practice is superior to the one that is being used in practice, at least from the perspective of validity, and that it is only inertia, as opposed to any serious suggestion that the incumbent method is superior (or even comparable), that keeps the inferior procedure in use, to the exclusion of the superior one. By focusing on these flawed research methods we can go beyond statements of potential harm from real conflicts of interest, and can more directly assess actual (not potential) harm.

  11. A curriculum-based approach for feature selection

    Science.gov (United States)

    Kalavala, Deepthi; Bhagvati, Chakravarthy

    2017-06-01

    Curriculum learning is a learning technique in which a classifier learns from easy samples first and then from increasingly difficult samples. On similar lines, a curriculum based feature selection framework is proposed for identifying most useful features in a dataset. Given a dataset, first, easy and difficult samples are identified. In general, the number of easy samples is assumed larger than difficult samples. Then, feature selection is done in two stages. In the first stage a fast feature selection method which gives feature scores is used. Feature scores are then updated incrementally with the set of difficult samples. The existing feature selection methods are not incremental in nature; entire data needs to be used in feature selection. The use of curriculum learning is expected to decrease the time needed for feature selection with classification accuracy comparable to the existing methods. Curriculum learning also allows incremental refinements in feature selection as new training samples become available. Our experiments on a number of standard datasets demonstrate that feature selection is indeed faster without sacrificing classification accuracy.

  12. Capturing the Flatness of a peer-to-peer lending network through random and selected perturbations

    Science.gov (United States)

    Karampourniotis, Panagiotis D.; Singh, Pramesh; Uparna, Jayaram; Horvat, Emoke-Agnes; Szymanski, Boleslaw K.; Korniss, Gyorgy; Bakdash, Jonathan Z.; Uzzi, Brian

    Null models are established tools that have been used in network analysis to uncover various structural patterns. They quantify the deviance of an observed network measure to that given by the null model. We construct a null model for weighted, directed networks to identify biased links (carrying significantly different weights than expected according to the null model) and thus quantify the flatness of the system. Using this model, we study the flatness of Kiva, a large international crownfinancing network of borrowers and lenders, aggregated to the country level. The dataset spans the years from 2006 to 2013. Our longitudinal analysis shows that flatness of the system is reducing over time, meaning the proportion of biased inter-country links is growing. We extend our analysis by testing the robustness of the flatness of the network in perturbations on the links' weights or the nodes themselves. Examples of such perturbations are event shocks (e.g. erecting walls) or regulatory shocks (e.g. Brexit). We find that flatness is unaffected by random shocks, but changes after shocks target links with a large weight or bias. The methods we use to capture the flatness are based on analytics, simulations, and numerical computations using Shannon's maximum entropy. Supported by ARL NS-CTA.

  13. Home-Based Versus Laboratory-Based Robotic Ankle Training for Children With Cerebral Palsy: A Pilot Randomized Comparative Trial.

    Science.gov (United States)

    Chen, Kai; Wu, Yi-Ning; Ren, Yupeng; Liu, Lin; Gaebler-Spira, Deborah; Tankard, Kelly; Lee, Julia; Song, Weiqun; Wang, Maobin; Zhang, Li-Qun

    2016-08-01

    To examine the outcomes of home-based robot-guided therapy and compare it to laboratory-based robot-guided therapy for the treatment of impaired ankles in children with cerebral palsy. A randomized comparative trial design comparing a home-based training group and a laboratory-based training group. Home versus laboratory within a research hospital. Children (N=41) with cerebral palsy who were at Gross Motor Function Classification System level I, II, or III were randomly assigned to 2 groups. Children in home-based and laboratory-based groups were 8.7±2.8 (n=23) and 10.7±6.0 (n=18) years old, respectively. Six-week combined passive stretching and active movement intervention of impaired ankle in a laboratory or home environment using a portable rehabilitation robot. Active dorsiflexion range of motion (as the primary outcome), mobility (6-minute walk test and timed Up and Go test), balance (Pediatric Balance Scale), Selective Motor Control Assessment of the Lower Extremity, Modified Ashworth Scale (MAS) for spasticity, passive range of motion (PROM), strength, and joint stiffness. Significant improvements were found for the home-based group in all biomechanical outcome measures except for PROM and all clinical outcome measures except the MAS. The laboratory-based group also showed significant improvements in all the biomechanical outcome measures and all clinical outcome measures except the MAS. There were no significant differences in the outcome measures between the 2 groups. These findings suggest that the translation of repetitive, goal-directed, biofeedback training through motivating games from the laboratory to the home environment is feasible. The benefits of home-based robot-guided therapy were similar to those of laboratory-based robot-guided therapy. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  14. Problem-based learning in continuing medical education: review of randomized controlled trials.

    Science.gov (United States)

    Al-Azri, Hilal; Ratnapalan, Savithiri

    2014-02-01

    To investigate the effects of problem-based learning (PBL) in continuing medical education. PubMed, MEDLINE, EMBASE, CINAHL, and ERIC databases were searched for randomized controlled trials published in English from January 2001 to May 2011 using key words problem-based learning, practice-based, self-directed, learner-centered, and active learning, combined with continuing medical education, continuing professional development, post professional, postgraduate, and adult learning. Randomized controlled trials that described the effects of PBL on knowledge enhancement, performance improvement, participants' satisfaction, or patients' health outcomes were selected for analysis. Fifteen studies were included in this review: 4 involved postgraduate trainee doctors, 10 involved practising physicians, and 1 had both groups. Online learning was used in 7 studies. Among postgraduate trainees PBL showed no significant differences in knowledge gain compared with lectures or non-case-based learning. In continuing education, PBL showed no significant difference in knowledge gain when compared with other methods. Several studies did not provide an educational intervention for the control group. Physician performance improvement showed an upward trend in groups participating in PBL, but no significant differences were noted in health outcomes. Online PBL is a useful method of delivering continuing medical education. There is limited evidence that PBL in continuing education would enhance physicians' performance or improve health outcomes.

  15. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

    Directory of Open Access Journals (Sweden)

    Jun-He Yang

    2017-01-01

    Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

  16. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

    Science.gov (United States)

    Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

    2017-01-01

    Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

  17. Selectivity of Chemoresistive Sensors Made of Chemically Functionalized Carbon Nanotube Random Networks for Volatile Organic Compounds (VOC

    Directory of Open Access Journals (Sweden)

    Jean-François Feller

    2014-01-01

    Full Text Available Different grades of chemically functionalized carbon nanotubes (CNT have been processed by spraying layer-by-layer (sLbL to obtain an array of chemoresistive transducers for volatile organic compound (VOC detection. The sLbL process led to random networks of CNT less conductive, but more sensitive to vapors than filtration under vacuum (bucky papers. Shorter CNT were also found to be more sensitive due to the less entangled and more easily disconnectable conducting networks they are making. Chemical functionalization of the CNT’ surface is changing their selectivity towards VOC, which makes it possible to easily discriminate methanol, chloroform and tetrahydrofuran (THF from toluene vapors after the assembly of CNT transducers into an array to make an e-nose. Interestingly, the amplitude of the CNT transducers’ responses can be enhanced by a factor of five (methanol to 100 (chloroform by dispersing them into a polymer matrix, such as poly(styrene (PS, poly(carbonate (PC or poly(methyl methacrylate (PMMA. COOH functionalization of CNT was found to penalize their dispersion in polymers and to decrease the sensors’ sensitivity. The resulting conductive polymer nanocomposites (CPCs not only allow for a more easy tuning of the sensors’ selectivity by changing the chemical nature of the matrix, but they also allow them to adjust their sensitivity by changing the average gap between CNT (acting on quantum tunneling in the CNT network. Quantum resistive sensors (QRSs appear promising for environmental monitoring and anticipated disease diagnostics that are both based on VOC analysis.

  18. Microbiota-based Signature of Gingivitis Treatments: A Randomized Study.

    Science.gov (United States)

    Huang, Shi; Li, Zhen; He, Tao; Bo, Cunpei; Chang, Jinlan; Li, Lin; He, Yanyan; Liu, Jiquan; Charbonneau, Duane; Li, Rui; Xu, Jian

    2016-04-20

    Plaque-induced gingivitis can be alleviated by various treatment regimens. To probe the impacts of various anti-gingivitis treatments on plaque microflora, here a double blinded, randomized controlled trial of 91 adults with moderate gingivitis was designed with two anti-gingivitis regimens: the brush-alone treatment and the brush-plus-rinse treatment. In the later group, more reduction in both Plaque Index (TMQHI) and Gingival Index (mean MGI) at Day 3, Day 11 and Day 27 was evident, and more dramatic changes were found between baseline and other time points for both supragingival plaque microbiota structure and salivary metabonomic profiles. A comparison of plaque microbiota changes was also performed between these two treatments and a third dataset where 50 subjects received regimen of dental scaling. Only Actinobaculum, TM7 and Leptotrichia were consistently reduced by all the three treatments, whereas the different microbial signatures of the three treatments during gingivitis relieve indicate distinct mechanisms of action. Our study suggests that microbiota based signatures can serve as a valuable approach for understanding and potentially comparing the modes of action for clinical treatments and oral-care products in the future.

  19. Random Harmonic Detection and Compensation Based on Synchronous Reference Frame

    Directory of Open Access Journals (Sweden)

    Yanbo Che

    2017-01-01

    Full Text Available Algorithms for harmonic detection and compensation are important guarantees for an active power filter (APF to achieve the harmonic control function and directly determine the overall performance. Existing algorithms usually need a large amount of computation, and the compensation effect of specified order harmonic is also limited. DC side capacitor voltage at sudden change of load is affected by the algorithm as well. This paper proposes a new algorithm for harmonic detection and compensation based on synchronous reference frame (SRF, in which a band-pass filter with center frequency of 6kth harmonic is designed in fundamental frequency SRF to extract random harmonic current with two different frequencies of (6k±1th harmonic in stationary reference frame. This new algorithm can rapidly detect any specified harmonic, and it can adjust the power factor to compensate reactive power. Meanwhile, it has few impacts on DC side capacitor voltage under complicated operating conditions such as sudden change of load. The correctness and effectiveness of this new algorithm are verified by simulation and experiment.

  20. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

    CERN Document Server

    Tangaro, Sabina; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Inglese, Paolo; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

    2015-01-01

    Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations and therefore be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach, for each voxel a number of local features were calculated. In this paper we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential Backward Elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects...

  1. Selection of principal components based on Fisher discriminant ratio

    Science.gov (United States)

    Zeng, Xiangyan; Naghedolfeizi, Masoud; Arora, Sanjeev; Yousif, Nabil; Aberra, Dawit

    2016-05-01

    Principal component analysis transforms a set of possibly correlated variables into uncorrelated variables, and is widely used as a technique of dimensionality reduction and feature extraction. In some applications of dimensionality reduction, the objective is to use a small number of principal components to represent most variation in the data. On the other hand, the main purpose of feature extraction is to facilitate subsequent pattern recognition and machine learning tasks, such as classification. Selecting principal components for classification tasks aims for more than dimensionality reduction. The capability of distinguishing different classes is another major concern. Components that have larger eigenvalues do not necessarily have better distinguishing capabilities. In this paper, we investigate a strategy of selecting principal components based on the Fisher discriminant ratio. The ratio of between class variance to within class variance is calculated for each component, based on which the principal components are selected. The number of relevant components is determined by the classification accuracy. To alleviate overfitting which is common when there are few training data available, we use a cross-validation procedure to determine the number of principal components. The main objective is to select the components that have large Fisher discriminant ratios so that adequate class separability is obtained. The number of selected components is determined by the classification accuracy of the validation data. The selection method is evaluated by face recognition experiments.

  2. Evolution of tag-based cooperation on Erdős-Rényi random graphs

    Science.gov (United States)

    Lima, F. W. S.; Hadzibeganovic, Tarik; Stauffer, Dietrich

    2014-12-01

    Here, we study an agent-based model of the evolution of tag-mediated cooperation on Erdős-Rényi random graphs. In our model, agents with heritable phenotypic traits play pairwise Prisoner's Dilemma-like games and follow one of the four possible strategies: Ethnocentric, altruistic, egoistic and cosmopolitan. Ethnocentric and cosmopolitan strategies are conditional, i.e. their selection depends upon the shared phenotypic similarity among interacting agents. The remaining two strategies are always unconditional, meaning that egoists always defect while altruists always cooperate. Our simulations revealed that ethnocentrism can win in both early and later evolutionary stages on directed random graphs when reproduction of artificial agents was asexual; however, under the sexual mode of reproduction on a directed random graph, we found that altruists dominate initially for a rather short period of time, whereas ethnocentrics and egoists suppress other strategists and compete for dominance in the intermediate and later evolutionary stages. Among our results, we also find surprisingly regular oscillations which are not damped in the course of time even after half a million Monte Carlo steps. Unlike most previous studies, our findings highlight conditions under which ethnocentrism is less stable or suppressed by other competing strategies.

  3. Linear regression-based feature selection for microarray data classification.

    Science.gov (United States)

    Abid Hasan, Md; Hasan, Md Kamrul; Abdul Mottalib, M

    2015-01-01

    Predicting the class of gene expression profiles helps improve the diagnosis and treatment of diseases. Analysing huge gene expression data otherwise known as microarray data is complicated due to its high dimensionality. Hence the traditional classifiers do not perform well where the number of features far exceeds the number of samples. A good set of features help classifiers to classify the dataset efficiently. Moreover, a manageable set of features is also desirable for the biologist for further analysis. In this paper, we have proposed a linear regression-based feature selection method for selecting discriminative features. Our main focus is to classify the dataset more accurately using less number of features than other traditional feature selection methods. Our method has been compared with several other methods and in almost every case the classification accuracy is higher using less number of features than the other popular feature selection methods.

  4. A generator for unique quantum random numbers based on vacuum states

    DEFF Research Database (Denmark)

    Gabriel, C.; Wittmann, C.; Sych, D.

    2010-01-01

    Random numbers are a valuable component in diverse applications that range from simulations(1) over gambling to cryptography(2,3). The quest for true randomness in these applications has engendered a large variety of different proposals for producing random numbers based on the foundational unpre...

  5. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

    Science.gov (United States)

    Zeng, Xueqiang; Luo, Gang

    2017-12-01

    Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

  6. The basic science and mathematics of random mutation and natural selection.

    Science.gov (United States)

    Kleinman, Alan

    2014-12-20

    The mutation and natural selection phenomenon can and often does cause the failure of antimicrobial, herbicidal, pesticide and cancer treatments selection pressures. This phenomenon operates in a mathematically predictable behavior, which when understood leads to approaches to reduce and prevent the failure of the use of these selection pressures. The mathematical behavior of mutation and selection is derived using the principles given by probability theory. The derivation of the equations describing the mutation and selection phenomenon is carried out in the context of an empirical example. Copyright © 2014 John Wiley & Sons, Ltd.

  7. [Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

    Science.gov (United States)

    Zhou, Jinzhi; Tang, Xiaofang

    2015-08-01

    In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

  8. Attribute based selection of thermoplastic resin for vacuum infusion process

    DEFF Research Database (Denmark)

    Prabhakaran, R.T. Durai; Lystrup, Aage; Løgstrup Andersen, Tom

    2011-01-01

    The composite industry looks toward a new material system (resins) based on thermoplastic polymers for the vacuum infusion process, similar to the infusion process using thermosetting polymers. A large number of thermoplastics are available in the market with a variety of properties suitable...... for different engineering applications, and few of those are available in a not yet polymerised form suitable for resin infusion. The proper selection of a new resin system among these thermoplastic polymers is a concern for manufactures in the current scenario and a special mathematical tool would...... be beneficial. In this paper, the authors introduce a new decision making tool for resin selection based on significant attributes. This article provides a broad overview of suitable thermoplastic material systems for vacuum infusion process available in today’s market. An illustrative example—resin selection...

  9. Selection-based virtual keyboard prototypes and data collection application.

    Science.gov (United States)

    Millet, Barbara; Asfour, Shihab; Lewis, James R

    2009-08-01

    An emerging area of research in engineering psychology is the evaluation of text entry for mobile devices using a small number of keys for the control of cursor direction and character selection from a matrix of characters (i.e., selection-based data entry). The present article describes a software tool designed to reduce time and effort in the development of prototypes of alternative selection-based text-entry schemes and their empirical evaluation. The tool, available for distribution to researchers, educators, and students, uses Action Script code compiled into an executable file that has an embedded Adobe Flash Player and is compatible with most operating systems (including Microsoft Windows, Apple OSX, and Linux).

  10. Lensless imaging based on coherent backscattering in random media

    Directory of Open Access Journals (Sweden)

    Lei Xu

    2014-08-01

    Full Text Available We studied lensless imaging due to coherent backscattering in random media both theoretically and experimentally. The point spread function of the lensless imaging system was derived. Parameters such as the volume fraction of the scatterer in the random scattering medium, the diameter of the scatterer, the distance between the object to be imaged and the surface of the random scattering medium were optimized to improve the image contrast and resolution. Moreover, for complicated objects, high contrast and quality images were achieved through the high-order intensity correlation measurement on the image plane, which may propel this imaging technique to practical applications.

  11. Random Forest Classifier for Zero-Shot Learning Based on Relative Attribute.

    Science.gov (United States)

    Cheng, Yuhu; Qiao, Xue; Wang, Xuesong; Yu, Qiang

    2017-03-21

    For the zero-shot image classification with relative attributes (RAs), the traditional method requires that not only all seen and unseen images obey Gaussian distribution, but also the classifications on testing samples are made by maximum likelihood estimation. We therefore propose a novel zero-shot image classifier called random forest based on relative attribute. First, based on the ordered and unordered pairs of images from the seen classes, the idea of ranking support vector machine is used to learn ranking functions for attributes. Then, according to the relative relationship between seen and unseen classes, the RA ranking-score model per attribute for each unseen image is built, where the appropriate seen classes are automatically selected to participate in the modeling process. In the third step, the random forest classifier is trained based on the RA ranking scores of attributes for all seen and unseen images. Finally, the class labels of testing images can be predicted via the trained RF. Experiments on Outdoor Scene Recognition, Pub Fig, and Shoes data sets show that our proposed method is superior to several state-of-the-art methods in terms of classification capability for zero-shot learning problems.

  12. An opinion formation based binary optimization approach for feature selection

    Science.gov (United States)

    Hamedmoghadam, Homayoun; Jalili, Mahdi; Yu, Xinghuo

    2018-02-01

    This paper proposed a novel optimization method based on opinion formation in complex network systems. The proposed optimization technique mimics human-human interaction mechanism based on a mathematical model derived from social sciences. Our method encodes a subset of selected features to the opinion of an artificial agent and simulates the opinion formation process among a population of agents to solve the feature selection problem. The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem. A number of mechanisms are employed to avoid getting trapped in local minima. We compare the performance of the proposed method with a number of classical population-based optimization methods and a state-of-the-art opinion formation based method. Our experiments on a number of high dimensional datasets reveal outperformance of the proposed algorithm over others.

  13. Adverse selection in a community-based health insurance scheme in rural Africa: implications for introducing targeted subsidies.

    Science.gov (United States)

    Parmar, Divya; Souares, Aurélia; de Allegri, Manuela; Savadogo, Germain; Sauerborn, Rainer

    2012-06-28

    Although most community-based health insurance (CBHI) schemes are voluntary, problem of adverse selection is hardly studied. Evidence on the impact of targeted subsidies on adverse selection is completely missing. This paper investigates adverse selection in a CBHI scheme in Burkina Faso. First, we studied the change in adverse selection over a period of 4 years. Second, we studied the effect of targeted subsidies on adverse selection. The study area, covering 41 villages and 1 town, was divided into 33 clusters and CBHI was randomly offered to these clusters during 2004-06. In 2007, premium subsidies were offered to the poor households. The data was collected by a household panel survey 2004-2007 from randomly selected households in these 33 clusters (n = 6795). We applied fixed effect models. We found weak evidence of adverse selection before the implementation of subsidies. Adverse selection significantly increased the next year and targeted subsidies largely explained this increase. Adverse selection is an important concern for any voluntary health insurance scheme. Targeted subsidies are often used as a tool to pursue the vision of universal coverage. At the same time targeted subsidies are also associated with increased adverse selection as found in this study. Therefore, it's essential that targeted subsidies for poor (or other high-risk groups) must be accompanied with a sound plan to bridge the financial gap due to adverse selection so that these schemes can continue to serve these populations.

  14. Automatic peak selection by a Benjamini-Hochberg-based algorithm.

    Science.gov (United States)

    Abbas, Ahmed; Kong, Xin-Bing; Liu, Zhi; Jing, Bing-Yi; Gao, Xin

    2013-01-01

    A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into [Formula: see text]-values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx.

  15. Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm

    KAUST Repository

    Abbas, Ahmed

    2013-01-07

    A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into p-values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013

  16. Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

    Science.gov (United States)

    Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

    2015-04-01

    Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Gear-based species selectivity and potential interactions between ...

    African Journals Online (AJOL)

    Gear-based species selectivity and potential interactions between artisanal and aquarium fisheries in coastal Kenya: implications for reef fisheries management. Gladys Okemwa, Boaz Kaunda-Arara, Edward Kimani, Benrick Ogotu, Harrison Ong'anda, Clay Obota, Mary Ontomwa ...

  18. An assessment of selected organisational-based factors on the ...

    African Journals Online (AJOL)

    The objective of this study was to investigate the influence of selected organisational-based factors on the perceived success of agribusinesses in South Africa. Business success, for the purposes of this study, was measured by means of two dependent variables, namely Business development and improvement and ...

  19. Solar Thermal AIR Collector Based on New Type Selective Coating

    Directory of Open Access Journals (Sweden)

    Musiy, R.Y.

    2014-01-01

    Full Text Available Based on the best for optical performance and selective coating solar thermal air collector, which operates by solar power on the principle of simultaneous ventilation and heating facilities, is designed. It can be used for vacation homes, museums, wooden churches, warehouses, garages, houses, greenhouses etc.

  20. Validity of selected cardiovascular field-based test among Malaysian ...

    African Journals Online (AJOL)

    Based on emerge obese problem among Malaysian, this research is formulated to validate published tests among healthy female adult. Selected test namely; 20 meter multi-stage shuttle run, 2.4km run test, 1 mile walk test and Harvard Step test were correlated with laboratory test (Bruce protocol) to find the criterion validity ...

  1. Health Effects Profiles for Searching Selected Lockheed DIALOG Data Bases.

    Science.gov (United States)

    Clement, Linda Lee

    This preliminary study attempted to determine the most effective search strategies for the topic "health effects" in relation to specific chemicals and/or pollutants--in this case, asbestos--for each of five selected Lockheed DIALOG data bases: BIOSIS Previews, Chemical Abstracts Condensates (Chemcon), NTIS, Enviroline, and Pollution…

  2. Classification and Target Group Selection Based Upon Frequent Patterns

    NARCIS (Netherlands)

    W.H.L.M. Pijls (Wim); R. Potharst (Rob)

    2000-01-01

    textabstractIn this technical report , two new algorithms based upon frequent patterns are proposed. One algorithm is a classification method. The other one is an algorithm for target group selection. In both algorithms, first of all, the collection of frequent patterns in the training set is

  3. A review of selection-based tests of abiotic surrogates for species representation.

    Science.gov (United States)

    Beier, Paul; Sutcliffe, Patricia; Hjort, Jan; Faith, Daniel P; Pressey, Robert L; Albuquerque, Fabio

    2015-06-01

    Because conservation planners typically lack data on where species occur, environmental surrogates--including geophysical settings and climate types--have been used to prioritize sites within a planning area. We reviewed 622 evaluations of the effectiveness of abiotic surrogates in representing species in 19 study areas. Sites selected using abiotic surrogates represented more species than an equal number of randomly selected sites in 43% of tests (55% for plants) and on average improved on random selection of sites by about 8% (21% for plants). Environmental diversity (ED) (42% median improvement on random selection) and biotically informed clusters showed promising results and merit additional testing. We suggest 4 ways to improve performance of abiotic surrogates. First, analysts should consider a broad spectrum of candidate variables to define surrogates, including rarely used variables related to geographic separation, distance from coast, hydrology, and within-site abiotic diversity. Second, abiotic surrogates should be defined at fine thematic resolution. Third, sites (the landscape units prioritized within a planning area) should be small enough to ensure that surrogates reflect species' environments and to produce prioritizations that match the spatial resolution of conservation decisions. Fourth, if species inventories are available for some planning units, planners should define surrogates based on the abiotic variables that most influence species turnover in the planning area. Although species inventories increase the cost of using abiotic surrogates, a modest number of inventories could provide the data needed to select variables and evaluate surrogates. Additional tests of nonclimate abiotic surrogates are needed to evaluate the utility of conserving nature's stage as a strategy for conservation planning in the face of climate change. © 2015 Society for Conservation Biology.

  4. Workplace based mindfulness practice and inflammation: a randomized trial.

    Science.gov (United States)

    Malarkey, William B; Jarjoura, David; Klatt, Maryanna

    2013-01-01

    We have developed a low dose Mindfulness-Based Intervention (MBI-ld) that reduces the time committed to meetings and formal mindfulness practice, while conducting the sessions during the workday. This reduced the barriers commonly mentioned for non-participation in mindfulness programs. In a controlled randomized trial we studied university faculty and staff (n=186) who were found to have an elevated CRP level,>3.0 mg/ml, and who either had, or were at risk for cardiovascular disease. This study was designed to evaluate if MBI-ld could produce a greater decrease in CRP, IL-6 and cortisol than an active control group receiving a lifestyle education program when measured at the end of the 2 month interventions. We found that MBI-ld significantly enhanced mindfulness by 2-months and it was maintained for up to a year when compared to the education control. No significant changes were noted between interventions in cortisol, IL-6 levels or self-reported measures of perceived stress, depression and sleep quality at 2-months. Although not statistically significant (p=.08), the CRP level at 2-months was one mg/ml lower in the MBI-ld group than in the education control group, a change which may have clinical significance (Ridker et al., 2000; Wassel et al., 2010). A larger MBI-ld effect on CRP (as compared to control) occurred among participants who had a baseline BMI 30 (-0.18 mg/ml). We conclude that MBI-ld should be more fully investigated as a low-cost self-directed complementary strategy for decreasing inflammation, and it seems most promising for non-obese subjects. Copyright © 2012 Elsevier Inc. All rights reserved.

  5. Bearing Fault Classification Based on Conditional Random Field

    Directory of Open Access Journals (Sweden)

    Guofeng Wang

    2013-01-01

    Full Text Available Condition monitoring of rolling element bearing is paramount for predicting the lifetime and performing effective maintenance of the mechanical equipment. To overcome the drawbacks of the hidden Markov model (HMM and improve the diagnosis accuracy, conditional random field (CRF model based classifier is proposed. In this model, the feature vectors sequences and the fault categories are linked by an undirected graphical model in which their relationship is represented by a global conditional probability distribution. In comparison with the HMM, the main advantage of the CRF model is that it can depict the temporal dynamic information between the observation sequences and state sequences without assuming the independence of the input feature vectors. Therefore, the interrelationship between the adjacent observation vectors can also be depicted and integrated into the model, which makes the classifier more robust and accurate than the HMM. To evaluate the effectiveness of the proposed method, four kinds of bearing vibration signals which correspond to normal, inner race pit, outer race pit and roller pit respectively are collected from the test rig. And the CRF and HMM models are built respectively to perform fault classification by taking the sub band energy features of wavelet packet decomposition (WPD as the observation sequences. Moreover, K-fold cross validation method is adopted to improve the evaluation accuracy of the classifier. The analysis and comparison under different fold times show that the accuracy rate of classification using the CRF model is higher than the HMM. This method brings some new lights on the accurate classification of the bearing faults.

  6. Novel random peptide libraries displayed on AAV serotype 9 for selection of endothelial cell-directed gene transfer vectors.

    Science.gov (United States)

    Varadi, K; Michelfelder, S; Korff, T; Hecker, M; Trepel, M; Katus, H A; Kleinschmidt, J A; Müller, O J

    2012-08-01

    We have demonstrated the potential of random peptide libraries displayed on adeno-associated virus (AAV)2 to select for AAV2 vectors with improved efficiency for cell type-directed gene transfer. AAV9, however, may have advantages over AAV2 because of a lower prevalence of neutralizing antibodies in humans and more efficient gene transfer in vivo. Here we provide evidence that random peptide libraries can be displayed on AAV9 and can be utilized to select for AAV9 capsids redirected to the cell type of interest. We generated an AAV9 peptide display library, which ensures that the displayed peptides correspond to the packaged genomes and performed four consecutive selection rounds on human coronary artery endothelial cells in vitro. This screening yielded AAV9 library capsids with distinct peptide motifs enabling up to 40-fold improved transduction efficiencies compared with wild-type (wt) AAV9 vectors. Incorporating sequences selected from AAV9 libraries into AAV2 capsids could not increase transduction as efficiently as in the AAV9 context. To analyze the potential on endothelial cells in the intact natural vascular context, human umbilical veins were incubated with the selected AAV in situ and endothelial cells were isolated. Fluorescence-activated cell sorting analysis revealed a 200-fold improved transduction efficiency compared with wt AAV9 vectors. Furthermore, AAV9 vectors with targeting sequences selected from AAV9 libraries revealed an increased transduction efficiency in the presence of human intravenous immunoglobulins, suggesting a reduced immunogenicity. We conclude that our novel AAV9 peptide library is functional and can be used to select for vectors for future preclinical and clinical gene transfer applications.

  7. A school-based randomized controlled trial to improve physical activity among Iranian high school girls

    Directory of Open Access Journals (Sweden)

    Ghofranipour Fazloalha

    2008-04-01

    Full Text Available Abstract Background Physical activity (PA rates decline precipitously during the high school years and are consistently lower among adolescent girls than adolescent boys. Due to cultural barriers, this problem might be exacerbated in female Iranian adolescents. However, little intervention research has been conducted to try to increase PA participation rates with this population. Because PA interventions in schools have the potential to reach many children and adolescents, this study reports on PA intervention research conducted in all-female Iranian high schools. Methods A randomized controlled trial was conducted to examine the effects of two six-month tailored interventions on potential determinants of PA and PA behavior. Students (N = 161 were randomly allocated to one of three conditions: an intervention based on Pender's Health Promotion model (HP, an intervention based on an integration of the health promotion model and selected constructs from the Transtheoretical model (THP, and a control group (CON. Measures were administered prior to the intervention, at post-intervention and at a six-month follow-up. Results Repeated measure ANOVAs showed a significant interaction between group and time for perceived benefits, self efficacy, interpersonal norms, social support, behavioral processes, and PA behavior, indicating that both intervention groups significantly improved across the 24-week intervention, whereas the control group did not. Participants in the THP group showed greater use of counter conditioning and stimulus control at post-intervention and at follow-up. While there were no significant differences in PA between the HP and CON groups at follow-up, a significant difference was still found between the THP and the CON group. Conclusion This study provides the first evidence of the effectiveness of a PA intervention based on Pender's HP model combined with selected aspects of the TTM on potential determinants to increase PA among

  8. Feature Selection for Image Retrieval based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Preeti Kushwaha

    2016-12-01

    Full Text Available This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.

  9. Transmit antenna selection based on shadowing side information

    KAUST Repository

    Yilmaz, Ferkan

    2011-05-01

    In this paper, we propose a new transmit antenna selection scheme based on shadowing side information. In the proposed scheme, single transmit antenna which has the highest shadowing coefficient is selected. By the proposed technique, usage of the feedback channel and channel estimation complexity at the receiver can be reduced. We consider independent but not identically distributed Generalized-K composite fading model, which is a general composite fading & shadowing channel model for wireless environments. Exact closed-form outage probability, moment generating function and symbol error probability expressions are derived. In addition, theoretical performance results are validated by Monte Carlo simulations. © 2011 IEEE.

  10. Effectiveness of Mindfulness-Based Stress Reduction Bibliotherapy: A Preliminary Randomized Controlled Trial.

    Science.gov (United States)

    Hazlett-Stevens, Holly; Oren, Yelena

    2017-06-01

    This randomized controlled investigation examined the effectiveness of a self-help bibliotherapy format of the evidence-based mindfulness-based stress reduction (MBSR) intervention. College students seeking stress reduction were randomly assigned to a 10-week MBSR bibliotherapy intervention group (n = 47) or a no-treatment control group (n = 45). Self-report measures were collected at baseline and postintervention. A total of 25 bibliotherapy and 43 control group participants provided final data following the intervention period. Compared to the control group, bibliotherapy participants reported increased mindfulness following the intervention. Significant decreases on measures of depression, anxiety, stress, perceived stress, and anxiety sensitivity also were reported postintervention as well as increased quality of life in physical health, psychological, and environmental domains. No statistically significant group effects were found for social relationships quality of life domain, worry, and experiential avoidance measures. This MBSR workbook may provide an acceptable and effective alternative for motivated individuals seeking to reduce stress, at least for a select group of individuals who are willing and able to sustain participation in the intervention. © 2016 Wiley Periodicals, Inc.

  11. Model-based sensor location selection for helicopter gearbox monitoring

    Science.gov (United States)

    Jammu, Vinay B.; Wang, Keming; Danai, Kourosh; Lewicki, David G.

    1996-01-01

    A new methodology is introduced to quantify the significance of accelerometer locations for fault diagnosis of helicopter gearboxes. The basis for this methodology is an influence model which represents the effect of various component faults on accelerometer readings. Based on this model, a set of selection indices are defined to characterize the diagnosability of each component, the coverage of each accelerometer, and the relative redundancy between the accelerometers. The effectiveness of these indices is evaluated experimentally by measurement-fault data obtained from an OH-58A main rotor gearbox. These data are used to obtain a ranking of individual accelerometers according to their significance in diagnosis. Comparison between the experimentally obtained rankings and those obtained from the selection indices indicates that the proposed methodology offers a systematic means for accelerometer location selection.

  12. The Jackprot Simulation Couples Mutation Rate with Natural Selection to Illustrate How Protein Evolution Is Not Random

    Science.gov (United States)

    Espinosa, Avelina; Bai, Chunyan Y.

    2016-01-01

    Protein evolution is not a random process. Views which attribute randomness to molecular change, deleterious nature to single-gene mutations, insufficient geological time, or population size for molecular improvements to occur, or invoke “design creationism” to account for complexity in molecular structures and biological processes, are unfounded. Scientific evidence suggests that natural selection tinkers with molecular improvements by retaining adaptive peptide sequence. We used slot-machine probabilities and ion channels to show biological directionality on molecular change. Because ion channels reside in the lipid bilayer of cell membranes, their residue location must be in balance with the membrane's hydrophobic/philic nature; a selective “pore” for ion passage is located within the hydrophobic region. We contrasted the random generation of DNA sequence for KcsA, a bacterial two-transmembrane-domain (2TM) potassium channel, from Streptomyces lividans, with an under-selection scenario, the “jackprot,” which predicted much faster evolution than by chance. We wrote a computer program in JAVA APPLET version 1.0 and designed an online interface, The Jackprot Simulation http://faculty.rwu.edu/cbai/JackprotSimulation.htm, to model a numerical interaction between mutation rate and natural selection during a scenario of polypeptide evolution. Winning the “jackprot,” or highest-fitness complete-peptide sequence, required cumulative smaller “wins” (rewarded by selection) at the first, second, and third positions in each of the 161 KcsA codons (“jackdons” that led to “jackacids” that led to the “jackprot”). The “jackprot” is a didactic tool to demonstrate how mutation rate coupled with natural selection suffices to explain the evolution of specialized proteins, such as the complex six-transmembrane (6TM) domain potassium, sodium, or calcium channels. Ancestral DNA sequences coding for 2TM-like proteins underwent nucleotide

  13. The Jackprot Simulation Couples Mutation Rate with Natural Selection to Illustrate How Protein Evolution Is Not Random.

    Science.gov (United States)

    Paz-Y-Miño C, Guillermo; Espinosa, Avelina; Bai, Chunyan Y

    2011-09-01

    Protein evolution is not a random process. Views which attribute randomness to molecular change, deleterious nature to single-gene mutations, insufficient geological time, or population size for molecular improvements to occur, or invoke "design creationism" to account for complexity in molecular structures and biological processes, are unfounded. Scientific evidence suggests that natural selection tinkers with molecular improvements by retaining adaptive peptide sequence. We used slot-machine probabilities and ion channels to show biological directionality on molecular change. Because ion channels reside in the lipid bilayer of cell membranes, their residue location must be in balance with the membrane's hydrophobic/philic nature; a selective "pore" for ion passage is located within the hydrophobic region. We contrasted the random generation of DNA sequence for KcsA, a bacterial two-transmembrane-domain (2TM) potassium channel, from Streptomyces lividans, with an under-selection scenario, the "jackprot," which predicted much faster evolution than by chance. We wrote a computer program in JAVA APPLET version 1.0 and designed an online interface, The Jackprot Simulation http://faculty.rwu.edu/cbai/JackprotSimulation.htm, to model a numerical interaction between mutation rate and natural selection during a scenario of polypeptide evolution. Winning the "jackprot," or highest-fitness complete-peptide sequence, required cumulative smaller "wins" (rewarded by selection) at the first, second, and third positions in each of the 161 KcsA codons ("jackdons" that led to "jackacids" that led to the "jackprot"). The "jackprot" is a didactic tool to demonstrate how mutation rate coupled with natural selection suffices to explain the evolution of specialized proteins, such as the complex six-transmembrane (6TM) domain potassium, sodium, or calcium channels. Ancestral DNA sequences coding for 2TM-like proteins underwent nucleotide "edition" and gene duplications to generate the 6

  14. Pseudo cluster randomization: a treatment allocation method to minimize contamination and selection bias.

    NARCIS (Netherlands)

    Borm, G.F.; Melis, R.J.F.; Teerenstra, S.; Peer, P.G.M.

    2005-01-01

    In some clinical trials, treatment allocation on a patient level is not feasible, and whole groups or clusters of patients are allocated to the same treatment. If, for example, a clinical trial is investigating the efficacy of various patient coaching methods and randomization is done on a patient

  15. Bias in the prediction of genetic gain due to mass and half-sib selection in random mating populations

    Directory of Open Access Journals (Sweden)

    José Marcelo Soriano Viana

    2009-01-01

    Full Text Available The prediction of gains from selection allows the comparison of breeding methods and selection strategies, although these estimates may be biased. The objective of this study was to investigate the extent of such bias in predicting genetic gain. For this, we simulated 10 cycles of a hypothetical breeding program that involved seven traits, three population classes, three experimental conditions and two breeding methods (mass and half-sib selection. Each combination of trait, population, heritability, method and cycle was repeated 10 times. The predicted gains were biased, even when the genetic parameters were estimated without error. Gain from selection in both genders is twice the gain from selection in a single gender only in the absence of dominance. The use of genotypic variance or broad sense heritability in the predictions represented an additional source of bias. Predictions based on additive variance and narrow sense heritability were equivalent, as were predictions based on genotypic variance and broad sense heritability. The predictions based on mass and family selection were suitable for comparing selection strategies, whereas those based on selection within progenies showed the largest bias and lower association with the realized gain.

  16. Selection of Construction Methods: A Knowledge-Based Approach

    Directory of Open Access Journals (Sweden)

    Ximena Ferrada

    2013-01-01

    Full Text Available The appropriate selection of construction methods to be used during the execution of a construction project is a major determinant of high productivity, but sometimes this selection process is performed without the care and the systematic approach that it deserves, bringing negative consequences. This paper proposes a knowledge management approach that will enable the intelligent use of corporate experience and information and help to improve the selection of construction methods for a project. Then a knowledge-based system to support this decision-making process is proposed and described. To define and design the system, semistructured interviews were conducted within three construction companies with the purpose of studying the way that the method’ selection process is carried out in practice and the knowledge associated with it. A prototype of a Construction Methods Knowledge System (CMKS was developed and then validated with construction industry professionals. As a conclusion, the CMKS was perceived as a valuable tool for construction methods’ selection, by helping companies to generate a corporate memory on this issue, reducing the reliance on individual knowledge and also the subjectivity of the decision-making process. The described benefits as provided by the system favor a better performance of construction projects.

  17. Comparing Random-based and k-Anonymity-Based Algorithms for Graph Anonymization

    OpenAIRE

    Casas Roma, Jordi; Torra, Vicenç; Herrera Joancomartí, Jordi

    2012-01-01

    Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random- ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in order to obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality...

  18. Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection.

    Science.gov (United States)

    Al-Jarrah, Omar Y; Alhussein, Omar; Yoo, Paul D; Muhaidat, Sami; Taha, Kamal; Kim, Kwangjo

    2016-08-01

    Botnets, which consist of remotely controlled compromised machines called bots, provide a distributed platform for several threats against cyber world entities and enterprises. Intrusion detection system (IDS) provides an efficient countermeasure against botnets. It continually monitors and analyzes network traffic for potential vulnerabilities and possible existence of active attacks. A payload-inspection-based IDS (PI-IDS) identifies active intrusion attempts by inspecting transmission control protocol and user datagram protocol packet's payload and comparing it with previously seen attacks signatures. However, the PI-IDS abilities to detect intrusions might be incapacitated by packet encryption. Traffic-based IDS (T-IDS) alleviates the shortcomings of PI-IDS, as it does not inspect packet payload; however, it analyzes packet header to identify intrusions. As the network's traffic grows rapidly, not only the detection-rate is critical, but also the efficiency and the scalability of IDS become more significant. In this paper, we propose a state-of-the-art T-IDS built on a novel randomized data partitioned learning model (RDPLM), relying on a compact network feature set and feature selection techniques, simplified subspacing and a multiple randomized meta-learning technique. The proposed model has achieved 99.984% accuracy and 21.38 s training time on a well-known benchmark botnet dataset. Experiment results demonstrate that the proposed methodology outperforms other well-known machine-learning models used in the same detection task, namely, sequential minimal optimization, deep neural network, C4.5, reduced error pruning tree, and randomTree.

  19. Impact of Selection Bias on Treatment Effect Size Estimates in Randomized Trials of Oral Health Interventions: A Meta-epidemiological Study.

    Science.gov (United States)

    Saltaji, H; Armijo-Olivo, S; Cummings, G G; Amin, M; da Costa, B R; Flores-Mir, C

    2018-01-01

    Emerging evidence suggests that design flaws of randomized controlled trials can result in over- or underestimation of the treatment effect size (ES). The objective of this study was to examine associations between treatment ES estimates and adequacy of sequence generation, allocation concealment, and baseline comparability among a sample of oral health randomized controlled trials. For our analysis, we selected all meta-analyses that included a minimum of 5 oral health randomized controlled trials and used continuous outcomes. We extracted data, in duplicate, related to items of selection bias (sequence generation, allocation concealment, and baseline comparability) in the Cochrane Risk of Bias tool. Using a 2-level meta-meta-analytic approach with a random effects model to allow for intra- and inter-meta-analysis heterogeneity, we quantified the impact of selection bias on the magnitude of ES estimates. We identified 64 meta-analyses, including 540 randomized controlled trials analyzing 137,957 patients. Sequence generation was judged to be adequate (at low risk of bias) in 32% ( n = 173) of trials, and baseline comparability was judged to be adequate in 77.8% of trials. Allocation concealment was unclear in the majority of trials ( n = 458, 84.8%). We identified significantly larger treatment ES estimates in trials that had inadequate/unknown sequence generation (difference in ES = 0.13; 95% CI: 0.01 to 0.25) and inadequate/unknown allocation concealment (difference in ES = 0.15; 95% CI: 0.02 to 0.27). In contrast, baseline imbalance (difference in ES = 0.01, 95% CI: -0.09 to 0.12) was not associated with inflated or underestimated ES. In conclusion, treatment ES estimates were 0.13 and 0.15 larger in trials with inadequate/unknown sequence generation and inadequate/unknown allocation concealment, respectively. Therefore, authors of systematic reviews using oral health randomized controlled trials should perform sensitivity analyses based on the adequacy of

  20. Evaluation of Randomly Selected Completed Medical Records Sheets in Teaching Hospitals of Jahrom University of Medical Sciences, 2009

    Directory of Open Access Journals (Sweden)

    Mohammad Parsa Mahjob

    2011-06-01

    Full Text Available Background and objective: Medical record documentation, often use to protect the patients legal rights, also providing information for medical researchers, general studies, education of health care staff and qualitative surveys is used. There is a need to control the amount of data entered in the medical record sheets of patients, considering the completion of these sheets is often carried out after completion of service delivery to the patients. Therefore, in this study the prevalence of completeness of medical history, operation reports, and physician order sheets by different documentaries in Jahrom teaching hospitals during year 2009 was analyzed. Methods and Materials: In this descriptive / retrospective study, the 400 medical record sheets of the patients from two teaching hospitals affiliated to Jahrom medical university was randomly selected. The tool of data collection was a checklist based on the content of medical history sheet, operation report and physician order sheets. The data were analyzed by SPSS (Version10 software and Microsoft Office Excel 2003. Results: Average of personal (Demography data entered in medical history, physician order and operation report sheets which is done by department's secretaries were 32.9, 35.8 and 40.18 percent. Average of clinical data entered by physician in medical history sheet is 38 percent. Surgical data entered by the surgeon in operation report sheet was 94.77 percent. Average of data entered by operation room's nurse in operation report sheet was 36.78 percent; Average of physician order data in physician order sheet entered by physician was 99.3 percent. Conclusion: According to this study, the rate of completed record papers reviewed by documentary in Jahrom teaching hospitals were not desirable and in some cases were very weak and incomplete. This deficiency was due to different reason such as medical record documentaries negligence, lack of adequate education for documentaries, High work

  1. Retention of Statistical Concepts in a Preliminary Randomization-Based Introductory Statistics Curriculum

    Science.gov (United States)

    Tintle, Nathan; Topliff, Kylie; VanderStoep, Jill; Holmes, Vicki-Lynn; Swanson, Todd

    2012-01-01

    Previous research suggests that a randomization-based introductory statistics course may improve student learning compared to the consensus curriculum. However, it is unclear whether these gains are retained by students post-course. We compared the conceptual understanding of a cohort of students who took a randomization-based curriculum (n = 76)…

  2. Event detection using population-based health care databases in randomized clinical trials

    DEFF Research Database (Denmark)

    Thuesen, Leif; Jensen, Lisette Okkels; Tilsted, Hans Henrik

    2013-01-01

    To describe a new research tool, designed to reflect routine clinical practice and relying on population-based health care databases to detect clinical events in randomized clinical trials.......To describe a new research tool, designed to reflect routine clinical practice and relying on population-based health care databases to detect clinical events in randomized clinical trials....

  3. Tropical systematic and random error energetics based on NCEP ...

    Indian Academy of Sciences (India)

    ... systematic and random error and their growth rates and different components of growth rate budgets like flux, pure generation, mixed generation and conversion in energy/variance form are investigated in physical domain for medium range tropical (30° S-30°N) weather forecast using daily horizontal wind field at 850 hPa ...

  4. Tropical systematic and random error energetics based on NCEP ...

    Indian Academy of Sciences (India)

    Systematic and random error and their growth rate and different components of growth rate budget in energy/variance form are investigated at wavenumber domain for medium range tropical (30°S-30°N) weather forecast using daily horizontal wind field of 850 hPa up to 5-day forecast for the month of June, 2000 of NCEP ...

  5. Random amplified polymorphic DNA (RAPD) based assessment of ...

    African Journals Online (AJOL)

    SAM

    2014-05-07

    May 7, 2014 ... Knowledge of genetic distances between genotypes is important for efficient organization and conservation of plant genetic resources for crop improvement programs. In this study genetic distances between genotype pairs (complements of Jaccard's similarity coefficient) were estimated from Random ...

  6. Dust storm detection using random forests and physical-based ...

    Indian Academy of Sciences (India)

    Keywords. Random forests; dust detection; MODIS; decision tree. Abstract. Dust storms are important phenomena over large regions of the arid and semi-arid areas of the Middle East. Due to the influences of dust aerosols on climate and human daily activities, dust detection plays a crucial role in environmental and climatic ...

  7. REVIEW ARTICLE: Random lasers based on organic epitaxial nanofibers

    Science.gov (United States)

    Quochi, Francesco

    2010-02-01

    We present a review on random lasing in organic nanofibers made of oligophenyl nanocrystals grown by molecular epitaxy on polar substrates. The nanofibers have sub-wavelength cross-sectional dimensions and can extend in length up to the millimeter scale. We report on random lasing properties of nanofibers, under subpicosecond photopumping, both in the coherent and incoherent regimes. With the aid of both optical and morphological studies on individual fibers, we get insight into one-dimensional coherent feedback taking place along the nanofibers' axes. Model calculations of light propagation in disordered media allow us to give a semiquantitative description of one-dimensional coherent random lasing near the lasing threshold. We also report on amplified simulated emission in individual nanofibers and demonstrate that nanoscale linear optical amplifiers can be obtained by molecular self-assembly at surfaces. Photophysical studies of nanofibers resorting to subpicosecond luminescence and pump-probe spectroscopy give us valuable information on temperature-dependent, excited-state nonlinear processes, such as exciton-exciton annihilation and photoinduced absorption. Excited-state effects strongly influence lasing thresholds under quasi-continuous-wave photoexcitation conditions, as demonstrated in photoexcitation experiments performed with nanosecond pulses. Last, we briefly discuss the potential of organic epitaxial nanofibers featuring low-threshold random lasing for photonic sensing applications.

  8. Random amplified polymorphic DNA (RAPD) based assessment of ...

    African Journals Online (AJOL)

    Knowledge of genetic distances between genotypes is important for efficient organization and conservation of plant genetic resources for crop improvement programs. In this study genetic distances between genotype pairs (complements of Jaccard's similarity coefficient) were estimated from Random Amplified Polymorphic ...

  9. Development of a Layered Conditional Random Field Based ...

    African Journals Online (AJOL)

    PROF. OLIVER OSUAGWA

    2014-12-01

    Dec 1, 2014 ... Conditional Estimation in NLP Models,” Proc. ACL Conf. Empirical. Methods in Natural Language Processing (EMNLP '02), Association for. Computational Linguistics, 10, (9-16). [39] Sutton, C. and McCallum, A (2006). “An Introduction to Conditional Random Fields for. Relational Learning,” Introduction to ...

  10. Presence of psychoactive substances in oral fluid from randomly selected drivers in Denmark

    DEFF Research Database (Denmark)

    Simonsen, K. Wiese; Steentoft, A.; Hels, Tove

    2012-01-01

    This roadside study is the Danish part of the EU-project DRUID (Driving under the Influence of Drugs, Alcohol, and Medicines) and included three representative regions in Denmark. Oral fluid samples (n = 3002) were collected randomly from drivers using a sampling scheme stratified by time, season...... of narcotic drugs. It can be concluded that driving under the influence of drugs is as serious a road safety problem as drunk driving.......This roadside study is the Danish part of the EU-project DRUID (Driving under the Influence of Drugs, Alcohol, and Medicines) and included three representative regions in Denmark. Oral fluid samples (n = 3002) were collected randomly from drivers using a sampling scheme stratified by time, season...

  11. Presence of psychoactive substances in oral fluid from randomly selected drivers in Denmark

    DEFF Research Database (Denmark)

    Simonsen, Kirsten Wiese; Steentoft, Anni; Hels, Tove

    2012-01-01

    This roadside study is the Danish part of the EU-project DRUID (Driving under the Influence of Drugs, Alcohol, and Medicines) and included three representative regions in Denmark. Oral fluid samples (n = 3002) were collected randomly from drivers using a sampling scheme stratified by time, season....... It can be concluded that driving under the influence of drugs is as serious a road safety problem as drunk driving.......This roadside study is the Danish part of the EU-project DRUID (Driving under the Influence of Drugs, Alcohol, and Medicines) and included three representative regions in Denmark. Oral fluid samples (n = 3002) were collected randomly from drivers using a sampling scheme stratified by time, season...

  12. Use of hyaluronan in the selection of sperm for intracytoplasmic sperm injection (ICSI): significant improvement in clinical outcomes--multicenter, double-blinded and randomized controlled trial.

    Science.gov (United States)

    Worrilow, K C; Eid, S; Woodhouse, D; Perloe, M; Smith, S; Witmyer, J; Ivani, K; Khoury, C; Ball, G D; Elliot, T; Lieberman, J

    2013-02-01

    Does the selection of sperm for ICSI based on their ability to bind to hyaluronan improve the clinical pregnancy rates (CPR) (primary end-point), implantation (IR) and pregnancy loss rates (PLR)? In couples where ≤ 65% of sperm bound hyaluronan, the selection of hyaluronan-bound (HB) sperm for ICSI led to a statistically significant reduction in PLR. HB sperm demonstrate enhanced developmental parameters which have been associated with successful fertilization and embryogenesis. Sperm selected for ICSI using a liquid source of hyaluronan achieved an improvement in IR. A pilot study by the primary author demonstrated that the use of HB sperm in ICSI was associated with improved CPR. The current study represents the single largest prospective, multicenter, double-blinded and randomized controlled trial to evaluate the use of hyaluronan in the selection of sperm for ICSI. Using the hyaluronan binding assay, an HB score was determined for the fresh or initial (I-HB) and processed or final semen specimen (F-HB). Patients were classified as >65% or ≤ 65% I-HB and stratified accordingly. Patients with I-HB scores ≤ 65% were randomized into control and HB selection (HYAL) groups whereas patients with I-HB >65% were randomized to non-participatory (NP), control or HYAL groups, in a ratio of 2:1:1. The NP group was included in the >65% study arm to balance the higher prevalence of patients with I-HB scores >65%. In the control group, oocytes received sperm selected via the conventional assessment of motility and morphology. In the HYAL group, HB sperm meeting the same visual criteria were selected for injection. Patient participants and clinical care providers were blinded to group assignment. Eight hundred two couples treated with ICSI in 10 private and hospital-based IVF programs were enrolled in this study. Of the 484 patients stratified to the I-HB > 65% arm, 115 participants were randomized to the control group, 122 participants were randomized to the HYAL group

  13. A random utility based estimation framework for the household activity pattern problem.

    Science.gov (United States)

    2016-06-01

    This paper develops a random utility based estimation framework for the Household Activity : Pattern Problem (HAPP). Based on the realization that output of complex activity-travel decisions : form a continuous pattern in space-time dimension, the es...

  14. Merit based selection and performance assessment for mineworkers

    Energy Technology Data Exchange (ETDEWEB)

    Edmonds-Ward, L.; Trendell, B. [Wambo Mining Corporation Pty. Ltd. (Australia)

    1998-12-31

    While objective selection and assessments are an accepted part of employing managers and other staff, they have had only a limited place when selecting mineworkers in Australia. Wambo Mining Corporation has used occupational testing as part of its recruitment process since 1994. In 1997 when Wambo undertook development of a new `on site` subsidiary underground mine called Wollemi Services, they wanted to select the most appropriate people in terms of skill and on the job performance. A process was agreed between management, employees and their local representatives to select and transfer people on merit from within shift groups. In the first intake, three supervisors and thirty-nine production workers were selected from an existing workforce of over two hundred. Part of the process to ensure validity and to help people feel comfortable was an objective job analysis for positions. From a computer-based analysis, person specifications were developed and appropriate test batteries identified to facilitate selection. A combination of a self-report occupational personality or work styles questionnaire and several ability tests were used. In addition, each employee and two supervisors completed an assessment of the employee`s current work performance. The validity of the self-report assessments has since been confirmed in a correlation analysis of the results with supervisor feedback on performance. It was essential that the overall process was confidential so that people would be prepared to participate and the vast majority of people took up the competitive challenge. In the four months since the process, there has been a significant breakdown of restrictive practices. As expected, there was a productivity improvement at the new site. In addition, at the existing mine there has been a significant realignment of individual performance with many individuals being dynamic, progressive and showing real responsibility in their work. 2 tabs.

  15. Selective nerve root blocks vs. caudal epidural injection for single level prolapsed lumbar intervertebral disc - A prospective randomized study.

    Science.gov (United States)

    Singh, Sudhir; Kumar, Sanjiv; Chahal, Gaurav; Verma, Reetu

    2017-01-01

    Chronic lumbar radiculopathy has a lifetime prevalence of 5.3% in men and 3.7% in women. It usually resolves spontaneously, but up to 30% cases will have pronounced symptoms even after one year. A prospective randomized single-blind study was conducted to compare the efficacy of caudal epidural steroid injection and selective nerve root block in management of pain and disability in cases of lumbar disc herniation. Eighty patients with confirmed single-level lumbar disc herniation were equally divided in two groups: (a) caudal epidural and (b) selective nerve root block group, by a computer-generated random allocation method. The caudal group received three injections of steroid mixed with local anesthetics while selective nerve root block group received single injection of steroid mixed with local anesthetic agent. Patients were assessed for pain relief and reduction in disability. In SNRB group, pain reduced by more than 50% up till 6 months, while in caudal group more than 50% reduction of pain was maintained till 1 year. The reduction in ODI in SNRB group was 52.8% till 3 months, 48.6% till 6 months, and 46.7% at 1 year, while in caudal group the improvement was 59.6%, 64.6%, 65.1%, and 65.4% at corresponding follow-up periods, respectively. Caudal epidural block is an easy and safe method with better pain relief and improvement in functional disability than selective nerve root block. Selective nerve root block injection is technically more demanding and has to be given by a skilled anesthetist.

  16. Randomization-based adjustment of multiple treatment hazard ratios for covariates with missing data.

    Science.gov (United States)

    Lam, Diana; Koch, Gary G; Preisser, John S; Saville, Benjamin R; Hussey, Michael A

    2017-01-01

    Clinical trials are designed to compare treatment effects when applied to samples from the same population. Randomization is used so that the samples are not biased with respect to baseline covariates that may influence the efficacy of the treatment. We develop randomization-based covariance adjustment methodology to estimate the log hazard ratios and their confidence intervals of multiple treatments in a randomized clinical trial with time-to-event outcomes and missingness among the baseline covariates. The randomization-based covariance adjustment method is a computationally straight-forward method for handling missing baseline covariate values.

  17. Selection of an autochthonous Saccharomyces strain starter for alcoholic fermentation of Sherry base wines.

    Science.gov (United States)

    Rodríguez-Palero, María Jesús; Fierro-Risco, Jesús; Codón, Antonio C; Benítez, Tahía; Valcárcel, Manuel J

    2013-06-01

    Several indigenous Saccharomyces strains from musts were isolated in the Jerez de la Frontera region, at the end of spontaneous fermentation, in order to select the most suitable autochthonous yeast starter, during the 2007 vintage. Five strains were chosen for their oenological abilities and fermentative kinetics to elaborate a Sherry base wine. The selected autochthonous strains were characterized by molecular methods: electrophoretic karyotype and random amplified polymorphic DNA-polymerase chain reaction (RAPD-PCR) and by physiological parameters: fermentative power, ethanol production, sugar consumption, acidity and volatile compound production, sensory quality, killer phenotype, desiccation, and sulphur dioxide tolerance. Laboratory- and pilot-scale fermentations were conducted with those autochthonous strains. One of them, named J4, was finally selected over all others for industrial fermentations. The J4 strain, which possesses exceptional fermentative properties and oenological qualities, prevails in industrial fermentations, and becomes the principal biological agent responsible for winemaking. Sherry base wine, industrially manufactured by means of the J4 strain, was analyzed, yielding, together with its sensory qualities, final average values of 0.9 g/l sugar content, 13.4 % (v/v) ethanol content and 0.26 g/l volatile acidity content; apart from a high acetaldehyde production, responsible for the distinctive aroma of "Fino". This base wine was selected for "Fino" Sherry elaboration and so it was fortified; it is at present being subjected to biological aging by the so-called "flor" yeasts. The "flor" velum formed so far is very high quality. To the best of our knowledge, this is the first study covering from laboratory to industrial scale of characterization and selection of autochthonous starter intended for alcoholic fermentation in Sherry base wines. Since the 2010 vintage, the indigenous J4 strain is employed to industrially manufacture a

  18. A model selection method for nonlinear system identification based FMRI effective connectivity analysis.

    Science.gov (United States)

    Li, Xingfeng; Coyle, Damien; Maguire, Liam; McGinnity, Thomas M; Benali, Habib

    2011-07-01

    In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.

  19. Nonextensive random-matrix theory based on Kaniadakis entropy

    OpenAIRE

    Abul-Magd, A. Y.

    2006-01-01

    The joint eigenvalue distributions of random-matrix ensembles are derived by applying the principle maximum entropy to the Renyi, Abe and Kaniadakis entropies. While the Renyi entropy produces essentially the same matrix-element distributions as the previously obtained expression by using the Tsallis entropy, and the Abe entropy does not lead to a closed form expression, the Kaniadakis entropy leads to a new generalized form of the Wigner surmise that describes a transition of the spacing dis...

  20. Randomness in the network inhibits cooperation based on the bounded rational collective altruistic decision

    Science.gov (United States)

    Ohdaira, Tetsushi

    2014-07-01

    Previous studies discussing cooperation employ the best decision that every player knows all information regarding the payoff matrix and selects the strategy of the highest payoff. Therefore, they do not discuss cooperation based on the altruistic decision with limited information (bounded rational altruistic decision). In addition, they do not cover the case where every player can submit his/her strategy several times in a match of the game. This paper is based on Ohdaira's reconsideration of the bounded rational altruistic decision, and also employs the framework of the prisoner's dilemma game (PDG) with sequential strategy. The distinction between this study and the Ohdaira's reconsideration is that the former covers the model of multiple groups, but the latter deals with the model of only two groups. Ohdaira's reconsideration shows that the bounded rational altruistic decision facilitates much more cooperation in the PDG with sequential strategy than Ohdaira and Terano's bounded rational second-best decision does. However, the detail of cooperation of multiple groups based on the bounded rational altruistic decision has not been resolved yet. This study, therefore, shows how randomness in the network composed of multiple groups affects the increase of the average frequency of mutual cooperation (cooperation between groups) based on the bounded rational altruistic decision of multiple groups. We also discuss the results of the model in comparison with related studies which employ the best decision.

  1. Mindfulness-based cognitive therapy in obsessive-compulsive disorder: protocol of a randomized controlled trial.

    Science.gov (United States)

    Külz, Anne Katrin; Landmann, Sarah; Cludius, Barbara; Hottenrott, Birgit; Rose, Nina; Heidenreich, Thomas; Hertenstein, Elisabeth; Voderholzer, Ulrich; Moritz, Steffen

    2014-11-18

    Obsessive-compulsive disorder (OCD) is a very disabling condition with a chronic course, if left untreated. Though cognitive behavioral treatment (CBT) with or without selective serotonin reuptake inhibitors (SSRI) is the method of choice, up to one third of individuals with obsessive-compulsive disorder (OCD) do not respond to treatment in terms of at least 35% improvement of symptoms. Mindfulness based cognitive therapy (MBCT) is an 8-week group program that could help OCD patients with no or only partial response to CBT to reduce OC symptoms and develop a helpful attitude towards obsessions and compulsive urges. This study is a prospective, bicentric, assessor-blinded, randomized, actively-controlled clinical trial. 128 patients with primary diagnosis of OCD according to DSM-IV and no or only partial response to CBT will be recruited from in- and outpatient services as well as online forums and the media. Patients will be randomized to either an MBCT intervention group or to a psycho-educative coaching group (OCD-EP) as an active control condition. All participants will undergo eight weekly sessions with a length of 120 minutes each of a structured group program. We hypothesize that MBCT will be superior to OCD-EP in reducing obsessive-compulsive symptoms as measured by the Yale-Brown-Obsessive-Compulsive Scale (Y-BOCS) following the intervention and at 6- and 12-months-follow-up. Secondary outcome measures include depressive symptoms, quality of life, metacognitive beliefs, self-compassion, mindful awareness and approach-avoidance tendencies as measured by an approach avoidance task. The results of this study will elucidate the benefits of MBCT for OCD patients who did not sufficiently benefit from CBT. To our knowledge, this is the first randomized controlled study assessing the effects of MBCT on symptom severity and associated parameters in OCD. German Clinical Trials Register DRKS00004525 . Registered 19 March 2013.

  2. Selective recognition of americium by peptide-based reagents.

    Science.gov (United States)

    Özçubukçu, Salih; Mandal, Kalyanaswer; Wegner, Seraphine; Jensen, Mark P; He, Chuan

    2011-09-05

    The separation of lanthanides from minor actinides such as americium and curium is an important step during the recycling process in the treatment of nuclear waste. However, the similar chemistry and ionic size of lanthanide and actinide ions make the separation challenging. Here, we report that a peptide-based reagent can selectively bind trivalent actinides over trivalent lanthanides by means of introducing soft-donor atoms into a peptide known as a lanthanide-binding tag (LBT). Fluorescence spectroscopy has been used to measure the dissociation constant of each metal/peptide complex. A 10-fold selectivity was obtained for Am(3+) over the similarly sized lanthanide cation, Nd(3+), when the asparagine on the fifth position of a LBT was mutated to a cysteine and further functionalized by a pyridine moiety.

  3. Numerical Model based Reliability Estimation of Selective Laser Melting Process

    DEFF Research Database (Denmark)

    Mohanty, Sankhya; Hattel, Jesper Henri

    2014-01-01

    parameters such as laser power, speed, beam profile, etc. Subsequently, uncertainties in the processing parameters are utilized to predict a range for the various outputs, using a Monte Carlo method based uncertainty analysis methodology, and the reliability of the process is established....... various numerical modelling and experimental studies are being carried out to better understand and control the process, there is still a lack of research into establishing the reliability of the process.In this paper, a combined modelling-experimental approach is introduced to establish the reliability...... of the selective laser melting process. A validated 3D finite-volume alternating-direction-implicit numerical technique is used to model the selective laser melting process, and is calibrated against results from single track formation experiments. Correlation coefficients are determined for process input...

  4. A Reliability Based Model for Wind Turbine Selection

    Directory of Open Access Journals (Sweden)

    A.K. Rajeevan

    2013-06-01

    Full Text Available A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.

  5. Heterogeneous Web Data Extraction Algorithm Based On Modified Hidden Conditional Random Fields

    OpenAIRE

    Cui Cheng

    2014-01-01

    As it is of great importance to extract useful information from heterogeneous Web data, in this paper, we propose a novel heterogeneous Web data extraction algorithm using a modified hidden conditional random fields model. Considering the traditional linear chain based conditional random fields can not effectively solve the problem of complex and heterogeneous Web data extraction, we modify the standard hidden conditional random fields in three aspects, which are 1) Using the hidden Markov mo...

  6. Frequency selective surfaces based high performance microstrip antenna

    CERN Document Server

    Narayan, Shiv; Jha, Rakesh Mohan

    2016-01-01

    This book focuses on performance enhancement of printed antennas using frequency selective surfaces (FSS) technology. The growing demand of stealth technology in strategic areas requires high-performance low-RCS (radar cross section) antennas. Such requirements may be accomplished by incorporating FSS into the antenna structure either in its ground plane or as the superstrate, due to the filter characteristics of FSS structure. In view of this, a novel approach based on FSS technology is presented in this book to enhance the performance of printed antennas including out-of-band structural RCS reduction. In this endeavor, the EM design of microstrip patch antennas (MPA) loaded with FSS-based (i) high impedance surface (HIS) ground plane, and (ii) the superstrates are discussed in detail. The EM analysis of proposed FSS-based antenna structures have been carried out using transmission line analogy, in combination with the reciprocity theorem. Further, various types of novel FSS structures are considered in desi...

  7. Distance based control system for machine vision-based selective spraying

    NARCIS (Netherlands)

    Steward, B.L.; Tian, L.F.; Tang, L.

    2002-01-01

    For effective operation of a selective sprayer with real-time local weed sensing, herbicides must be delivered, accurately to weed targets in the field. With a machine vision-based selective spraying system, acquiring sequential images and switching nozzles on and off at the correct locations are

  8. Designing randomized-controlled trials to improve head-louse treatment: systematic review using a vignette-based method.

    Science.gov (United States)

    Do-Pham, Giao; Le Cleach, Laurence; Giraudeau, Bruno; Maruani, Annabel; Chosidow, Olivier; Ravaud, Philippe

    2014-03-01

    Head-louse infestation remains a public health problem. Despite published randomized-controlled trials, no consensus-based clinical practice guidelines for its management emerged because of the heterogeneity of trial methodologies. Our study was undertaken to attempt to find an optimal trial framework: minimizing the risk of bias, while taking feasibility into account. To do so, we used the vignette-based method. A systematic review first identified trials on head-louse infestation; 49 were selected and their methodological constraints assessed. Methodological features were extracted and combined by arborescence to generate a broad spectrum of potential designs, called vignettes, yielding 357 vignettes. A panel of 48 experts then rated one-on-one comparisons of those vignettes to obtain a ranking of the designs. Methodological items retained for vignette generation were income level of the population, types of treatments compared, randomization unit, blinding, treatment-administration site, diagnosis method and criteria, and primary outcome measure. The expert panel selected vignettes with cluster randomization, centralized treatment administration, and blinding of the outcome assessor. The vignette method identified optimal designs to standardize future head-louse treatment trials, thereby obtaining valid conclusions and comparable data from future trials, and appears to be a reliable way to generate evidence-based guidelines.

  9. Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope.

    Science.gov (United States)

    Quan, Wei; Lv, Lin; Liu, Baiqi

    2014-11-01

    In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.

  10. Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope

    Energy Technology Data Exchange (ETDEWEB)

    Quan, Wei; Lv, Lin, E-mail: lvlinlch1990@163.com; Liu, Baiqi [School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191 (China)

    2014-11-15

    In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.

  11. Random search optimization based on genetic algorithm and discriminant function

    Science.gov (United States)

    Kiciman, M. O.; Akgul, M.; Erarslanoglu, G.

    1990-01-01

    The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.

  12. Robosonic: Randomness-Based Manipulation of Sounds Assisted by Robots

    Science.gov (United States)

    Luz, Filipe Costa; Jorge, Rui Pereira; Bila, Vasco

    In this text, we intend to explore the possibilities of sound manipulation in a context of augmented reality (AR) through the use of robots. We use the random behaviour of robots in a limited space for the real-time modulation of two sound characteristics: amplitude and frequency. We add the possibility of interaction with these robots, providing the user the opportunity to manipulate the physical interface by placing markers in the action space, which alter the behaviour of the robots and, consequently, the audible result produced.

  13. On a Notion of Data Depth Based on Random Simplices

    OpenAIRE

    Liu, Regina Y.

    1990-01-01

    For a distribution $F$ on $\\mathbb{R}^p$ and a point $x$ in $\\mathbb{R}^p$, the simplical depth $D(x)$ is introduced, which is the probability that the point $x$ is contained inside a random simplex whose vertices are $p + 1$ independent observations from $F$. Mathematically and heuristically it is argued that $D(x)$ indeed can be viewed as a measure of depth of the point $x$ with respect to $F$. An empirical version of $D(\\cdot)$ gives rise to a natural ordering of the data points from the c...

  14. Nonextensive random-matrix theory based on Kaniadakis entropy

    Science.gov (United States)

    Abul-Magd, A. Y.

    2007-02-01

    The joint eigenvalue distributions of random-matrix ensembles are derived by applying the principle maximum entropy to the Rényi, Abe and Kaniadakis entropies. While the Rényi entropy produces essentially the same matrix-element distributions as the previously obtained expression by using the Tsallis entropy, and the Abe entropy does not lead to a closed form expression, the Kaniadakis entropy leads to a new generalized form of the Wigner surmise that describes a transition of the spacing distribution from chaos to order. This expression is compared with the corresponding expression obtained by assuming Tsallis' entropy as well as the results of a previous numerical experiment.

  15. Nonextensive random-matrix theory based on Kaniadakis entropy

    Energy Technology Data Exchange (ETDEWEB)

    Abul-Magd, A.Y. [Department of Mathematics, Faculty of Science, Zagazig University, Zagazig (Egypt)]. E-mail: a_y_abul_magd@hotmail.com

    2007-02-12

    The joint eigenvalue distributions of random-matrix ensembles are derived by applying the principle maximum entropy to the Renyi, Abe and Kaniadakis entropies. While the Renyi entropy produces essentially the same matrix-element distributions as the previously obtained expression by using the Tsallis entropy, and the Abe entropy does not lead to a closed form expression, the Kaniadakis entropy leads to a new generalized form of the Wigner surmise that describes a transition of the spacing distribution from chaos to order. This expression is compared with the corresponding expression obtained by assuming Tsallis' entropy as well as the results of a previous numerical experiment.

  16. Using PSO-Based Hierarchical Feature Selection Algorithm

    Directory of Open Access Journals (Sweden)

    Zhiwei Ji

    2014-01-01

    Full Text Available Hepatocellular carcinoma (HCC is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.

  17. Core Business Selection Based on Ant Colony Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Yu Lan

    2014-01-01

    Full Text Available Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.

  18. A prediction scheme of tropical cyclone frequency based on lasso and random forest

    Science.gov (United States)

    Tan, Jinkai; Liu, Hexiang; Li, Mengya; Wang, Jun

    2017-07-01

    This study aims to propose a novel prediction scheme of tropical cyclone frequency (TCF) over the Western North Pacific (WNP). We concerned the large-scale meteorological factors inclusive of the sea surface temperature, sea level pressure, the Niño-3.4 index, the wind shear, the vorticity, the subtropical high, and the sea ice cover, since the chronic change of these factors in the context of climate change would cause a gradual variation of the annual TCF. Specifically, we focus on the correlation between the year-to-year increment of these factors and TCF. The least absolute shrinkage and selection operator (Lasso) method was used for variable selection and dimension reduction from 11 initial predictors. Then, a prediction model based on random forest (RF) was established by using the training samples (1978-2011) for calibration and the testing samples (2012-2016) for validation. The RF model presents a major variation and trend of TCF in the period of calibration, and also fitted well with the observed TCF in the period of validation though there were some deviations. The leave-one-out cross validation of the model exhibited most of the predicted TCF are in consistence with the observed TCF with a high correlation coefficient. A comparison between results of the RF model and the multiple linear regression (MLR) model suggested the RF is more practical and capable of giving reliable results of TCF prediction over the WNP.

  19. Phonon structures of GaN-based random semiconductor alloys

    Science.gov (United States)

    Zhou, Mei; Chen, Xiaobin; Li, Gang; Zheng, Fawei; Zhang, Ping

    2017-12-01

    Accurate modeling of thermal properties is strikingly important for developing next-generation electronics with high performance. Many thermal properties are closely related to phonon dispersions, such as sound velocity. However, random substituted semiconductor alloys AxB1-x usually lack translational symmetry, and simulation with periodic boundary conditions often requires large supercells, which makes phonon dispersion highly folded and hardly comparable with experimental results. Here, we adopt a large supercell with randomly distributed A and B atoms to investigate substitution effect on the phonon dispersions of semiconductor alloys systematically by using phonon unfolding method [F. Zheng, P. Zhang, Comput. Mater. Sci. 125, 218 (2016)]. The results reveal the extent to which phonon band characteristics in (In,Ga)N and Ga(N,P) are preserved or lost at different compositions and q points. Generally, most characteristics of phonon dispersions can be preserved with indium substitution of gallium in GaN, while substitution of nitrogen with phosphorus strongly perturbs the phonon dispersion of GaN, showing a rapid disintegration of the Bloch characteristics of optical modes and introducing localized impurity modes. In addition, the sound velocities of both (In,Ga)N and Ga(N,P) display a nearly linear behavior as a function of substitution compositions. Supplementary material in the form of one pdf file available from the Journal web page at http://https://doi.org/10.1140/epjb/e2017-80481-0.

  20. Embedded Memory Hierarchy Exploration Based on Magnetic Random Access Memory

    Directory of Open Access Journals (Sweden)

    Luís Vitório Cargnini

    2014-08-01

    Full Text Available Static random access memory (SRAM is the most commonly employed semiconductor in the design of on-chip processor memory. However, it is unlikely that the SRAM technology will have a cell size that will continue to scale below 45 nm, due to the leakage current that is caused by the quantum tunneling effect. Magnetic random access memory (MRAM is a candidate technology to replace SRAM, assuming appropriate dimensioning given an operating threshold voltage. The write current of spin transfer torque (STT-MRAM is a known limitation; however, this has been recently mitigated by leveraging perpendicular magnetic tunneling junctions. In this article, we present a comprehensive comparison of spin transfer torque-MRAM (STT-MRAM and SRAM cache set banks. The non-volatility of STT-MRAM allows the definition of new instant on/off policies and leakage current optimizations. Through our experiments, we demonstrate that STT-MRAM is a candidate for the memory hierarchy of embedded systems, due to the higher densities and reduced leakage of MRAM.We demonstrate that adopting STT-MRAM in L1 and L2 caches mitigates the impact of higher write latencies and increased current draw due to the use of MRAM. With the correct system-on-chip (SoC design, we believe that STT-MRAM is a viable alternative to SRAM, which minimizes leakage current and the total power consumed by the SoC.

  1. Carbon Nanotube-Based Ion Selective Sensors for Wearable Applications.

    Science.gov (United States)

    Roy, Soumyendu; David-Pur, Moshe; Hanein, Yael

    2017-10-11

    Wearable electronics offer new opportunities in a wide range of applications, especially sweat analysis using skin sensors. A fundamental challenge in these applications is the formation of sensitive and stable electrodes. In this article we report the development of a wearable sensor based on carbon nanotube (CNT) electrode arrays for sweat sensing. Solid-state ion selective electrodes (ISEs), sensitive to Na(+) ions, were prepared by drop coating plasticized poly(vinyl chloride) (PVC) doped with ionophore and ion exchanger on CNT electrodes. The ion selective membrane (ISM) filled the intertubular spaces of the highly porous CNT film and formed an attachment that was stronger than that achieved with flat Au, Pt, or carbon electrodes. Concentration of the ISM solution used influenced the attachment to the CNT film, the ISM surface morphology, and the overall performance of the sensor. Sensitivity of 56 ± 3 mV/decade to Na(+) ions was achieved. Optimized solid-state reference electrodes (REs), suitable for wearable applications, were prepared by coating CNT electrodes with colloidal dispersion of Ag/AgCl, agarose hydrogel with 0.5 M NaCl, and a passivation layer of PVC doped with NaCl. The CNT-based REs had low sensitivity (-1.7 ± 1.2 mV/decade) toward the NaCl solution and high repeatability and were superior to bare Ag/AgCl, metals, carbon, and CNT films, reported previously as REs. CNT-based ISEs were calibrated against CNT-based REs, and the short-term stability of the system was tested. We demonstrate that CNT-based devices implemented on a flexible support are a very attractive platform for future wearable technology devices.

  2. Patch-based visual tracking with online representative sample selection

    Science.gov (United States)

    Ou, Weihua; Yuan, Di; Li, Donghao; Liu, Bin; Xia, Daoxun; Zeng, Wu

    2017-05-01

    Occlusion is one of the most challenging problems in visual object tracking. Recently, a lot of discriminative methods have been proposed to deal with this problem. For the discriminative methods, it is difficult to select the representative samples for the target template updating. In general, the holistic bounding boxes that contain tracked results are selected as the positive samples. However, when the objects are occluded, this simple strategy easily introduces the noises into the training data set and the target template and then leads the tracker to drift away from the target seriously. To address this problem, we propose a robust patch-based visual tracker with online representative sample selection. Different from previous works, we divide the object and the candidates into several patches uniformly and propose a score function to calculate the score of each patch independently. Then, the average score is adopted to determine the optimal candidate. Finally, we utilize the non-negative least square method to find the representative samples, which are used to update the target template. The experimental results on the object tracking benchmark 2013 and on the 13 challenging sequences show that the proposed method is robust to the occlusion and achieves promising results.

  3. Tyrosinase-Based Biosensors for Selective Dopamine Detection

    Directory of Open Access Journals (Sweden)

    Monica Florescu

    2017-06-01

    Full Text Available A novel tyrosinase-based biosensor was developed for the detection of dopamine (DA. For increased selectivity, gold electrodes were previously modified with cobalt (II-porphyrin (CoP film with electrocatalytic activity, to act both as an electrochemical mediator and an enzyme support, upon which the enzyme tyrosinase (Tyr was cross-linked. Differential pulse voltammetry was used for electrochemical detection and the reduction current of dopamine-quinone was measured as a function of dopamine concentration. Our experiments demonstrated that the presence of CoP improves the selectivity of the electrode towards dopamine in the presence of ascorbic acid (AA, with a linear trend of concentration dependence in the range of 2–30 µM. By optimizing the conditioning parameters, a separation of 130 mV between the peak potentials for ascorbic acid AA and DA was obtained, allowing the selective detection of DA. The biosensor had a sensitivity of 1.22 ± 0.02 µA·cm−2·µM−1 and a detection limit of 0.43 µM. Biosensor performances were tested in the presence of dopamine medication, with satisfactory results in terms of recovery (96%, and relative standard deviation values below 5%. These results confirmed the applicability of the biosensors in real samples such as human urine and blood serum.

  4. Quantum random number generator based on quantum nature of vacuum fluctuations

    Science.gov (United States)

    Ivanova, A. E.; Chivilikhin, S. A.; Gleim, A. V.

    2017-11-01

    Quantum random number generator (QRNG) allows obtaining true random bit sequences. In QRNG based on quantum nature of vacuum, optical beam splitter with two inputs and two outputs is normally used. We compare mathematical descriptions of spatial beam splitter and fiber Y-splitter in the quantum model for QRNG, based on homodyne detection. These descriptions were identical, that allows to use fiber Y-splitters in practical QRNG schemes, simplifying the setup. Also we receive relations between the input radiation and the resulting differential current in homodyne detector. We experimentally demonstrate possibility of true random bits generation by using QRNG based on homodyne detection with Y-splitter.

  5. A model-based approach to selection of tag SNPs

    Directory of Open Access Journals (Sweden)

    Sun Fengzhu

    2006-06-01

    Full Text Available Abstract Background Single Nucleotide Polymorphisms (SNPs are the most common type of polymorphisms found in the human genome. Effective genetic association studies require the identification of sets of tag SNPs that capture as much haplotype information as possible. Tag SNP selection is analogous to the problem of data compression in information theory. According to Shannon's framework, the optimal tag set maximizes the entropy of the tag SNPs subject to constraints on the number of SNPs. This approach requires an appropriate probabilistic model. Compared to simple measures of Linkage Disequilibrium (LD, a good model of haplotype sequences can more accurately account for LD structure. It also provides a machinery for the prediction of tagged SNPs and thereby to assess the performances of tag sets through their ability to predict larger SNP sets. Results Here, we compute the description code-lengths of SNP data for an array of models and we develop tag SNP selection methods based on these models and the strategy of entropy maximization. Using data sets from the HapMap and ENCODE projects, we show that the hidden Markov model introduced by Li and Stephens outperforms the other models in several aspects: description code-length of SNP data, information content of tag sets, and prediction of tagged SNPs. This is the first use of this model in the context of tag SNP selection. Conclusion Our study provides strong evidence that the tag sets selected by our best method, based on Li and Stephens model, outperform those chosen by several existing methods. The results also suggest that information content evaluated with a good model is more sensitive for assessing the quality of a tagging set than the correct prediction rate of tagged SNPs. Besides, we show that haplotype phase uncertainty has an almost negligible impact on the ability of good tag sets to predict tagged SNPs. This justifies the selection of tag SNPs on the basis of haplotype

  6. A system identification technique based on the random decrement signatures. Part 1: Theory and simulation

    Science.gov (United States)

    Bedewi, Nabih E.; Yang, Jackson C. S.

    1987-01-01

    Identification of the system parameters of a randomly excited structure may be treated using a variety of statistical techniques. Of all these techniques, the Random Decrement is unique in that it provides the homogeneous component of the system response. Using this quality, a system identification technique was developed based on a least-squares fit of the signatures to estimate the mass, damping, and stiffness matrices of a linear randomly excited system. The mathematics of the technique is presented in addition to the results of computer simulations conducted to demonstrate the prediction of the response of the system and the random forcing function initially introduced to excite the system.

  7. On the design of henon and logistic map-based random number generator

    Science.gov (United States)

    Magfirawaty; Suryadi, M. T.; Ramli, Kalamullah

    2017-10-01

    The key sequence is one of the main elements in the cryptosystem. True Random Number Generators (TRNG) method is one of the approaches to generating the key sequence. The randomness source of the TRNG divided into three main groups, i.e. electrical noise based, jitter based and chaos based. The chaos based utilizes a non-linear dynamic system (continuous time or discrete time) as an entropy source. In this study, a new design of TRNG based on discrete time chaotic system is proposed, which is then simulated in LabVIEW. The principle of the design consists of combining 2D and 1D chaotic systems. A mathematical model is implemented for numerical simulations. We used comparator process as a harvester method to obtain the series of random bits. Without any post processing, the proposed design generated random bit sequence with high entropy value and passed all NIST 800.22 statistical tests.

  8. Overproduction and selective abortion of ovules based on the order of fertilization revisited.

    Science.gov (United States)

    Sakai, Satoki; Kojima, Tomomi

    2009-10-07

    Given that seeds fertilized by slowly growing pollen are of low quality genetically, we theoretically reanalyzed the hypothesis that plants selectively abort ovules fertilized later to enhance the mean quality of resulting seeds. We assumed that both superior and inferior pollen exist, the superior pollen growing faster to fertilize ovules, resulting in seeds of higher quality than those of ovules fertilized by inferior pollen. We developed two models to determine the conditions under which selective abortion is favored. In the first model, ovules in one flower are fertilized by pollen grains that arrive at different times, with each visit bringing both fast- and slow-growing pollen. In the second model, ovules in two flowers are fertilized by all pollen grains that arrive at the same time. In the first model, we found that selective abortion based on the order of fertilization is never advantageous irrespective of the duration of the time lag between the two visits. Rather, random abortion is possibly favored. In the second model, although selective abortion based on the order of fertilization can be advantageous, the parameter region favoring it is rather restricted. This is because overproduction can be advantageous only if the quantity of the superior pollen is not limited in one flower but is limited in the other flower. In addition, the degree of overproduction was very low, implying that the merit of overproduction (increase in the number of superior seeds) is low compared to the cost of overproducing ovules. These results suggest that selective abortion of ovules based on the order of fertilization is not as advantageous as previously considered.

  9. Fuzzy Axiomatic Design approach based green supplier selection

    DEFF Research Database (Denmark)

    Kannan, Devika; Govindan, Kannan; Rajendran, Sivakumar

    2015-01-01

    proposes a multi-criteria decision-making (MCDM) approach called Fuzzy Axiomatic Design (FAD) to select the best green supplier for Singapore-based plastic manufacturing company. At first, the environmental criteria was developed along with the traditional criteria based on the literature review...... and company requirements. Next, the FAD methodology evaluates the requirements of both the manufacturer (design needs) and the supplier (functional needs), and because multiple criteria must be considered, a multi-objective optimization model of a fuzzy nature must be developed. The application...... responsible in addition to being efficiently managed. A significant way to implement responsible GSCM is to reconsider, in innovative ways, the purchase and supply cycle, and a preliminary step would be to ensure that the supplier of goods successfully incorporates green criteria. Therefore, this paper...

  10. Tunable antenna radome based on graphene frequency selective surface

    Directory of Open Access Journals (Sweden)

    Meijun Qu

    2017-09-01

    Full Text Available In this paper, a graphene-based frequency selective surface (FSS is proposed. The proposed FSS exhibits a tunable bandpass filtering characteristic due to the alterable conductivity of the graphene strips which is controlled by chemical potential. Based on the reconfigurable bandpass property of the proposed FSS, a cylindrical antenna radome is designed using the FSS unit cells. A conventional omnidirectional dipole can realize a two-beam directional pattern when it is placed into the proposed antenna radome. Forward and backward endfire radiations of the dipole loaded with the radome is realized by properly adjusting the chemical potential. The proposed antenna radome is extremely promising for beam-scanning in terahertz and mid-infrared plasmonic devices and systems when the gain of a conventional antenna needs to be enhanced.

  11. SELECTION OF RECIPIENTS FOR HEART TRANSPLANTATION BASED ON URGENCY STATUS

    Directory of Open Access Journals (Sweden)

    O. A. Sujayeva

    2014-01-01

    Full Text Available The article provides the overview of current international recommendations dedicated to selection of heart transplantation recipients based on urgency status. Authors found that cardiopulmonary bicycle stress test allowed to reveal additional criteria of high death risk within 1 year. These additional criteria were: the maximal oxygen consumption VO2max < 30% of the expected considering the age; VD/VT (ratio of physiologic dead space over tidal volume increasing during the test; maximal tolerance to physical loading ≤50 Wt and/or < 20% of the expected considering the age. Authors created mathematical model for prediction of death within 1 year based on above mentioned data. Special software estimating the probability of death within 1 year was also created.

  12. Tunable antenna radome based on graphene frequency selective surface

    Science.gov (United States)

    Qu, Meijun; Rao, Menglou; Li, Shufang; Deng, Li

    2017-09-01

    In this paper, a graphene-based frequency selective surface (FSS) is proposed. The proposed FSS exhibits a tunable bandpass filtering characteristic due to the alterable conductivity of the graphene strips which is controlled by chemical potential. Based on the reconfigurable bandpass property of the proposed FSS, a cylindrical antenna radome is designed using the FSS unit cells. A conventional omnidirectional dipole can realize a two-beam directional pattern when it is placed into the proposed antenna radome. Forward and backward endfire radiations of the dipole loaded with the radome is realized by properly adjusting the chemical potential. The proposed antenna radome is extremely promising for beam-scanning in terahertz and mid-infrared plasmonic devices and systems when the gain of a conventional antenna needs to be enhanced.

  13. Synthetic Cell-Based Sensors with Programmed Selectivity and Sensitivity.

    Science.gov (United States)

    Bernard, Elvis; Wang, Baojun

    2017-01-01

    Bacteria live in an ever changing environment and, to adapt their physiology, they have to sense the changes. Our current understanding of the mechanisms and elements involved in the detection and processing of these environmental signals grant us access to an array of genetic components able to process such information. As engineers can use different electronic components to build a circuit, we can rewire the cellular components to create digital logic and analogue gene circuits that will program cell behaviour in a designed manner in response to a specific stimulus. Here we present the methods and protocols for designing and implementing synthetic cell-based biosensors that use engineered genetic logic and analogue amplifying circuits to significantly increase selectivity and sensitivity, for example, for heavy metal ions in an aqueous environment. The approach is modular and can be readily applied to improving the sensing limit and performance of a range of microbial cell-based sensors to meet their real world detection requirement.

  14. High-speed true random number generation based on paired memristors for security electronics

    Science.gov (United States)

    Zhang, Teng; Yin, Minghui; Xu, Changmin; Lu, Xiayan; Sun, Xinhao; Yang, Yuchao; Huang, Ru

    2017-11-01

    True random number generator (TRNG) is a critical component in hardware security that is increasingly important in the era of mobile computing and internet of things. Here we demonstrate a TRNG using intrinsic variation of memristors as a natural source of entropy that is otherwise undesirable in most applications. The random bits were produced by cyclically switching a pair of tantalum oxide based memristors and comparing their resistance values in the off state, taking advantage of the more pronounced resistance variation compared with that in the on state. Using an alternating read scheme in the designed TRNG circuit, the unbiasedness of the random numbers was significantly improved, and the bitstream passed standard randomness tests. The Pt/TaO x /Ta memristors fabricated in this work have fast programming/erasing speeds of ∼30 ns, suggesting a high random number throughput. The approach proposed here thus holds great promise for physically-implemented random number generation.

  15. Role of selective V2-receptor-antagonism in septic shock: a randomized, controlled, experimental study

    OpenAIRE

    Rehberg, Sebastian; Ertmer, Christian; Lange, Matthias; Morelli, Andrea; Whorton, Elbert; Strohhäcker, Anne-Katrin; Dünser, Martin Wolfgang; Lipke, Erik; Kampmeier, Tim G; Aken, Hugo; Traber, Daniel L; Westphal, Martin

    2010-01-01

    ABSTRACT : INTRODUCTION : V2-receptor (V2R) stimulation potentially aggravates sepsis-induced vasodilation, fluid accumulation and microvascular thrombosis. Therefore, the present study was performed to determine the effects of a first-line therapy with the selective V2R-antagonist (Propionyl1-D-Tyr(Et)2-Val4-Abu6-Arg8,9)-Vasopressin on cardiopulmonary hemodynamics and organ function vs. the mixed V1aR/V2R-agonist arginine vasopressin (AVP) or placebo in an established ovine model of septic s...

  16. Environmental time series interpolation based on Spartan random processes

    Science.gov (United States)

    Žukovič, Milan; Hristopulos, D. T.

    In many environmental applications, time series are either incomplete or irregularly spaced. We investigate the application of the Spartan random process to missing data prediction. We employ a novel modified method of moments (MMoM) and the established method of maximum likelihood (ML) for parameter inference. The CPU time of MMoM is shown to be much faster than that of ML estimation and almost independent of the data size. We formulate an explicit Spartan interpolator for estimating missing data. The model validation is performed on both synthetic data and real time series of atmospheric aerosol concentrations. The prediction performance is shown to be comparable with that attained by means of the best linear unbiased (Kolmogorov-Wiener) predictor at reduced computational cost.

  17. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    Directory of Open Access Journals (Sweden)

    Sungho Kim

    2016-07-01

    Full Text Available Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR images or infrared (IR images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter and an asymmetric morphological closing filter (AMCF, post-filter into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic

  18. The influence of extrinsic motivation on competition-based selection.

    Science.gov (United States)

    Sänger, Jessica; Wascher, Edmund

    2011-10-10

    The biased competition approach to visuo-spatial attention proposes that the selection of competing information is effected by the saliency of the stimulus as well as by an intention-based bias of attention towards behavioural goals. Wascher and Beste (2010) [32] showed that the detection of relevant information depends on its relative saliency compared to irrelevant conflicting stimuli. Furthermore the N1pc, N2pc and N2 of the EEG varied with the strength of the conflict. However, this system could also be modulated by rather global mechanisms like attentional effort. The present study investigates such modulations by testing the influence of extrinsic motivation on the selection of competing stimuli. Participants had to detect a luminance change in various conditions among others against an irrelevant orientation change. Half of the participants were motivated to maximize their performance by the announcement of a monetary reward for correct responses. Participants who were motivated had lower error rates than participants who were not motivated. The event-related lateralizations of the EEG showed no motivation-related effect on the N1pc, which reflects the initial saliency driven orientation of attention towards the more salient stimulus. The subsequent N2pc was enhanced in the motivation condition. Extrinsic motivation was also accompanied by enhanced fronto-central negativities. Thus, the data provide evidence that the improvement of selection performance when participants were extrinsically motivated by announcing a reward was not due to changes in the initial saliency based processing of information but was foremost mediated by improved higher-level mechanisms. Copyright © 2011 Elsevier B.V. All rights reserved.

  19. A randomized controlled trial investigating the use of a predictive nomogram for the selection of the FSH starting dose in IVF/ICSI cycles.

    Science.gov (United States)

    Allegra, Adolfo; Marino, Angelo; Volpes, Aldo; Coffaro, Francesco; Scaglione, Piero; Gullo, Salvatore; La Marca, Antonio

    2017-04-01

    The number of oocytes retrieved is a relevant intermediate outcome in women undergoing IVF/intracytoplasmic sperm injection (ICSI). This trial compared the efficiency of the selection of the FSH starting dose according to a nomogram based on multiple biomarkers (age, day 3 FSH, anti-Müllerian hormone) versus an age-based strategy. The primary outcome measure was the proportion of women with an optimal number of retrieved oocytes defined as 8-14. At their first IVF/ICSI cycle, 191 patients underwent a long gonadotrophin-releasing hormone agonist protocol and were randomized to receive a starting dose of recombinant (human) FSH, based on their age (150 IU if ≤35 years, 225 IU if >35 years) or based on the nomogram. Optimal response was observed in 58/92 patients (63%) in the nomogram group and in 42/99 (42%) in the control group (+21%, 95% CI = 0.07 to 0.35, P = 0.0037). No significant differences were found in the clinical pregnancy rate or the number of embryos cryopreserved per patient. The study showed that the FSH starting dose selected according to ovarian reserve is associated with an increase in the proportion of patients with an optimal response: large trials are recommended to investigate any possible effect on the live-birth rate. Copyright © 2017 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

  20. Participant-selected music and physical activity in older adults following cardiac rehabilitation: a randomized controlled trial.

    Science.gov (United States)

    Clark, Imogen N; Baker, Felicity A; Peiris, Casey L; Shoebridge, Georgie; Taylor, Nicholas F

    2017-03-01

    To evaluate effects of participant-selected music on older adults' achievement of activity levels recommended in the physical activity guidelines following cardiac rehabilitation. A parallel group randomized controlled trial with measurements at Weeks 0, 6 and 26. A multisite outpatient rehabilitation programme of a publicly funded metropolitan health service. Adults aged 60 years and older who had completed a cardiac rehabilitation programme. Experimental participants selected music to support walking with guidance from a music therapist. Control participants received usual care only. The primary outcome was the proportion of participants achieving activity levels recommended in physical activity guidelines. Secondary outcomes compared amounts of physical activity, exercise capacity, cardiac risk factors, and exercise self-efficacy. A total of 56 participants, mean age 68.2 years (SD = 6.5), were randomized to the experimental ( n = 28) and control groups ( n = 28). There were no differences between groups in proportions of participants achieving activity recommended in physical activity guidelines at Week 6 or 26. Secondary outcomes demonstrated between-group differences in male waist circumference at both measurements (Week 6 difference -2.0 cm, 95% CI -4.0 to 0; Week 26 difference -2.8 cm, 95% CI -5.4 to -0.1), and observed effect sizes favoured the experimental group for amounts of physical activity (d = 0.30), exercise capacity (d = 0.48), and blood pressure (d = -0.32). Participant-selected music did not increase the proportion of participants achieving recommended amounts of physical activity, but may have contributed to exercise-related benefits.

  1. Benefits of Selected Physical Exercise Programs in Detention: A Randomized Controlled Study

    Directory of Open Access Journals (Sweden)

    Claudia Battaglia

    2013-10-01

    Full Text Available The aim of the study was to determine which kind of physical activity could be useful to inmate populations to improve their health status and fitness levels. A repeated measure design was used to evaluate the effects of two different training protocols on subjects in a state of detention, tested pre- and post-experimental protocol.Seventy-five male subjects were enrolled in the studyand randomly allocated to three groups: the cardiovascular plus resistance training protocol group (CRT (n = 25; mean age 30.9 ± 8.9 years,the high-intensity strength training protocol group (HIST (n = 25; mean age 33.9 ± 6.8 years, and a control group (C (n = 25; mean age 32.9 ± 8.9 years receiving no treatment. All subjects underwent a clinical assessmentandfitness tests. MANOVA revealed significant multivariate effects on group (p < 0.01 and group-training interaction (p < 0.05. CRT protocol resulted the most effective protocol to reach the best outcome in fitness tests. Both CRT and HIST protocols produced significant gains in the functional capacity (cardio-respiratory capacity and cardiovascular disease risk decrease of incarcerated males. The significant gains obtained in functional capacity reflect the great potential of supervised exercise interventions for improving the health status of incarcerated people.

  2. Gesture Recognition using Latent-Dynamic based Conditional Random Fields and Scalar Features

    Science.gov (United States)

    Yulita, I. N.; Fanany, M. I.; Arymurthy, A. M.

    2017-02-01

    The need for segmentation and labeling of sequence data appears in several fields. The use of the conditional models such as Conditional Random Fields is widely used to solve this problem. In the pattern recognition, Conditional Random Fields specify the possibilities of a sequence label. This method constructs its full label sequence to be a probabilistic graphical model based on its observation. However, Conditional Random Fields can not capture the internal structure so that Latent-based Dynamic Conditional Random Fields is developed without leaving external dynamics of inter-label. This study proposes the use of Latent-Dynamic Conditional Random Fields for Gesture Recognition and comparison between both methods. Besides, this study also proposes the use of a scalar features to gesture recognition. The results show that performance of Latent-dynamic based Conditional Random Fields is not better than the Conditional Random Fields, and scalar features are effective for both methods are in gesture recognition. Therefore, it recommends implementing Conditional Random Fields and scalar features in gesture recognition for better performance

  3. Selective Sequential Zero-Base Budgeting Procedures Based on Total Factor Productivity Indicators

    OpenAIRE

    Ishikawa, A.; E. F. Sudit

    1981-01-01

    The authors' purpose in this paper is to develop productivity-based sequential budgeting procedures designed to expedite identification of major problem areas in bugetary performance, as well as to reduce the costs associated with comprehensive zero-base analyses. The concept of total factor productivity is reviewed and its relations to ordinary and zero-based budgeting are discussed in detail. An outline for a selective sequential analysis based on monitoring of three key indicators of (a) i...

  4. Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

    Science.gov (United States)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

    2017-01-01

    We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

  5. Comparison of sensor selection mechanisms for an ERP-based brain-computer interface.

    Science.gov (United States)

    Feess, David; Krell, Mario M; Metzen, Jan H

    2013-01-01

    A major barrier for a broad applicability of brain-computer interfaces (BCIs) based on electroencephalography (EEG) is the large number of EEG sensor electrodes typically used. The necessity for this results from the fact that the relevant information for the BCI is often spread over the scalp in complex patterns that differ depending on subjects and application scenarios. Recently, a number of methods have been proposed to determine an individual optimal sensor selection. These methods have, however, rarely been compared against each other or against any type of baseline. In this paper, we review several selection approaches and propose one additional selection criterion based on the evaluation of the performance of a BCI system using a reduced set of sensors. We evaluate the methods in the context of a passive BCI system that is designed to detect a P300 event-related potential and compare the performance of the methods against randomly generated sensor constellations. For a realistic estimation of the reduced system's performance we transfer sensor constellations found on one experimental session to a different session for evaluation. We identified notable (and unanticipated) differences among the methods and could demonstrate that the best method in our setup is able to reduce the required number of sensors considerably. Though our application focuses on EEG data, all presented algorithms and evaluation schemes can be transferred to any binary classification task on sensor arrays.

  6. Comparison of sensor selection mechanisms for an ERP-based brain-computer interface.

    Directory of Open Access Journals (Sweden)

    David Feess

    Full Text Available A major barrier for a broad applicability of brain-computer interfaces (BCIs based on electroencephalography (EEG is the large number of EEG sensor electrodes typically used. The necessity for this results from the fact that the relevant information for the BCI is often spread over the scalp in complex patterns that differ depending on subjects and application scenarios. Recently, a number of methods have been proposed to determine an individual optimal sensor selection. These methods have, however, rarely been compared against each other or against any type of baseline. In this paper, we review several selection approaches and propose one additional selection criterion based on the evaluation of the performance of a BCI system using a reduced set of sensors. We evaluate the methods in the context of a passive BCI system that is designed to detect a P300 event-related potential and compare the performance of the methods against randomly generated sensor constellations. For a realistic estimation of the reduced system's performance we transfer sensor constellations found on one experimental session to a different session for evaluation. We identified notable (and unanticipated differences among the methods and could demonstrate that the best method in our setup is able to reduce the required number of sensors considerably. Though our application focuses on EEG data, all presented algorithms and evaluation schemes can be transferred to any binary classification task on sensor arrays.

  7. Reduced plasma aldosterone concentrations in randomly selected patients with insulin-dependent diabetes mellitus.

    LENUS (Irish Health Repository)

    Cronin, C C

    2012-02-03

    Abnormalities of the renin-angiotensin system have been reported in patients with diabetes mellitus and with diabetic complications. In this study, plasma concentrations of prorenin, renin, and aldosterone were measured in a stratified random sample of 110 insulin-dependent (Type 1) diabetic patients attending our outpatient clinic. Fifty-four age- and sex-matched control subjects were also examined. Plasma prorenin concentration was higher in patients without complications than in control subjects when upright (geometric mean (95% confidence intervals (CI): 75.9 (55.0-105.6) vs 45.1 (31.6-64.3) mU I-1, p < 0.05). There was no difference in plasma prorenin concentration between patients without and with microalbuminuria and between patients without and with background retinopathy. Plasma renin concentration, both when supine and upright, was similar in control subjects, in patients without complications, and in patients with varying degrees of diabetic microangiopathy. Plasma aldosterone was suppressed in patients without complications in comparison to control subjects (74 (58-95) vs 167 (140-199) ng I-1, p < 0.001) and was also suppressed in patients with microvascular disease. Plasma potassium was significantly higher in patients than in control subjects (mean +\\/- standard deviation: 4.10 +\\/- 0.36 vs 3.89 +\\/- 0.26 mmol I-1; p < 0.001) and plasma sodium was significantly lower (138 +\\/- 4 vs 140 +\\/- 2 mmol I-1; p < 0.001). We conclude that plasma prorenin is not a useful early marker for diabetic microvascular disease. Despite apparently normal plasma renin concentrations, plasma aldosterone is suppressed in insulin-dependent diabetic patients.

  8. Cirrhosis Classification Based on Texture Classification of Random Features

    Directory of Open Access Journals (Sweden)

    Hui Liu

    2014-01-01

    Full Text Available Accurate staging of hepatic cirrhosis is important in investigating the cause and slowing down the effects of cirrhosis. Computer-aided diagnosis (CAD can provide doctors with an alternative second opinion and assist them to make a specific treatment with accurate cirrhosis stage. MRI has many advantages, including high resolution for soft tissue, no radiation, and multiparameters imaging modalities. So in this paper, multisequences MRIs, including T1-weighted, T2-weighted, arterial, portal venous, and equilibrium phase, are applied. However, CAD does not meet the clinical needs of cirrhosis and few researchers are concerned with it at present. Cirrhosis is characterized by the presence of widespread fibrosis and regenerative nodules in the hepatic, leading to different texture patterns of different stages. So, extracting texture feature is the primary task. Compared with typical gray level cooccurrence matrix (GLCM features, texture classification from random features provides an effective way, and we adopt it and propose CCTCRF for triple classification (normal, early, and middle and advanced stage. CCTCRF does not need strong assumptions except the sparse character of image, contains sufficient texture information, includes concise and effective process, and makes case decision with high accuracy. Experimental results also illustrate the satisfying performance and they are also compared with typical NN with GLCM.

  9. A two-stage noise source identification technique based on a farfield random parametric array.

    Science.gov (United States)

    Bai, Mingsian R; Chen, You Siang; Lo, Yi-Yang

    2017-05-01

    A farfield random array is implemented for noise source identification. Microphone positions are optimized, with the aid of the simulated annealing method. A two-stage localization and separation algorithm is devised on the basis of the equivalent source method (ESM). In the localization stage, the active source regions are located by using the delay-and-sum method, followed by a parametric localization procedure, stochastic maximum likelihood algorithm. Multidimensional nonlinear optimization is exploited in the bearing estimation process. In the separation stage, source amplitudes are extracted by formulating an inverse problem based on the preceding source bearings identified. The number of equivalent sources is selected to be less than that of microphones to render an overdetermined problem which can be readily solved by using the Tikhonov regularization. Alternatively, the separation problem can be augmented into an underdetermined problem which can be solved by using the compressive sensing technique. Traditionally, farfield arrays only give a relative distribution of source field. However, by using the proposed method, the acoustic variables including sound pressure, particle velocity, sound intensity, and sound power can be calculated based on ESM. Numerical and experimental results of several objective and subjective tests are presented.

  10. Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation.

    Science.gov (United States)

    Huang, Weimin; Yang, Yongzhong; Lin, Zhiping; Huang, Guang-Bin; Zhou, Jiayin; Duan, Yuping; Xiong, Wei

    2014-01-01

    This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results.

  11. A weight based genetic algorithm for selecting views

    Science.gov (United States)

    Talebian, Seyed H.; Kareem, Sameem A.

    2013-03-01

    Data warehouse is a technology designed for supporting decision making. Data warehouse is made by extracting large amount of data from different operational systems; transforming it to a consistent form and loading it to the central repository. The type of queries in data warehouse environment differs from those in operational systems. In contrast to operational systems, the analytical queries that are issued in data warehouses involve summarization of large volume of data and therefore in normal circumstance take a long time to be answered. On the other hand, the result of these queries must be answered in a short time to enable managers to make decisions as short time as possible. As a result, an essential need in this environment is in improving the performances of queries. One of the most popular methods to do this task is utilizing pre-computed result of queries. In this method, whenever a new query is submitted by the user instead of calculating the query on the fly through a large underlying database, the pre-computed result or views are used to answer the queries. Although, the ideal option would be pre-computing and saving all possible views, but, in practice due to disk space constraint and overhead due to view updates it is not considered as a feasible choice. Therefore, we need to select a subset of possible views to save on disk. The problem of selecting the right subset of views is considered as an important challenge in data warehousing. In this paper we suggest a Weighted Based Genetic Algorithm (WBGA) for solving the view selection problem with two objectives.

  12. A prototype of behavior selection mechanism based on emotion

    Science.gov (United States)

    Zhang, Guofeng; Li, Zushu

    2007-12-01

    In bionic methodology rather than in design methodology more familiar with, summarizing the psychological researches of emotion, we propose the biologic mechanism of emotion, emotion selection role in creature evolution and a anima framework including emotion similar to the classical control structure; and consulting Prospect Theory, build an Emotion Characteristic Functions(ECF) that computer emotion; two more emotion theories are added to them that higher emotion is preferred and middle emotion makes brain run more efficiently, emotional behavior mechanism comes into being. A simulation of proposed mechanism are designed and carried out on Alife Swarm software platform. In this simulation, a virtual grassland ecosystem is achieved where there are two kinds of artificial animals: herbivore and preyer. These artificial animals execute four types of behavior: wandering, escaping, finding food, finding sex partner in their lives. According the theories of animal ethnology, escaping from preyer is prior to other behaviors for its existence, finding food is secondly important behavior, rating is third one and wandering is last behavior. In keeping this behavior order, based on our behavior characteristic function theory, the specific functions of emotion computing are built of artificial autonomous animals. The result of simulation confirms the behavior selection mechanism.

  13. Preference option randomized design (PORD) for comparative effectiveness research: Statistical power for testing comparative effect, preference effect, selection effect, intent-to-treat effect, and overall effect.

    Science.gov (United States)

    Heo, Moonseong; Meissner, Paul; Litwin, Alain H; Arnsten, Julia H; McKee, M Diane; Karasz, Alison; McKinley, Paula; Rehm, Colin D; Chambers, Earle C; Yeh, Ming-Chin; Wylie-Rosett, Judith

    2017-01-01

    Comparative effectiveness research trials in real-world settings may require participants to choose between preferred intervention options. A randomized clinical trial with parallel experimental and control arms is straightforward and regarded as a gold standard design, but by design it forces and anticipates the participants to comply with a randomly assigned intervention regardless of their preference. Therefore, the randomized clinical trial may impose impractical limitations when planning comparative effectiveness research trials. To accommodate participants' preference if they are expressed, and to maintain randomization, we propose an alternative design that allows participants' preference after randomization, which we call a "preference option randomized design (PORD)". In contrast to other preference designs, which ask whether or not participants consent to the assigned intervention after randomization, the crucial feature of preference option randomized design is its unique informed consent process before randomization. Specifically, the preference option randomized design consent process informs participants that they can opt out and switch to the other intervention only if after randomization they actively express the desire to do so. Participants who do not independently express explicit alternate preference or assent to the randomly assigned intervention are considered to not have an alternate preference. In sum, preference option randomized design intends to maximize retention, minimize possibility of forced assignment for any participants, and to maintain randomization by allowing participants with no or equal preference to represent random assignments. This design scheme enables to define five effects that are interconnected with each other through common design parameters-comparative, preference, selection, intent-to-treat, and overall/as-treated-to collectively guide decision making between interventions. Statistical power functions for testing

  14. Age-related Cataract in a Randomized Trial of Selenium and Vitamin E in Men: The SELECT Eye Endpoints (SEE) Study

    Science.gov (United States)

    Christen, William G.; Glynn, Robert J.; Gaziano, J. Michael; Darke, Amy K.; Crowley, John J.; Goodman, Phyllis J.; Lippman, Scott M.; Lad, Thomas E.; Bearden, James D.; Goodman, Gary E.; Minasian, Lori M.; Thompson, Ian M.; Blanke, Charles D.; Klein, Eric A.

    2014-01-01

    Importance Observational studies suggest a role for dietary nutrients such as vitamin E and selenium in cataract prevention. However, the results of randomized trials of vitamin E supplements and cataract have been disappointing, and are not yet available for selenium. Objective To test whether long-term supplementation with selenium and vitamin E affects the incidence of cataract in a large cohort of men. Design, Setting, and Participants The SELECT Eye Endpoints (SEE) study was an ancillary study of the SWOG-coordinated Selenium and Vitamin E Cancer Prevention Trial (SELECT), a randomized, placebo-controlled, four arm trial of selenium and vitamin E conducted among 35,533 men aged 50 years and older for African Americans and 55 and older for all other men, at 427 participating sites in the US, Canada, and Puerto Rico. A total of 11,267 SELECT participants from 128 SELECT sites participated in the SEE ancillary study. Intervention Individual supplements of selenium (200 µg/d from L-selenomethionine) and vitamin E (400 IU/d of all rac-α-tocopheryl acetate). Main Outcome Measures Incident cataract, defined as a lens opacity, age-related in origin, responsible for a reduction in best-corrected visual acuity to 20/30 or worse based on self-report confirmed by medical record review, and cataract extraction, defined as the surgical removal of an incident cataract. Results During a mean (SD) of 5.6 (1.2) years of treatment and follow-up, 389 cases of cataract were documented. There were 185 cataracts in the selenium group and 204 in the no selenium group (hazard ratio [HR], 0.91; 95 percent confidence interval [CI], 0.75 to 1.11; P=.37). For vitamin E, there were 197 cases in the treated group and 192 in the placebo group (HR, 1.02; CI, 0.84 to 1.25; P=.81). Similar results were observed for cataract extraction. Conclusions and Relevance These randomized trial data from a large cohort of apparently healthy men indicate that long-term daily supplementation with selenium

  15. Feature selection gait-based gender classification under different circumstances

    Science.gov (United States)

    Sabir, Azhin; Al-Jawad, Naseer; Jassim, Sabah

    2014-05-01

    This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.

  16. Do Culture-based Segments Predict Selection of Market Strategy?

    Directory of Open Access Journals (Sweden)

    Veronika Jadczaková

    2015-01-01

    Full Text Available Academists and practitioners have already acknowledged the importance of unobservable segmentation bases (such as psychographics yet still focusing on how well these bases are capable of describing relevant segments (the identifiability criterion rather than on how precisely these segments can predict (the predictability criterion. Therefore, this paper intends to add a debate to this topic by exploring whether culture-based segments do account for a selection of market strategy. To do so, a set of market strategy variables over a sample of 251 manufacturing firms was first regressed on a set of 19 cultural variables using canonical correlation analysis. Having found significant relationship in the first canonical function, it was further examined by means of correspondence analysis which cultural segments – if any – are linked to which market strategies. However, as correspondence analysis failed to find a significant relationship, it may be concluded that business culture might relate to the adoption of market strategy but not to the cultural groupings presented in the paper.

  17. Edge-Based Defocus Blur Estimation With Adaptive Scale Selection.

    Science.gov (United States)

    Karaali, Ali; Jung, Claudio Rosito

    2018-03-01

    Objects that do not lie at the focal distance of a digital camera generate defocused regions in the captured image. This paper presents a new edge-based method for spatially varying defocus blur estimation using a single image based on reblurred gradient magnitudes. The proposed approach initially computes a scale-consistent edge map of the input image and selects a local reblurring scale aiming to cope with noise, edge mis-localization, and interfering edges. An initial blur estimate is computed at the detected scale-consistent edge points and a novel connected edge filter is proposed to smooth the sparse blur map based on pixel connectivity within detected edge contours. Finally, a fast guided filter is used to propagate the sparse blur map through the whole image. Experimental results show that the proposed approach presents a very good compromise between estimation error and running time when compared with the state-of-the-art methods. We also explore our blur estimation method in the context of image deblurring, and show that metrics typically used to evaluate blur estimation may not correlate as expected with the visual quality of the deblurred image.

  18. Bacteriophage-based genetic system for selection of nonsplicing inteins.

    Science.gov (United States)

    Cann, Isaac K O; Amaya, Kensey R; Southworth, Maurice W; Perler, Francine B

    2004-05-01

    A genetic selection system that detects splicing and nonsplicing activities of inteins was developed based on the ability to rescue a T4 phage strain with a conditionally inactive DNA polymerase. This phage defect can be complemented by expression of plasmid-encoded phage RB69 DNA polymerase. Insertion of an intein gene into the active site of the RB69 DNA polymerase gene renders polymerase activity and phage viability dependent on protein splicing. The effectiveness of the system was tested by screening for thermosensitive splicing mutants. Development of genetic systems with the potential of identifying protein splicing inhibitors is a first step towards controlling proliferation of pathogenic microbes harboring inteins in essential proteins.

  19. Selection of complementary foods based on optimal nutritional values

    DEFF Research Database (Denmark)

    Sen, Partho; Mardinogulu, Adil; Nielsen, Jens

    2017-01-01

    Human milk is beneficial for growth and development of infants. Several factors result in mothers ceasing breastfeeding which leads to introduction of breast-milk substitutes (BMS). In some communities traditional foods are given as BMS, in others they are given as complementary foods during...... weaning. Improper food selection at this stage is associated with a high prevalence of malnutrition in children under 5 years. Here we listed the traditional foods from four continents and compared them with human milk based on their dietary contents. Vitamins such as thiamine (similar to[2-10] folds......), riboflavin (similar to[4-10] folds) and ascorbic acid (foods were markedly lower. In order to extend the search for foods that includes similar dietary constituents as human milk, we designed a strategy of screening 8654 foods. 12 foods were identified and these foods...

  20. Outsourcing and vendor selection model based on Taguchi loss function

    Directory of Open Access Journals (Sweden)

    Jirarat Teeravaraprug

    2008-07-01

    Full Text Available In today’s fiercely competitive environment, there is an emergence of the extended enterprise of interdependentorganizations. This leads to a steady increase in part and service outsourcing. The decisions relating to this topic are whetheroutsourcing is appropriate and which vendors should be selected. To make the decision, many attributes need to beconsidered—both cash and non-cash. Cash impacts can be measured directly where as non-cash impacts are hardly measured.This paper applies Taguchi loss function to measure the non-cash impacts. The non-cash impacts considered in this paperinclude quality, speed, dependability, and flexibility. A mathematical model is given based on both cash and non-cash impacts.A numerical example is given to illustrate the model. Finally, conclusions and discussions are given.

  1. Single-chain lipopeptide vaccines for the induction of virus-specific cytotoxic T cell responses in randomly selected populations.

    Science.gov (United States)

    Gras-Masse, H

    2001-12-01

    Effective vaccine development is now taking advantage of the rapidly accumulating information concerning the molecular basis of a protective immune response. Analysts and medicinal chemists have joined forces with immunologists and taken up the clear challenge of identifying immunologically active structural elements and synthesizing them in pure, reproducible forms. Current literature reveals the growing interest for extremely reductionist approaches aiming at producing totally synthetic vaccines that would be fully defined at the molecular level and particularly safe. The sequential information contained in these formulations tends to be minimized to those epitopes which elicit neutralizing antibodies, or cell-mediated responses. In the following review, we describe some of our results in developing fully synthetic, clinically acceptable lipopeptide vaccines for inducing cytotoxic T lymphocytes (CTL) responses in randomly selected populations.

  2. Condition-Based Conveyor Belt Replacement Strategy in Lignite Mines with Random Belt Deterioration

    Science.gov (United States)

    Blazej, Ryszard; Jurdziak, Leszek

    2017-12-01

    In Polish lignite surface mines, condition-based belt replacement strategies are applied in order to assure profitable refurbishment of worn out belts performed by external firms specializing in belt maintenance. In two of three lignite mines, staff asses belt condition subjectively during visual inspections. Only one mine applies specialized diagnostic device (HRDS) allowing objective magnetic evaluation of belt core condition in order to choose the most profitable moment for the dismantling of worn out belt segments from conveyors and sending them to the maintenance firm which provides their refurbishment. This article describes the advantages of a new diagnostic device called DiagBelt. It was developed at the Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology. Economic gains from its application are calculated for the lignite mine and for the belt maintenance firm, taking into account random life (durability) of new and reconditioned belts (after the 1st and the 2nd refurbishment). Recursive calculations for following years allow the estimation of the length and costs of replaced, reconditioned and purchased belts on an annual basis, while the use of the Monte Carlo method allows the estimation of their variability caused by random deterioration of belts. Savings are obtained due to better selection of moments (times) for the replacement of belt segments and die to the possibility to qualify worn out belts for refurbishment without the need to remove their covers. In effect, increased belt durability and lowered share of waste belts (which were not qualified for reconditioning) create savings which can quickly cover expenditures on new diagnostic tools and regular belt inspections in the mine.

  3. SHER: a colored petri net based random mobility model for wireless communications.

    Directory of Open Access Journals (Sweden)

    Naeem Akhtar Khan

    Full Text Available In wireless network research, simulation is the most imperative technique to investigate the network's behavior and validation. Wireless networks typically consist of mobile hosts; therefore, the degree of validation is influenced by the underlying mobility model, and synthetic models are implemented in simulators because real life traces are not widely available. In wireless communications, mobility is an integral part while the key role of a mobility model is to mimic the real life traveling patterns to study. The performance of routing protocols and mobility management strategies e.g. paging, registration and handoff is highly dependent to the selected mobility model. In this paper, we devise and evaluate the Show Home and Exclusive Regions (SHER, a novel two-dimensional (2-D Colored Petri net (CPN based formal random mobility model, which exhibits sociological behavior of a user. The model captures hotspots where a user frequently visits and spends time. Our solution eliminates six key issues of the random mobility models, i.e., sudden stops, memoryless movements, border effect, temporal dependency of velocity, pause time dependency, and speed decay in a single model. The proposed model is able to predict the future location of a mobile user and ultimately improves the performance of wireless communication networks. The model follows a uniform nodal distribution and is a mini simulator, which exhibits interesting mobility patterns. The model is also helpful to those who are not familiar with the formal modeling, and users can extract meaningful information with a single mouse-click. It is noteworthy that capturing dynamic mobility patterns through CPN is the most challenging and virulent activity of the presented research. Statistical and reachability analysis techniques are presented to elucidate and validate the performance of our proposed mobility model. The state space methods allow us to algorithmically derive the system behavior and

  4. SHER: a colored petri net based random mobility model for wireless communications.

    Science.gov (United States)

    Khan, Naeem Akhtar; Ahmad, Farooq; Khan, Sher Afzal

    2015-01-01

    In wireless network research, simulation is the most imperative technique to investigate the network's behavior and validation. Wireless networks typically consist of mobile hosts; therefore, the degree of validation is influenced by the underlying mobility model, and synthetic models are implemented in simulators because real life traces are not widely available. In wireless communications, mobility is an integral part while the key role of a mobility model is to mimic the real life traveling patterns to study. The performance of routing protocols and mobility management strategies e.g. paging, registration and handoff is highly dependent to the selected mobility model. In this paper, we devise and evaluate the Show Home and Exclusive Regions (SHER), a novel two-dimensional (2-D) Colored Petri net (CPN) based formal random mobility model, which exhibits sociological behavior of a user. The model captures hotspots where a user frequently visits and spends time. Our solution eliminates six key issues of the random mobility models, i.e., sudden stops, memoryless movements, border effect, temporal dependency of velocity, pause time dependency, and speed decay in a single model. The proposed model is able to predict the future location of a mobile user and ultimately improves the performance of wireless communication networks. The model follows a uniform nodal distribution and is a mini simulator, which exhibits interesting mobility patterns. The model is also helpful to those who are not familiar with the formal modeling, and users can extract meaningful information with a single mouse-click. It is noteworthy that capturing dynamic mobility patterns through CPN is the most challenging and virulent activity of the presented research. Statistical and reachability analysis techniques are presented to elucidate and validate the performance of our proposed mobility model. The state space methods allow us to algorithmically derive the system behavior and rectify the errors

  5. Unsupervised Feature Selection Based on the Morisita Index

    Science.gov (United States)

    Golay, Jean; Kanevski, Mikhail

    2016-04-01

    Recent breakthroughs in technology have radically improved our ability to collect and store data. As a consequence, the size of datasets has been increasing rapidly both in terms of number of variables (or features) and number of instances. Since the mechanism of many phenomena is not well known, too many variables are sampled. A lot of them are redundant and contribute to the emergence of three major challenges in data mining: (1) the complexity of result interpretation, (2) the necessity to develop new methods and tools for data processing, (3) the possible reduction in the accuracy of learning algorithms because of the curse of dimensionality. This research deals with a new algorithm for selecting the smallest subset of features conveying all the information of a dataset (i.e. an algorithm for removing redundant features). It is a new version of the Fractal Dimensionality Reduction (FDR) algorithm [1] and it relies on two ideas: (a) In general, data lie on non-linear manifolds of much lower dimension than that of the spaces where they are embedded. (b) The situation describes in (a) is partly due to redundant variables, since they do not contribute to increasing the dimension of manifolds, called Intrinsic Dimension (ID). The suggested algorithm implements these ideas by selecting only the variables influencing the data ID. Unlike the FDR algorithm, it resorts to a recently introduced ID estimator [2] based on the Morisita index of clustering and to a sequential forward search strategy. Consequently, in addition to its ability to capture non-linear dependences, it can deal with large datasets and its implementation is straightforward in any programming environment. Many real world case studies are considered. They are related to environmental pollution and renewable resources. References [1] C. Traina Jr., A.J.M. Traina, L. Wu, C. Faloutsos, Fast feature selection using fractal dimension, in: Proceedings of the XV Brazilian Symposium on Databases, SBBD, pp. 158

  6. Query-Based Sampling: Can we do Better than Random?

    NARCIS (Netherlands)

    Tigelaar, A.S.; Hiemstra, Djoerd

    2010-01-01

    Many servers on the web offer content that is only accessible via a search interface. These are part of the deep web. Using conventional crawling to index the content of these remote servers is impossible without some form of cooperation. Query-based sampling provides an alternative to crawling

  7. Phenomenological analysis of random telegraph noise in amorphous TiOx-based bipolar resistive switching random access memory devices.

    Science.gov (United States)

    Lee, Jung-Kyu; Lee, Ju-Wan; Bae, Jong-Ho; Park, Jinwon; Chung, Sung-Woong; Roh, Jae Sung; Hong, Sung-Joo; Lee, Jong-Ho

    2012-07-01

    As dimensions of resistive random access memories (RRAMs) devices continue to shrink, the low-frequency noise of nanoscale devices has become increasingly important in evaluating the device reliability. Thus, we investigated random telegraph noise (RTN) caused by capture and emission of an electron at traps. We physically analyzed capture and emission processes through systematic measurements of amorphous TiOx (alpha-TiOx)-based bipolar RRAMs. RTNs were observed during high-resistance state (HRS) in most devices. However, discrete switching behavior was scarcely observed in low-resistance state (LRS) as most of traps in the alpha-TiOx were filled with mobile ions such as O2- in LRS. The capture and emission processes of an electron at traps are largely divided into two groups: (1) both capture and emission processes are mainly affected by electric field; and (2) one of the capture and emission processes is only influenced by the thermal process. This paper provides fundamental physics required to understand the mechanism of RTNs in alpha-TiOx-based bipolar RRAMs.

  8. Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network

    Science.gov (United States)

    Wang, Zhen-yu; Zhang, Li-jie

    2017-10-01

    Measure error of the sensor can be effectively compensated with prediction. Aiming at large random drift error of MEMS(Micro Electro Mechanical System))gyroscope, an improved learning algorithm of Radial Basis Function(RBF) Neural Network(NN) based on K-means clustering and Orthogonal Least-Squares (OLS) is proposed in this paper. The algorithm selects the typical samples as the initial cluster centers of RBF NN firstly, candidates centers with K-means algorithm secondly, and optimizes the candidate centers with OLS algorithm thirdly, which makes the network structure simpler and makes the prediction performance better. Experimental results show that the proposed K-means clustering OLS learning algorithm can predict the random drift of MEMS gyroscope effectively, the prediction error of which is 9.8019e-007°/s and the prediction time of which is 2.4169e-006s

  9. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

    Science.gov (United States)

    Tangaro, Sabina; Amoroso, Nicola; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Paolo, Inglese; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

    2015-01-01

    Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

  10. Social learning theory parenting intervention promotes attachment-based caregiving in young children: randomized clinical trial.

    Science.gov (United States)

    O'Connor, Thomas G; Matias, Carla; Futh, Annabel; Tantam, Grace; Scott, Stephen

    2013-01-01

    Parenting programs for school-aged children are typically based on behavioral principles as applied in social learning theory. It is not yet clear if the benefits of these interventions extend beyond aspects of the parent-child relationship quality conceptualized by social learning theory. The current study examined the extent to which a social learning theory-based treatment promoted change in qualities of parent-child relationship derived from attachment theory. A randomized clinical trial of 174 four- to six-year-olds selected from a high-need urban area and stratified by conduct problems were assigned to a parenting program plus a reading intervention (n = 88) or nonintervention condition (n = 86). In-home observations of parent-child interactions were assessed in three tasks: (a) free play, (b) challenge task, and (c) tidy up. Parenting behavior was coded according to behavior theory using standard count measures of positive and negative parenting, and for attachment theory using measures of sensitive responding and mutuality; children's attachment narratives were also assessed. Compared to the parents in the nonintervention group, parents allocated to the intervention showed increases in the positive behavioral counts and sensitive responding; change in behavioral count measures overlapped modestly with change in attachment-based changes. There was no reliable change in children's attachment narratives associated with the intervention. The findings demonstrate that standard social learning theory-based parenting interventions can change broader aspects of parent-child relationship quality and raise clinical and conceptual questions about the distinctiveness of existing treatment models in parenting research.

  11. Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms

    Directory of Open Access Journals (Sweden)

    Fernando A. Auat Cheein

    2013-01-01

    Full Text Available Process modeling by means of Gaussian-based algorithms often suffers from redundant information which usually increases the estimation computational complexity without significantly improving the estimation performance. In this article, a non-arbitrary measurement selection criterion for Gaussian-based algorithms is proposed. The measurement selection criterion is based on the determination of the most significant measurement from both an estimation convergence perspective and the covariance matrix associated with the measurement. The selection criterion is independent from the nature of the measured variable. This criterion is used in conjunction with three Gaussian-based algorithms: the EIF (Extended Information Filter, the EKF (Extended Kalman Filter and the UKF (Unscented Kalman Filter. Nevertheless, the measurement selection criterion shown herein can also be applied to other Gaussian-based algorithms. Although this work is focused on environment modeling, the results shown herein can be applied to other Gaussian-based algorithm implementations. Mathematical descriptions and implementation results that validate the proposal are also included in this work.

  12. Mindfulness-based prevention for eating disorders: A school-based cluster randomized controlled study.

    Science.gov (United States)

    Atkinson, Melissa J; Wade, Tracey D

    2015-11-01

    Successful prevention of eating disorders represents an important goal due to damaging long-term impacts on health and well-being, modest treatment outcomes, and low treatment seeking among individuals at risk. Mindfulness-based approaches have received early support in the treatment of eating disorders, but have not been evaluated as a prevention strategy. This study aimed to assess the feasibility, acceptability, and efficacy of a novel mindfulness-based intervention for reducing the risk of eating disorders among adolescent females, under both optimal (trained facilitator) and task-shifted (non-expert facilitator) conditions. A school-based cluster randomized controlled trial was conducted in which 19 classes of adolescent girls (N = 347) were allocated to a three-session mindfulness-based intervention, dissonance-based intervention, or classes as usual control. A subset of classes (N = 156) receiving expert facilitation were analyzed separately as a proxy for delivery under optimal conditions. Task-shifted facilitation showed no significant intervention effects across outcomes. Under optimal facilitation, students receiving mindfulness demonstrated significant reductions in weight and shape concern, dietary restraint, thin-ideal internalization, eating disorder symptoms, and psychosocial impairment relative to control by 6-month follow-up. Students receiving dissonance showed significant reductions in socio-cultural pressures. There were no statistically significant differences between the two interventions. Moderate intervention acceptability was reported by both students and teaching staff. Findings show promise for the application of mindfulness in the prevention of eating disorders; however, further work is required to increase both impact and acceptability, and to enable successful outcomes when delivered by less expert providers. © 2015 Wiley Periodicals, Inc.

  13. EEG-based mild depressive detection using feature selection methods and classifiers.

    Science.gov (United States)

    Li, Xiaowei; Hu, Bin; Sun, Shuting; Cai, Hanshu

    2016-11-01

    Depression has become a major health burden worldwide, and effectively detection of such disorder is a great challenge which requires latest technological tool, such as Electroencephalography (EEG). This EEG-based research seeks to find prominent frequency band and brain regions that are most related to mild depression, as well as an optimal combination of classification algorithms and feature selection methods which can be used in future mild depression detection. An experiment based on facial expression viewing task (Emo_block and Neu_block) was conducted, and EEG data of 37 university students were collected using a 128 channel HydroCel Geodesic Sensor Net (HCGSN). For discriminating mild depressive patients and normal controls, BayesNet (BN), Support Vector Machine (SVM), Logistic Regression (LR), k-nearest neighbor (KNN) and RandomForest (RF) classifiers were used. And BestFirst (BF), GreedyStepwise (GSW), GeneticSearch (GS), LinearForwordSelection (LFS) and RankSearch (RS) based on Correlation Features Selection (CFS) were applied for linear and non-linear EEG features selection. Independent Samples T-test with Bonferroni correction was used to find the significantly discriminant electrodes and features. Data mining results indicate that optimal performance is achieved using a combination of feature selection method GSW based on CFS and classifier KNN for beta frequency band. Accuracies achieved 92.00% and 98.00%, and AUC achieved 0.957 and 0.997, for Emo_block and Neu_block beta band data respectively. T-test results validate the effectiveness of selected features by search method GSW. Simplified EEG system with only FP1, FP2, F3, O2, T3 electrodes was also explored with linear features, which yielded accuracies of 91.70% and 96.00%, AUC of 0.952 and 0.972, for Emo_block and Neu_block respectively. Classification results obtained by GSW + KNN are encouraging and better than previously published results. In the spatial distribution of features, we find

  14. Mutual information-based feature selection for radiomics

    Science.gov (United States)

    Oubel, Estanislao; Beaumont, Hubert; Iannessi, Antoine

    2016-03-01

    Background The extraction and analysis of image features (radiomics) is a promising field in the precision medicine era, with applications to prognosis, prediction, and response to treatment quantification. In this work, we present a mutual information - based method for quantifying reproducibility of features, a necessary step for qualification before their inclusion in big data systems. Materials and Methods Ten patients with Non-Small Cell Lung Cancer (NSCLC) lesions were followed over time (7 time points in average) with Computed Tomography (CT). Five observers segmented lesions by using a semi-automatic method and 27 features describing shape and intensity distribution were extracted. Inter-observer reproducibility was assessed by computing the multi-information (MI) of feature changes over time, and the variability of global extrema. Results The highest MI values were obtained for volume-based features (VBF). The lesion mass (M), surface to volume ratio (SVR) and volume (V) presented statistically significant higher values of MI than the rest of features. Within the same VBF group, SVR showed also the lowest variability of extrema. The correlation coefficient (CC) of feature values was unable to make a difference between features. Conclusions MI allowed to discriminate three features (M, SVR, and V) from the rest in a statistically significant manner. This result is consistent with the order obtained when sorting features by increasing values of extrema variability. MI is a promising alternative for selecting features to be considered as surrogate biomarkers in a precision medicine context.

  15. Implementation of evidence-based antenatal care in Mozambique: a cluster randomized controlled trial: study protocol.

    Science.gov (United States)

    Chavane, Leonardo; Merialdi, Mario; Betrán, Ana Pilar; Requejo-Harris, Jennifer; Bergel, Eduardo; Aleman, Alicia; Colomar, Mercedes; Cafferata, Maria Luisa; Carbonell, Alicia; Crahay, Beatrice; Delvaux, Therese; Geelhoed, Diederike; Gülmezoglu, Metin; Malapende, Celsa Regina; Melo, Armando; Nguyen, My Huong; Osman, Nafissa Bique; Widmer, Mariana; Temmerman, Marleen; Althabe, Fernando

    2014-05-21

    Antenatal care (ANC) reduces maternal and perinatal morbidity and mortality directly through the detection and treatment of pregnancy-related illnesses, and indirectly through the detection of women at increased risk of delivery complications. The potential benefits of quality antenatal care services are most significant in low-resource countries where morbidity and mortality levels among women of reproductive age and neonates are higher.WHO developed an ANC model that recommended the delivery of services scientifically proven to improve maternal, perinatal and neonatal outcomes. The aim of this study is to determine the effect of an intervention designed to increase the use of the package of evidence-based services included in the WHO ANC model in Mozambique. The primary hypothesis is that the intervention will increase the use of evidence-based practices during ANC visits in comparison to the standard dissemination channels currently used in the country. This is a demonstration project to be developed through a facility-based cluster randomized controlled trial with a stepped wedge design. The intervention was tailored, based on formative research findings, to be readily applicable to local prenatal care services and acceptable to local pregnant women and health providers. The intervention includes four components: the provision of kits with all necessary medicines and laboratory supplies for ANC (medical and non-medical equipment), a storage system, a tracking system, and training sessions for health care providers. Ten clinics were selected and will start receiving the intervention in a random order. Outcomes will be computed at each time point when a new clinic starts the intervention. The primary outcomes are the delivery of selected health care practices to women attending the first ANC visit, and secondary outcomes are the delivery of selected health care practices to women attending second and higher ANC visits as well as the attitude of midwives in

  16. IMRT QA: Selecting gamma criteria based on error detection sensitivity

    Energy Technology Data Exchange (ETDEWEB)

    Steers, Jennifer M. [Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California 90048 and Physics and Biology in Medicine IDP, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095 (United States); Fraass, Benedick A., E-mail: benedick.fraass@cshs.org [Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California 90048 (United States)

    2016-04-15

    Purpose: The gamma comparison is widely used to evaluate the agreement between measurements and treatment planning system calculations in patient-specific intensity modulated radiation therapy (IMRT) quality assurance (QA). However, recent publications have raised concerns about the lack of sensitivity when employing commonly used gamma criteria. Understanding the actual sensitivity of a wide range of different gamma criteria may allow the definition of more meaningful gamma criteria and tolerance limits in IMRT QA. We present a method that allows the quantitative determination of gamma criteria sensitivity to induced errors which can be applied to any unique combination of device, delivery technique, and software utilized in a specific clinic. Methods: A total of 21 DMLC IMRT QA measurements (ArcCHECK®, Sun Nuclear) were compared to QA plan calculations with induced errors. Three scenarios were studied: MU errors, multi-leaf collimator (MLC) errors, and the sensitivity of the gamma comparison to changes in penumbra width. Gamma comparisons were performed between measurements and error-induced calculations using a wide range of gamma criteria, resulting in a total of over 20 000 gamma comparisons. Gamma passing rates for each error class and case were graphed against error magnitude to create error curves in order to represent the range of missed errors in routine IMRT QA using 36 different gamma criteria. Results: This study demonstrates that systematic errors and case-specific errors can be detected by the error curve analysis. Depending on the location of the error curve peak (e.g., not centered about zero), 3%/3 mm threshold = 10% at 90% pixels passing may miss errors as large as 15% MU errors and ±1 cm random MLC errors for some cases. As the dose threshold parameter was increased for a given %Diff/distance-to-agreement (DTA) setting, error sensitivity was increased by up to a factor of two for select cases. This increased sensitivity with increasing dose

  17. Selection of experimental strawberry (Fragaria x ananassa) hybrids based on selection indices.

    Science.gov (United States)

    Vieira, S D; de Souza, D C; Martins, I A; Ribeiro, G H M R; Resende, L V; Ferraz, A K L; Galvão, A G; de Resende, J T V

    2017-03-08

    The strawberry (Fragaria x ananassa Dutch.), is the only vegetable belonging to the rosacea family. All strawberry species have now emerged from wild species and belong to the genus Fragaria, being that this genus presents more than 45 described species, and only 11 are considered natural species. Due to the octoploid nature of strawberry and its variability after hybridization, selecting one or more characters may result in unfavorable genotypes and even the exclusion of promising ones, because negative genetic correlations have been observed among them that cause inefficient selection. Therefore, the objective of this study was to verify the efficiency of selection indices in selecting experimental strawberry hybrids for in natura consumption and processing. Seven commercial cultivars and 103 hybrids were used, which were obtained from populations derived from their crossings. The experiment was conducted in augmented blocks, in which four agronomical traits (total mass, amount of commercial fruit, amount of noncommercial fruit, and average fruit mass) and seven physical-chemical traits (soluble solids, soluble solids:titratable acidity ratio, total sugars, total pectin, vigor, and internal and external coloration) were evaluated. For hybrid selection, the following indices were used: Mulamba and Mock (1978), Smith (1936), Hazel (1943), and genotype-ideotype, which selected 20% of the genotypes evaluated. The three indices selected about 9% of the hybrids. The selection of two experimental hybrids (89 and 495) and the use of selection indices resulted in larger estimates of selection gains. The Mulamba and Mock (1978), Smith (1936), and Hazel (1943) indices had the highest percentage of gains on selection, and are therefore recommended for the selection of strawberry clones.

  18. Multiple Attribute Decision Making Based Relay Vehicle Selection for Electric Vehicle Communication

    Directory of Open Access Journals (Sweden)

    Zhao Qiang

    2015-01-01

    Full Text Available Large-scale electric vehicle integration into power grid and charging randomly will cause serious impacts on the normal operation of power grid. Therefore, it is necessary to control the charging behavior of electric vehicle, while information transmission for electric vehicle is significant. Due to the highly mobile characteristics of vehicle, transferring information to power grid directly might be inaccessible. Relay vehicle (RV can be used for supporting multi-hop connection between SV and power grid. This paper proposes a multiple attribute decision making (MADM-based RV selection algorithm, which considers multiple attribute, including data transfer rate, delay, route duration. It takes the characteristics of electric vehicle communication into account, which can provide protection for the communication services of electric vehicle charging and discharging. Numerical results demonstrate that compared to previous algorithm, the proposed algorithm offer better performance in terms of throughput, transmission delay.

  19. Voxel-Based Dose Prediction with Multi-Patient Atlas Selection for Automated Radiotherapy Treatment Planning

    CERN Document Server

    McIntosh, Chris

    2016-01-01

    Automating the radiotherapy treatment planning process is a technically challenging problem. The majority of automated approaches have focused on customizing and inferring dose volume objectives to used in plan optimization. In this work we outline a multi-patient atlas-based dose prediction approach that learns to predict the dose-per-voxel for a novel patient directly from the computed tomography (CT) planning scan without the requirement of specifying any objectives. Our method learns to automatically select the most effective atlases for a novel patient, and then map the dose from those atlases onto the novel patient. We extend our previous work to include a conditional random field for the optimization of a joint distribution prior that matches the complementary goals of an accurately spatially distributed dose distribution while still adhering to the desired dose volume histograms. The resulting distribution can then be used for inverse-planning with a new spatial dose objective, or to create typical do...

  20. Thin layer coulometry with ionophore based ion-selective membranes.

    Science.gov (United States)

    Grygolowicz-Pawlak, Ewa; Bakker, Eric

    2010-06-01

    We are demonstrating here for the first time a thin layer coulometric detection mode for ionophore based liquid ion-selective membranes. Coulometry promises to achieve the design of robust, calibration free sensors that are especially attractive for applications where recalibration in situ is difficult or undesirable. This readout principle is here achieved with porous polypropylene tubing doped with the membrane material and which contains a chlorinated silver wire in the inner compartment, together with the fluidically delivered sample solution. The membrane material consists of the lipophilic plasticizer dodecyl 2-nitrophenyl ether, the lipophilic electrolyte ETH 500, and the calcium ionophore ETH 5234. Importantly and in contrast to earlier work on voltammetric liquid membrane electrodes, the membrane also contains a cation-exchanger salt, KTFPB. This renders the membrane permselective and allows one to observe open circuit potentiometric responses for the device, which is confirmed to follow the expected Nernstian equation. Moreover, as the same cationic species is now potential determining at both interfaces of the membrane, it is possible to use rapidly diffusing and/or thin membrane systems where transport processes at the inner and outer interface of the membrane do not perturb each other or the overall composition of the membrane. The tubing is immersed in an electrolyte solution where the counter and working electrode are placed, and the potentials are applied relative to the measured open circuit potentials. Exhaustive current decays are observed in the range of 10 to 100 muM calcium chloride. The observed charge, calculated as integrated currents, is linearly dependent on concentration and forms the basis for the coulometric readout of ion-selective membrane electrodes.

  1. Randomized trial of switching from prescribed non-selective non-steroidal anti-inflammatory drugs to prescribed celecoxib

    DEFF Research Database (Denmark)

    Macdonald, Thomas M; Hawkey, Chris J; Ford, Ian

    2017-01-01

    BACKGROUND: Selective cyclooxygenase-2 inhibitors and conventional non-selective non-steroidal anti-inflammatory drugs (nsNSAIDs) have been associated with adverse cardiovascular (CV) effects. We compared the CV safety of switching to celecoxib vs. continuing nsNSAID therapy in a European setting....... METHOD: Patients aged 60 years and over with osteoarthritis or rheumatoid arthritis, free from established CV disease and taking chronic prescribed nsNSAIDs, were randomized to switch to celecoxib or to continue their previous nsNSAID. The primary endpoint was hospitalization for non-fatal myocardial...... expected developed an on-treatment (OT) primary CV event and the rate was similar for celecoxib, 0.95 per 100 patient-years, and nsNSAIDs, 0.86 per 100 patient-years (HR = 1.12, 95% confidence interval, 0.81-1.55; P = 0.50). Comparable intention-to-treat (ITT) rates were 1.14 per 100 patient...

  2. A Security Mechanism for Cluster-Based WSN against Selective Forwarding

    Directory of Open Access Journals (Sweden)

    Hai Zhou

    2016-09-01

    Full Text Available A wireless sensor network (WSN faces a number of outsider and insider attacks, and it is difficult to detect and defend against insider attacks. In particular, an insider selective-forwarding attack, in which the attackers select some of the received packets to drop, most threatens a WSN. Compared to a distributed WSN, a cluster-based WSN will suffer more losses, even the whole network’s destruction, if the cluster head is attacked. In this paper, a scheme solving the above issues is proposed with three types of nodes, the Cluster Head (CH, the Inspector Node (IN and Member Nodes (MNs. The IN monitors the CH’s transmission to protect the cluster against a selective-forwarding attack; the CH forwards packets from MNs and other CHs, and randomly checks the IN to ascertain if it works properly; and the MNs send the gathered data packets to the CH and evaluate the behaviors of the CH and IN based on their own reputation mechanism. The novelty of our scheme is that in order to take both the safety and the lifespan of a network into consideration, the composite reputation value (CRV including forwarding rate, detecting malicious nodes, and surplus energy of the node is utilized to select CH and IN under the new suggested network arrangement, and the use of a node’s surplus energy can balance the energy consumption of a node, thereby prolonging the network lifespan. Theoretical analysis and simulation results indicate that the proposed scheme can detect the malicious node accurately and efficiently, so the false alarm rate is lowered by 25.7% compared with Watchdog and the network lifespan is prolonged by 54.84% compared with LEACH (Low Energy Adaptive Clustering Hierarchy.

  3. A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model

    Directory of Open Access Journals (Sweden)

    Apisit Eiumnoh

    2013-10-01

    Full Text Available Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF model to simultaneously align two or more images and obtain a land cover map (LCM of the scene. The expectation maximization (EM algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced.

  4. Land cover mapping based on random forest classification of multitemporal spectral and thermal images.

    Science.gov (United States)

    Eisavi, Vahid; Homayouni, Saeid; Yazdi, Ahmad Maleknezhad; Alimohammadi, Abbas

    2015-05-01

    Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8's spectral and thermal imageries using a random forest (RF) classifier. We used a grid search approach based on the out-of-bag (OOB) estimate of error to optimize the RF parameters. Four different scenarios were considered in this research: (1) RF classification of multitemporal spectral images, (2) RF classification of multitemporal LST images, (3) RF classification of all multitemporal LST and spectral images, and (4) RF classification of selected important or optimum features. The study area in this research was Naghadeh city and its surrounding region, located in West Azerbaijan Province, northwest of Iran. The overall accuracies of first, second, third, and fourth scenarios were equal to 86.48, 82.26, 90.63, and 91.82%, respectively. The quantitative assessments of the results demonstrated that the most important or optimum features increase the class separability, while the spectral and thermal features produced a more moderate increase in the land cover mapping accuracy. In addition, the contribution of the multitemporal thermal information led to a considerable increase in the user and producer accuracies of classes with a rapid temporal change behavior, such as crops and vegetation.

  5. Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.

    Science.gov (United States)

    Ramírez, J; Górriz, J M; Ortiz, A; Martínez-Murcia, F J; Segovia, F; Salas-Gonzalez, D; Castillo-Barnes, D; Illán, I A; Puntonet, C G

    2017-12-11

    Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10-15% per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments. The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of One vs. Rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level. The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25% classification score on the test subset which consists of 160 real subjects. The classifier yielded the best performance when compared to: (i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, (ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and (iii) bagging ensemble learning. A robust method has been proposed for the international challenge on MCI prediction based on MRI data. The system yielded the second best performance during the competition with an accuracy rate of 56.25% when evaluated on the real subjects of the test set. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Partner- and planning-based interventions to reduce fat consumption: randomized controlled trial.

    OpenAIRE

    Prestwich, A; Conner, MT; Lawton, RJ; Ward, JK; Ayres, K; McEachan, RRC

    2014-01-01

    OBJECTIVE: The research tested the efficacy of partner- and planning-based interventions to reduce dietary fat intake over a 6-month period. DESIGN: Randomized controlled, blinded, parallel trial. METHODS: A computer randomization feature was used to allocate council employees (N = 427, of which 393 completed baseline measures) to one of four conditions (partner + implementation intentions, partner-only, implementation intentions, and control group) before they completed measures at baseline ...

  7. K-Ras(G12D)-selective inhibitory peptides generated by random peptide T7 phage display technology.

    Science.gov (United States)

    Sakamoto, Kotaro; Kamada, Yusuke; Sameshima, Tomoya; Yaguchi, Masahiro; Niida, Ayumu; Sasaki, Shigekazu; Miwa, Masanori; Ohkubo, Shoichi; Sakamoto, Jun-Ichi; Kamaura, Masahiro; Cho, Nobuo; Tani, Akiyoshi

    2017-03-11

    Amino-acid mutations of Gly 12 (e.g. G12D, G12V, G12C) of V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (K-Ras), the most promising drug target in cancer therapy, are major growth drivers in various cancers. Although over 30 years have passed since the discovery of these mutations in most cancer patients, effective mutated K-Ras inhibitors have not been marketed. Here, we report novel and selective inhibitory peptides to K-Ras(G12D). We screened random peptide libraries displayed on T7 phage against purified recombinant K-Ras(G12D), with thorough subtraction of phages bound to wild-type K-Ras, and obtained KRpep-2 (Ac-RRCPLYISYDPVCRR-NH 2 ) as a consensus sequence. KRpep-2 showed more than 10-fold binding- and inhibition-selectivity to K-Ras(G12D), both in SPR analysis and GDP/GTP exchange enzyme assay. K D and IC 50 values were 51 and 8.9 nM, respectively. After subsequent sequence optimization, we successfully generated KRpep-2d (Ac-RRRRCPLYISYDPVCRRRR-NH 2 ) that inhibited enzyme activity of K-Ras(G12D) with IC 50  = 1.6 nM and significantly suppressed ERK-phosphorylation, downstream of K-Ras(G12D), along with A427 cancer cell proliferation at 30 μM peptide concentration. To our knowledge, this is the first report of a K-Ras(G12D)-selective inhibitor, contributing to the development and study of K-Ras(G12D)-targeting drugs. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Wavelength Selection Method Based on Differential Evolution for Precise Quantitative Analysis Using Terahertz Time-Domain Spectroscopy.

    Science.gov (United States)

    Li, Zhi; Chen, Weidong; Lian, Feiyu; Ge, Hongyi; Guan, Aihong

    2017-12-01

    Quantitative analysis of component mixtures is an important application of terahertz time-domain spectroscopy (THz-TDS) and has attracted broad interest in recent research. Although the accuracy of quantitative analysis using THz-TDS is affected by a host of factors, wavelength selection from the sample's THz absorption spectrum is the most crucial component. The raw spectrum consists of signals from the sample and scattering and other random disturbances that can critically influence the quantitative accuracy. For precise quantitative analysis using THz-TDS, the signal from the sample needs to be retained while the scattering and other noise sources are eliminated. In this paper, a novel wavelength selection method based on differential evolution (DE) is investigated. By performing quantitative experiments on a series of binary amino acid mixtures using THz-TDS, we demonstrate the efficacy of the DE-based wavelength selection method, which yields an error rate below 5%.

  9. Selection for Surgical Training: An Evidence-Based Review.

    Science.gov (United States)

    Schaverien, Mark V

    2016-01-01

    The predictive relationship between candidate selection criteria for surgical training programs and future performance during and at the completion of training has been investigated for several surgical specialties, however there is no interspecialty agreement regarding which selection criteria should be used. Better understanding the predictive reliability between factors at selection and future performance may help to optimize the process and lead to greater standardization of the surgical selection process. PubMed and Ovid MEDLINE databases were searched. Over 560 potentially relevant publications were identified using the search strategy and screened using the Cochrane Collaboration Data Extraction and Assessment Template. 57 studies met the inclusion criteria. Several selection criteria used in the traditional selection demonstrated inconsistent correlation with subsequent performance during and at the end of surgical training. The following selection criteria, however, demonstrated good predictive relationships with subsequent resident performance: USMLE examination scores, Letters of Recommendation (LOR) including the Medical Student Performance Evaluation (MSPE), academic performance during clinical clerkships, the interview process, displaying excellence in extracurricular activities, and the use of unadjusted rank lists. This systematic review supports that the current selection process needs to be further evaluated and improved. Multicenter studies using standardized outcome measures of success are now required to improve the reliability of the selection process to select the best trainees. Published by Elsevier Inc.

  10. Turbo codes based image transmission for channels with random errors

    Science.gov (United States)

    Yao, Lei; Cao, Lei

    2006-10-01

    In this paper, we propose a joint source-channel coding scheme for progressive image transmission over binary symmetric channels(BSCs). The algorithm of set partitioning in hierarchical trees (SPIHT) is used for source coding. Rate-compatible punctured Turbo codes (RCPT) concatenated with multiple cyclic redundancy check (CRC) codes are adopted for channel protection. For a fixed transmission rate, the source and channel code rates are jointly optimized to maximize the expected image quality at the receiver. Two technical components which are different from existing methods are presented. First, a long data packet is divided into multiple CRC blocks before being coded by turbo codes. This is to secure a high coding gain of Turbo codes which is proportional to the interleaver size. In the mean time, the beginning blocks in a packet may still be useable although the decoding of the entire packet fails. Second, instead of using exhaustive search, we give a genetic algorithm (GA) based optimization method to find the appropriate channel code rates with low complexity. The effectiveness of the scheme is demonstrated through simulations.

  11. Towards an Automatic and Application-Based EigensolverSelection

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Yeliang; Li, Xiaoye S.; Marques, Osni

    2005-09-09

    The computation of eigenvalues and eigenvectors is an important and often time-consuming phase in computer simulations. Recent efforts in the development of eigensolver libraries have given users good algorithms without the need for users to spend much time in programming. Yet, given the variety of numerical algorithms that are available to domain scientists, choosing the ''best'' algorithm suited for a particular application is a daunting task. As simulations become increasingly sophisticated and larger, it becomes infeasible for a user to try out every reasonable algorithm configuration in a timely fashion. Therefore, there is a need for an intelligent engine that can guide the user through the maze of various solvers with various configurations. In this paper, we present a methodology and a software architecture aiming at determining the best solver based on the application type and the matrix properties. We combine a decision tree and an intelligent engine to select a solver and a preconditioner combination for the application submitted by the user. We also discuss how our system interface is implemented with third party numerical libraries. In the case study, we demonstrate the feasibility and usefulness of our system with a simplified linear solving system. Our experiments show that our proposed intelligent engine is quite adept in choosing a suitable algorithm for different applications.

  12. Towards Identify Selective Antibacterial Peptides Based on Abstracts Meaning.

    Science.gov (United States)

    Barbosa-Santillán, Liliana I; Sánchez-Escobar, Juan J; Calixto-Romo, M Angeles; Barbosa-Santillán, Luis F

    2016-01-01

    We present an Identify Selective Antibacterial Peptides (ISAP) approach based on abstracts meaning. Laboratories and researchers have significantly increased the report of their discoveries related to antibacterial peptides in primary publications. It is important to find antibacterial peptides that have been reported in primary publications because they can produce antibiotics of different generations that attack and destroy the bacteria. Unfortunately, researchers used heterogeneous forms of natural language to describe their discoveries (sometimes without the sequence of the peptides). Thus, we propose that learning the words meaning instead of the antibacterial peptides sequence is possible to identify and predict antibacterial peptides reported in the PubMed engine. The ISAP approach consists of two stages: training and discovering. ISAP founds that the 35% of the abstracts sample had antibacterial peptides and we tested in the updated Antimicrobial Peptide Database 2 (APD2). ISAP predicted that 45% of the abstracts had antibacterial peptides. That is, ISAP found that 810 antibacterial peptides were not classified like that, so they are not reported in APD2. As a result, this new search tool would complement the APD2 with a set of peptides that are candidates to be antibacterial. Finally, 20% of the abstracts were not semantic related to APD2.

  13. Moldless PEGDA-Based Optoelectrofluidic Platform for Microparticle Selection

    Directory of Open Access Journals (Sweden)

    Shih-Mo Yang

    2011-01-01

    Full Text Available This paper reports on an optoelectrofluidic platform which consists of the organic photoconductive material, titanium oxide phthalocyanine (TiOPc, and the photocrosslinkable polymer, poly (ethylene glycol diacrylate (PEGDA. TiOPc simplifies the fabrication process of the optoelectronic chip due to requiring only a single spin-coating step. PEGDA is applied to embed the moldless PEGDA-based microchannel between the top ITO glass and the bottom TiOPc substrate. A real-time control interface via a touch panel screen is utilized to select the target 15 μm polystyrene particles. When the microparticles flow to an illuminating light bar, which is oblique to the microfluidic flow path, the lateral driving force diverts the microparticles. Two light patterns, the switching oblique light bar and the optoelectronic ladder phenomenon, are designed to demonstrate the features. This work integrating the new material design, TiOPc and PEGDA, and the ability of mobile microparticle manipulation demonstrates the potential of optoelectronic approach.

  14. History-based action selection bias in posterior parietal cortex.

    Science.gov (United States)

    Hwang, Eun Jung; Dahlen, Jeffrey E; Mukundan, Madan; Komiyama, Takaki

    2017-11-01

    Making decisions based on choice-outcome history is a crucial, adaptive ability in life. However, the neural circuit mechanisms underlying history-dependent decision-making are poorly understood. In particular, history-related signals have been found in many brain areas during various decision-making tasks, but the causal involvement of these signals in guiding behavior is unclear. Here we addressed this issue utilizing behavioral modeling, two-photon calcium imaging, and optogenetic inactivation in mice. We report that a subset of neurons in the posterior parietal cortex (PPC) closely reflect the choice-outcome history and history-dependent decision biases, and PPC inactivation diminishes the history dependency of choice. Specifically, many PPC neurons show history- and bias-tuning during the inter-trial intervals (ITI), and history dependency of choice is affected by PPC inactivation during ITI and not during trial. These results indicate that PPC is a critical region mediating the subjective use of history in biasing action selection.

  15. A Lightweight Structure Redesign Method Based on Selective Laser Melting

    Directory of Open Access Journals (Sweden)

    Li Tang

    2016-11-01

    Full Text Available The purpose of this paper is to present a new design method of lightweight parts fabricated by selective laser melting (SLM based on the “Skin-Frame” and to explore the influence of fabrication defects on SLM parts with different sizes. Some standard lattice parts were designed according to the Chinese GB/T 1452-2005 standard and manufactured by SLM. Then these samples were tested in an MTS Insight 30 compression testing machine to study the trends of the yield process with different structure sizes. A set of standard cylinder samples were also designed according to the Chinese GB/T 228-2010 standard. These samples, which were made of iron-nickel alloy (IN718, were also processed by SLM, and then tested in the universal material testing machine INSTRON 1346 to obtain their tensile strength. Furthermore, a lightweight redesigned method was researched. Then some common parts such as a stopper and connecting plate were redesigned using this method. These redesigned parts were fabricated and some application tests have already been performed. The compression testing results show that when the minimum structure size is larger than 1.5 mm, the mechanical characteristics will hardly be affected by process defects. The cylinder parts were fractured by the universal material testing machine at about 1069.6 MPa. These redesigned parts worked well in application tests, with both the weight and fabrication time of these parts reduced more than 20%.

  16. Towards Identify Selective Antibacterial Peptides Based on Abstracts Meaning

    Directory of Open Access Journals (Sweden)

    Liliana I. Barbosa-Santillán

    2016-01-01

    Full Text Available We present an Identify Selective Antibacterial Peptides (ISAP approach based on abstracts meaning. Laboratories and researchers have significantly increased the report of their discoveries related to antibacterial peptides in primary publications. It is important to find antibacterial peptides that have been reported in primary publications because they can produce antibiotics of different generations that attack and destroy the bacteria. Unfortunately, researchers used heterogeneous forms of natural language to describe their discoveries (sometimes without the sequence of the peptides. Thus, we propose that learning the words meaning instead of the antibacterial peptides sequence is possible to identify and predict antibacterial peptides reported in the PubMed engine. The ISAP approach consists of two stages: training and discovering. ISAP founds that the 35% of the abstracts sample had antibacterial peptides and we tested in the updated Antimicrobial Peptide Database 2 (APD2. ISAP predicted that 45% of the abstracts had antibacterial peptides. That is, ISAP found that 810 antibacterial peptides were not classified like that, so they are not reported in APD2. As a result, this new search tool would complement the APD2 with a set of peptides that are candidates to be antibacterial. Finally, 20% of the abstracts were not semantic related to APD2.

  17. Performance Measurement Model for the Supplier Selection Based on AHP

    OpenAIRE

    Fabio De Felice; Deldoost, Mostafa H.; Mohsen Faizollahi; Antonella Petrillo

    2015-01-01

    The performance of the supplier is a crucial factor for the success or failure of any company. Rational and effective decision making in terms of the supplier selection process can help the organization to optimize cost and quality functions. The nature of supplier selection processes is generally complex, especially when the company has a large variety of products and vendors. Over the years, several solutions and methods have emerged for addressing the supplier selection problem (SSP). Expe...

  18. Zeta Sperm Selection Improves Pregnancy Rate and Alters Sex Ratio in Male Factor Infertility Patients: A Double-Blind, Randomized Clinical Trial

    Directory of Open Access Journals (Sweden)

    Nasr Esfahani Mohammad Hossein

    2016-07-01

    Full Text Available Background Selection of sperm for intra-cytoplasmic sperm injection (ICSI is usually considered as the ultimate technique to alleviate male-factor infertility. In routine ICSI, selection is based on morphology and viability which does not necessarily preclude the chance injection of DNA-damaged or apoptotic sperm into the oocyte. Sperm with high negative surface electrical charge, named “Zeta potential”, are mature and more likely to have intact chromatin. In addition, X-bearing spermatozoa carry more negative charge. Therefore, we aimed to compare the clinical outcomes of Zeta procedure with routine sperm selection in infertile men candidate for ICSI. Materials and Methods From a total of 203 ICSI cycles studied, 101 cycles were allocated to density gradient centrifugation (DGC/Zeta group and the remaining 102 were included in the DGC group in this prospective study. Clinical outcomes were com- pared between the two groups. The ratios of Xand Y bearing sperm were assessed by fluorescence in situ hybridization (FISH and quantitative polymerase chain reaction (qPCR methods in 17 independent semen samples. Results In the present double-blind randomized clinical trial, a significant increase in top quality embryos and pregnancy rate were observed in DGC/Zeta group compared to DGC group. Moreover, sex ratio (XY/XX at birth significantly was lower in the DGC/Zeta group compared to DGC group despite similar ratio of X/Y bearings sper- matozoa following Zeta selection. Conclusion Zeta method not only improves the percentage of top embryo quality and pregnancy outcome but also alters the sex ratio compared to the conventional DGC method, despite no significant change in the ratio of Xand Ybearing sperm population (Registration number: IRCT201108047223N1.

  19. Unsupervised Feature Selection Based on the Morisita Index for Hyperspectral Images

    Science.gov (United States)

    Golay, Jean; Kanevski, Mikhail

    2017-04-01

    Hyperspectral sensors are capable of acquiring images with hundreds of narrow and contiguous spectral bands. Compared with traditional multispectral imagery, the use of hyperspectral images allows better performance in discriminating between land-cover classes, but it also results in large redundancy and high computational data processing. To alleviate such issues, unsupervised feature selection techniques for redundancy minimization can be implemented. Their goal is to select the smallest subset of features (or bands) in such a way that all the information content of a data set is preserved as much as possible. The present research deals with the application to hyperspectral images of a recently introduced technique of unsupervised feature selection: the Morisita-Based filter for Redundancy Minimization (MBRM). MBRM is based on the (multipoint) Morisita index of clustering and on the Morisita estimator of Intrinsic Dimension (ID). The fundamental idea of the technique is to retain only the bands which contribute to increasing the ID of an image. In this way, redundant bands are disregarded, since they have no impact on the ID. Besides, MBRM has several advantages over benchmark techniques: in addition to its ability to deal with large data sets, it can capture highly-nonlinear dependences and its implementation is straightforward in any programming environment. Experimental results on freely available hyperspectral images show the good effectiveness of MBRM in remote sensing data processing. Comparisons with benchmark techniques are carried out and random forests are used to assess the performance of MBRM in reducing the data dimensionality without loss of relevant information. References [1] C. Traina Jr., A.J.M. Traina, L. Wu, C. Faloutsos, Fast feature selection using fractal dimension, in: Proceedings of the XV Brazilian Symposium on Databases, SBBD, pp. 158-171, 2000. [2] J. Golay, M. Kanevski, A new estimator of intrinsic dimension based on the multipoint

  20. Randomized controlled trial of outpatient mentalization-based treatment versus structured clinical management for borderline personality disorder.

    Science.gov (United States)

    Bateman, Anthony; Fonagy, Peter

    2009-12-01

    This randomized controlled trial tested the effectiveness of an 18-month mentalization-based treatment (MBT) approach in an outpatient context against a structured clinical management (SCM) outpatient approach for treatment of borderline personality disorder. Patients (N=134) consecutively referred to a specialist personality disorder treatment center and meeting selection criteria were randomly allocated to MBT or SCM. Eleven mental health professionals equal in years of experience and training served as therapists. Independent evaluators blind to treatment allocation conducted assessments every 6 months. The primary outcome was the occurrence of crisis events, a composite of suicidal and severe self-injurious behaviors and hospitalization. Secondary outcomes included social and interpersonal functioning and self-reported symptoms. Outcome measures, assessed at 6-month intervals, were analyzed using mixed effects logistic regressions for binary data, Poisson regression models for count data, and mixed effects linear growth curve models for self-report variables. Substantial improvements were observed in both conditions across all outcome variables. Patients randomly assigned to MBT showed a steeper decline of both self-reported and clinically significant problems, including suicide attempts and hospitalization. Structured treatments improve outcomes for individuals with borderline personality disorder. A focus on specific psychological processes brings additional benefits to structured clinical support. Mentalization-based treatment is relatively undemanding in terms of training so it may be useful for implementation into general mental health services. Further evaluations by independent research groups are now required.

  1. Effects of Video Game Training on Measures of Selective Attention and Working Memory in Older Adults: Results from a Randomized Controlled Trial

    Science.gov (United States)

    Ballesteros, Soledad; Mayas, Julia; Prieto, Antonio; Ruiz-Marquez, Eloísa; Toril, Pilar; Reales, José M.

    2017-01-01

    Video game training with older adults potentially enhances aspects of cognition that decline with aging and could therefore offer a promising training approach. Although, previous published studies suggest that training can produce transfer, many of them have certain shortcomings. This randomized controlled trial (RCT; Clinicaltrials.gov ID: NCT02796508) tried to overcome some of these limitations by incorporating an active control group and the assessment of motivation and expectations. Seventy-five older volunteers were randomly assigned to the experimental group trained for 16 sessions with non-action video games from Lumosity, a commercial platform (http://www.lumosity.com/) or to an active control group trained for the same number of sessions with simulation strategy games. The final sample included 55 older adults (30 in the experimental group and 25 in the active control group). Participants were tested individually before and after training to assess working memory (WM) and selective attention and also reported their perceived improvement, motivation and engagement. The results showed improved performance across the training sessions. The main results were: (1) the experimental group did not show greater improvements in measures of selective attention and working memory than the active control group (the opposite occurred in the oddball task); (2) a marginal training effect was observed for the N-back task, but not for the Stroop task while both groups improved in the Corsi Blocks task. Based on these results, one can conclude that training with non-action games provide modest benefits for untrained tasks. The effect is not specific for that kind of training as a similar effect was observed for strategy video games. Groups did not differ in motivation, engagement or expectations. PMID:29163136

  2. Effects of Video Game Training on Measures of Selective Attention and Working Memory in Older Adults: Results from a Randomized Controlled Trial.

    Science.gov (United States)

    Ballesteros, Soledad; Mayas, Julia; Prieto, Antonio; Ruiz-Marquez, Eloísa; Toril, Pilar; Reales, José M

    2017-01-01

    Video game training with older adults potentially enhances aspects of cognition that decline with aging and could therefore offer a promising training approach. Although, previous published studies suggest that training can produce transfer, many of them have certain shortcomings. This randomized controlled trial (RCT; Clinicaltrials.gov ID: NCT02796508) tried to overcome some of these limitations by incorporating an active control group and the assessment of motivation and expectations. Seventy-five older volunteers were randomly assigned to the experimental group trained for 16 sessions with non-action video games from Lumosity, a commercial platform (http://www.lumosity.com/) or to an active control group trained for the same number of sessions with simulation strategy games. The final sample included 55 older adults (30 in the experimental group and 25 in the active control group). Participants were tested individually before and after training to assess working memory (WM) and selective attention and also reported their perceived improvement, motivation and engagement. The results showed improved performance across the training sessions. The main results were: (1) the experimental group did not show greater improvements in measures of selective attention and working memory than the active control group (the opposite occurred in the oddball task); (2) a marginal training effect was observed for the N-back task, but not for the Stroop task while both groups improved in the Corsi Blocks task. Based on these results, one can conclude that training with non-action games provide modest benefits for untrained tasks. The effect is not specific for that kind of training as a similar effect was observed for strategy video games. Groups did not differ in motivation, engagement or expectations.

  3. Effects of Video Game Training on Measures of Selective Attention and Working Memory in Older Adults: Results from a Randomized Controlled Trial

    Directory of Open Access Journals (Sweden)

    Soledad Ballesteros

    2017-11-01

    Full Text Available Video game training with older adults potentially enhances aspects of cognition that decline with aging and could therefore offer a promising training approach. Although, previous published studies suggest that training can produce transfer, many of them have certain shortcomings. This randomized controlled trial (RCT; Clinicaltrials.gov ID: NCT02796508 tried to overcome some of these limitations by incorporating an active control group and the assessment of motivation and expectations. Seventy-five older volunteers were randomly assigned to the experimental group trained for 16 sessions with non-action video games from Lumosity, a commercial platform (http://www.lumosity.com/ or to an active control group trained for the same number of sessions with simulation strategy games. The final sample included 55 older adults (30 in the experimental group and 25 in the active control group. Participants were tested individually before and after training to assess working memory (WM and selective attention and also reported their perceived improvement, motivation and engagement. The results showed improved performance across the training sessions. The main results were: (1 the experimental group did not show greater improvements in measures of selective attention and working memory than the active control group (the opposite occurred in the oddball task; (2 a marginal training effect was observed for the N-back task, but not for the Stroop task while both groups improved in the Corsi Blocks task. Based on these results, one can conclude that training with non-action games provide modest benefits for untrained tasks. The effect is not specific for that kind of training as a similar effect was observed for strategy video games. Groups did not differ in motivation, engagement or expectations.

  4. Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine

    OpenAIRE

    Yang Liu; Bo He; Diya Dong; Yue Shen,; Tianhong Yan; Rui Nian; Amaury Lendasse

    2015-01-01

    A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, inclu...

  5. Random sample community-based health surveys: does the effort to reach participants matter?

    Science.gov (United States)

    Messiah, Antoine; Castro, Grettel; Rodríguez de la Vega, Pura; Acuna, Juan M

    2014-12-15

    Conducting health surveys with community-based random samples are essential to capture an otherwise unreachable population, but these surveys can be biased if the effort to reach participants is insufficient. This study determines the desirable amount of effort to minimise such bias. A household-based health survey with random sampling and face-to-face interviews. Up to 11 visits, organised by canvassing rounds, were made to obtain an interview. Single-family homes in an underserved and understudied population in North Miami-Dade County, Florida, USA. Of a probabilistic sample of 2200 household addresses, 30 corresponded to empty lots, 74 were abandoned houses, 625 households declined to participate and 265 could not be reached and interviewed within 11 attempts. Analyses were performed on the 1206 remaining households. Each household was asked if any of their members had been told by a doctor that they had high blood pressure, heart disease including heart attack, cancer, diabetes, anxiety/ depression, obesity or asthma. Responses to these questions were analysed by the number of visit attempts needed to obtain the interview. Return per visit fell below 10% after four attempts, below 5% after six attempts and below 2% after eight attempts. As the effort increased, household size decreased, while household income and the percentage of interviewees active and employed increased; proportion of the seven health conditions decreased, four of which did so significantly: heart disease 20.4-9.2%, high blood pressure 63.5-58.1%, anxiety/depression 24.4-9.2% and obesity 21.8-12.6%. Beyond the fifth attempt, however, cumulative percentages varied by less than 1% and precision varied by less than 0.1%. In spite of the early and steep drop, sustaining at least five attempts to reach participants is necessary to reduce selection bias. 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.

  6. ANALYSIS OF FUZZY QUEUES: PARAMETRIC PROGRAMMING APPROACH BASED ON RANDOMNESS - FUZZINESS CONSISTENCY PRINCIPLE

    Directory of Open Access Journals (Sweden)

    Dhruba Das

    2015-04-01

    Full Text Available In this article, based on Zadeh’s extension principle we have apply the parametric programming approach to construct the membership functions of the performance measures when the interarrival time and the service time are fuzzy numbers based on the Baruah’s Randomness- Fuzziness Consistency Principle. The Randomness-Fuzziness Consistency Principle leads to defining a normal law of fuzziness using two different laws of randomness. In this article, two fuzzy queues FM/M/1 and M/FM/1 has been studied and constructed their membership functions of the system characteristics based on the aforesaid principle. The former represents a queue with fuzzy exponential arrivals and exponential service rate while the latter represents a queue with exponential arrival rate and fuzzy exponential service rate.

  7. [Evidence-based medicine: can we trust the results of well-designed randomized trials?].

    Science.gov (United States)

    Maturana, A; Benaglio, C

    2014-10-01

    Evidence based medicine assists in clinical decision-making by integrating critically appraised information with patient's values and preferences within an existing clinical context. A fundamental concept in this paradigm is the hierarchy of information. The randomized clinical trial is recognized as one of the designs that is less prone to bias and therefore of higher methodological quality. Clinical guidelines are one of the principal tools that evidence based medicine uses to transfer scientific information to clinical practice and many of their recommendations are based on these type of studies. In this review we present some of the limitations that the results can have, in even well designed and executed randomized clinical trials. We also discuss why valid results in these types of studies could not necessarily be extrapolated to the general population. Although the randomized clinical trial continues to be one of the best methodological designs, we suggest that the reader be careful when interpreting its results.

  8. Observer-based H(infinity) control for networked nonlinear systems with random packet losses.

    Science.gov (United States)

    Li, Jian Guo; Yuan, Jing Qi; Lu, Jun Guo

    2010-01-01

    This paper investigates the observer-based H(infinity) control problem of networked nonlinear systems with global Lipschitz nonlinearities and random communication packet losses. The random packet loss is modelled as a Bernoulli distributed white sequence with a known conditional probability distribution. In the presence of random packet losses, sufficient conditions for the existence of an observer-based feedback controller are derived, such that the closed-loop networked nonlinear system is exponentially stable in the mean-square sense, and a prescribed H(infinity) disturbance-rejection-attenuation performance is also achieved. Then a linear matrix inequality (LMI) approach for designing such an observer-based H(infinity) controller is presented. Finally, a simulation example is used to demonstrate the effectiveness of the proposed method. 2009. Published by Elsevier Ltd.

  9. Channel selection based on phase measurement in P300-based brain-computer interface.

    Directory of Open Access Journals (Sweden)

    Minpeng Xu

    Full Text Available Most EEG-based brain-computer interface (BCI paradigms include specific electrode positions. As the structures and activities of the brain vary with each individual, contributing channels should be chosen based on original records of BCIs. Phase measurement is an important approach in EEG analyses, but seldom used for channel selections. In this paper, the phase locking and concentrating value-based recursive feature elimination approach (PLCV-RFE is proposed to produce robust-EEG channel selections in a P300 speller. The PLCV-RFE, deriving from the phase resetting mechanism, measures the phase relation between EEGs and ranks channels by the recursive strategy. Data recorded from 32 electrodes on 9 subjects are used to evaluate the proposed method. The results show that the PLCV-RFE substantially reduces channel sets and improves recognition accuracies significantly. Moreover, compared with other state-of-the-art feature selection methods (SSNRSF and SVM-RFE, the PLCV-RFE achieves better performance. Thus the phase measurement is available in the channel selection of BCI and it may be an evidence to indirectly support that phase resetting is at least one reason for ERP generations.

  10. based 2D dynamic metal-organic framework showing selective ...

    Indian Academy of Sciences (India)

    The selective water uptake over alcohols along with visible colour change demonstrates the potential of the present compound in bio-alcohol purification. Keywords. Metal-organic frameworks; coordination polymers; selective uptake; dynamic framework. 1. Introduction. The limited natural resources like gas and oil have.

  11. Durable phosphate-selective electrodes based on uranyl salophenes

    NARCIS (Netherlands)

    Wroblewski, Wojciech; Wojciechowski, Kamil; Dybko, Artur; Brzozka, Zbigniew; Egberink, Richard J.M.; Ruel, Bianca H.M.; Reinhoudt, David

    2001-01-01

    Lipophilic uranyl salophenes derivatives were used as ionophores in durable phosphate-selective electrodes. The influence of the ionophore structure and membrane composition (polarity of plasticizer, the amount of incorporated ionic sites) on the electrode selectivity and long-term stability were

  12. Mahalanobis Taguchi system based criteria selection tool for ...

    Indian Academy of Sciences (India)

    Agriculture crop selection cannot be formulated from one criterion but from multiple criteria. A list of criteria for crop selection was identified through literature survey and agricultural experts. The identified criteria were grouped into seven main criteria namely, soil, water, season, input, support, facilities and threats.

  13. Intention-based and stimulus-based mechanisms in action selection.

    Science.gov (United States)

    Waszak, Florian; Wascher, Edmund; Keller, Peter; Koch, Iring; Aschersleben, Gisa; Rosenbaum, David A; Prinz, Wolfgang

    2005-04-01

    Human actions can be classified as being either more stimulus-based or more intention-based. According to the ideomotor framework of action control, intention-based actions primarily refer to anticipated action effects (in other words response-stimulus [R-S] bindings), whereas stimulus-based actions are commonly assumed to be more strongly determined by stimulus-response [S-R] bindings. We explored differences in the functional signatures of both modes of action control in a temporal bisection task. Participants either performed a choice response by pressing one out of two keys in response to a preceding stimulus (stimulus-based action), or pressed one out of two keys to produce the next stimulus (intention-based action). In line with the ideomotor framework, we found intention-based actions to be shifted in time towards their anticipated effects (the next stimulus), whereas stimulus-based actions were shifted towards their preceding stimulus. Event-related potentials (ERPs) in the EEG revealed marked differences in action preparation for the two tasks. The data as a whole provide converging evidence for functional differences in the selection of motor actions as a function of their triggering conditions, and support the notion of two different modes of action selection, one being exogenous or mainly stimulus-driven, the other being endogenous or mainly intention-driven.

  14. Secure identity-based encryption in the quantum random oracle model

    Science.gov (United States)

    Zhandry, Mark

    2015-04-01

    We give the first proof of security for an identity-based encryption (IBE) scheme in the quantum random oracle model. This is the first proof of security for any scheme in this model that does not rely on the assumed existence of so-called quantum-secure pseudorandom functions (PRFs). Our techniques are quite general and we use them to obtain security proofs for two random oracle hierarchical IBE schemes and a random oracle signature scheme, all of which have previously resisted quantum security proofs, even assuming quantum-secure PRFs. We also explain how to remove quantum-secure PRFs from prior quantum random oracle model proofs. We accomplish these results by developing new tools for arguing that quantum algorithms cannot distinguish between two oracle distributions. Using a particular class of oracle distributions that we call semi-constant distributions, we argue that the aforementioned cryptosystems are secure against quantum adversaries.

  15. Object-based gully system prediction from medium resolution imagery using Random Forests

    Science.gov (United States)

    Shruthi, Rajesh B. V.; Kerle, Norman; Jetten, Victor; Stein, Alfred

    2014-07-01

    Erosion, in particular gully erosion, is a widespread problem. Its mapping is crucial for erosion monitoring and remediation of degraded areas. In addition, mapping of areas with high potential for future gully erosion can be used to assist prevention strategies. Good relations with topographic variables collected from the field are appropriate for determining areas susceptible to gullying. Image analysis of high resolution remotely sensed imagery (HRI) in combination with field verification has proven to be a good approach, although dependent on expensive imagery. Automatic and semi-automatic methods, such as object-oriented analysis (OOA), are rapid and reproducible. However, HRI data are not always available. We therefore attempted to identify gully systems using statistical modeling of image features from medium resolution imagery, here ASTER. These data were used for determining areas within gully system boundaries (GSB) using a semi-automatic method based on OOA. We assess if the selection of useful object features can be done in an objective and transferable way, using Random Forests (RF) for prediction of gully systems at regional scale, here in the Sehoul region, near Rabat, Morocco. Moderate success was achieved using a semi-automatic object-based RF model (out-of-bag error of 18.8%). Besides compensating for the imbalance between gully and non-gully classes, the procedure followed in this study enabled us to balance the classification error rates. The user's and producer's accuracy of the data with a balanced set of class showed an improved accuracy of the spatial estimates of gully systems, when compared to the data with imbalanced class. The model over-predicted the area within the GSB (13-27%), but its overall performance demonstrated that medium resolution satellite images contain sufficient information to identify gully systems, so that large areas can be mapped with relatively little effort and acceptable accuracy.

  16. Performance Measurement Model for the Supplier Selection Based on AHP

    Directory of Open Access Journals (Sweden)

    Fabio De Felice

    2015-10-01

    Full Text Available The performance of the supplier is a crucial factor for the success or failure of any company. Rational and effective decision making in terms of the supplier selection process can help the organization to optimize cost and quality functions. The nature of supplier selection processes is generally complex, especially when the company has a large variety of products and vendors. Over the years, several solutions and methods have emerged for addressing the supplier selection problem (SSP. Experience and studies have shown that there is no best way for evaluating and selecting a specific supplier process, but that it varies from one organization to another. The aim of this research is to demonstrate how a multiple attribute decision making approach can be effectively applied for the supplier selection process.

  17. Personal drug selection: problem-based learning in pharmacology: experience from a medical school in Nepal.

    Science.gov (United States)

    Shankar, P Ravi; Palaian, Subish; Gyawali, Sudesh; Mishra, Pranaya; Mohan, Lalit

    2007-06-13

    At the Manipal College of Medical Sciences, Pokhara, Nepal, Pharmacology is taught during the first four semesters of the undergraduate medical course. Personal or P-drug selection is an important exercise. The present study was carried out to obtain student opinion about the P-drug learning sessions, the assessment examinations, and on the small group dynamics. The practical sessions on P-drug selection are carried out in small groups. Student feedback about the session was obtained using focus group discussions. The focus groups were selected to represent both genders and the three main nationalities, Nepalese, Indians, and Sri Lankans. There were four Nepalese, five Indians, and three Sri Lankans. Within each nationality and gender category the students were randomly selected. The respondents were explained the objectives of the study and were invited to participate. Written informed consent was obtained. The discussion lasted around two hours and was conducted in the afternoon in two groups of six students each. The first author (PRS) acted as a facilitator. The responses were recorded and analyzed qualitatively. The overall student opinion was positive. Around 25% (3 respondents) of respondents were confused about whether P-drugs were for a disease or a patient. Group consensus was commonly used to give numerical values for the different criteria. The large number of brands created problems in calculating cost. The students wanted more time for the exercise in the examination. Formative assessment during the learning sessions may be considered. The group members usually got along well. Absenteeism was a problem and not all members put in their full effort. The physical working environment should be improved. Based on what the students say, the sessions on P-drugs should be continued and strengthened. Modifications in the sessions are required. Sessions during the clinical years and internship training can be considered.

  18. Personal drug selection: problem-based learning in pharmacology: experience from a medical school in Nepal.

    Directory of Open Access Journals (Sweden)

    P Ravi Shankar

    Full Text Available BACKGROUND: At the Manipal College of Medical Sciences, Pokhara, Nepal, Pharmacology is taught during the first four semesters of the undergraduate medical course. Personal or P-drug selection is an important exercise. The present study was carried out to obtain student opinion about the P-drug learning sessions, the assessment examinations, and on the small group dynamics. METHOD: The practical sessions on P-drug selection are carried out in small groups. Student feedback about the session was obtained using focus group discussions. The focus groups were selected to represent both genders and the three main nationalities, Nepalese, Indians, and Sri Lankans. There were four Nepalese, five Indians, and three Sri Lankans. Within each nationality and gender category the students were randomly selected. The respondents were explained the objectives of the study and were invited to participate. Written informed consent was obtained. The discussion lasted around two hours and was conducted in the afternoon in two groups of six students each. The first author (PRS acted as a facilitator. The responses were recorded and analyzed qualitatively. RESULTS: The overall student opinion was positive. Around 25% (3 respondents of respondents were confused about whether P-drugs were for a disease or a patient. Group consensus was commonly used to give numerical values for the different criteria. The large number of brands created problems in calculating cost. The students wanted more time for the exercise in the examination. Formative assessment during the learning sessions may be considered. The group members usually got along well. Absenteeism was a problem and not all members put in their full effort. The physical working environment should be improved. CONCLUSIONS: Based on what the students say, the sessions on P-drugs should be continued and strengthened. Modifications in the sessions are required. Sessions during the clinical years and internship training

  19. Prevalence of at-risk genotypes for genotoxic effects decreases with age in a randomly selected population in Flanders: a cross sectional study

    Directory of Open Access Journals (Sweden)

    van Delft Joost HM

    2011-10-01

    Full Text Available Abstract Background We hypothesized that in Flanders (Belgium, the prevalence of at-risk genotypes for genotoxic effects decreases with age due to morbidity and mortality resulting from chronic diseases. Rather than polymorphisms in single genes, the interaction of multiple genetic polymorphisms in low penetrance genes involved in genotoxic effects might be of relevance. Methods Genotyping was performed on 399 randomly selected adults (aged 50-65 and on 442 randomly selected adolescents. Based on their involvement in processes relevant to genotoxicity, 28 low penetrance polymorphisms affecting the phenotype in 19 genes were selected (xenobiotic metabolism, oxidative stress defense and DNA repair, respectively 13, 6 and 9 polymorphisms. Polymorphisms which, based on available literature, could not clearly be categorized a priori as leading to an 'increased risk' or a 'protective effect' were excluded. Results The mean number of risk alleles for all investigated polymorphisms was found to be lower in the 'elderly' (17.0 ± 2.9 than the 'adolescent' (17.6 ± 3.1 subpopulation (P = 0.002. These results were not affected by gender nor smoking. The prevalence of a high (> 17 = median number of risk alleles was less frequent in the 'elderly' (40.6% than the 'adolescent' (51.4% subpopulation (P = 0.002. In particular for phase II enzymes, the mean number of risk alleles was lower in the 'elderly' (4.3 ± 1.6 than the 'adolescent' age group (4.8 ± 1.9 P 4 = median number of risk alleles was less frequent in the 'elderly' (41.3% than the adolescent subpopulation (56.3%, P 8 = median number of risk alleles for DNA repair enzyme-coding genes was lower in the 'elderly' (37,3% than the 'adolescent' subpopulation (45.6%, P = 0.017. Conclusions These observations are consistent with the hypothesis that, in Flanders, the prevalence of at-risk alleles in genes involved in genotoxic effects decreases with age, suggesting that persons carrying a higher number of

  20. Global stocks of selected mineral-based commodities

    Science.gov (United States)

    Wilburn, David R.; Bleiwas, Donald I.; Karl, Nick A.

    2016-12-05

    IntroductionThe U.S. Geological Survey, National Minerals Information Center, analyzes mineral and metal supply chains by identifying and describing major components of mineral and material flows from ore extraction, through intermediate forms, to a final product. This report focuses on an important component of the world’s supply chain: the amounts and global distribution of major consumer, producer, and exchange stocks of selected mineral commodities. In this report, the term “stock” is used instead of “inventory” and refers to accumulations of mined ore, intermediate products, and refined mineral-based commodities that are in a form that meets the agreed-upon specifications of a buyer or processor of intermediate products. These may include certain ores such as bauxite, concentrates, smelter products, and refined metals. Materials sometimes referred to as inventory for accounting purposes, such as ore contained in a deposit or in a leach pile, or materials that need to be further processed before they can be shipped to a consumer, are not considered. Stocks may be held (owned) by consumers, governments, investors, producers, and traders. They may serve as (1) a means to achieve economic, social, and strategic goals through government policies; (2) a secure source of supply to meet demand and to mitigate potential shortages in the supply chain; (3) a hedge to mitigate price volatility; and (4) vehicles for speculative investment.The paucity and uneven reliability of data for stocks of ores and concentrates and for material held by producers, consumers, and merchants hinder the accurate estimating of the size and distribution of this portion of the supply chain for certain commodities. This paper reviews the more visible stocks held in commodity exchange warehouses distributed throughout the world.

  1. FPGA-based RF spectrum merging and adaptive hopset selection

    Science.gov (United States)

    McLean, R. K.; Flatley, B. N.; Silvius, M. D.; Hopkinson, K. M.

    The radio frequency (RF) spectrum is a limited resource. Spectrum allotment disputes stem from this scarcity as many radio devices are confined to a fixed frequency or frequency sequence. One alternative is to incorporate cognition within a reconfigurable radio platform, therefore enabling the radio to adapt to dynamic RF spectrum environments. In this way, the radio is able to actively sense the RF spectrum, decide, and act accordingly, thereby sharing the spectrum and operating in more flexible manner. In this paper, we present a novel solution for merging many distributed RF spectrum maps into one map and for subsequently creating an adaptive hopset. We also provide an example of our system in operation, the result of which is a pseudorandom adaptive hopset. The paper then presents a novel hardware design for the frequency merger and adaptive hopset selector, both of which are written in VHDL and implemented as a custom IP core on an FPGA-based embedded system using the Xilinx Embedded Development Kit (EDK) software tool. The design of the custom IP core is optimized for area, and it can process a high-volume digital input via a low-latency circuit architecture. The complete embedded system includes the Xilinx PowerPC microprocessor, UART serial connection, and compact flash memory card IP cores, and our custom map merging/hopset selection IP core, all of which are targeted to the Virtex IV FPGA. This system is then incorporated into a cognitive radio prototype on a Rice University Wireless Open Access Research Platform (WARP) reconfigurable radio.

  2. Fuzzy System-Based Target Selection for a NIR Camera-Based Gaze Tracker

    Science.gov (United States)

    Naqvi, Rizwan Ali; Arsalan, Muhammad; Park, Kang Ryoung

    2017-01-01

    Gaze-based interaction (GBI) techniques have been a popular subject of research in the last few decades. Among other applications, GBI can be used by persons with disabilities to perform everyday tasks, as a game interface, and can play a pivotal role in the human computer interface (HCI) field. While gaze tracking systems have shown high accuracy in GBI, detecting a user’s gaze for target selection is a challenging problem that needs to be considered while using a gaze detection system. Past research has used the blinking of the eyes for this purpose as well as dwell time-based methods, but these techniques are either inconvenient for the user or requires a long time for target selection. Therefore, in this paper, we propose a method for fuzzy system-based target selection for near-infrared (NIR) camera-based gaze trackers. The results of experiments performed in addition to tests of the usability and on-screen keyboard use of the proposed method show that it is better than previous methods. PMID:28420114

  3. Fuzzy System-Based Target Selection for a NIR Camera-Based Gaze Tracker.

    Science.gov (United States)

    Naqvi, Rizwan Ali; Arsalan, Muhammad; Park, Kang Ryoung

    2017-04-14

    Gaze-based interaction (GBI) techniques have been a popular subject of research in the last few decades. Among other applications, GBI can be used by persons with disabilities to perform everyday tasks, as a game interface, and can play a pivotal role in the human computer interface (HCI) field. While gaze tracking systems have shown high accuracy in GBI, detecting a user's gaze for target selection is a challenging problem that needs to be considered while using a gaze detection system. Past research has used the blinking of the eyes for this purpose as well as dwell time-based methods, but these techniques are either inconvenient for the user or requires a long time for target selection. Therefore, in this paper, we propose a method for fuzzy system-based target selection for near-infrared (NIR) camera-based gaze trackers. The results of experiments performed in addition to tests of the usability and on-screen keyboard use of the proposed method show that it is better than previous methods.

  4. Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling

    Directory of Open Access Journals (Sweden)

    Fuqun Zhou

    2016-10-01

    Full Text Available Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS. It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2–3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests’ features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

  5. Biased random key genetic algorithm with insertion and gender selection for capacitated vehicle routing problem with time windows

    Science.gov (United States)

    Rochman, Auliya Noor; Prasetyo, Hari; Nugroho, Munajat Tri

    2017-06-01

    Vehicle Routing Problem (VRP) often occurs when the manufacturers need to distribute their product to some customers/outlets. The distribution process is typically restricted by the capacity of the vehicle and the working hours at the distributor. This type of VRP is also known as Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). A Biased Random Key Genetic Algorithm (BRKGA) was designed and coded in MATLAB to solve the CVRPTW case of soft drink distribution. The standard BRKGA was then modified by applying chromosome insertion into the initial population and defining chromosome gender for parent undergoing crossover operation. The performance of the established algorithms was then compared to a heuristic procedure for solving a soft drink distribution. Some findings are revealed (1) the total distribution cost of BRKGA with insertion (BRKGA-I) results in a cost saving of 39% compared to the total cost of heuristic method, (2) BRKGA with the gender selection (BRKGA-GS) could further improve the performance of the heuristic method. However, the BRKGA-GS tends to yield worse results compared to that obtained from the standard BRKGA.

  6. A preliminary investigation of the jack-bean urease inhibition by randomly selected traditionally used herbal medicine.

    Science.gov (United States)

    Biglar, Mahmood; Soltani, Khadijeh; Nabati, Farzaneh; Bazl, Roya; Mojab, Faraz; Amanlou, Massoud

    2012-01-01

    Helicobacter pylori (H. pylori) infection leads to different clinical and pathological outcomes in humans, including chronic gastritis, peptic ulcer disease and gastric neoplasia and even gastric cancer and its eradiation dependst upon multi-drug therapy. The most effective therapy is still unknown and prompts people to make great efforts to find better and more modern natural or synthetic anti-H. pylori agents. In this report 21 randomly selected herbal methanolic extracts were evaluated for their effect on inhibition of Jack-bean urease using the indophenol method as described by Weatherburn. The inhibition potency was measured by UV spectroscopy technique at 630 nm which attributes to released ammonium. Among these extracts, five showed potent inhibitory activities with IC50 ranges of 18-35 μg/mL. These plants are Matricaria disciforme (IC50:35 μg/mL), Nasturtium officinale (IC50:18 μg/mL), Punica granatum (IC50:30 μg/mL), Camelia sinensis (IC50:35 μg/mL), Citrus aurantifolia (IC50:28 μg/mL).

  7. Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

    Science.gov (United States)

    Zhou, Fuqun; Zhang, Aining

    2016-10-25

    Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

  8. DNA-based frequency selective electromagnetic interference shielding

    Science.gov (United States)

    Grote, James; Ouchen, Fahima; Kreit, Eric; Buskohl, Phillip; Steffan, Thomas; Rogers, Charles; Salour, Michael

    2017-10-01

    A method of modeling RF properties of multilayered polymer host - metal nanoparticle guest composite films, using the transmission matrix method (TMM) model is presented. This is an alternate, pattern-less, dielectric approach to frequency selective surface electromagnetic interference shielding.

  9. Neural bases of selective attention in action video game players

    National Research Council Canada - National Science Library

    Bavelier, D; Achtman, R L; Mani, M; Föcker, J

    2012-01-01

    Over the past few years, the very act of playing action video games has been shown to enhance several different aspects of visual selective attention, yet little is known about the neural mechanisms...

  10. Locality-Sensitive Hashing with Margin Based Feature Selection

    OpenAIRE

    Konoshima, Makiko; Noma, Yui

    2012-01-01

    We propose a learning method with feature selection for Locality-Sensitive Hashing. Locality-Sensitive Hashing converts feature vectors into bit arrays. These bit arrays can be used to perform similarity searches and personal authentication. The proposed method uses bit arrays longer than those used in the end for similarity and other searches and by learning selects the bits that will be used. We demonstrated this method can effectively perform optimization for cases such as fingerprint imag...

  11. Efficacy of a brief school-based program for selective prevention of childhood anxiety.

    Science.gov (United States)

    Balle, Maria; Tortella-Feliu, Miquel

    2010-01-01

    Anxiety sensitivity (AS) is recognized as an early risk factor for the development of anxiety disorders. This study evaluates whether a brief school-based selective prevention program reduces AS and anxious and depressive symptoms in children and youth. Participants scoring high in AS but without any current psychopathological disorder were selected from a sample of 613 individuals (61% female, 11-17 years old) and randomly assigned to the prevention program (n=47) or to a waiting-list control (WLC) (n=45) group. A normal control (NC) group (n=53) was also included. After treatment, a significant decrease in AS and in anxiety and depressive symptoms were observed in both prevention and WLC groups. Differences between experimental conditions only emerged, partially, at six-month follow-up (FU) with the prevention group (PG) exhibiting significantly lower AS (p<.05), and equalling NCs. Although the magnitude of change in the PG is comparable to that reported in previous studies with longer and more complex prevention programs, a parallel reduction in the WLCs suggests that the observed decrease in the short term could be mostly time-linked. Despite this, our results encourage research into brief preventive interventions at an individual level.

  12. CHull: a generic convex-hull-based model selection method.

    Science.gov (United States)

    Wilderjans, Tom F; Ceulemans, Eva; Meers, Kristof

    2013-03-01

    When analyzing data, researchers are often confronted with a model selection problem (e.g., determining the number of components/factors in principal components analysis [PCA]/factor analysis or identifying the most important predictors in a regression analysis). To tackle such a problem, researchers may apply some objective procedure, like parallel analysis in PCA/factor analysis or stepwise selection methods in regression analysis. A drawback of these procedures is that they can only be applied to the model selection problem at hand. An interesting alternative is the CHull model selection procedure, which was originally developed for multiway analysis (e.g., multimode partitioning). However, the key idea behind the CHull procedure--identifying a model that optimally balances model goodness of fit/misfit and model complexity--is quite generic. Therefore, the procedure may also be used when applying many other analysis techniques. The aim of this article is twofold. First, we demonstrate the wide applicability of the CHull method by showing how it can be used to solve various model selection problems in the context of PCA, reduced K-means, best-subset regression, and partial least squares regression. Moreover, a comparison of CHull with standard model selection methods for these problems is performed. Second, we present the CHULL software, which may be downloaded from http://ppw.kuleuven.be/okp/software/CHULL/, to assist the user in applying the CHull procedure.

  13. A Bidirectional Generalized Synchronization Theorem-Based Chaotic Pseudo-random Number Generator

    Directory of Open Access Journals (Sweden)

    Han Shuangshuang

    2013-07-01

    Full Text Available Based on a bidirectional generalized synchronization theorem for discrete chaos system, this paper introduces a new 5-dimensional bidirectional generalized chaos synchronization system (BGCSDS, whose prototype is a novel chaotic system introduced in [12]. Numerical simulation showed that two pair variables of the BGCSDS achieve generalized chaos synchronization via a transform H.A chaos-based pseudo-random number generator (CPNG was designed by the new BGCSDS. Using the FIPS-140-2 tests issued by the National Institute of Standard and Technology (NIST verified the randomness of the 1000 binary number sequences generated via the CPNG and the RC4 algorithm respectively. The results showed that all the tested sequences passed the FIPS-140-2 tests. The confidence interval analysis showed the statistical properties of the randomness of the sequences generated via the CPNG and the RC4 algorithm do not have significant differences.

  14. A Simple K-Map Based Variable Selection Scheme in the Direct ...

    African Journals Online (AJOL)

    A multiplexer with (n-l) data select inputs can realise directly a function of n variables. In this paper, a simple k-map based variable selection scheme is proposed such that an n variable logic function can be synthesised using a multiplexer with (n-q) data input variables and q data select variables. The procedure is based on ...

  15. A theory-based video messaging mobile phone intervention for smoking cessation: randomized controlled trial.

    Science.gov (United States)

    Whittaker, Robyn; Dorey, Enid; Bramley, Dale; Bullen, Chris; Denny, Simon; Elley, C Raina; Maddison, Ralph; McRobbie, Hayden; Parag, Varsha; Rodgers, Anthony; Salmon, Penny

    2011-01-21

    Advances in technology allowed the development of a novel smoking cessation program delivered by video messages sent to mobile phones. This social cognitive theory-based intervention (called "STUB IT") used observational learning via short video diary messages from role models going through the quitting process to teach behavioral change techniques. The objective of our study was to assess the effectiveness of a multimedia mobile phone intervention for smoking cessation. A randomized controlled trial was conducted with 6-month follow-up. Participants had to be 16 years of age or over, be current daily smokers, be ready to quit, and have a video message-capable phone. Recruitment targeted younger adults predominantly through radio and online advertising. Registration and data collection were completed online, prompted by text messages. The intervention group received an automated package of video and text messages over 6 months that was tailored to self-selected quit date, role model, and timing of messages. Extra messages were available on demand to beat cravings and address lapses. The control group also set a quit date and received a general health video message sent to their phone every 2 weeks. The target sample size was not achieved due to difficulty recruiting young adult quitters. Of the 226 randomized participants, 47% (107/226) were female and 24% (54/226) were Maori (indigenous population of New Zealand). Their mean age was 27 years (SD 8.7), and there was a high level of nicotine addiction. Continuous abstinence at 6 months was 26.4% (29/110) in the intervention group and 27.6% (32/116) in the control group (P = .8). Feedback from participants indicated that the support provided by the video role models was important and appreciated. This study was not able to demonstrate a statistically significant effect of the complex video messaging mobile phone intervention compared with simple general health video messages via mobile phone. However, there was

  16. Theory of mind selectively predicts preschoolers' knowledge-based selective word learning.

    Science.gov (United States)

    Brosseau-Liard, Patricia; Penney, Danielle; Poulin-Dubois, Diane

    2015-11-01

    Children can selectively attend to various attributes of a model, such as past accuracy or physical strength, to guide their social learning. There is a debate regarding whether a relation exists between theory-of-mind skills and selective learning. We hypothesized that high performance on theory-of-mind tasks would predict preference for learning new words from accurate informants (an epistemic attribute), but not from physically strong informants (a non-epistemic attribute). Three- and 4-year-olds (N = 65) completed two selective learning tasks, and their theory-of-mind abilities were assessed. As expected, performance on a theory-of-mind battery predicted children's preference to learn from more accurate informants but not from physically stronger informants. Results thus suggest that preschoolers with more advanced theory of mind have a better understanding of knowledge and apply that understanding to guide their selection of informants. This work has important implications for research on children's developing social cognition and early learning. © 2015 The British Psychological Society.

  17. Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications*

    Science.gov (United States)

    Murphy, Thomas Brendan; Dean, Nema; Raftery, Adrian E.

    2010-01-01

    Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented. The discriminant analysis model is fitted in a semi-supervised manner using both labeled and unlabeled data. The method is shown to give excellent classification performance on several high-dimensional multiclass food authenticity datasets with more variables than observations. The variables selected by the proposed method provide information about which variables are meaningful for classification purposes. A headlong search strategy for variable selection is shown to be efficient in terms of computation and achieves excellent classification performance. In applications to several food authenticity datasets, our proposed method outperformed default implementations of Random Forests, AdaBoost, transductive SVMs and Bayesian Multinomial Regression by substantial margins. PMID:20936055

  18. A Distributed Dynamic Super Peer Selection Method Based on Evolutionary Game for Heterogeneous P2P Streaming Systems

    Directory of Open Access Journals (Sweden)

    Jing Chen

    2013-01-01

    Full Text Available Due to high efficiency and good scalability, hierarchical hybrid P2P architecture has drawn more and more attention in P2P streaming research and application fields recently. The problem about super peer selection, which is the key problem in hybrid heterogeneous P2P architecture, is becoming highly challenging because super peers must be selected from a huge and dynamically changing network. A distributed super peer selection (SPS algorithm for hybrid heterogeneous P2P streaming system based on evolutionary game is proposed in this paper. The super peer selection procedure is modeled based on evolutionary game framework firstly, and its evolutionarily stable strategies are analyzed. Then a distributed Q-learning algorithm (ESS-SPS according to the mixed strategies by analysis is proposed for the peers to converge to the ESSs based on its own payoff history. Compared to the traditional randomly super peer selection scheme, experiments results show that the proposed ESS-SPS algorithm achieves better performance in terms of social welfare and average upload rate of super peers and keeps the upload capacity of the P2P streaming system increasing steadily with the number of peers increasing.

  19. Enumeration of Escherichia coli cells on chicken carcasses as a potential measure of microbial process control in a random selection of slaughter establishments in the United States

    Science.gov (United States)

    The purpose of this study was to evaluate whether the measurement of Escherichia coli levels at two points during the chicken slaughter process has utility as a measure of quality control. A one year long survey was conducted during 2004 and 2005 in 20 randomly selected United States chicken slaught...

  20. Impact of amoxicillin therapy on resistance selection in patients with community-acquired lower respiratory tract infections : A randomized, placebo-controlled study

    NARCIS (Netherlands)

    Malhotra-Kumar, Surbhi; Van Heirstraeten, Liesbet; Coenen, Samuel; Lammens, Christine; Adriaenssens, Niels; Kowalczyk, Anna; Godycki-Cwirko, Maciek; Bielicka, Zuzana; Hupkova, Helena; Lannering, Christina; Mölstad, Sigvard; Fernandez-Vandellos, Patricia; Torres, Antoni; Parizel, Maxim; Ieven, Margareta; Butler, Chris C.; Verheij, Theo; Little, Paul; Goossens, Hermanon; Frimodt-Møller, Niels; Bruno, Pascale; Hering, Iris; Lemiengre, Marieke; Loens, Katherine; Malmvall, Bo Eric; Muras, Magdalena; Romano, Nuria Sanchez; Prat, Matteu Serra; Svab, Igor; Swain, Jackie; Tarsia, Paolo; Leus, Frank; Veen, Robert; Worby, Tricia

    2016-01-01

    Objectives: To determine the effect of amoxicillin treatment on resistance selection in patients with community-acquired lower respiratory tract infections in a randomized, placebo-controlled trial. Methods: Patients were prescribed amoxicillin 1 g, three times daily (n = 52) or placebo (n = 50) for

  1. The Long-Term Effectiveness of a Selective, Personality-Targeted Prevention Program in Reducing Alcohol Use and Related Harms: A Cluster Randomized Controlled Trial

    Science.gov (United States)

    Newton, Nicola C.; Conrod, Patricia J.; Slade, Tim; Carragher, Natacha; Champion, Katrina E.; Barrett, Emma L.; Kelly, Erin V.; Nair, Natasha K.; Stapinski, Lexine; Teesson, Maree

    2016-01-01

    Background: This study investigated the long-term effectiveness of Preventure, a selective personality-targeted prevention program, in reducing the uptake of alcohol, harmful use of alcohol, and alcohol-related harms over a 3-year period. Methods: A cluster randomized controlled trial was conducted to assess the effectiveness of Preventure.…

  2. Early stage hot spot analysis through standard cell base random pattern generation

    Science.gov (United States)

    Jeon, Joong-Won; Song, Jaewan; Kim, Jeong-Lim; Park, Seongyul; Yang, Seung-Hune; Lee, Sooryong; Kang, Hokyu; Madkour, Kareem; ElManhawy, Wael; Lee, SeungJo; Kwan, Joe

    2017-04-01

    Due to limited availability of DRC clean patterns during the process and RET recipe development, OPC recipes are not tested with high pattern coverage. Various kinds of pattern can help OPC engineer to detect sensitive patterns to lithographic effects. Random pattern generation is needed to secure robust OPC recipe. However, simple random patterns without considering real product layout style can't cover patterning hotspot in production levels. It is not effective to use them for OPC optimization thus it is important to generate random patterns similar to real product patterns. This paper presents a strategy for generating random patterns based on design architecture information and preventing hotspot in early process development stage through a tool called Layout Schema Generator (LSG). Using LSG, we generate standard cell based on random patterns reflecting real design cell structure - fin pitch, gate pitch and cell height. The output standard cells from LSG are applied to an analysis methodology to assess their hotspot severity by assigning a score according to their optical image parameters - NILS, MEEF, %PV band and thus potential hotspots can be defined by determining their ranking. This flow is demonstrated on Samsung 7nm technology optimizing OPC recipe and early enough in the process avoiding using problematic patterns.

  3. GoDisco: Selective Gossip Based Dissemination of Information in Social Community Based Overlays

    Science.gov (United States)

    Datta, Anwitaman; Sharma, Rajesh

    We propose and investigate a gossip based, social principles and behavior inspired decentralized mechanism (GoDisco) to disseminate information in online social community networks, using exclusively social links and exploiting semantic context to keep the dissemination process selective to relevant nodes. Such a designed dissemination scheme using gossiping over a egocentric social network is unique and is arguably a concept whose time has arrived, emulating word of mouth behavior and can have interesting applications like probabilistic publish/subscribe, decentralized recommendation and contextual advertisement systems, to name a few. Simulation based experiments show that despite using only local knowledge and contacts, the system has good global coverage and behavior.

  4. Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy.

    Science.gov (United States)

    Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao; Song, Qing

    Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution). The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest), SMO (Sequential Minimal Optimization), NNA (Nearest Neighbor Algorithm), and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew's Correlation Coefficient) of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc.

  5. A Selective Encryption Algorithm Based on AES for Medical Information.

    Science.gov (United States)

    Oh, Ju-Young; Yang, Dong-Il; Chon, Ki-Hwan

    2010-03-01

    The transmission of medical information is currently a daily routine. Medical information needs efficient, robust and secure encryption modes, but cryptography is primarily a computationally intensive process. Towards this direction, we design a selective encryption scheme for critical data transmission. We expand the advandced encrytion stanard (AES)-Rijndael with five criteria: the first is the compression of plain data, the second is the variable size of the block, the third is the selectable round, the fourth is the optimization of software implementation and the fifth is the selective function of the whole routine. We have tested our selective encryption scheme by C(++) and it was compiled with Code::Blocks using a MinGW GCC compiler. The experimental results showed that our selective encryption scheme achieves a faster execution speed of encryption/decryption. In future work, we intend to use resource optimization to enhance the round operations, such as SubByte/InvSubByte, by exploiting similarities between encryption and decryption. As encryption schemes become more widely used, the concept of hardware and software co-design is also a growing new area of interest.

  6. Gender differences and a school-based obesity prevention program in Argentina: a randomized trial

    Directory of Open Access Journals (Sweden)

    Cecile Rausch Herscovici

    2013-08-01

    Full Text Available OBJECTIVE: To evaluate the impact of a school-based obesity prevention program that seeks to change food intake among students at schools in Rosario, Argentina. METHODS: This was a prospective study involving 405 children 9-11 years of age at six schools in the poor areas of Rosario, Argentina, in May-October 2008. After matching for socioeconomic status, schools were selected by simple randomization; participants were assessed at baseline (T1 and again 6 months later, after completion of the intervention (T2. The program focused on increasing the children's knowledge of healthy nutrition and exercise through four workshops; educating the parents/caregivers; and offering healthy options at the school snack bar. The main outcome measures were the children's intake of healthy and unhealthy foods (assessed with a weekly food frequency questionnaire and their body mass index (BMI. RESULTS: Of the 387 children assessed at T1, 369 were reassessed at T2 (205 intervention; 164 control. Girls at the schools where the intervention occurred increased their intake of three of the five healthy food items promoted by the program (fruits, vegetables, low-sugar cereals. Statistical significance was reached for skim milk (P = 0.03 and for pure orange juice (P = 0.05. Boys of both the intervention and control groups failed to improve their intake of healthy foods, but those of the intervention arm significantly reduced their intake of hamburgers and hot dogs (P = 0.001. CONCLUSIONS: Girls were more amenable to improving their dietary intake. Overall, the program was more likely to increase consumption of healthy food than to decrease intake of unhealthy foods. Gender differences should be taken into account when designing preventive interventions.

  7. Random Walk Graph Laplacian-Based Smoothness Prior for Soft Decoding of JPEG Images.

    Science.gov (United States)

    Liu, Xianming; Cheung, Gene; Wu, Xiaolin; Zhao, Debin

    2017-02-01

    Given the prevalence of joint photographic experts group (JPEG) compressed images, optimizing image reconstruction from the compressed format remains an important problem. Instead of simply reconstructing a pixel block from the centers of indexed discrete cosine transform (DCT) coefficient quantization bins (hard decoding), soft decoding reconstructs a block by selecting appropriate coefficient values within the indexed bins with the help of signal priors. The challenge thus lies in how to define suitable priors and apply them effectively. In this paper, we combine three image priors-Laplacian prior for DCT coefficients, sparsity prior, and graph-signal smoothness prior for image patches-to construct an efficient JPEG soft decoding algorithm. Specifically, we first use the Laplacian prior to compute a minimum mean square error initial solution for each code block. Next, we show that while the sparsity prior can reduce block artifacts, limiting the size of the overcomplete dictionary (to lower computation) would lead to poor recovery of high DCT frequencies. To alleviate this problem, we design a new graph-signal smoothness prior (desired signal has mainly low graph frequencies) based on the left eigenvectors of the random walk graph Laplacian matrix (LERaG). Compared with the previous graph-signal smoothness priors, LERaG has desirable image filtering properties with low computation overhead. We demonstrate how LERaG can facilitate recovery of high DCT frequencies of a piecewise smooth signal via an interpretation of low graph frequency components as relaxed solutions to normalized cut in spectral clustering. Finally, we construct a soft decoding algorithm using the three signal priors with appropriate prior weights. Experimental results show that our proposal outperforms the state-of-the-art soft decoding algorithms in both objective and subjective evaluations noticeably.

  8. Perceptions of genetic testing for personalized nutrition: a randomized trial of DNA-based dietary advice.

    Science.gov (United States)

    Nielsen, Daiva E; Shih, Sarah; El-Sohemy, Ahmed

    2014-01-01

    Direct-to-consumer (DTC) genetic tests have facilitated easy access to personal genetic information related to health and nutrition; however, consumer perceptions of the nutritional information provided by these tests have not been evaluated. The objectives of this study were to assess individual perceptions of personalized nutrition and genetic testing and to determine whether a personalized nutrition intervention modifies perceptions. A double-blind, parallel-group, randomized controlled trial was conducted among healthy men and women aged 20-35 years (n = 138). Participants in the intervention group (n = 92) were given a report of DNA-based dietary advice and those in the control group (n = 46) were given a general dietary advice report. A survey was completed at baseline and 3 and 12 months after distributing the reports to assess perceptions between the two groups. No significant differences in perceptions of personalized nutrition and genetic testing were observed between the intervention and control group, so responses of both groups were combined. As compared to baseline, participant responses increased significantly toward the positive end of a Likert scale at 3 months for the statement 'I am interested in the relationship between diet and genetics' (mean change ± SD: 0.28 ± 0.99, p = 0.0002). The majority of participants indicated that a university research lab (47%) or health care professional (41%) were the best sources for obtaining accurate personal genetic information, while a DTC genetic testing company received the fewest selections (12%). Most participants (56%) considered dietitians to be the best source of personalized nutrition followed by medical doctors (27%), naturopaths (8%) and nurses (6%). These results suggest that perceptions of personalized nutrition changed over the course of the intervention. Individuals view a research lab or health care professional as better providers of genetic information than a DTC genetic testing company

  9. A Randomized Trial of a Multimodal Community-Based Prisoner Reentry Program Emphasizing Substance Abuse Treatment

    Science.gov (United States)

    Grommon, Eric; Davidson, William S., II; Bynum, Timothy S.

    2013-01-01

    Prisoner reentry programs continue to be developed and implemented to ease the process of transition into the community and to curtail fiscal pressures. This study describes and provides relapse and recidivism outcome findings related to a randomized trial evaluating a multimodal, community-based reentry program that prioritized substance abuse…

  10. The Efficiency and Efficacy of Equivalence-Based Learning: A Randomized Controlled Trial

    Science.gov (United States)

    Zinn, Tracy E.; Newland, M. Christopher; Ritchie, Katie E.

    2015-01-01

    Because it employs an emergent-learning framework, equivalence-based instruction (EBI) is said to be highly efficient, but its presumed benefits must be compared quantitatively with alternative techniques. In a randomized controlled trial, 61 college students attempted to learn 32 pairs of proprietary and generic drug names using computer-based…

  11. Mindfulness-Based Stress Reduction for the Treatment of Adolescent Psychiatric Outpatients: A Randomized Clinical Trial

    Science.gov (United States)

    Biegel, Gina M.; Brown, Kirk Warren; Shapiro, Shauna L.; Schubert, Christine M.

    2009-01-01

    Research has shown that mindfulness-based treatment interventions may be effective for a range of mental and physical health disorders in adult populations, but little is known about the effectiveness of such interventions for treating adolescent conditions. The present randomized clinical trial was designed to assess the effect of the…

  12. Reporting of positive results in randomized controlled trials of mindfulness-based mental health interventions

    NARCIS (Netherlands)

    Coronado-Montoya, S.; Levis, A.W.; Kwakkenbos, C.M.C.; Steele, R.J.; Turner, E.H.; Thombs, B.D.

    2016-01-01

    Background A large proportion of mindfulness-based therapy trials report statistically significant results, even in the context of very low statistical power. The objective of the present study was to characterize the reporting of "positive" results in randomized controlled trials of

  13. The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.

    Science.gov (United States)

    Gu, Weiwei; Gong, Li; Lou, Xiaodan; Zhang, Jiang

    2017-10-13

    Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.

  14. Attachment-Based Family Therapy for Adolescents with Suicidal Ideation: A Randomized Controlled Trial

    Science.gov (United States)

    Diamond, Guy S.; Wintersteen, Matthew B.; Brown, Gregory K.; Diamond, Gary M.; Gallop, Robert; Shelef, Karni; Levy, Suzanne

    2010-01-01

    Objective: To evaluate whether Attachment-Based Family Therapy (ABFT) is more effective than Enhanced Usual Care (EUC) for reducing suicidal ideation and depressive symptoms in adolescents. Method: This was a randomized controlled trial of suicidal adolescents between the ages of 12 and 17, identified in primary care and emergency departments. Of…

  15. Mentalization-Based Treatment for Self-Harm in Adolescents: A Randomized Controlled Trial

    Science.gov (United States)

    Rossouw, Trudie I.; Fonagy, Peter

    2012-01-01

    Objective: We examined whether mentalization-based treatment for adolescents (MBT-A) is more effective than treatment as usual (TAU) for adolescents who self-harm. Method: A total of 80 adolescents (85% female) consecutively presenting to mental health services with self-harm and comorbid depression were randomly allocated to either MBT-A or TAU.…

  16. Mindfulness-based stress reduction and physiological activity during acute stress : A randomized controlled trial

    NARCIS (Netherlands)

    Nyklicek, I.; Mommersteeg, P.M.C.; van Beugen, S.; Ramakers, C.; van Boxtel, G.J.M.

    2013-01-01

    Objective: The aim was to examine the effects of a Mindfulness-Based Stress Reduction (MBSR) intervention on cardiovascular and cortisol activity during acute stress. Method: Eighty-eight healthy community-dwelling individuals reporting elevated stress levels were randomly assigned to the MBSR

  17. Mindfulness-based stress reduction and physiological activity during acute stress: a randomized controlled trial

    NARCIS (Netherlands)

    Nyklicek, I.; Mommersteeg, P.M.; Beugen, S. van; Ramakers, C.; Boxtel, G.J.M. van

    2013-01-01

    OBJECTIVE: The aim was to examine the effects of a Mindfulness-Based Stress Reduction (MBSR) intervention on cardiovascular and cortisol activity during acute stress. METHOD: Eighty-eight healthy community-dwelling individuals reporting elevated stress levels were randomly assigned to the MBSR

  18. HABITAT ASSESSMENT USING A RANDOM PROBABILITY BASED SAMPLING DESIGN: ESCAMBIA RIVER DELTA, FLORIDA

    Science.gov (United States)

    Smith, Lisa M., Darrin D. Dantin and Steve Jordan. In press. Habitat Assessment Using a Random Probability Based Sampling Design: Escambia River Delta, Florida (Abstract). To be presented at the SWS/GERS Fall Joint Society Meeting: Communication and Collaboration: Coastal Systems...

  19. T3, a Combinator-based Random Testing Tool for Java: Benchmarking

    NARCIS (Netherlands)

    Prasetya, I.S.W.B.

    2014-01-01

    T3 is the next generation of the light weight automated testing tool T2 for Java. In the heart T3 is still a random testing tool; but it now comes with some new features: pair-wise testing, concurrent generators, and a combinator-based approach ala QuickCheck. This paper presents the result of

  20. Universal-Based Prevention of Syndromal and Subsyndromal Social Anxiety: A Randomized Controlled Study

    Science.gov (United States)

    Aune, Tore; Stiles, Tore C.

    2009-01-01

    This article reports results from a universal preventive program aimed at (a) reducing social anxiety and (b) preventing the development of syndromal social anxiety among a population-based sample of older children and young adolescents during a 1-year period. Pupils (N = 1,748) from 2 counties were cluster randomized to either an intervention or…

  1. Reinforcing Sampling Distributions through a Randomization-Based Activity for Introducing ANOVA

    Science.gov (United States)

    Taylor, Laura; Doehler, Kirsten

    2015-01-01

    This paper examines the use of a randomization-based activity to introduce the ANOVA F-test to students. The two main goals of this activity are to successfully teach students to comprehend ANOVA F-tests and to increase student comprehension of sampling distributions. Four sections of students in an advanced introductory statistics course…

  2. Effectiveness in practice-based research: Looking for alternatives to the randomized controlled trial (RCT)

    NARCIS (Netherlands)

    Tavecchio, L.

    2015-01-01

    Over the last decade, the status of the randomized controlled trial (RCT), hallmark of evidence-based medicine (research), has been growing strongly in general practice, social work and public health. But this type of research is only practicable under strictly controlled and well-defined settings

  3. CHEMICALLY MODIFIED FIELD-EFFECT TRANSISTORS - POTENTIOMETRIC AG+ SELECTIVITY OF PVC MEMBRANES BASED ON MACROCYCLIC THIOETHERS

    NARCIS (Netherlands)

    BRZOZKA, Z; COBBEN, PLHM; REINHOUDT, DN; EDEMA, JJH; KELLOGG, RM

    1993-01-01

    A chemically modified field-effect transistor (CHEMFET) with satisfactory Ag+ selectivity is described. The potentiometric Ag+ selectivities of CHEMFETs with plasticized PVC membranes based on macrocyclic thioethers have been determined. All the macrocyclic thioethers tested showed silver response

  4. Novel web service selection model based on discrete group search.

    Science.gov (United States)

    Zhai, Jie; Shao, Zhiqing; Guo, Yi; Zhang, Haiteng

    2014-01-01

    In our earlier work, we present a novel formal method for the semiautomatic verification of specifications and for describing web service composition components by using abstract concepts. After verification, the instantiations of components were selected to satisfy the complex service performance constraints. However, selecting an optimal instantiation, which comprises different candidate services for each generic service, from a large number of instantiations is difficult. Therefore, we present a new evolutionary approach on the basis of the discrete group search service (D-GSS) model. With regard to obtaining the optimal multiconstraint instantiation of the complex component, the D-GSS model has competitive performance compared with other service selection models in terms of accuracy, efficiency, and ability to solve high-dimensional service composition component problems. We propose the cost function and the discrete group search optimizer (D-GSO) algorithm and study the convergence of the D-GSS model through verification and test cases.

  5. An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

    Science.gov (United States)

    Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

    2016-01-01

    For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

  6. Directory of selected forestry-related bibliographic data bases

    Science.gov (United States)

    Peter A. Evans

    1979-01-01

    This compilation lists 117 bibliographic data bases maintained by scientists of the Forest Service, U.S. Department of Agriculture. For each data base, the following information is provided; name of the data base; originator; date started; coverage by subject; geographic area, and size of collection; base format; retrieval format; ways to query; who to query; and...

  7. Variable Selection in the Presence of Missing Data: Imputation-based Methods.

    Science.gov (United States)

    Zhao, Yize; Long, Qi

    2017-01-01

    Variable selection plays an essential role in regression analysis as it identifies important variables that associated with outcomes and is known to improve predictive accuracy of resulting models. Variable selection methods have been widely investigated for fully observed data. However, in the presence of missing data, methods for variable selection need to be carefully designed to account for missing data mechanisms and statistical techniques used for handling missing data. Since imputation is arguably the most popular method for handling missing data due to its ease of use, statistical methods for variable selection that are combined with imputation are of particular interest. These methods, valid used under the assumptions of missing at random (MAR) and missing completely at random (MCAR), largely fall into three general strategies. The first strategy applies existing variable selection methods to each imputed dataset and then combine variable selection results across all imputed datasets. The second strategy applies existing variable selection methods to stacked imputed datasets. The third variable selection strategy combines resampling techniques such as bootstrap with imputation. Despite recent advances, this area remains under-developed and offers fertile ground for further research.

  8. Risk Assessment of Distribution Network Based on Random set Theory and Sensitivity Analysis

    Science.gov (United States)

    Zhang, Sh; Bai, C. X.; Liang, J.; Jiao, L.; Hou, Z.; Liu, B. Zh

    2017-05-01

    Considering the complexity and uncertainty of operating information in distribution network, this paper introduces the use of random set for risk assessment. The proposed method is based on the operating conditions defined in the random set framework to obtain the upper and lower cumulative probability functions of risk indices. Moreover, the sensitivity of risk indices can effectually reflect information about system reliability and operating conditions, and by use of these information the bottlenecks that suppress system reliability can be found. The analysis about a typical radial distribution network shows that the proposed method is reasonable and effective.

  9. Live cell imaging based on surface plasmon-enhanced fluorescence microscopy using random nanostructures

    Science.gov (United States)

    Oh, Youngjin; Lee, Wonju; Son, Taehwang; Kim, Sook Young; Shin, Jeon-Soo; Kim, Donghyun

    2014-02-01

    Localized surface plasmon enhanced microscopy based on nanoislands of random spatial distribution was demonstrated for imaging live cells and molecular interactions. Nanoislands were produced without lithography by high temperature annealing under various processing conditions. The localization of near-field distribution that is associated with localized surface plasmon on metallic random nanoislands was analyzed theoretically and experimentally in comparison with periodic nanostructures. For experimental validation in live cell imaging, mouse macrophage-like cell line stained with Alexa Fluor 488 was prepared on nanoislands. The results suggest the possibility of attaining the imaging resolution on the order of 80 nm.

  10. Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood

    Directory of Open Access Journals (Sweden)

    Yunquan Song

    2014-01-01

    Full Text Available Empirical likelihood is a very popular method and has been widely used in the fields of artificial intelligence (AI and data mining as tablets and mobile application and social media dominate the technology landscape. This paper proposes an empirical likelihood shrinkage method to efficiently estimate unknown parameters and select correct moment conditions simultaneously, when the model is defined by moment restrictions in which some are possibly misspecified. We show that our method enjoys oracle-like properties; that is, it consistently selects the correct moment conditions and at the same time its estimator is as efficient as the empirical likelihood estimator obtained by all correct moment conditions. Moreover, unlike the GMM, our proposed method allows us to carry out confidence regions for the parameters included in the model without estimating the covariances of the estimators. For empirical implementation, we provide some data-driven procedures for selecting the tuning parameter of the penalty function. The simulation results show that the method works remarkably well in terms of correct moment selection and the finite sample properties of the estimators. Also, a real-life example is carried out to illustrate the new methodology.

  11. Hyperspectral image classification based on NMF Features Selection Method

    Science.gov (United States)

    Abe, Bolanle T.; Jordaan, J. A.

    2013-12-01

    Hyperspectral instruments are capable of collecting hundreds of images corresponding to wavelength channels for the same area on the earth surface. Due to the huge number of features (bands) in hyperspectral imagery, land cover classification procedures are computationally expensive and pose a problem known as the curse of dimensionality. In addition, higher correlation among contiguous bands increases the redundancy within the bands. Hence, dimension reduction of hyperspectral data is very crucial so as to obtain good classification accuracy results. This paper presents a new feature selection technique. Non-negative Matrix Factorization (NMF) algorithm is proposed to obtain reduced relevant features in the input domain of each class label. This aimed to reduce classification error and dimensionality of classification challenges. Indiana pines of the Northwest Indiana dataset is used to evaluate the performance of the proposed method through experiments of features selection and classification. The Waikato Environment for Knowledge Analysis (WEKA) data mining framework is selected as a tool to implement the classification using Support Vector Machines and Neural Network. The selected features subsets are subjected to land cover classification to investigate the performance of the classifiers and how the features size affects classification accuracy. Results obtained shows that performances of the classifiers are significant. The study makes a positive contribution to the problems of hyperspectral imagery by exploring NMF, SVMs and NN to improve classification accuracy. The performances of the classifiers are valuable for decision maker to consider tradeoffs in method accuracy versus method complexity.

  12. Gender based disruptive selection maintains body size polymorphism

    Indian Academy of Sciences (India)

    Darwinian fitness in holometabolous insects like the fruit fly Drosophila melanogaster is reported to be positively correlated with body size. If large individuals in a population have higher fitness, then one would expect directional selection to operate leading to uniformly large individuals. However, size polymorphism persists ...

  13. Fluorescent naphthalene-based benzene tripod for selective ...

    Indian Academy of Sciences (India)

    tive probe for the selective detection of fluoride. In order to demonstrate the practical utility of the new assay, we further applied it to sense fluoride in tap water and waste water, which provide more competitive media. 2. Experimental. 2.1 General information and materials. All the materials used for synthesis were purchased.

  14. Pheromones-based sexual selection in a rapidly changing world.

    Science.gov (United States)

    Henneken, Jessica; Jones, Therésa M

    2017-12-01

    Insects utilise chemical cues for a range of different purposes and the complexity and degree of specificity of these signals is arguably unparalleled in the animal kingdom. Chemical signals are particularly important for insect reproduction and the selective pressures driving their evolution and maintenance have been the subject of previous reviews. However, the world in which chemical cues evolved and are maintained is changing at an unprecedented rate. How (or indeed whether) chemical signals used in sexual selection will respond is largely unknown. Here, we explore how recent increases in urbanisation and associated anthropogenic impacts may affect how chemical signals are produced and perceived. We focus on four anthropomorphic influences which have the potential to interact with pheromone-mediated sexual selection processes; climatic temperature shifts, exposure to chemical pollutants, the presence of artificial light at night and nutrient availability. Our aim is to provide a broad overview of key areas where the rapidly changing environment of the future might specifically affect pheromones utilised in sexual selection. Copyright © 2017. Published by Elsevier Inc.

  15. Reporter-based screening and selection of enzymes

    NARCIS (Netherlands)

    Rossum, van T.; Kengen, S.W.M.; Oost, van der J.

    2013-01-01

    The biotech industry is continuously seeking for new or improved biocatalysts. The success of these efforts is often hampered by the lack of an efficient screening assay. Thus, to be able to extend the number of enzymes available for industrial applications, high-throughput screening and selection

  16. Alkali resistivity of Cu based selective catalytic reduction catalysts

    DEFF Research Database (Denmark)

    Putluru, Siva Sankar Reddy; Jensen, Anker Degn; Riisager, Anders

    2012-01-01

    The deactivation of V2O5–WO3–TiO2, Cu–HZSM5 and Cu–HMOR plate type monolithic catalysts was investigated when exposed to KCl aerosols in a bench-scale reactor. Fresh and exposed catalysts were characterized by selective catalytic reduction (SCR) activity measurements, scanning electron microscope...

  17. Instance Pointcuts: Selecting Object Sets Based on Their Usage History

    NARCIS (Netherlands)

    Bockisch, Christoph; Hatun, Kardelen; Aksit, Mehmet

    At runtime, how objects have to be handled frequently depends on how they were used before. But with current programminglanguage support, selecting objects according to their previous usage patterns often results in scattered and tangled code. In this study, we propose a new kind of pointcut, called

  18. Selection of okra parents based on performance and genetic ...

    African Journals Online (AJOL)

    A total of 200 okra accessions with wide variability and a potential for genetic improvement were stored in the Vegetables Germplasm Bank of the Federal University of Viçosa (UFV-BGH) in Viçosa, Minas Gerais State, Brazil. The objective of this work was to select parents by genetic divergence and behavior per se in 70 ...

  19. MySQL based selection of appropriate indexing technique in ...

    African Journals Online (AJOL)

    This paper deals with selection of appropriate indexing technique applied on MySQL Database for a health care system and related performance issues using multiclass support vector machine (SVM). The patient database is generally huge and contains lot of variations. For the quick search or fast retrieval of the desired ...

  20. Surveillance for cancer recurrence in long-term young breast cancer survivors randomly selected from a statewide cancer registry.

    Science.gov (United States)

    Jones, Tarsha; Duquette, Debra; Underhill, Meghan; Ming, Chang; Mendelsohn-Victor, Kari E; Anderson, Beth; Milliron, Kara J; Copeland, Glenn; Janz, Nancy K; Northouse, Laurel L; Duffy, Sonia M; Merajver, Sofia D; Katapodi, Maria C

    2018-01-20

    This study examined clinical breast exam (CBE) and mammography surveillance in long-term young breast cancer survivors (YBCS) and identified barriers and facilitators to cancer surveillance practices. Data collected with a self-administered survey from a statewide, randomly selected sample of YBCS diagnosed with invasive breast cancer or ductal carcinoma in situ younger than 45 years old, stratified by race (Black vs. White/Other). Multivariate logistic regression models identified predictors of annual CBEs and mammograms. Among 859 YBCS (n = 340 Black; n = 519 White/Other; mean age = 51.0 ± 5.9; diagnosed 11.0 ± 4.0 years ago), the majority (> 85%) reported an annual CBE and a mammogram. Black YBCS in the study were more likely to report lower rates of annual mammography and more barriers accessing care compared to White/Other YBCS. Having a routine source of care, confidence to use healthcare services, perceived expectations from family members and healthcare providers to engage in cancer surveillance, and motivation to comply with these expectations were significant predictors of having annual CBEs and annual mammograms. Cost-related lack of access to care was a significant barrier to annual mammograms. Routine source of post-treatment care facilitated breast cancer surveillance above national average rates. Persistent disparities regarding access to mammography surveillance were identified for Black YBCS, primarily due to lack of access to routine source of care and high out-of-pocket costs. Public health action targeting cancer surveillance in YBCS should ensure routine source of post-treatment care and address cost-related barriers. Clinical Trials Registration Number: NCT01612338.

  1. Copper (II) ion selective liquid membrane electrode based on new Schiff base carrier.

    Science.gov (United States)

    Sadeghi, Susan; Vardini, Mohammad Taghi; Naeimi, Hossein

    2006-01-01

    Cu2+ selective PVC membrane electrode based on new Schiff base 2, 2'-[1,9 nonanediyl bis (nitriloethylidyne)]-bis-(1-naphthol) as a selective carrier was constructed. The electrode exhibited a linear potential response within the activity range of 1.0 x 10(-6) - 5.0 x 10(-3) moll(-1) with a Nernstian slope of 29 +/- 1 mV decade(-1) of Cu2+ activity and a limit of detection 8.0 x 10(-7) mol l(-1). The response time of the electrode was fast, 10 s, and stable potentials were obtained within the pH range of 3.5- 6.5. The potentiometric selectivity coefficients were evaluated using two solution method and revealed no important interferences except for Ag+ ion. The proposed electrode was applied as an indicator electrode to potentiometric titration of Cu2+ ions and determination of Cu2+ content in real samples such as black tea leaves and multivitamin capsule.

  2. Sexual selection has minimal impact on effective population sizes in species with high rates of random offspring mortality: An empirical demonstration using fitness distributions.

    Science.gov (United States)

    Pischedda, Alison; Friberg, Urban; Stewart, Andrew D; Miller, Paige M; Rice, William R

    2015-10-01

    The effective population size (N(e)) is a fundamental parameter in population genetics that influences the rate of loss of genetic diversity. Sexual selection has the potential to reduce N(e) by causing the sex-specific distributions of individuals that successfully reproduce to diverge. To empirically estimate the effect of sexual selection on N(e), we obtained fitness distributions for males and females from an outbred, laboratory-adapted population of Drosophila melanogaster. We observed strong sexual selection in this population (the variance in male reproductive success was ∼14 times higher than that for females), but found that sexual selection had only a modest effect on N(e), which was 75% of the census size. This occurs because the substantial random offspring mortality in this population diminishes the effects of sexual selection on N(e), a result that necessarily applies to other high fecundity species. The inclusion of this random offspring mortality creates a scaling effect that reduces the variance/mean ratios for male and female reproductive success and causes them to converge. Our results demonstrate that measuring reproductive success without considering offspring mortality can underestimate Ne and overestimate the genetic consequences of sexual selection. Similarly, comparing genetic diversity among different genomic components may fail to detect strong sexual selection. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

  3. Uncovering Molecular Bases Underlying Bone Morphogenetic Protein Receptor Inhibitor Selectivity.

    Directory of Open Access Journals (Sweden)

    Abdelaziz Alsamarah

    Full Text Available Abnormal alteration of bone morphogenetic protein (BMP signaling is implicated in many types of diseases including cancer and heterotopic ossifications. Hence, small molecules targeting BMP type I receptors (BMPRI to interrupt BMP signaling are believed to be an effective approach to treat these diseases. However, lack of understanding of the molecular determinants responsible for the binding selectivity of current BMP inhibitors has been a big hindrance to the development of BMP inhibitors for clinical use. To address this issue, we carried out in silico experiments to test whether computational methods can reproduce and explain the high selectivity of a small molecule BMP inhibitor DMH1 on BMPRI kinase ALK2 vs. the closely related TGF-β type I receptor kinase ALK5 and vascular endothelial growth factor receptor type 2 (VEGFR2 tyrosine kinase. We found that, while the rigid docking method used here gave nearly identical binding affinity scores among the three kinases; free energy perturbation coupled with Hamiltonian replica-exchange molecular dynamics (FEP/H-REMD simulations reproduced the absolute binding free energies in excellent agreement with experimental data. Furthermore, the binding poses identified by FEP/H-REMD led to a quantitative analysis of physical/chemical determinants governing DMH1 selectivity. The current work illustrates that small changes in the binding site residue type (e.g. pre-hinge region in ALK2 vs. ALK5 or side chain orientation (e.g. Tyr219 in caALK2 vs. wtALK2, as well as a subtle structural modification on the ligand (e.g. DMH1 vs. LDN193189 will cause distinct binding profiles and selectivity among BMP inhibitors. Therefore, the current computational approach represents a new way of investigating BMP inhibitors. Our results provide critical information for designing exclusively selective BMP inhibitors for the development of effective pharmacotherapy for diseases caused by aberrant BMP signaling.

  4. Effects of a community-based healthy heart program on increasing healthy women's physical activity: a randomized controlled trial guided by Community-based Participatory Research (CBPR

    Directory of Open Access Journals (Sweden)

    Seyednezami Nasrin

    2007-08-01

    Full Text Available Abstract Background Cardiovascular disease remains the leading killer of women in most developed areas of the world. Rates of physical inactivity and poor nutrition, which are two of the most important modifiable risk factors for cardiovascular disease in women, are substantial. This study sought to examine the effectiveness of a community-based lifestyle-modification program on increasing women's physical activity in a randomized trial guided by community-based participatory research (CBPR methods. Methods A total of 335 healthy, 25–64 years old women who had been selected by a multiple-stage stratified cluster random sampling method in Bushehr Port/I.R. Iran, were randomized into control and intervention groups. The intervention group completed an 8-week lifestyle modification program for increasing their physical activity, based on a revised form of Choose to Move program; an American Heart Association Physical Activity Program for Women. Audio-taped activity instructions with music and practical usage of the educational package were given to the intervention group in weekly home-visits by 53 volunteers from local non-governmental and community-based organizations. Results Among the participants, the percentage who reported being active (at lease 30 minutes of moderate intensity physical activity for at least 5 days a week, or at least 20 minutes of vigorous physical activity for at least three days a week increased from 3% and 2.7% at baseline to 13.4% and 3% (p Conclusion An intervention based on CBPR methods can be effective for the short-term adoption of physical activity behavior among women. The development of participatory process to support the adequate delivery of lifestyle-modification programs is feasible and an effective healthcare delivery strategy for cardiovascular community health promotion. Trial Registration ACTRNO12606000521527

  5. Milk yield persistency in Brazilian Gyr cattle based on a random regression model.

    Science.gov (United States)

    Pereira, R J; Verneque, R S; Lopes, P S; Santana, M L; Lagrotta, M R; Torres, R A; Vercesi Filho, A E; Machado, M A

    2012-06-15

    With the objective of evaluating measures of milk yield persistency, 27,000 test-day milk yield records from 3362 first lactations of Brazilian Gyr cows that calved between 1990 and 2007 were analyzed with a random regression model. Random, additive genetic and permanent environmental effects were modeled using Legendre polynomials of order 4 and 5, respectively. Residual variance was modeled using five classes. The average lactation curve was modeled using a fourth-order Legendre polynomial. Heritability estimates for measures of persistency ranged from 0.10 to 0.25. Genetic correlations between measures of persistency and 305-day milk yield (Y305) ranged from -0.52 to 0.03. At high selection intensities for persistency measures and Y305, few animals were selected in common. As the selection intensity for the two traits decreased, a higher percentage of animals were selected in common. The average predicted breeding values for Y305 according to year of birth of the cows had a substantial annual genetic gain. In contrast, no improvement in the average persistency breeding value was observed. We conclude that selection for total milk yield during lactation does not identify bulls or cows that are genetically superior in terms of milk yield persistency. A measure of persistency represented by the sum of deviations of estimated breeding value for days 31 to 280 in relation to estimated breeding value for day 30 should be preferred in genetic evaluations of this trait in the Gyr breed, since this measure showed a medium heritability and a genetic correlation with 305-day milk yield close to zero. In addition, this measure is more adequate at the time of peak lactation, which occurs between days 25 and 30 after calving in this breed.

  6. Possibility/Necessity-Based Probabilistic Expectation Models for Linear Programming Problems with Discrete Fuzzy Random Variables

    Directory of Open Access Journals (Sweden)

    Hideki Katagiri

    2017-10-01

    Full Text Available This paper considers linear programming problems (LPPs where the objective functions involve discrete fuzzy random variables (fuzzy set-valued discrete random variables. New decision making models, which are useful in fuzzy stochastic environments, are proposed based on both possibility theory and probability theory. In multi-objective cases, Pareto optimal solutions of the proposed models are newly defined. Computational algorithms for obtaining the Pareto optimal solutions of the proposed models are provided. It is shown that problems involving discrete fuzzy random variables can be transformed into deterministic nonlinear mathematical programming problems which can be solved through a conventional mathematical programming solver under practically reasonable assumptions. A numerical example of agriculture production problems is given to demonstrate the applicability of the proposed models to real-world problems in fuzzy stochastic environments.

  7. A Simple Density with Distance Based Initial Seed Selection Technique for K Means Algorithm

    Directory of Open Access Journals (Sweden)

    Sajidha Syed Azimuddin

    2017-01-01

    Full Text Available Open issues with respect to K means algorithm are identifying the number of clusters, initial seed concept selection, clustering tendency, handling empty clusters, identifying outliers etc. In this paper we propose a novel and a simple technique considering both density and distance of the concepts in a dataset to identify initial seed concepts for clustering. Many authors have proposed different techniques to identify initial seed concepts; but our method ensures that the initial seed concepts are chosen from different clusters that are to be generated by the clustering solution. The hallmark of our algorithm is that it is a single pass algorithm that does not require any extra parameters to be estimated. Further, our seed concepts are one among the actual concepts and not the mean of representative concepts as is the case in many other algorithms. We have implemented our proposed algorithm and compared the results with the interval based technique of Fouad Khan. We see that our method outperforms the interval based method. We have also compared our method with the original random K means and K Means++ algorithms.

  8. Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Michael H. Thaut

    2005-11-01

    Full Text Available Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as “yes” or “no” or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct “yes”/“no” BCI from a single session. Blind source separation (BSS and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA wrapped around a support vector machine (SVM classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a “direct,” single-session BCI.

  9. BIM-Based Decision Support System for Material Selection Based on Supplier Rating

    Directory of Open Access Journals (Sweden)

    Abiola Akanmu

    2015-12-01

    Full Text Available Material selection is a delicate process, typically hinged on a number of factors which can be either cost or environmental related. This process becomes more complicated when designers are faced with several material options of building elements and each option can be supplied by different suppliers whose selection criteria may affect the budgetary and environmental requirements of the project. This paper presents the development of a decision support system based on the integration of building information models, a modified harmony search algorithm and supplier performance rating. The system is capable of producing the cost and environmental implications of different material combinations or building designs. A case study is presented to illustrate the functionality of the developed system.

  10. A Randomized Controlled Trial of a Theoretically-Based Behavioral Nutrition Intervention for Community Elders: Lessons Learned from the Behavioral Nutrition Intervention for Community Elders Study

    OpenAIRE

    Locher, JL; Vickers, KS; Buys, DR; Ellis, A; Lawrence, JC; Newton, LE; Roth, DL; Ritchie, CS; Bales, CW

    2013-01-01

    Older adults with multiple comorbidities are often undernourished or at high risk for becoming so, especially after a recent hospitalization. Randomized controlled trials of effective, innovative interventions are needed to support evidence-based approaches for solving nutritional problems in this population. Self-management approaches where participants select their own behavioral goals can enhance success of interventions. The purpose of this study was to evaluate the feasibility and effica...

  11. Inversion-based data-driven time-space domain random noise attenuation method

    Science.gov (United States)

    Zhao, Yu-Min; Li, Guo-Fa; Wang, Wei; Zhou, Zhen-Xiao; Tang, Bo-Wen; Zhang, Wen-Bo

    2017-12-01

    Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data's space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.

  12. Conflict-cost based random sampling design for parallel MRI with low rank constraints

    Science.gov (United States)

    Kim, Wan; Zhou, Yihang; Lyu, Jingyuan; Ying, Leslie

    2015-05-01

    In compressed sensing MRI, it is very important to design sampling pattern for random sampling. For example, SAKE (simultaneous auto-calibrating and k-space estimation) is a parallel MRI reconstruction method using random undersampling. It formulates image reconstruction as a structured low-rank matrix completion problem. Variable density (VD) Poisson discs are typically adopted for 2D random sampling. The basic concept of Poisson disc generation is to guarantee samples are neither too close to nor too far away from each other. However, it is difficult to meet such a condition especially in the high density region. Therefore the sampling becomes inefficient. In this paper, we present an improved random sampling pattern for SAKE reconstruction. The pattern is generated based on a conflict cost with a probability model. The conflict cost measures how many dense samples already assigned are around a target location, while the probability model adopts the generalized Gaussian distribution which includes uniform and Gaussian-like distributions as special cases. Our method preferentially assigns a sample to a k-space location with the least conflict cost on the circle of the highest probability. To evaluate the effectiveness of the proposed random pattern, we compare the performance of SAKEs using both VD Poisson discs and the proposed pattern. Experimental results for brain data show that the proposed pattern yields lower normalized mean square error (NMSE) than VD Poisson discs.

  13. Calculating radiotherapy margins based on Bayesian modelling of patient specific random errors

    Science.gov (United States)

    Herschtal, A.; te Marvelde, L.; Mengersen, K.; Hosseinifard, Z.; Foroudi, F.; Devereux, T.; Pham, D.; Ball, D.; Greer, P. B.; Pichler, P.; Eade, T.; Kneebone, A.; Bell, L.; Caine, H.; Hindson, B.; Kron, T.

    2015-02-01

    Collected real-life clinical target volume (CTV) displacement data show that some patients undergoing external beam radiotherapy (EBRT) demonstrate significantly more fraction-to-fraction variability in their displacement (‘random error’) than others. This contrasts with the common assumption made by historical recipes for margin estimation for EBRT, that the random error is constant across patients. In this work we present statistical models of CTV displacements in which random errors are characterised by an inverse gamma (IG) distribution in order to assess the impact of random error variability on CTV-to-PTV margin widths, for eight real world patient cohorts from four institutions, and for different sites of malignancy. We considered a variety of clinical treatment requirements and penumbral widths. The eight cohorts consisted of a total of 874 patients and 27 391 treatment sessions. Compared to a traditional margin recipe that assumes constant random errors across patients, for a typical 4 mm penumbral width, the IG based margin model mandates that in order to satisfy the common clinical requirement that 90% of patients receive at least 95% of prescribed RT dose to the entire CTV, margins be increased by a median of 10% (range over the eight cohorts -19% to +35%). This substantially reduces the proportion of patients for whom margins are too small to satisfy clinical requirements.

  14. On analysis-based two-step interpolation methods for randomly sampled seismic data

    Science.gov (United States)

    Yang, Pengliang; Gao, Jinghuai; Chen, Wenchao

    2013-02-01

    Interpolating the missing traces of regularly or irregularly sampled seismic record is an exceedingly important issue in the geophysical community. Many modern acquisition and reconstruction methods are designed to exploit the transform domain sparsity of the few randomly recorded but informative seismic data using thresholding techniques. In this paper, to regularize randomly sampled seismic data, we introduce two accelerated, analysis-based two-step interpolation algorithms, the analysis-based FISTA (fast iterative shrinkage-thresholding algorithm) and the FPOCS (fast projection onto convex sets) algorithm from the IST (iterative shrinkage-thresholding) algorithm and the POCS (projection onto convex sets) algorithm. A MATLAB package is developed for the implementation of these thresholding-related interpolation methods. Based on this package, we compare the reconstruction performance of these algorithms, using synthetic and real seismic data. Combined with several thresholding strategies, the accelerated convergence of the proposed methods is also highlighted.

  15. Cheminformatics based selection and cytotoxic effects of herbal extracts.

    OpenAIRE

    Sardari, Soroush; Shokrgozar, Mohammad Ali; Ghavami, Ghazaleh

    2009-01-01

    International audience; Bioinformatics and traditional medicine can be used in discovery and design of novel candidate drugs to efficient cancer chemotherapy. In this study, similarity search tools employed to screen and introduce novel herbs with antitumor property. Several novel herbs have been selected by using logical computational algorithms and assayed on six cancerous cell lines. Complementary assays involved hemolytic and antifungal MIC tests have been performed to determine selectivi...

  16. Categorical variables with many categories are preferentially selected in bootstrap-based model selection procedures for multivariable regression models.

    Science.gov (United States)

    Rospleszcz, Susanne; Janitza, Silke; Boulesteix, Anne-Laure

    2016-05-01

    Automated variable selection procedures, such as backward elimination, are commonly employed to perform model selection in the context of multivariable regression. The stability of such procedures can be investigated using a bootstrap-based approach. The idea is to apply the variable selection procedure on a large number of bootstrap samples successively and to examine the obtained models, for instance, in terms of the inclusion of specific predictor variables. In this paper, we aim to investigate a particular important problem affecting this method in the case of categorical predictor variables with different numbers of categories and to give recommendations on how to avoid it. For this purpose, we systematically assess the behavior of automated variable selection based on the likelihood ratio test using either bootstrap samples drawn with replacement or subsamples drawn without replacement from the original dataset. Our study consists of extensive simulations and a real data example from the NHANES study. Our main result is that if automated variable selection is conducted on bootstrap samples, variables with more categories are substantially favored over variables with fewer categories and over metric variables even if none of them have any effect. Importantly, variables with no effect and many categories may be (wrongly) preferred to variables with an effect but few categories. We suggest the use of subsamples instead of bootstrap samples to bypass these drawbacks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Effects of a community-based healthy heart program on increasing healthy women's physical activity: a randomized controlled trial guided by Community-based Participatory Research (CBPR).

    Science.gov (United States)

    Pazoki, Raha; Nabipour, Iraj; Seyednezami, Nasrin; Imami, Seyed Reza

    2007-08-23

    Cardiovascular disease remains the leading killer of women in most developed areas of the world. Rates of physical inactivity and poor nutrition, which are two of the most important modifiable risk factors for cardiovascular disease in women, are substantial. This study sought to examine the effectiveness of a community-based lifestyle-modification program on increasing women's physical activity in a randomized trial guided by community-based participatory research (CBPR) methods. A total of 335 healthy, 25-64 years old women who had been selected by a multiple-stage stratified cluster random sampling method in Bushehr Port/I.R. Iran, were randomized into control and intervention groups. The intervention group completed an 8-week lifestyle modification program for increasing their physical activity, based on a revised form of Choose to Move program; an American Heart Association Physical Activity Program for Women. Audio-taped activity instructions with music and practical usage of the educational package were given to the intervention group in weekly home-visits by 53 volunteers from local non-governmental and community-based organizations. Among the participants, the percentage who reported being active (at lease 30 minutes of moderate intensity physical activity for at least 5 days a week, or at least 20 minutes of vigorous physical activity for at least three days a week) increased from 3% and 2.7% at baseline to 13.4% and 3% (p physical activity per week (mean = 139.81, SE = 23.35) than women in the control group (mean = 40.14, SE = 12.65) at week 8 (p effective for the short-term adoption of physical activity behavior among women. The development of participatory process to support the adequate delivery of lifestyle-modification programs is feasible and an effective healthcare delivery strategy for cardiovascular community health promotion. ACTRNO12606000521527.

  18. Frame-Based Random Access with Interference Cancellation across Frames for Massive Machine Type Communications

    Directory of Open Access Journals (Sweden)

    Minjoong Rim

    2017-01-01

    Full Text Available One of the main requirements for next generation mobile or wireless communication systems is to effectively support a large number of machine type communication devices for Internet of things applications. In order to improve the random access capability in frame-based slotted Aloha environments, coded random access techniques have been proposed, in which multiple copies of a packet are transmitted per frame and the copies are cancelled out from the received signal if any single copy is successfully received. They, however, may increase the transmission power by sending multiple copies per frame. Above all, for systems with a small number of slots per frame, they may not be able to improve the performance by readily reaching a congested state. This paper proposes a new frame-based random access scheme, which sends at most one copy of a packet per frame but uses interference cancellation to improve the performance. If a successfully received packet is a retransmitted one, the previously transmitted signals for the packet can be cancelled out from the received signals for trying to decode other received packets. The proposed scheme has different characteristics than coded random access schemes and can be also combined with them to further improve the performance.

  19. Dynamic Educational e-Content Selection Using Multiple Criteria in Web-Based Personalized Learning Environments.

    Science.gov (United States)

    Manouselis, Nikos; Sampson, Demetrios

    This paper focuses on the way a multi-criteria decision making methodology is applied in the case of agent-based selection of offered learning objects. The problem of selection is modeled as a decision making one, with the decision variables being the learner model and the learning objects' educational description. In this way, selection of…

  20. a simple k-map based variable selection scheme in the direct

    African Journals Online (AJOL)

    Dr Obe

    ABSTRACT. A multiplexer with (n-l) data select inputs can realise directly a function of n variables. In this paper, a simple k-map based variable selection scheme is proposed such that an n variable logic function can be synthesised using a multiplexer with (n-q) data input variables and q data select variables.

  1. Measurement-Based Transmission Line Parameter Estimation with Adaptive Data Selection Scheme

    DEFF Research Database (Denmark)

    Li, Changgang; Zhang, Yaping; Zhang, Hengxu

    2017-01-01

    estimation model with an adaptive data selection scheme based on measured data. Data selection scheme, defined with time window and number of data points, is introduced in the estimation model as additional variables to optimize. The data selection scheme is adaptively adjusted to minimize the relative...

  2. Proposal of a super trait for the optimum selection of popcorn progenies based on path analysis.

    Science.gov (United States)

    do Amaral Júnior, A T; Dos Santos, A; Gerhardt, I F S; Kurosawa, R N F; Moreira, N F; Pereira, M G; de A Gravina, G; de L Silva, F H

    2016-12-19

    A challenge faced by popcorn breeding programs is the existence of a negative correlation between the two main traits, popping expansion and yield, which hinders simultaneous gains. The objective of this study was to investigate the use of a new variable or super trait, which favors the reliable selection of superior progenies. The super trait 'expanded popcorn volume per hectare' was introduced in the evaluation of 200 full-sib families of the eighth recurrent intrapopulation selection cycle, which were arranged in randomized blocks with three replicates in two environments. Although the inability to obtain simultaneous gains through selection via popping expansion or yield was confirmed, the super trait was positively associated with both yield and popping expansion, allowing simultaneous gains via indirect selection using 'expanded popcorn volume per hectare' as the main trait. This approach is recommended because this super trait can be used in breeding programs to optimize selective gains for the crop.

  3. GeneRIF indexing: sentence selection based on machine learning.

    Science.gov (United States)

    Jimeno-Yepes, Antonio J; Sticco, J Caitlin; Mork, James G; Aronson, Alan R

    2013-05-31

    A Gene Reference Into Function (GeneRIF) describes novel functionality of genes. GeneRIFs are available from the National Center for Biotechnology Information (NCBI) Gene database. GeneRIF indexing is performed manually, and the intention of our work is to provide methods to support creating the GeneRIF entries. The creation of GeneRIF entries involves the identification of the genes mentioned in MEDLINE®; citations and the sentences describing a novel function. We have compared several learning algorithms and several features extracted or derived from MEDLINE sentences to determine if a sentence should be selected for GeneRIF indexing. Features are derived from the sentences or using mechanisms to augment the information provided by them: assigning a discourse label using a previously trained model, for example. We show that machine learning approaches with specific feature combinations achieve results close to one of the annotators. We have evaluated different feature sets and learning algorithms. In particular, Naïve Bayes achieves better performance with a selection of features similar to one used in related work, which considers the location of the sentence, the discourse of the sentence and the functional terminology in it. The current performance is at a level similar to human annotation and it shows that machine learning can be used to automate the task of sentence selection for GeneRIF annotation. The current experiments are limited to the human species. We would like to see how the methodology can be extended to other species, specifically the normalization of gene mentions in other species.

  4. Reliability-based server selection for heterogeneous VANETs

    Directory of Open Access Journals (Sweden)

    Seyedali Hosseininezhad

    2011-09-01

    Full Text Available Heterogeneous wireless networks are capable of providing customers with better services while service providers can offer more applications to more customers with lower costs. To provide services, some applications rely on existing servers in the network. In a vehicular ad-hoc network (VANET some mobile nodes may function as servers. Due to high mobility of nodes and short lifetime of links, server-to-client and server-to-server communications become challenging. In this paper we propose to enhance the performance of server selection by taking link reliability into consideration in the server selection mechanism, thereby avoiding extra client-to-server hand-offs and reducing the need of server-to-server synchronization. As a case study we focus on location management service in a heterogeneous VANET. We provide a routing algorithm for transactions between location servers and mobile nodes. We assume that location servers are vehicles equipped with at least one long- range and one short-range radio interfaces, whereas regular nodes (clients are only equipped with a short-range radio interface. The primary goal of our design is to minimize hand-offs between location servers while limiting the delays of location updates. Taking advantage of vehicle mobility patterns, we propose a mobility-aware server selection scheme and show that it can reduce the number of hand-offs and yet avoid large delays during location updates. We present simulation results to show that proposed scheme significantly lowers the costs of signaling and rate of server hand-offs by increasing the connection lifetimes between clients and servers.

  5. Resonant Inerter Based Absorbers for a Selected Global Mode

    DEFF Research Database (Denmark)

    Krenk, Steen

    2016-01-01

    The paper presents calibration and efficiency analyses for two different configurations of a resonant vibration absorber consisting of a spring, a damper and an inerter element. In the two configurations the damper is either in parallel with the spring or with the inerter element. A calibration......-resonant modes. The calibration procedure is given a unified format for the two absorber types, and the high efficiency – evaluated as the ability to reproduce the selected dynamic amplification level of the resonant mode – is demonstrated....

  6. Broadband metamaterial absorber based on coupling resistive frequency selective surface.

    Science.gov (United States)

    Sun, LiangKui; Cheng, HaiFeng; Zhou, YongJiang; Wang, Jun

    2012-02-13

    We report the design, fabrication, and measurement of a broadband metamaterial absorber, which consists of lossy frequency selective surface (FSS) and a metallic ground plane separated by a dielectric layer. The compact single unit cell of the FSS contains crisscross and fractal square patch which couple with each other. Both qualitative analysis by equivalent circuit and accurate numeric calculation show that the coupling between the crisscross and the fractal square patch can enhance the bandwidth with the reflectivity below -10dB in the frequency range of 2-18GHz by producing a third absorption null. In the end, the designed absorber was realized by experiment.

  7. Annotation-Based Whole Genomic Prediction and Selection

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Do, Duy Ngoc; Janss, Luc

    Genomic selection is widely used in both animal and plant species, however, it is performed with no input from known genomic or biological role of genetic variants and therefore is a black box approach in a genomic era. This study investigated the role of different genomic regions and detected QTLs...... groups. Genomic prediction has accuracy comparable to an own phenotype and use of genomic prediction can be cost effective by replacing feed intake measurement. Use of genomic annotation of SNPs and QTL information had no largely significant impact on predictive accuracy for the current traits but may...

  8. Portfolio Selection Based on Distance between Fuzzy Variables

    Directory of Open Access Journals (Sweden)

    Weiyi Qian

    2014-01-01

    Full Text Available This paper researches portfolio selection problem in fuzzy environment. We introduce a new simple method in which the distance between fuzzy variables is used to measure the divergence of fuzzy investment return from a prior one. Firstly, two new mathematical models are proposed by expressing divergence as distance, investment return as expected value, and risk as variance and semivariance, respectively. Secondly, the crisp forms of the new models are also provided for different types of fuzzy variables. Finally, several numerical examples are given to illustrate the effectiveness of the proposed approach.

  9. Exploring ART intake scenes in a human rights-based intervention to improve adherence: a randomized controlled trial.

    Science.gov (United States)

    Basso, Cáritas Relva; Helena, Ernani Tiaraju Santa; Caraciolo, Joselita Maria Magalhães; Paiva, Vera; Nemes, Maria Ines Battistela

    2013-01-01

    To assess the effectiveness of a psychosocial individual intervention to improve adherence to ART in a Brazilian reference-center, consenting PLHIV with viral load >50 copies/ml were selected. After 4 weeks of MEMS cap use, participants were randomized into an intervention group (IG) (n = 64) or control group (CG) (n = 57). CG received usual care only. The IG participated in a human rights-based intervention approach entailing four dialogical meetings focused on medication intake scenes. Comparison between IG and CG revealed no statistically significant difference in adherence measured at weeks 8, 12, 16, 20 and 24. Viral load (VL) decreased in both groups (p < 0.0001) with no significant difference between study groups. The lower number of eligible patients than expected underpowered the study. Ongoing qualitative analysis should provide deeper understanding of the trial results. NIH Clinical Trials: NCT00716040.

  10. Influence of selected biotopes on chironomid-based bioassessment ...

    African Journals Online (AJOL)

    Impact of pollution on aquatic biota is usually assessed by comparing the assemblage at an impacted site with those at a control or reference site. In South Africa, except in rivers where not all biotopes are represented, the characterisation of a macroinvertebrate-based pollution effect is usually based on samples collected ...

  11. Sustainable selective oxidations using ceria-based materials

    NARCIS (Netherlands)

    Beckers, J.; Rothenberg, G.

    2010-01-01

    This Perspective covers sustainable oxidation processes using doped cerias, ceria-supported catalysts and ceria-based mixed oxides. Firstly, we consider the general properties of ceria-based catalysts. We outline the advantages of the ceria redox cycle, and explain the dynamic behaviour of these

  12. Chromium(III) selective membrane sensors based on Schiff bases as chelating ionophores.

    Science.gov (United States)

    Singh, A K; Gupta, V K; Gupta, Barkha

    2007-02-28

    The two chromium chelates of Schiff bases, N-(acetoacetanilide)-1,2-diaminoethane (L(1)) and N,N'-bis(acetoacetanilide)-triethylenetetraammine (L(2)), have been synthesized and explored as neutral ionophores for preparing poly(vinylchloride) (PVC) based membrane sensors selective to Cr(III). The addition of lipophilic anion excluder (NaTPB) and various plasticizers viz. o-Nitrophenyloctyl ether (o-NPOE), dioctylpthalate (DOP), dibutylphthalate (DBP), tris(2-ethylhexyl)phosphate (TEHP), and benzyl acetate (BA) have found to improve the performance of the sensors. The best performance was obtained for the membrane sensor having a composition of L(1):PVC:DBP:NaTPB in the ratio 5:150:250:3 (w/w). The sensor exhibits Nernstian response in the concentration range 8.9 x 10(-8) to 1.0 x 10(-1) M Cr(3+) with limit of detection 5.6 x 10(-8) M. The proposed sensor manifest advantages of relatively fast response (10s) and good selectivity over some alkali, alkaline earth, transition and heavy metal ions. The selectivity behavior of the proposed electrode revealed a considerable improvement as compared to the best previously PVC-membrane electrode for chromium(III) ion. The potentiometric response of the proposed sensor was independent of pH of the test solution in the range of 2.0-7.0. The sensor has found to work satisfactorily in partially non-aqueous media up to 20% (v/v) content of methanol, ethanol and acetonitrile and could be used for a period of 3 months. The proposed electrode was used as an indicator electrode in potentiometric titration of chromium ion with EDTA and in direct determination in different water and food samples.

  13. Synchronization of random bit generators based on coupled chaotic lasers and application to cryptography.

    Science.gov (United States)

    Kanter, Ido; Butkovski, Maria; Peleg, Yitzhak; Zigzag, Meital; Aviad, Yaara; Reidler, Igor; Rosenbluh, Michael; Kinzel, Wolfgang

    2010-08-16

    Random bit generators (RBGs) constitute an important tool in cryptography, stochastic simulations and secure communications. The later in particular has some difficult requirements: high generation rate of unpredictable bit strings and secure key-exchange protocols over public channels. Deterministic algorithms generate pseudo-random number sequences at high rates, however, their unpredictability is limited by the very nature of their deterministic origin. Recently, physical RBGs based on chaotic semiconductor lasers were shown to exceed Gbit/s rates. Whether secure synchronization of two high rate physical RBGs is possible remains an open question. Here we propose a method, whereby two fast RBGs based on mutually coupled chaotic lasers, are synchronized. Using information theoretic analysis we demonstrate security against a powerful computational eavesdropper, capable of noiseless amplification, where all parameters are publicly known. The method is also extended to secure synchronization of a small network of three RBGs.

  14. Double image encryption based on random phase encoding in the fractional Fourier domain.

    Science.gov (United States)

    Tao, Ran; Xin, Yi; Wang, Yue

    2007-11-26

    A novel image encryption method is proposed by utilizing random phase encoding in the fractional Fourier domain to encrypt two images into one encrypted image with stationary white distribution. By applying the correct keys which consist of the fractional orders, the random phase masks and the pixel scrambling operator, the two primary images can be recovered without cross-talk. The decryption process is robust against the loss of data. The phase-based image with a larger key space is more sensitive to keys and disturbances than the amplitude-based image. The pixel scrambling operation improves the quality of the decrypted image when noise perturbation occurs. The novel approach is verified by simulations.

  15. Internet based gripper teleoperation with random time delay by using haptic feedback and SEMG

    Science.gov (United States)

    Xu, Xiaonong; Song, Aiguo; Zhang, Huatao; Ji, Peng

    2016-10-01

    Random time delay may cause instability in the internet based teleoperation system. Transparency and intuitiveness are also very important for operator to control the system to accurately perform the desired action, especially for the gripper teleoperation system. This paper presents a new grip force control method of gripper teleoperation system with haptic feedback. The system employs the SEMG signal as the control parameter in order to enhance the intuitive control experience for operator. In order to eliminate the impacts on the system stability caused by random time delay, a non-time based teleoperation method is applied to the control process. Besides, neural network and designed fuzzy logic controller is also utilized to improve this control method. The effectiveness of the proposed method is demonstrated by experiment results.

  16. Copper(II) selective electrochemical sensor based on Schiff Base complexes.

    Science.gov (United States)

    Singh, Lok P; Bhatnagar, Jitendra M

    2004-10-08

    Plasticized membranes using Schiff Base complexes, derived from 2,3-diaminopyridine and o-vanilin have been prepared and explored as Cu(2+)-selective sensors. Effect of various plasticizers viz., dibutyl phthalate (DBP), dioctylphthalate (DOP), chloronaphthalene (CN), tri-n-butylphosphate (TBP) etc. and anion excluder, sodium tetraphenylborate (NaTPB) was studied in detail and improved performance was observed at several instances. Optimum performance was observed with Schiff Base (B) having a membrane composition of B(1%):PVC(33%):DOP(65%):NaTPB(1%). The sensor works satisfactorily in the concentration range 5.0x10(-6) to 1.0x10(-1)M (detection limit 0.3ppm) with a Nernstian slope of 29.6mV per decade of activity. Wide pH range (1.9-5.2), fast response time (4 months) indicate the vital utility of the proposed sensor. The potentiometric selectivity coefficient values as determined by match potential method (MPM) indicate good response for Cu(2+) in presence of interfering ions. The tolerance level of Hg(2+), which causes serious interference in the determination of Cu(2+) ions (K(Cu(2+)Hg(2+))(Pot)(MPM): 0.45), was determined as a function of Cu(2+) concentration in simulated mixtures. The sensor was also used in the potentiometric titration of Cu(2+) with EDTA.

  17. Tunable antenna radome based on graphene frequency selective surface

    National Research Council Canada - National Science Library

    Qu, Meijun; Rao, Menglou; Li, Shufang; Deng, Li

    2017-01-01

    ... to the alterable conductivity of the graphene strips which is controlled by chemical potential. Based on the reconfigurable bandpass property of the proposed FSS, a cylindrical antenna radome is designed using the FSS unit cells...

  18. Subcellular carrier-based optical ion-selective nanosensors

    Directory of Open Access Journals (Sweden)

    Susana eCarregal Romero

    2012-04-01

    Full Text Available In this review we discuss two systems based on nanotechnology for real-time sensing of biologically relevant analytes (ions or other biological molecules inside cells in a non-invasive way.

  19. Transmission performance improvement using random DFB laser based Raman amplification and bidirectional second-order pumping

    OpenAIRE

    Tan, M.; Rosa, P; Le, S.T.; Iqbal, Md A.; Phillips, I.D.; Harper, P.

    2016-01-01

    We demonstrate that a distributed Raman amplification scheme based on random distributed feedback (DFB) fiber laser enables bidirectional second-order Raman pumping without increasing relative intensity noise (RIN) of the signal. This extends the reach of 10 × 116 Gb/s DP-QPSK WDM transmission up to 7915 km, compared with conventional Raman amplification schemes. Moreover, this scheme gives the longest maximum transmission distance among all the Raman amplification schemes presented in this p...

  20. Characterization of coded random access with compressive sensing based multi user detection

    DEFF Research Database (Denmark)

    Ji, Yalei; Stefanovic, Cedomir; Bockelmann, Carsten

    2014-01-01

    and is inherently capable to take advantage of the capture effect from the PHY layer. Furthermore, at the PHY layer, compressive sensing based multi-user detection (CS-MUD) is a novel technique that exploits sparsity in multi-user detection to achieve a joint activity and data detection. In this paper, we combine...... coded random access with CS-MUD on the PHY layer and show very promising results for the resulting protocol....

  1. Random Coding Bounds for DNA Codes Based on Fibonacci Ensembles of DNA Sequences

    Science.gov (United States)

    2008-07-01

    Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC...COVERED (From - To) 6 Jul 08 – 11 Jul 08 4. TITLE AND SUBTITLE RANDOM CODING BOUNDS FOR DNA CODES BASED ON FIBONACCI ENSEMBLES OF DNA SEQUENCES...sequences which are generalizations of the Fibonacci sequences. 15. SUBJECT TERMS DNA Codes, Fibonacci Ensembles, DNA Computing, Code Optimization 16

  2. Supplier Selection based on the Performance by using PROMETHEE Method

    Science.gov (United States)

    Sinaga, T. S.; Siregar, K.

    2017-03-01

    Generally, companies faced problem to identify vendors that can provide excellent service in availability raw material and on time delivery. The performance of suppliers in a company have to be monitored to ensure the availability to fulfill the company needs. This research is intended to explain how to assess suppliers to improve manufacturing performance. The criteria that considered in evaluating suppliers is criteria of Dickson. There are four main criteria which further split into seven sub-criteria, namely compliance with accuracy, consistency, on-time delivery, right quantity order, flexibility and negotiation, timely of order confirmation, and responsiveness. This research uses PROMETHEE methodology in assessing the supplier performances and obtaining a selected supplier as the best one that shown from the degree of alternative comparison preference between suppliers.

  3. Boosting feature selection for Neural Network based regression.

    Science.gov (United States)

    Bailly, Kevin; Milgram, Maurice

    2009-01-01

    The head pose estimation problem is well known to be a challenging task in computer vision and is a useful tool for several applications involving human-computer interaction. This problem can be stated as a regression one where the input is an image and the output is pan and tilt angles. Finding the optimal regression is a hard problem because of the high dimensionality of the input (number of image pixels) and the large variety of morphologies and illumination. We propose a new method combining a boosting strategy for feature selection and a neural network for the regression. Potential features are a very large set of Haar-like wavelets which are well known to be adapted to face image processing. To achieve the feature selection, a new Fuzzy Functional Criterion (FFC) is introduced which is able to evaluate the link between a feature and the output without any estimation of the joint probability density function as in the Mutual Information. The boosting strategy uses this criterion at each step: features are evaluated by the FFC using weights on examples computed from the error produced by the neural network trained at the previous step. Tests are carried out on the commonly used Pointing 04 database and compared with three state-of-the-art methods. We also evaluate the accuracy of the estimation on FacePix, a database with a high angular resolution. Our method is compared positively to a Convolutional Neural Network, which is well known to incorporate feature extraction in its first layers.

  4. Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism

    Directory of Open Access Journals (Sweden)

    Surya G. Nurzaman

    2014-05-01

    Full Text Available Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics.

  5. Affinity selection of Nipah and Hendra virus-related vaccine candidates from a complex random peptide library displayed on bacteriophage virus-like particles

    Energy Technology Data Exchange (ETDEWEB)

    Peabody, David S.; Chackerian, Bryce; Ashley, Carlee; Carnes, Eric; Negrete, Oscar

    2017-01-24

    The invention relates to virus-like particles of bacteriophage MS2 (MS2 VLPs) displaying peptide epitopes or peptide mimics of epitopes of Nipah Virus envelope glycoprotein that elicit an immune response against Nipah Virus upon vaccination of humans or animals. Affinity selection on Nipah Virus-neutralizing monoclonal antibodies using random sequence peptide libraries on MS2 VLPs selected peptides with sequence similarity to peptide sequences found within the envelope glycoprotein of Nipah itself, thus identifying the epitopes the antibodies recognize. The selected peptide sequences themselves are not necessarily identical in all respects to a sequence within Nipah Virus glycoprotein, and therefore may be referred to as epitope mimics VLPs displaying these epitope mimics can serve as vaccine. On the other hand, display of the corresponding wild-type sequence derived from Nipah Virus and corresponding to the epitope mapped by affinity selection, may also be used as a vaccine.

  6. Landslide Displacement Prediction With Uncertainty Based on Neural Networks With Random Hidden Weights.

    Science.gov (United States)

    Lian, Cheng; Zeng, Zhigang; Yao, Wei; Tang, Huiming; Chen, Chun Lung Philip

    2016-12-01

    In this paper, we propose a new approach to establish a landslide displacement forecasting model based on artificial neural networks (ANNs) with random hidden weights. To quantify the uncertainty associated with the predictions, a framework for probabilistic forecasting of landslide displacement is developed. The aim of this paper is to construct prediction intervals (PIs) instead of deterministic forecasting. A lower-upper bound estimation (LUBE) method is adopted to construct ANN-based PIs, while a new single hidden layer feedforward ANN with random hidden weights for LUBE is proposed. Unlike the original implementation of LUBE, the input weights and hidden biases of the ANN are randomly chosen, and only the output weights need to be adjusted. Combining particle swarm optimization (PSO) and gravitational search algorithm (GSA), a hybrid evolutionary algorithm, PSOGSA, is utilized to optimize the output weights. Furthermore, a new ANN objective function, which combines a modified combinational coverage width-based criterion with one-norm regularization, is proposed. Two benchmark data sets and two real-world landslide data sets are presented to illustrate the capability and merit of our method. Experimental results reveal that the proposed method can construct high-quality PIs.

  7. Peptide based diagnostics: are random-sequence peptides more useful than tiling proteome sequences?

    Science.gov (United States)

    Navalkar, Krupa Arun; Johnston, Stephan Albert; Stafford, Phillip

    2015-02-01

    Diagnostics using peptide ligands have been available for decades. However, their adoption in diagnostics has been limited, not because of poor sensitivity but in many cases due to diminished specificity. Numerous reports suggest that protein-based rather than peptide-based disease detection is more specific. We examined two different approaches to peptide-based diagnostics using Coccidioides (aka Valley Fever) as the disease model. Although the pathogen was discovered more than a century ago, a highly sensitive diagnostic remains unavailable. We present a case study where two different approaches to diagnosing Valley Fever were used: first, overlapping Valley Fever epitopes representing immunodominant Coccidioides antigens were tiled using a microarray format of presynthesized peptides. Second, a set of random sequence peptides identified using a 10,000 peptide immunosignaturing microarray was compared for sensitivity and specificity. The scientific hypothesis tested was that actual epitope peptides from Coccidioides would provide sufficient sensitivity and specificity as a diagnostic. Results demonstrated that random sequence peptides exhibited higher accuracy when classifying different stages of Valley Fever infection vs. epitope peptides. The epitope peptide array did provide better performance than the existing immunodiffusion array, but when directly compared to the random sequence peptides, reported lower overall accuracy. This study suggests that there are competing aspects of antibody recognition that involve conservation of pathogen sequence and aspects of mimotope recognition and amino acid substitutions. These factors may prove critical when developing the next generation of high-performance immunodiagnostics. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Gain in student understanding of the role of random variation in evolution following teaching intervention based on luria-delbruck experiment.

    Science.gov (United States)

    Robson, Rachel L; Burns, Susan

    2011-01-01

    Undergraduate students in introductory biology classes are typically saddled with pre-existing popular beliefs that impede their ability to learn about biological evolution. One of the most common misconceptions about evolution is that the environment causes advantageous mutations, rather than the correct view that mutations occur randomly and the environment only selects for mutants with advantageous traits. In this study, a significant gain in student understanding of the role of randomness in evolution was observed after students participated in an inquiry-based pedagogical intervention based on the Luria-Delbruck experiment. Questionnaires with isomorphic questions regarding environmental selection among random mutants were administered to study participants (N = 82) in five separate sections of a sophomore-level microbiology class before and after the teaching intervention. Demographic data on each participant was also collected, in a way that preserved anonymity. Repeated measures analysis showed that post-test scores were significantly higher than pre-test scores with regard to the questions about evolution (F(1, 77) = 25.913, p evolution had no significant effect on gain in understanding of this concept. This study indicates that conducting and discussing an experiment about phage resistance in E. coli may improve student understanding of the role of stochastic events in evolution more broadly, as post-test answers showed that students were able to apply the lesson of the Luria-Delbruck experiment to other organisms subjected to other kinds of selection.

  9. Recruitment strategies should not be randomly selected: empirically improving recruitment success and diversity in developmental psychology research

    Science.gov (United States)

    Sugden, Nicole A.; Moulson, Margaret C.

    2015-01-01

    Psychological and developmental research have been critiqued for the lack of diversity of research samples. Because differences in culture, race, and ethnicity can influence participant behavior, limited diversity limits the generalizability of the findings. These differences may also impact how participants behave in response to recruitment attempts, which suggests that recruitment itself may be leveraged to increase sample diversity. The goal of the current study was to determine what factors, within a recruitment interaction, could be leveraged to increase success and diversity when recruiting families with children for developmental research. Study 1 found three factors influenced success: (1) recruitment was more successful when other potential participants were also interested (i.e., recruiters were busy), (2) recruiters of particular races were more successful than recruiters of other races, and (3) differences in success were related to what the recruiter said to engage the potential participant (i.e., the script). The latter two factors interacted, suggesting some recruiters were using less optimal scripts. To improve success rates, study 2 randomly assigned scripts to recruiters and encouraged them to recruit more vigorously during busy periods. Study 2 found that two factors influenced success: (1) some scripts were more successful than others and (2) we were more successful at recruiting non-White potential participants than White participants. These two interacted, with some scripts being more successful with White and other scripts being more successful with non-White families. This intervention significantly increased recruitment success rate by 8.1% and the overall number of families recruited by 15.3%. These findings reveal that empirically evaluating and tailoring recruitment efforts based on the most successful strategies is effective in boosting diversity through increased participation of children from non-White families. PMID:25972829

  10. Recruitment strategies should not be randomly selected: empirically improving recruitment success and diversity in developmental psychology research.

    Science.gov (United States)

    Sugden, Nicole A; Moulson, Margaret C

    2015-01-01

    Psychological and developmental research have been critiqued for the lack of diversity of research samples. Because differences in culture, race, and ethnicity can influence participant behavior, limited diversity limits the generalizability of the findings. These differences may also impact how participants behave in response to recruitment attempts, which suggests that recruitment itself may be leveraged to increase sample diversity. The goal of the current study was to determine what factors, within a recruitment interaction, could be leveraged to increase success and diversity when recruiting families with children for developmental research. Study 1 found three factors influenced success: (1) recruitment was more successful when other potential participants were also interested (i.e., recruiters were busy), (2) recruiters of particular races were more successful than recruiters of other races, and (3) differences in success were related to what the recruiter said to engage the potential participant (i.e., the script). The latter two factors interacted, suggesting some recruiters were using less optimal scripts. To improve success rates, study 2 randomly assigned scripts to recruiters and encouraged them to recruit more vigorously during busy periods. Study 2 found that two factors influenced success: (1) some scripts were more successful than others and (2) we were more successful at recruiting non-White potential participants than White participants. These two interacted, with some scripts being more successful with White and other scripts being more successful with non-White families. This intervention significantly increased recruitment success rate by 8.1% and the overall number of families recruited by 15.3%. These findings reveal that empirically evaluating and tailoring recruitment efforts based on the most successful strategies is effective in boosting diversity through increased participation of children from non-White families.

  11. Recruitment strategies shouldn’t be randomly selected: Empirically improving recruitment success and diversity in developmental psychology research

    Directory of Open Access Journals (Sweden)

    Nicole Andrea Sugden

    2015-04-01

    Full Text Available Psychological and developmental research have been critiqued for the lack of diversity of research samples. Because differences in culture, race, and ethnicity can influence participant behavior, limited diversity limits the generalizability of the findings. These differences may also impact how participants behave in response to recruitment attempts, which suggests that recruitment itself may be leveraged to increase sample diversity. The goal of the current study was to determine what factors, within a recruitment interaction, could be leveraged to increase success and diversity when recruiting families with children for developmental research. Study 1 found three factors influenced success: 1 recruitment was more successful when other potential participants were also interested (i.e., recruiters were busy, 2 recruiters of particular races were more successful than recruiters of other races, and 3 differences in success were related to what the recruiter said to engage the potential participant (i.e., the script. The latter two factors interacted, suggesting some recruiters were using less optimal scripts. To improve success rates, study 2 randomly assigned scripts to recruiters and encouraged them to recruit more vigorously during busy periods. Study 2 found that two factors influenced success: 1 some scripts were more successful than others and 2 we were more successful at recruiting non-White potential participants than White participants. These two interacted, with some scripts being more successful with White and other scripts being more successful with non-White families. This intervention significantly increased recruitment success rate by 8.1% and the overall number of families recruited by 15.3%. These findings reveal that empirically evaluating and tailoring recruitment efforts based on the most successful strategies is effective in boosting diversity through increased participation of children from non-White families.

  12. Automated confidence ranked classification of randomized controlled trial articles: an aid to evidence-based medicine

    Science.gov (United States)

    Smalheiser, Neil R; McDonagh, Marian S; Yu, Clement; Adams, Clive E; Davis, John M; Yu, Philip S

    2015-01-01

    Objective: For many literature review tasks, including systematic review (SR) and other aspects of evidence-based medicine, it is important to know whether an article describes a randomized controlled trial (RCT). Current manual annotation is not complete or flexible enough for the SR process. In this work, highly accurate machine learning predictive models were built that include confidence predictions of whether an article is an RCT. Materials and Methods: The LibSVM classifier was used with forward selection of potential feature sets on a large human-related subset of MEDLINE to create a classification model requiring only the citation, abstract, and MeSH terms for each article. Results: The model achieved an area under the receiver operating characteristic curve of 0.973 and mean squared error of 0.013 on the held out year 2011 data. Accurate confidence estimates were confirmed on a manually reviewed set of test articles. A second model not requiring MeSH terms was also created, and performs almost as well. Discussion: Both models accurately rank and predict article RCT confidence. Using the model and the manually reviewed samples, it is estimated that about 8000 (3%) additional RCTs can be identified in MEDLINE, and that 5% of articles tagged as RCTs in Medline may not be identified. Conclusion: Retagging human-related studies with a continuously valued RCT confidence is potentially more useful for article ranking and review than a simple yes/no prediction. The automated RCT tagging tool should offer significant savings of time and effort during the process of writing SRs, and is a key component of a multistep text mining pipeline that we are building to streamline SR workflow. In addition, the model may be useful for identifying errors in MEDLINE publication types. The RCT confidence predictions described here have been made available to users as a web service with a user query form front end at: http://arrowsmith.psych

  13. Towards improving compound selection in structure-based virtual screening.

    Science.gov (United States)

    Waszkowycz, Bohdan

    2008-03-01

    Structure-based virtual screening is now an established technology for supporting hit finding and lead optimisation in drug discovery. Recent validation studies have highlighted the poor performance of currently used scoring functions in estimating binding affinity and hence in ranking large datasets of docked ligands. Progress in the analysis of large datasets can be made through the use of appropriate data mining techniques and the derivation of a broader range of descriptors relevant to receptor-ligand binding. In addition, simple scoring functions can be supplemented by simulation-based scoring protocols. Developments in workflow design allow the automation of repetitive tasks, and also encourage the routine use of simulation-based methods and the rapid prototyping of novel modelling and analysis procedures.

  14. An efficient ERP-based brain-computer interface using random set presentation and face familiarity.

    Directory of Open Access Journals (Sweden)

    Seul-Ki Yeom

    Full Text Available Event-related potential (ERP-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC-based paradigm with our approach that combines a random set presentation paradigm with (non- self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup.

  15. Risk-based audit selection of dairy farms

    NARCIS (Netherlands)

    Asseldonk, van M.A.P.M.; Velthuis, A.G.J.

    2014-01-01

    Dairy farms are audited in the Netherlands on numerous process standards. Each farm is audited once every 2 years. Increasing demands for cost-effectiveness in farm audits can be met by introducing risk-based principles. This implies targeting subpopulations with a higher risk of poor process

  16. Evaluation Of Selected Commercial Banks Financing Of Agro Based ...

    African Journals Online (AJOL)

    ... their activities and form cooperatives for easy access to bank funding. Banks should have effective and efficient loan monitoring and supervision departments to prevent loan diversion and default. Keywords: Commercial Banks, Financing, Agro-Based Enterprises International Journal of Agriculture and Development Vol.

  17. DFT-based inhibitor and promoter selection criteria for pentagonal ...

    Indian Academy of Sciences (India)

    Methane hydrate formation inhibition by methanol and ethylene glycol as well as methane hydrate stabilization by cyclopentane and tetrahydrofuran are critically analysed based on the interaction energy, free energy change, dipole moment and infrared frequency calculation. Calculation of free energy change for formation ...

  18. Exposure level from selected base station tower around Kuala Nerus

    African Journals Online (AJOL)

    ... of the tower, tilt and direction of the antennas fixed on the top of the tower, the number of antennas on single tower, the type of radiation pattern, the direction of main beam of radiation, the feeding power and the operating frequency. Keywords: base station tower; exposure level; radiofrequency; electromagnetic radiation ...

  19. Natural ingredients based cosmetics. Content of selected fragrance sensitizers

    DEFF Research Database (Denmark)

    Rastogi, Suresh Chandra; Johansen, J D; Menné, T

    1996-01-01

    -hexylcinnamic aldehyde. The study revealed that the 91% (20/22) of the natural ingredients based perfumes contained 0.027%-7.706% of 1 to 7 of the target fragrances. Between 1 and 5 of the chemically defined synthetic constituents of fragrance mix were found in 82% (18/22) of the perfumes. 35% (7/20) of the other...

  20. Development of Base Transceiver Station Selection Algorithm for ...

    African Journals Online (AJOL)

    Placement of base transceiver station (BTSs) by different operators on a particular site as collocation site, so as to save cost and reduce the number of people who are at risk of radiation in BTSs located places as compared to each operator having different BTSs is the new trend in Nigeria telecommunication industries ...