Curvelet-domain multiple matching method combined with cubic B-spline function

Science.gov (United States)

Wang, Tong; Wang, Deli; Tian, Mi; Hu, Bin; Liu, Chengming

2018-05-01

Since the large amount of surface-related multiple existed in the marine data would influence the results of data processing and interpretation seriously, many researchers had attempted to develop effective methods to remove them. The most successful surface-related multiple elimination method was proposed based on data-driven theory. However, the elimination effect was unsatisfactory due to the existence of amplitude and phase errors. Although the subsequent curvelet-domain multiple-primary separation method achieved better results, poor computational efficiency prevented its application. In this paper, we adopt the cubic B-spline function to improve the traditional curvelet multiple matching method. First, select a little number of unknowns as the basis points of the matching coefficient; second, apply the cubic B-spline function on these basis points to reconstruct the matching array; third, build constraint solving equation based on the relationships of predicted multiple, matching coefficients, and actual data; finally, use the BFGS algorithm to iterate and realize the fast-solving sparse constraint of multiple matching algorithm. Moreover, the soft-threshold method is used to make the method perform better. With the cubic B-spline function, the differences between predicted multiple and original data diminish, which results in less processing time to obtain optimal solutions and fewer iterative loops in the solving procedure based on the L1 norm constraint. The applications to synthetic and field-derived data both validate the practicability and validity of the method.

Exploration of machine learning techniques in predicting multiple sclerosis disease course

OpenAIRE

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

2017-01-01

Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 1693 CLIMB study patients were classified as increased EDSS?1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up. Results Baseline data...

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

Science.gov (United States)

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

Development of a regional ensemble prediction method for probabilistic weather prediction

International Nuclear Information System (INIS)

Nohara, Daisuke; Tamura, Hidetoshi; Hirakuchi, Hiromaru

2015-01-01

A regional ensemble prediction method has been developed to provide probabilistic weather prediction using a numerical weather prediction model. To obtain consistent perturbations with the synoptic weather pattern, both of initial and lateral boundary perturbations were given by differences between control and ensemble member of the Japan Meteorological Agency (JMA)'s operational one-week ensemble forecast. The method provides a multiple ensemble member with a horizontal resolution of 15 km for 48-hour based on a downscaling of the JMA's operational global forecast accompanied with the perturbations. The ensemble prediction was examined in the case of heavy snow fall event in Kanto area on January 14, 2013. The results showed that the predictions represent different features of high-resolution spatiotemporal distribution of precipitation affected by intensity and location of extra-tropical cyclone in each ensemble member. Although the ensemble prediction has model bias of mean values and variances in some variables such as wind speed and solar radiation, the ensemble prediction has a potential to append a probabilistic information to a deterministic prediction. (author)

Predicting Harmonic Distortion of Multiple Converters in a Power System

Directory of Open Access Journals (Sweden)

P. M. Ivry

2017-01-01

Full Text Available Various uncertainties arise in the operation and management of power systems containing Renewable Energy Sources (RES that affect the systems power quality. These uncertainties may arise due to system parameter changes or design parameter choice. In this work, the impact of uncertainties on the prediction of harmonics in a power system containing multiple Voltage Source Converters (VSCs is investigated. The study focuses on the prediction of harmonic distortion level in multiple VSCs when some system or design parameters are only known within certain constraints. The Univariate Dimension Reduction (UDR method was utilized in this study as an efficient predictive tool for the level of harmonic distortion of the VSCs measured at the Point of Common Coupling (PCC to the grid. Two case studies were considered and the UDR technique was also experimentally validated. The obtained results were compared with that of the Monte Carlo Simulation (MCS results.

A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction

International Nuclear Information System (INIS)

Wu, Xuedong; Zhu, Zhiyu; Su, Xunliang; Fan, Shaosheng; Du, Zhaoping; Chang, Yanchao; Zeng, Qingjun

2015-01-01

Wind speed prediction is one important methods to guarantee the wind energy integrated into the whole power system smoothly. However, wind power has a non–schedulable nature due to the strong stochastic nature and dynamic uncertainty nature of wind speed. Therefore, wind speed prediction is an indispensable requirement for power system operators. Two new approaches for hourly wind speed prediction are developed in this study by integrating the single multiplicative neuron model and the iterated nonlinear filters for updating the wind speed sequence accurately. In the presented methods, a nonlinear state–space model is first formed based on the single multiplicative neuron model and then the iterated nonlinear filters are employed to perform dynamic state estimation on wind speed sequence with stochastic uncertainty. The suggested approaches are demonstrated using three cases wind speed data and are compared with autoregressive moving average, artificial neural network, kernel ridge regression based residual active learning and single multiplicative neuron model methods. Three types of prediction errors, mean absolute error improvement ratio and running time are employed for different models’ performance comparison. Comparison results from Tables 1–3 indicate that the presented strategies have much better performance for hourly wind speed prediction than other technologies. - Highlights: • Developed two novel hybrid modeling methods for hourly wind speed prediction. • Uncertainty and fluctuations of wind speed can be better explained by novel methods. • Proposed strategies have online adaptive learning ability. • Proposed approaches have shown better performance compared with existed approaches. • Comparison and analysis of two proposed novel models for three cases are provided

An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions

Directory of Open Access Journals (Sweden)

Barash Danny

2008-04-01

Full Text Available Abstract Background RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure. Results Using RNAsubopt, the suboptimal solutions for a given wild-type sequence are calculated once. Then, specific mutations are selected that are most likely to cause a conformational rearrangement. For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3, for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations. Conclusion A highly efficient addition to RNAmute that is as user friendly as the original application but that facilitates the practical analysis of multiple-point mutations is presented. Such an extension can now be exploited prior to site-directed mutagenesis experiments by virologists, for example, who investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary

Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

Science.gov (United States)

Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

2004-03-05

Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

Burst Pressure Prediction of Multiple Cracks in Pipelines

International Nuclear Information System (INIS)

Razak, N A; Alang, N A; Murad, M A

2013-01-01

Available industrial code such as ASME B1G, modified ASME B1G and DNV RP-F101 to assess pipeline defects appear more conservative for multiple crack like- defects than single crack-like defects. Thus, this paper presents burst pressure prediction of pipe with multiple cracks like defects. A finite element model was developed and the burst pressure prediction was compared with the available code. The model was used to investigate the effect of the distance between the cracks and the crack length. The coalescence diagram was also developed to evaluate the burst pressure of the multiple cracks. It was found as the distance between crack increases, the interaction effect comes to fade away and multiple cracks behave like two independent single cracks

Power capability prediction for lithium-ion batteries based on multiple constraints analysis

International Nuclear Information System (INIS)

Pan, Rui; Wang, Yujie; Zhang, Xu; Yang, Duo; Chen, Zonghai

2017-01-01

Highlights: • Multiple constraints for peak power capability prediction are deeply analyzed. • Multi-limited method is proposed for the peak power capability prediction of LIBs. • The EKF is used for the model based peak power capability prediction. • The FUDS and UDDS profiles are executed to evaluate the proposed method. - Abstract: The power capability of the lithium-ion battery is a key performance indicator for electric vehicle, and it is intimately correlated with the acceleration, regenerative braking and gradient climbing power requirements. Therefore, an accurate power capability or state-of-power prediction is critical to a battery management system, which can help the battery to work in suitable area and prevent the battery from over-charging and over-discharging. However, the power capability is easily affected by dynamic load, voltage variation and temperature. In this paper, three different constraints in power capability prediction are introduced, and the advantages and disadvantages of the three methods are deeply analyzed. Furthermore, a multi-limited approach for the power capability prediction is proposed, which can overcome the drawbacks of the three methods. Subsequently, the extended Kalman filter algorithm is employed for model based state-of-power prediction. In order to verify the proposed method, diverse experiments are executed to explore the efficiency, robustness, and precision. The results indicate that the proposed method can improve the precision and robustness obviously.

Motor degradation prediction methods

Energy Technology Data Exchange (ETDEWEB)

Arnold, J.R.; Kelly, J.F.; Delzingaro, M.J.

1996-12-01

Motor Operated Valve (MOV) squirrel cage AC motor rotors are susceptible to degradation under certain conditions. Premature failure can result due to high humidity/temperature environments, high running load conditions, extended periods at locked rotor conditions (i.e. > 15 seconds) or exceeding the motor`s duty cycle by frequent starts or multiple valve stroking. Exposure to high heat and moisture due to packing leaks, pressure seal ring leakage or other causes can significantly accelerate the degradation. ComEd and Liberty Technologies have worked together to provide and validate a non-intrusive method using motor power diagnostics to evaluate MOV rotor condition and predict failure. These techniques have provided a quick, low radiation dose method to evaluate inaccessible motors, identify degradation and allow scheduled replacement of motors prior to catastrophic failures.

Motor degradation prediction methods

International Nuclear Information System (INIS)

Arnold, J.R.; Kelly, J.F.; Delzingaro, M.J.

1996-01-01

Motor Operated Valve (MOV) squirrel cage AC motor rotors are susceptible to degradation under certain conditions. Premature failure can result due to high humidity/temperature environments, high running load conditions, extended periods at locked rotor conditions (i.e. > 15 seconds) or exceeding the motor's duty cycle by frequent starts or multiple valve stroking. Exposure to high heat and moisture due to packing leaks, pressure seal ring leakage or other causes can significantly accelerate the degradation. ComEd and Liberty Technologies have worked together to provide and validate a non-intrusive method using motor power diagnostics to evaluate MOV rotor condition and predict failure. These techniques have provided a quick, low radiation dose method to evaluate inaccessible motors, identify degradation and allow scheduled replacement of motors prior to catastrophic failures

MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing

DEFF Research Database (Denmark)

Lindgreen, Stinus; Gardner, Paul P; Krogh, Anders

2007-01-01

function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency. AVAILABILITY: Source code available from http://mastr.binf.ku.dk/......MOTIVATION: As more non-coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore......, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction. RESULT: We present a novel solution to the problem of simultaneous structure prediction...

Effect of reheating on predictions following multiple-field inflation

Science.gov (United States)

Hotinli, Selim C.; Frazer, Jonathan; Jaffe, Andrew H.; Meyers, Joel; Price, Layne C.; Tarrant, Ewan R. M.

2018-01-01

We study the sensitivity of cosmological observables to the reheating phase following inflation driven by many scalar fields. We describe a method which allows semianalytic treatment of the impact of perturbative reheating on cosmological perturbations using the sudden decay approximation. Focusing on N -quadratic inflation, we show how the scalar spectral index and tensor-to-scalar ratio are affected by the rates at which the scalar fields decay into radiation. We find that for certain choices of decay rates, reheating following multiple-field inflation can have a significant impact on the prediction of cosmological observables.

Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model.

Science.gov (United States)

Aridhi, Sabeur; Sghaier, Haïtham; Zoghlami, Manel; Maddouri, Mondher; Nguifo, Engelbert Mephu

2016-01-01

Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype-phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.

EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to Improve Prediction Accuracy.

Science.gov (United States)

Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor; Irie, Hanna; Zhang, Bin

2018-04-24

Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. We first designed an expression weighted cosine method (EWCos) to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed EMUDRA (Ensemble of Multiple Drug Repositioning Approaches) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. The EMUDRA R package is available at doi:10.7303/syn11510888. bin.zhang@mssm.edu or zhangb@hotmail.com. Supplementary data are available at Bioinformatics online.

Prediction With Dimension Reduction of Multiple Molecular Data Sources for Patient Survival

Directory of Open Access Journals (Sweden)

Adam Kaplan

2017-07-01

Full Text Available Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal component analysis (PCA. However, the application of PCA is not straightforward for multisource data, wherein multiple sources of ‘omics data measure different but related biological components. In this article, we use recent advances in the dimension reduction of multisource data for predictive modeling. In particular, we apply exploratory results from Joint and Individual Variation Explained (JIVE, an extension of PCA for multisource data, for prediction of differing response types. We conduct illustrative simulations to illustrate the practical advantages and interpretability of our approach. As an application example, we consider predicting survival for patients with glioblastoma multiforme from 3 data sources measuring messenger RNA expression, microRNA expression, and DNA methylation. We also introduce a method to estimate JIVE scores for new samples that were not used in the initial dimension reduction and study its theoretical properties; this method is implemented in the R package R.JIVE on CRAN, in the function jive.predict.

Vitamin D Levels Predict Multiple Sclerosis Progression

Science.gov (United States)

... Research Matters NIH Research Matters February 3, 2014 Vitamin D Levels Predict Multiple Sclerosis Progression Among people ... sclerosis (MS), those with higher blood levels of vitamin D had better outcomes during 5 years of ...

Multiple-point statistical prediction on fracture networks at Yucca Mountain

International Nuclear Information System (INIS)

Liu, X.Y; Zhang, C.Y.; Liu, Q.S.; Birkholzer, J.T.

2009-01-01

In many underground nuclear waste repository systems, such as at Yucca Mountain, water flow rate and amount of water seepage into the waste emplacement drifts are mainly determined by hydrological properties of fracture network in the surrounding rock mass. Natural fracture network system is not easy to describe, especially with respect to its connectivity which is critically important for simulating the water flow field. In this paper, we introduced a new method for fracture network description and prediction, termed multi-point-statistics (MPS). The process of the MPS method is to record multiple-point statistics concerning the connectivity patterns of a fracture network from a known fracture map, and to reproduce multiple-scale training fracture patterns in a stochastic manner, implicitly and directly. It is applied to fracture data to study flow field behavior at the Yucca Mountain waste repository system. First, the MPS method is used to create a fracture network with an original fracture training image from Yucca Mountain dataset. After we adopt a harmonic and arithmetic average method to upscale the permeability to a coarse grid, THM simulation is carried out to study near-field water flow in the surrounding waste emplacement drifts. Our study shows that connectivity or patterns of fracture networks can be grasped and reconstructed by MPS methods. In theory, it will lead to better prediction of fracture system characteristics and flow behavior. Meanwhile, we can obtain variance from flow field, which gives us a way to quantify model uncertainty even in complicated coupled THM simulations. It indicates that MPS can potentially characterize and reconstruct natural fracture networks in a fractured rock mass with advantages of quantifying connectivity of fracture system and its simulation uncertainty simultaneously.

Label-free morphology-based prediction of multiple differentiation potentials of human mesenchymal stem cells for early evaluation of intact cells.

Directory of Open Access Journals (Sweden)

Hiroto Sasaki

Full Text Available Precise quantification of cellular potential of stem cells, such as human bone marrow-derived mesenchymal stem cells (hBMSCs, is important for achieving stable and effective outcomes in clinical stem cell therapy. Here, we report a method for image-based prediction of the multiple differentiation potentials of hBMSCs. This method has four major advantages: (1 the cells used for potential prediction are fully intact, and therefore directly usable for clinical applications; (2 predictions of potentials are generated before differentiation cultures are initiated; (3 prediction of multiple potentials can be provided simultaneously for each sample; and (4 predictions of potentials yield quantitative values that correlate strongly with the experimental data. Our results show that the collapse of hBMSC differentiation potentials, triggered by in vitro expansion, can be quantitatively predicted far in advance by predicting multiple potentials, multi-lineage differentiation potentials (osteogenic, adipogenic, and chondrogenic and population doubling potential using morphological features apparent during the first 4 days of expansion culture. In order to understand how such morphological features can be effective for advance predictions, we measured gene-expression profiles of the same early undifferentiated cells. Both senescence-related genes (p16 and p21 and cytoskeleton-related genes (PTK2, CD146, and CD49 already correlated to the decrease of potentials at this stage. To objectively compare the performance of morphology and gene expression for such early prediction, we tested a range of models using various combinations of features. Such comparison of predictive performances revealed that morphological features performed better overall than gene-expression profiles, balancing the predictive accuracy with the effort required for model construction. This benchmark list of various prediction models not only identifies the best morphological feature

Prediction of beta-turns and beta-turn types by a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN).

Science.gov (United States)

Kirschner, Andreas; Frishman, Dmitrij

2008-10-01

Prediction of beta-turns from amino acid sequences has long been recognized as an important problem in structural bioinformatics due to their frequent occurrence as well as their structural and functional significance. Because various structural features of proteins are intercorrelated, secondary structure information has been often employed as an additional input for machine learning algorithms while predicting beta-turns. Here we present a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN) capable of predicting multiple mutually dependent structural motifs and demonstrate its efficiency in recognizing three aspects of protein structure: beta-turns, beta-turn types, and secondary structure. The advantage of our method compared to other predictors is that it does not require any external input except for sequence profiles because interdependencies between different structural features are taken into account implicitly during the learning process. In a sevenfold cross-validation experiment on a standard test dataset our method exhibits the total prediction accuracy of 77.9% and the Mathew's Correlation Coefficient of 0.45, the highest performance reported so far. It also outperforms other known methods in delineating individual turn types. We demonstrate how simultaneous prediction of multiple targets influences prediction performance on single targets. The MOLEBRNN presented here is a generic method applicable in a variety of research fields where multiple mutually depending target classes need to be predicted. http://webclu.bio.wzw.tum.de/predator-web/.

Predicting Speech Intelligibility with a Multiple Speech Subsystems Approach in Children with Cerebral Palsy

Science.gov (United States)

Lee, Jimin; Hustad, Katherine C.; Weismer, Gary

2014-01-01

Purpose: Speech acoustic characteristics of children with cerebral palsy (CP) were examined with a multiple speech subsystems approach; speech intelligibility was evaluated using a prediction model in which acoustic measures were selected to represent three speech subsystems. Method: Nine acoustic variables reflecting different subsystems, and…

Comparison of Methods to Trace Multiple Subskills: Is LR-DBN Best?

Science.gov (United States)

Xu, Yanbo; Mostow, Jack

2012-01-01

A long-standing challenge for knowledge tracing is how to update estimates of multiple subskills that underlie a single observable step. We characterize approaches to this problem by how they model knowledge tracing, fit its parameters, predict performance, and update subskill estimates. Previous methods allocated blame or credit among subskills…

Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions

DEFF Research Database (Denmark)

Seemann, Ernst Stefan; Richter, Andreas S.; Gorodkin, Jan

2010-01-01

of that used for individual multiple alignments. Results: We derived a rather extensive algorithm. One of the advantages of our approach (in contrast to other RNARNA interaction prediction methods) is the application of covariance detection and prediction of pseudoknots between intra- and inter-molecular base...... pairs. As a proof of concept, we show an example and discuss the strengths and weaknesses of the approach....

Brain atrophy and lesion load predict long term disability in multiple sclerosis

DEFF Research Database (Denmark)

Popescu, Veronica; Agosta, Federica; Hulst, Hanneke E

2013-01-01

To determine whether brain atrophy and lesion volumes predict subsequent 10 year clinical evolution in multiple sclerosis (MS).......To determine whether brain atrophy and lesion volumes predict subsequent 10 year clinical evolution in multiple sclerosis (MS)....

Prediction of Protein–Protein Interactions by Evidence Combining Methods

Directory of Open Access Journals (Sweden)

Ji-Wei Chang

2016-11-01

Full Text Available Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.

Science.gov (United States)

Chen, Ruoying; Zhang, Zhiwang; Wu, Di; Zhang, Peng; Zhang, Xinyang; Wang, Yong; Shi, Yong

2011-01-21

Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids. Copyright © 2010 Elsevier Ltd. All rights reserved.

Prediction of the neutrons subcritical multiplication using the diffusion hybrid equation with external neutron sources

Energy Technology Data Exchange (ETDEWEB)

Costa da Silva, Adilson; Carvalho da Silva, Fernando [COPPE/UFRJ, Programa de Engenharia Nuclear, Caixa Postal 68509, 21941-914, Rio de Janeiro (Brazil); Senra Martinez, Aquilino, E-mail: aquilino@lmp.ufrj.br [COPPE/UFRJ, Programa de Engenharia Nuclear, Caixa Postal 68509, 21941-914, Rio de Janeiro (Brazil)

2011-07-15

Highlights: > We proposed a new neutron diffusion hybrid equation with external neutron source. > A coarse mesh finite difference method for the adjoint flux and reactivity calculation was developed. > 1/M curve to predict the criticality condition is used. - Abstract: We used the neutron diffusion hybrid equation, in cartesian geometry with external neutron sources to predict the subcritical multiplication of neutrons in a pressurized water reactor, using a 1/M curve to predict the criticality condition. A Coarse Mesh Finite Difference Method was developed for the adjoint flux calculation and to obtain the reactivity values of the reactor. The results obtained were compared with benchmark values in order to validate the methodology presented in this paper.

Prediction of the neutrons subcritical multiplication using the diffusion hybrid equation with external neutron sources

International Nuclear Information System (INIS)

Costa da Silva, Adilson; Carvalho da Silva, Fernando; Senra Martinez, Aquilino

2011-01-01

Highlights: → We proposed a new neutron diffusion hybrid equation with external neutron source. → A coarse mesh finite difference method for the adjoint flux and reactivity calculation was developed. → 1/M curve to predict the criticality condition is used. - Abstract: We used the neutron diffusion hybrid equation, in cartesian geometry with external neutron sources to predict the subcritical multiplication of neutrons in a pressurized water reactor, using a 1/M curve to predict the criticality condition. A Coarse Mesh Finite Difference Method was developed for the adjoint flux calculation and to obtain the reactivity values of the reactor. The results obtained were compared with benchmark values in order to validate the methodology presented in this paper.

An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

Science.gov (United States)

Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY

OpenAIRE

Chayalakshmi C.L

2018-01-01

MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY ABSTRACT Calculation of boiler efficiency is essential if its parameters need to be controlled for either maintaining or enhancing its efficiency. But determination of boiler efficiency using conventional method is time consuming and very expensive. Hence, it is not recommended to find boiler efficiency frequently. The work presented in this paper deals with establishing the statistical mo...

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

Science.gov (United States)

Xie, Weihong; Yu, Yang

2017-01-01

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. PMID:29124062

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

Directory of Open Access Journals (Sweden)

Fan Liang

2017-01-01

Full Text Available Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

Multiple predictor smoothing methods for sensitivity analysis.

Energy Technology Data Exchange (ETDEWEB)

Helton, Jon Craig; Storlie, Curtis B.

2006-08-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.

Multiple predictor smoothing methods for sensitivity analysis

International Nuclear Information System (INIS)

Helton, Jon Craig; Storlie, Curtis B.

2006-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

Distributed Model Predictive Control over Multiple Groups of Vehicles in Highway Intelligent Space for Large Scale System

Directory of Open Access Journals (Sweden)

Tang Xiaofeng

2014-01-01

Full Text Available The paper presents the three time warning distances for solving the large scale system of multiple groups of vehicles safety driving characteristics towards highway tunnel environment based on distributed model prediction control approach. Generally speaking, the system includes two parts. First, multiple vehicles are divided into multiple groups. Meanwhile, the distributed model predictive control approach is proposed to calculate the information framework of each group. Each group of optimization performance considers the local optimization and the neighboring subgroup of optimization characteristics, which could ensure the global optimization performance. Second, the three time warning distances are studied based on the basic principles used for highway intelligent space (HIS and the information framework concept is proposed according to the multiple groups of vehicles. The math model is built to avoid the chain avoidance of vehicles. The results demonstrate that the proposed highway intelligent space method could effectively ensure driving safety of multiple groups of vehicles under the environment of fog, rain, or snow.

Neutron source multiplication method

International Nuclear Information System (INIS)

Clayton, E.D.

1985-01-01

Extensive use has been made of neutron source multiplication in thousands of measurements of critical masses and configurations and in subcritical neutron-multiplication measurements in situ that provide data for criticality prevention and control in nuclear materials operations. There is continuing interest in developing reliable methods for monitoring the reactivity, or k/sub eff/, of plant operations, but the required measurements are difficult to carry out and interpret on the far subcritical configurations usually encountered. The relationship between neutron multiplication and reactivity is briefly discussed and data presented to illustrate problems associated with the absolute measurement of neutron multiplication and reactivity in subcritical systems. A number of curves of inverse multiplication have been selected from a variety of experiments showing variations observed in multiplication during the course of critical and subcritical experiments where different methods of reactivity addition were used, with different neutron source detector position locations. Concern is raised regarding the meaning and interpretation of k/sub eff/ as might be measured in a far subcritical system because of the modal effects and spectrum differences that exist between the subcritical and critical systems. Because of this, the calculation of k/sub eff/ identical with unity for the critical assembly, although necessary, may not be sufficient to assure safety margins in calculations pertaining to far subcritical systems. Further study is needed on the interpretation and meaning of k/sub eff/ in the far subcritical system

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

Science.gov (United States)

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

Science.gov (United States)

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes. PMID:26294903

Earthquake prediction by Kina Method

International Nuclear Information System (INIS)

Kianoosh, H.; Keypour, H.; Naderzadeh, A.; Motlagh, H.F.

2005-01-01

Earthquake prediction has been one of the earliest desires of the man. Scientists have worked hard to predict earthquakes for a long time. The results of these efforts can generally be divided into two methods of prediction: 1) Statistical Method, and 2) Empirical Method. In the first method, earthquakes are predicted using statistics and probabilities, while the second method utilizes variety of precursors for earthquake prediction. The latter method is time consuming and more costly. However, the result of neither method has fully satisfied the man up to now. In this paper a new method entitled 'Kiana Method' is introduced for earthquake prediction. This method offers more accurate results yet lower cost comparing to other conventional methods. In Kiana method the electrical and magnetic precursors are measured in an area. Then, the time and the magnitude of an earthquake in the future is calculated using electrical, and in particular, electrical capacitors formulas. In this method, by daily measurement of electrical resistance in an area we make clear that the area is capable of earthquake occurrence in the future or not. If the result shows a positive sign, then the occurrence time and the magnitude can be estimated by the measured quantities. This paper explains the procedure and details of this prediction method. (authors)

Predictive ability of machine learning methods for massive crop yield prediction

Directory of Open Access Journals (Sweden)

Alberto Gonzalez-Sanchez

2014-04-01

Full Text Available An important issue for agricultural planning purposes is the accurate yield estimation for the numerous crops involved in the planning. Machine learning (ML is an essential approach for achieving practical and effective solutions for this problem. Many comparisons of ML methods for yield prediction have been made, seeking for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide enough information for agricultural planning purposes. This paper compares the predictive accuracy of ML and linear regression techniques for crop yield prediction in ten crop datasets. Multiple linear regression, M5-Prime regression trees, perceptron multilayer neural networks, support vector regression and k-nearest neighbor methods were ranked. Four accuracy metrics were used to validate the models: the root mean square error (RMS, root relative square error (RRSE, normalized mean absolute error (MAE, and correlation factor (R. Real data of an irrigation zone of Mexico were used for building the models. Models were tested with samples of two consecutive years. The results show that M5-Prime and k-nearest neighbor techniques obtain the lowest average RMSE errors (5.14 and 4.91, the lowest RRSE errors (79.46% and 79.78%, the lowest average MAE errors (18.12% and 19.42%, and the highest average correlation factors (0.41 and 0.42. Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning.

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

Science.gov (United States)

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

Bayesian Methods for Predicting the Shape of Chinese Yam in Terms of Key Diameters

Directory of Open Access Journals (Sweden)

Mitsunori Kayano

2017-01-01

Full Text Available This paper proposes Bayesian methods for the shape estimation of Chinese yam (Dioscorea opposita using a few key diameters of yam. Shape prediction of yam is applicable to determining optimal cutoff positions of a yam for producing seed yams. Our Bayesian method, which is a combination of Bayesian estimation model and predictive model, enables automatic, rapid, and low-cost processing of yam. After the construction of the proposed models using a sample data set in Japan, the models provide whole shape prediction of yam based on only a few key diameters. The Bayesian method performed well on the shape prediction in terms of minimizing the mean squared error between measured shape and the prediction. In particular, a multiple regression method with key diameters at two fixed positions attained the highest performance for shape prediction. We have developed automatic, rapid, and low-cost yam-processing machines based on the Bayesian estimation model and predictive model. Development of such shape prediction approaches, including our Bayesian method, can be a valuable aid in reducing the cost and time in food processing.

Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances

Directory of Open Access Journals (Sweden)

Abut F

2015-08-01

Full Text Available Fatih Abut, Mehmet Fatih AkayDepartment of Computer Engineering, Çukurova University, Adana, TurkeyAbstract: Maximal oxygen uptake (VO2max indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance

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

Directory of Open Access Journals (Sweden)

Yijun Zhao

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

Multiplicity of Plasmodium falciparum infection predicts antimalarial ...

African Journals Online (AJOL)

Background: In areas with intense malaria transmission, individuals are often simultaneously infected with multiple parasite strains. This study assessed the effect of multiple infections on treatment response in Ugandan children with uncomplicated malaria. Methods: Four hundred and seventy six blood specimens were ...

Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances.

Science.gov (United States)

Abut, Fatih; Akay, Mehmet Fatih

2015-01-01

Maximal oxygen uptake (VO2max) indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R) and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance.

Predictive performance models and multiple task performance

Science.gov (United States)

Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

1989-01-01

Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

High accuracy prediction of beta-turns and their types using propensities and multiple alignments.

Science.gov (United States)

Fuchs, Patrick F J; Alix, Alain J P

2005-06-01

We have developed a method that predicts both the presence and the type of beta-turns, using a straightforward approach based on propensities and multiple alignments. The propensities were calculated classically, but the way to use them for prediction was completely new: starting from a tetrapeptide sequence on which one wants to evaluate the presence of a beta-turn, the propensity for a given residue is modified by taking into account all the residues present in the multiple alignment at this position. The evaluation of a score is then done by weighting these propensities by the use of Position-specific score matrices generated by PSI-BLAST. The introduction of secondary structure information predicted by PSIPRED or SSPRO2 as well as taking into account the flanking residues around the tetrapeptide improved the accuracy greatly. This latter evaluated on a database of 426 reference proteins (previously used on other studies) by a sevenfold crossvalidation gave very good results with a Matthews Correlation Coefficient (MCC) of 0.42 and an overall prediction accuracy of 74.8%; this places our method among the best ones. A jackknife test was also done, which gave results within the same range. This shows that it is possible to reach neural networks accuracy with considerably less computional cost and complexity. Furthermore, propensities remain excellent descriptors of amino acid tendencies to belong to beta-turns, which can be useful for peptide or protein engineering and design. For beta-turn type prediction, we reached the best accuracy ever published in terms of MCC (except for the irregular type IV) in the range of 0.25-0.30 for types I, II, and I' and 0.13-0.15 for types VIII, II', and IV. To our knowledge, our method is the only one available on the Web that predicts types I' and II'. The accuracy evaluated on two larger databases of 547 and 823 proteins was not improved significantly. All of this was implemented into a Web server called COUDES (French acronym

A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier

Directory of Open Access Journals (Sweden)

Yanzhen Zhou

2016-09-01

Full Text Available Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, different ensemble classifiers with different response times are built to construct different layers of the proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems.

Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method

Science.gov (United States)

Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty

2017-03-01

Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.

A deep learning-based multi-model ensemble method for cancer prediction.

Science.gov (United States)

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Multiple Crack Growth Prediction in AA2024-T3 Friction Stir Welded Joints, Including Manufacturing Effects

DEFF Research Database (Denmark)

Carlone, Pierpaolo; Citarella, Roberto; Sonne, Mads Rostgaard

2016-01-01

A great deal of attention is currently paid by several industries toward the friction stir welding process to realize lightweight structures. Within this aim, the realistic prediction of fatigue behavior of welded assemblies is a key factor. In this work an integrated finite element method - dual...... boundary element method (FEM-DBEM) procedure, coupling the welding process simulation to the subsequent crack growth assessment, is proposed and applied to simulate multiple crack propagation, with allowance for manufacturing effects. The friction stir butt welding process of the precipitation hardened AA...... on a notched specimen. The whole procedure was finally tested comparing simulation outcomes with experimental data. The good agreement obtained highlights the predictive capability of the method. The influence of the residual stress distribution on crack growth and the mutual interaction between propagating...

On multiple level-set regularization methods for inverse problems

International Nuclear Information System (INIS)

DeCezaro, A; Leitão, A; Tai, X-C

2009-01-01

We analyze a multiple level-set method for solving inverse problems with piecewise constant solutions. This method corresponds to an iterated Tikhonov method for a particular Tikhonov functional G α based on TV–H 1 penalization. We define generalized minimizers for our Tikhonov functional and establish an existence result. Moreover, we prove convergence and stability results of the proposed Tikhonov method. A multiple level-set algorithm is derived from the first-order optimality conditions for the Tikhonov functional G α , similarly as the iterated Tikhonov method. The proposed multiple level-set method is tested on an inverse potential problem. Numerical experiments show that the method is able to recover multiple objects as well as multiple contrast levels

Nonlinear-drifted Brownian motion with multiple hidden states for remaining useful life prediction of rechargeable batteries

Science.gov (United States)

Wang, Dong; Zhao, Yang; Yang, Fangfang; Tsui, Kwok-Leung

2017-09-01

Brownian motion with adaptive drift has attracted much attention in prognostics because its first hitting time is highly relevant to remaining useful life prediction and it follows the inverse Gaussian distribution. Besides linear degradation modeling, nonlinear-drifted Brownian motion has been developed to model nonlinear degradation. Moreover, the first hitting time distribution of the nonlinear-drifted Brownian motion has been approximated by time-space transformation. In the previous studies, the drift coefficient is the only hidden state used in state space modeling of the nonlinear-drifted Brownian motion. Besides the drift coefficient, parameters of a nonlinear function used in the nonlinear-drifted Brownian motion should be treated as additional hidden states of state space modeling to make the nonlinear-drifted Brownian motion more flexible. In this paper, a prognostic method based on nonlinear-drifted Brownian motion with multiple hidden states is proposed and then it is applied to predict remaining useful life of rechargeable batteries. 26 sets of rechargeable battery degradation samples are analyzed to validate the effectiveness of the proposed prognostic method. Moreover, some comparisons with a standard particle filter based prognostic method, a spherical cubature particle filter based prognostic method and two classic Bayesian prognostic methods are conducted to highlight the superiority of the proposed prognostic method. Results show that the proposed prognostic method has lower average prediction errors than the particle filter based prognostic methods and the classic Bayesian prognostic methods for battery remaining useful life prediction.

Prediction of human core body temperature using non-invasive measurement methods.

Science.gov (United States)

Niedermann, Reto; Wyss, Eva; Annaheim, Simon; Psikuta, Agnes; Davey, Sarah; Rossi, René Michel

2014-01-01

The measurement of core body temperature is an efficient method for monitoring heat stress amongst workers in hot conditions. However, invasive measurement of core body temperature (e.g. rectal, intestinal, oesophageal temperature) is impractical for such applications. Therefore, the aim of this study was to define relevant non-invasive measures to predict core body temperature under various conditions. We conducted two human subject studies with different experimental protocols, different environmental temperatures (10 °C, 30 °C) and different subjects. In both studies the same non-invasive measurement methods (skin temperature, skin heat flux, heart rate) were applied. A principle component analysis was conducted to extract independent factors, which were then used in a linear regression model. We identified six parameters (three skin temperatures, two skin heat fluxes and heart rate), which were included for the calculation of two factors. The predictive value of these factors for core body temperature was evaluated by a multiple regression analysis. The calculated root mean square deviation (rmsd) was in the range from 0.28 °C to 0.34 °C for all environmental conditions. These errors are similar to previous models using non-invasive measures to predict core body temperature. The results from this study illustrate that multiple physiological parameters (e.g. skin temperature and skin heat fluxes) are needed to predict core body temperature. In addition, the physiological measurements chosen in this study and the algorithm defined in this work are potentially applicable as real-time core body temperature monitoring to assess health risk in broad range of working conditions.

Multiple attenuation to reflection seismic data using Radon filter and Wave Equation Multiple Rejection (WEMR) method

Energy Technology Data Exchange (ETDEWEB)

Erlangga, Mokhammad Puput [Geophysical Engineering, Institut Teknologi Bandung, Ganesha Street no.10 Basic Science B Buliding fl.2-3 Bandung, 40132, West Java Indonesia puput.erlangga@gmail.com (Indonesia)

2015-04-16

Separation between signal and noise, incoherent or coherent, is important in seismic data processing. Although we have processed the seismic data, the coherent noise is still mixing with the primary signal. Multiple reflections are a kind of coherent noise. In this research, we processed seismic data to attenuate multiple reflections in the both synthetic and real seismic data of Mentawai. There are several methods to attenuate multiple reflection, one of them is Radon filter method that discriminates between primary reflection and multiple reflection in the τ-p domain based on move out difference between primary reflection and multiple reflection. However, in case where the move out difference is too small, the Radon filter method is not enough to attenuate the multiple reflections. The Radon filter also produces the artifacts on the gathers data. Except the Radon filter method, we also use the Wave Equation Multiple Elimination (WEMR) method to attenuate the long period multiple reflection. The WEMR method can attenuate the long period multiple reflection based on wave equation inversion. Refer to the inversion of wave equation and the magnitude of the seismic wave amplitude that observed on the free surface, we get the water bottom reflectivity which is used to eliminate the multiple reflections. The WEMR method does not depend on the move out difference to attenuate the long period multiple reflection. Therefore, the WEMR method can be applied to the seismic data which has small move out difference as the Mentawai seismic data. The small move out difference on the Mentawai seismic data is caused by the restrictiveness of far offset, which is only 705 meter. We compared the real free multiple stacking data after processing with Radon filter and WEMR process. The conclusion is the WEMR method can more attenuate the long period multiple reflection than the Radon filter method on the real (Mentawai) seismic data.

DDR: Efficient computational method to predict drug–target interactions using graph mining and machine learning approaches

KAUST Repository

Olayan, Rawan S.

2017-11-23

Motivation Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. Results We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using five repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new, and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs.

Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

Directory of Open Access Journals (Sweden)

Avval Zhila Mohajeri

2015-01-01

Full Text Available This paper deals with developing a linear quantitative structure-activity relationship (QSAR model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-α] indole, diazepino [1,2-α] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR technique combined with the stepwise (SW and the genetic algorithm (GA methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.

A multiple model approach to respiratory motion prediction for real-time IGRT

International Nuclear Information System (INIS)

Putra, Devi; Haas, Olivier C L; Burnham, Keith J; Mills, John A

2008-01-01

Respiration induces significant movement of tumours in the vicinity of thoracic and abdominal structures. Real-time image-guided radiotherapy (IGRT) aims to adapt radiation delivery to tumour motion during irradiation. One of the main problems for achieving this objective is the presence of time lag between the acquisition of tumour position and the radiation delivery. Such time lag causes significant beam positioning errors and affects the dose coverage. A method to solve this problem is to employ an algorithm that is able to predict future tumour positions from available tumour position measurements. This paper presents a multiple model approach to respiratory-induced tumour motion prediction using the interacting multiple model (IMM) filter. A combination of two models, constant velocity (CV) and constant acceleration (CA), is used to capture respiratory-induced tumour motion. A Kalman filter is designed for each of the local models and the IMM filter is applied to combine the predictions of these Kalman filters for obtaining the predicted tumour position. The IMM filter, likewise the Kalman filter, is a recursive algorithm that is suitable for real-time applications. In addition, this paper proposes a confidence interval (CI) criterion to evaluate the performance of tumour motion prediction algorithms for IGRT. The proposed CI criterion provides a relevant measure for the prediction performance in terms of clinical applications and can be used to specify the margin to accommodate prediction errors. The prediction performance of the IMM filter has been evaluated using 110 traces of 4-minute free-breathing motion collected from 24 lung-cancer patients. The simulation study was carried out for prediction time 0.1-0.6 s with sampling rates 3, 5 and 10 Hz. It was found that the prediction of the IMM filter was consistently better than the prediction of the Kalman filter with the CV or CA model. There was no significant difference of prediction errors for the

A consensus successive projections algorithm--multiple linear regression method for analyzing near infrared spectra.

Science.gov (United States)

Liu, Ke; Chen, Xiaojing; Li, Limin; Chen, Huiling; Ruan, Xiukai; Liu, Wenbin

2015-02-09

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named "consensus SPA-MLR" (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques. Copyright © 2014 Elsevier B.V. All rights reserved.

In Silico Perspectives on the Prediction of the PLP's Epitopes involved in Multiple Sclerosis.

Science.gov (United States)

Zamanzadeh, Zahra; Ataei, Mitra; Nabavi, Seyed Massood; Ahangari, Ghasem; Sadeghi, Mehdi; Sanati, Mohammad Hosein

2017-03-01

Multiple sclerosis (MS) is the most common autoimmune disease of the central nervous system (CNS). The main cause of the MS is yet to be revealed, but the most probable theory is based on the molecular mimicry that concludes some infections in the activation of T cells against brain auto-antigens that initiate the disease cascade. The Purpose of this research is the prediction of the auto-antigen potency of the myelin proteolipid protein (PLP) in multiple sclerosis. As there wasn't any tertiary structure of PLP available in the Protein Data Bank (PDB) and in order to characterize the structural properties of the protein, we modeled this protein using prediction servers. Meta prediction method, as a new perspective in silico , was performed to fi nd PLPs epitopes. For this purpose, several T cell epitope prediction web servers were used to predict PLPs epitopes against Human Leukocyte Antigens (HLA). The overlap regions, as were predicted by most web servers were selected as immunogenic epitopes and were subjected to the BLASTP against microorganisms. Three common regions, AA 58-74 , AA 161-177 , and AA 238-254 were detected as immunodominant regions through meta-prediction. Investigating peptides with more than 50% similarity to that of candidate epitope AA 58-74 in bacteria showed a similar peptide in bacteria (mainly consistent with that of clostridium and mycobacterium) and spike protein of Alphacoronavirus 1, Canine coronavirus, and Feline coronavirus. These results suggest that cross reaction of the immune system to PLP may have originated from a bacteria or viral infection, and therefore molecular mimicry might have an important role in the progression of MS. Through reliable and accurate prediction of the consensus epitopes, it is not necessary to synthesize all PLP fragments and examine their immunogenicity experimentally ( in vitro ). In this study, the best encephalitogenic antigens were predicted based on bioinformatics tools that may provide reliable

Application of multiple correlation analysis method to the prognosis and evaluation of uranium metallogenisys in Jiangzha region

International Nuclear Information System (INIS)

Zhu Hongxun; Pan Hongping; Jian Xingxiang

2008-01-01

Prognosis and evaluation of uranium resources in Jiangzha region, Sichuan province are carried out through the multiple correlation analysis method. Through combining the characteristics of the methods and geology circumstance of areas to be predict, the uranium source, rock types, structure, terrain, hot springs and red basin are selected as estimation variable (factor). The original data of reference and predict unit are listed first, then correlation degree is calculated and uranium mineralization prospect areas are discriminated finally. The result shows that the method is credible, and should be applied to the whole Ruoergai uranium metallogenic area. (authors)

Fuzzy multiple attribute decision making methods and applications

CERN Document Server

Chen, Shu-Jen

1992-01-01

This monograph is intended for an advanced undergraduate or graduate course as well as for researchers, who want a compilation of developments in this rapidly growing field of operations research. This is a sequel to our previous works: "Multiple Objective Decision Making--Methods and Applications: A state-of-the-Art Survey" (No.164 of the Lecture Notes); "Multiple Attribute Decision Making--Methods and Applications: A State-of-the-Art Survey" (No.186 of the Lecture Notes); and "Group Decision Making under Multiple Criteria--Methods and Applications" (No.281 of the Lecture Notes). In this monograph, the literature on methods of fuzzy Multiple Attribute Decision Making (MADM) has been reviewed thoroughly and critically, and classified systematically. This study provides readers with a capsule look into the existing methods, their characteristics, and applicability to the analysis of fuzzy MADM problems. The basic concepts and algorithms from the classical MADM methods have been used in the development of the f...

Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior

Directory of Open Access Journals (Sweden)

Khalil Muhammad Zuhaib

2017-09-01

Full Text Available Navigating a robot in a dynamic environment is a challenging task, especially when the behavior of other agents such as pedestrians, is only partially predictable. Also, the kinodynamic constraints on robot motion add an extra challenge. This paper proposes a novel navigational strategy for collision avoidance of a kinodynamically constrained robot from multiple moving passive agents with partially predictable behavior. Specifically, this paper presents a new approach to identify the set of control inputs to the robot, named control obstacle, which leads it towards a collision with a passive agent moving along an arbitrary path. The proposed method is developed by generalizing the concept of nonlinear velocity obstacle (NLVO, which is used to avoid collision with a passive agent, and takes into account the kinodynamic constraints on robot motion. Further, it formulates the navigational problem as an optimization problem, which allows the robot to make a safe decision in the presence of various sources of unmodelled uncertainties. Finally, the performance of the algorithm is evaluated for different parameters and is compared to existing velocity obstacle-based approaches. The simulated experiments show the excellent performance of the proposed approach in term of computation time and success rate.

System for prediction and determination of the sub critic multiplication

International Nuclear Information System (INIS)

Martinez, Aquilino S.; Pereira, Valmir; Silva, Fernando C. da

1997-01-01

It is presented a concept of a system which may be used to calculate and anticipate the subcritical multiplication of a PWR nuclear power plant. The system is divided into two different modules. The first module allows the theoretical prediction of the subcritical multiplication factor through the solution of the multigroup diffusion equation. The second module determines this factor based on the data acquired from the neutron detectors of a NPP external nuclear detection system. (author). 3 refs., 3 figs., 2 tabs

Climate Prediction for Brazil's Nordeste: Performance of Empirical and Numerical Modeling Methods.

Science.gov (United States)

Moura, Antonio Divino; Hastenrath, Stefan

2004-07-01

Comparisons of performance of climate forecast methods require consistency in the predictand and a long common reference period. For Brazil's Nordeste, empirical methods developed at the University of Wisconsin use preseason (October January) rainfall and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific as input to stepwise multiple regression and neural networking. These are used to predict the March June rainfall at a network of 27 stations. An experiment at the International Research Institute for Climate Prediction, Columbia University, with a numerical model (ECHAM4.5) used global SST information through February to predict the March June rainfall at three grid points in the Nordeste. The predictands for the empirical and numerical model forecasts are correlated at +0.96, and the period common to the independent portion of record of the empirical prediction and the numerical modeling is 1968 99. Over this period, predicted versus observed rainfall are evaluated in terms of correlation, root-mean-square error, absolute error, and bias. Performance is high for both approaches. Numerical modeling produces a correlation of +0.68, moderate errors, and strong negative bias. For the empirical methods, errors and bias are small, and correlations of +0.73 and +0.82 are reached between predicted and observed rainfall.

Computational predictive methods for fracture and fatigue

Science.gov (United States)

Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.

1994-09-01

The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.

Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

Science.gov (United States)

Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

2017-12-01

Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

VAN method of short-term earthquake prediction shows promise

Science.gov (United States)

Uyeda, Seiya

Although optimism prevailed in the 1970s, the present consensus on earthquake prediction appears to be quite pessimistic. However, short-term prediction based on geoelectric potential monitoring has stood the test of time in Greece for more than a decade [VarotsosandKulhanek, 1993] Lighthill, 1996]. The method used is called the VAN method.The geoelectric potential changes constantly due to causes such as magnetotelluric effects, lightning, rainfall, leakage from manmade sources, and electrochemical instabilities of electrodes. All of this noise must be eliminated before preseismic signals are identified, if they exist at all. The VAN group apparently accomplished this task for the first time. They installed multiple short (100-200m) dipoles with different lengths in both north-south and east-west directions and long (1-10 km) dipoles in appropriate orientations at their stations (one of their mega-stations, Ioannina, for example, now has 137 dipoles in operation) and found that practically all of the noise could be eliminated by applying a set of criteria to the data.

TEHRAN AIR POLLUTANTS PREDICTION BASED ON RANDOM FOREST FEATURE SELECTION METHOD

Directory of Open Access Journals (Sweden)

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.

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.

Statistical tests for equal predictive ability across multiple forecasting methods

DEFF Research Database (Denmark)

Borup, Daniel; Thyrsgaard, Martin

We develop a multivariate generalization of the Giacomini-White tests for equal conditional predictive ability. The tests are applicable to a mixture of nested and non-nested models, incorporate estimation uncertainty explicitly, and allow for misspecification of the forecasting model as well as ...

Cervical length measurement for the prediction of preterm birth in multiple pregnancies : a systematic review and bivariate meta-analysis

NARCIS (Netherlands)

Lim, A. C.; Hegeman, M. A.; In 't Veld, M. A. Huis; Opmeer, B. C.; Bruinse, H. W.; Mol, B. W. J.

Objectives To review the literature on cervical length as a predictor of preterm birth in asymptomatic women with a multiple pregnancy. Methods We searched MEDLINE, Embase and reference lists of included articles to identify all studies that reported on the accuracy of cervical length for predicting

A new method for class prediction based on signed-rank algorithms applied to Affymetrix® microarray experiments

Directory of Open Access Journals (Sweden)

Vassal Aurélien

2008-01-01

Full Text Available Abstract Background The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix® technology provides both a quantitative fluorescence signal and a decision (detection call: absent or present based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM patients. Results After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM. Conclusion This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with

Multiple Method Contraception Use among African American Adolescents in Four US Cities

Directory of Open Access Journals (Sweden)

Jennifer L. Brown

2011-01-01

Full Text Available We report on African American adolescents' (N=850; M age = 15.4 contraceptive practices and type of contraception utilized during their last sexual encounter. Respondents completed measures of demographics, contraceptive use, sexual partner type, and ability to select “safe” sexual partners. 40% endorsed use of dual or multiple contraceptive methods; a total of 35 different contraceptive combinations were reported. Perceived ability to select “safe” partners was associated with not using contraception (OR = 1.25, using less effective contraceptive methods (OR = 1.23, or hormonal birth control (OR = 1.50. Female gender predicted hormonal birth control use (OR = 2.33, use of less effective contraceptive methods (e.g., withdrawal; OR = 2.47, and using no contraception (OR = 2.37. Respondents' age and partner type did not predict contraception use. Adolescents used contraceptive methods with limited ability to prevent both unintended pregnancies and STD/HIV. Adolescents who believed their partners posed low risk were more likely to use contraceptive practices other than condoms or no contraception. Reproductive health practitioners are encouraged to help youth negotiate contraceptive use with partners, regardless of the partner's perceived riskiness.

Quantitative Prediction of Coalbed Gas Content Based on Seismic Multiple-Attribute Analyses

Directory of Open Access Journals (Sweden)

Renfang Pan

2015-09-01

Full Text Available Accurate prediction of gas planar distribution is crucial to selection and development of new CBM exploration areas. Based on seismic attributes, well logging and testing data we found that seismic absorption attenuation, after eliminating the effects of burial depth, shows an evident correlation with CBM gas content; (positive structure curvature has a negative correlation with gas content; and density has a negative correlation with gas content. It is feasible to use the hydrocarbon index (P*G and pseudo-Poisson ratio attributes for detection of gas enrichment zones. Based on seismic multiple-attribute analyses, a multiple linear regression equation was established between the seismic attributes and gas content at the drilling wells. Application of this equation to the seismic attributes at locations other than the drilling wells yielded a quantitative prediction of planar gas distribution. Prediction calculations were performed for two different models, one using pre-stack inversion and the other one disregarding pre-stack inversion. A comparison of the results indicates that both models predicted a similar trend for gas content distribution, except that the model using pre-stack inversion yielded a prediction result with considerably higher precision than the other model.

Machine learning methods for metabolic pathway prediction

Directory of Open Access Journals (Sweden)

Karp Peter D

2010-01-01

Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

Machine learning methods for metabolic pathway prediction

Science.gov (United States)

2010-01-01

Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

Multiple predictor smoothing methods for sensitivity analysis: Example results

International Nuclear Information System (INIS)

Storlie, Curtis B.; Helton, Jon C.

2008-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described in the first part of this presentation: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. In this, the second and concluding part of the presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

Basic thinking patterns and working methods for multiple DFX

DEFF Research Database (Denmark)

Andreasen, Mogens Myrup; Mortensen, Niels Henrik

1997-01-01

This paper attempts to describe the theory and methodologies behind DFX and linking multiple DFX's together. The contribution is an articulation of basic thinking patterns and description of some working methods for handling multiple DFX.......This paper attempts to describe the theory and methodologies behind DFX and linking multiple DFX's together. The contribution is an articulation of basic thinking patterns and description of some working methods for handling multiple DFX....

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Science.gov (United States)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

Science.gov (United States)

Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

2018-05-10

Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

Investigating the interactive role of stressful life events, reinforcement sensitivity and personality traits in prediction of the severity of Multiple Sclerosis (MS symptoms

Directory of Open Access Journals (Sweden)

2017-06-01

Full Text Available Background & Objective: Multiple sclerosis is a chronic neurological condition recognized by demyelination in the central nervous system. The present study was conducted to investigate the interactive role of stressful life events, reinforcement sensitivity, and personality traits in prediction of the severity of symptoms of Multiple sclerosis (MS symptoms. Materials & Methods: This is a correlational study whose statistical population consisted of all the patients suffering from Multiple Sclerosis in Shiraz in the first half of 1394, among whom 162 patients were included in this research by means of purposive sampling method. Five-Factor Personality Inventory, Jackson Personality Inventory, Stressful Life Events Scale, and Expanded Disability Status Scale (EDSS were utilised as research tools. In order to analyze the data, descriptive and inferential methods were used. The data were analysed using Pearson correlation and hierarchical regression. Results: The findings revealed that stressful life events (β = 0.41, p <0.001, Behavioral Inhibition System (β = 0.26, p<0.05, and neuroticism index (β = 0.92, p <0.05 were able to predict variance of scores of the severity of symptoms of Multiple Sclerosis significantly. Conclusion: Stressful life events, Behavioral Inhibition System, and neuroticism showed a significant relationship with the severity of symptoms of Multiple Sclerosis; thus, it seems that interaction of personality traits and environmental conditions are among influential factors of the severity of symptoms of Multiple Sclerosis. This fact implies that individuals' personal traits play an eminent role in the progression of the disease.

Prediction methods and databases within chemoinformatics

DEFF Research Database (Denmark)

Jónsdóttir, Svava Osk; Jørgensen, Flemming Steen; Brunak, Søren

2005-01-01

MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information...... about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability...

Decision tree methods: applications for classification and prediction.

Science.gov (United States)

Song, Yan-Yan; Lu, Ying

2015-04-25

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

Science.gov (United States)

Smith, Kent W.; Sasaki, M. S.

1979-01-01

A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

Use of Multiple Imputation Method to Improve Estimation of Missing Baseline Serum Creatinine in Acute Kidney Injury Research

Science.gov (United States)

Peterson, Josh F.; Eden, Svetlana K.; Moons, Karel G.; Ikizler, T. Alp; Matheny, Michael E.

2013-01-01

Summary Background and objectives Baseline creatinine (BCr) is frequently missing in AKI studies. Common surrogate estimates can misclassify AKI and adversely affect the study of related outcomes. This study examined whether multiple imputation improved accuracy of estimating missing BCr beyond current recommendations to apply assumed estimated GFR (eGFR) of 75 ml/min per 1.73 m2 (eGFR 75). Design, setting, participants, & measurements From 41,114 unique adult admissions (13,003 with and 28,111 without BCr data) at Vanderbilt University Hospital between 2006 and 2008, a propensity score model was developed to predict likelihood of missing BCr. Propensity scoring identified 6502 patients with highest likelihood of missing BCr among 13,003 patients with known BCr to simulate a “missing” data scenario while preserving actual reference BCr. Within this cohort (n=6502), the ability of various multiple-imputation approaches to estimate BCr and classify AKI were compared with that of eGFR 75. Results All multiple-imputation methods except the basic one more closely approximated actual BCr than did eGFR 75. Total AKI misclassification was lower with multiple imputation (full multiple imputation + serum creatinine) (9.0%) than with eGFR 75 (12.3%; Pcreatinine) (15.3%) versus eGFR 75 (40.5%; P<0.001). Multiple imputation improved specificity and positive predictive value for detecting AKI at the expense of modestly decreasing sensitivity relative to eGFR 75. Conclusions Multiple imputation can improve accuracy in estimating missing BCr and reduce misclassification of AKI beyond currently proposed methods. PMID:23037980

NEURAL METHODS FOR THE FINANCIAL PREDICTION

OpenAIRE

Jerzy Balicki; Piotr Dryja; Waldemar Korłub; Piotr Przybyłek; Maciej Tyszka; Marcin Zadroga; Marcin Zakidalski

2016-01-01

Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

NEURAL METHODS FOR THE FINANCIAL PREDICTION

Directory of Open Access Journals (Sweden)

Jerzy Balicki

2016-06-01

Full Text Available Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

Development of a predictive model for distribution coefficient (Kd) of 13'7Cs and 60Co in marine sediments using multiple linear regression analysis

International Nuclear Information System (INIS)

Kumar, Ajay; Ravi, P.M.; Guneshwar, S.L.; Rout, Sabyasachi; Mishra, Manish K.; Pulhani, Vandana; Tripathi, R.M.

2018-01-01

Numerous common methods (batch laboratory, the column laboratory, field-batch method, field modeling and K 0c method) are used frequently for determination of K d values. Recently, multiple regression models are considered as new best estimates for predicting the K d of radionuclides in the environment. It is also well known fact that the K d value is highly influenced by physico-chemical properties of sediment. Due to the significant variability in influencing parameters, the measured K d values can range over several orders of magnitude under different environmental conditions. The aim of this study is to develop a predictive model for K d values of 137 Cs and 60 Co based on the sediment properties using multiple linear regression analysis

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

Science.gov (United States)

Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

2018-01-01

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

Methodology to predict the initiation of multiple transverse fractures from horizontal wellbores

Energy Technology Data Exchange (ETDEWEB)

Crosby, D. G.; Yang, Z.; Rahman, S. S. [Univ. of New South Wales (Australia)

2001-10-01

The criterion based on Drucker and Prager which is designed to predict the pressure required to initiate secondary multiple transverse fractures in close proximity to primary fractures is discussed. Results based on this criterion compare favorably with those measured during a series of laboratory-scale hydraulic fracture interaction tests. It is concluded that the multiple fracture criterion and laboratory results demonstrate that transversely fractured horizontal wellbores have a limited capacity to resist the initiation of multiple fractures from adjacent perforations, or intersecting induced and natural fractures. 23 refs., 1 tab., 9 figs.

The System of Inventory Forecasting in PT. XYZ by using the Method of Holt Winter Multiplicative

Science.gov (United States)

Shaleh, W.; Rasim; Wahyudin

2018-01-01

Problems at PT. XYZ currently only rely on manual bookkeeping, then the cost of production will swell and all investments invested to be less to predict sales and inventory of goods. If the inventory prediction of goods is to large, then the cost of production will swell and all investments invested to be less efficient. Vice versa, if the inventory prediction is too small it will impact on consumers, so that consumers are forced to wait for the desired product. Therefore, in this era of globalization, the development of computer technology has become a very important part in every business plan. Almost of all companies, both large and small, use computer technology. By utilizing computer technology, people can make time in solving complex business problems. Computer technology for companies has become an indispensable activity to provide enhancements to the business services they manage but systems and technologies are not limited to the distribution model and data processing but the existing system must be able to analyze the possibilities of future company capabilities. Therefore, the company must be able to forecast conditions and circumstances, either from inventory of goods, force, or profits to be obtained. To forecast it, the data of total sales from December 2014 to December 2016 will be calculated by using the method of Holt Winters, which is the method of time series prediction (Multiplicative Seasonal Method) it is seasonal data that has increased and decreased, also has 4 equations i.e. Single Smoothing, Trending Smoothing, Seasonal Smoothing and Forecasting. From the results of research conducted, error value in the form of MAPE is below 1%, so it can be concluded that forecasting with the method of Holt Winter Multiplicative.

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

Science.gov (United States)

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

Measuring multiple residual-stress components using the contour method and multiple cuts

Energy Technology Data Exchange (ETDEWEB)

Prime, Michael B [Los Alamos National Laboratory; Swenson, Hunter [Los Alamos National Laboratory; Pagliaro, Pierluigi [U. PALERMO; Zuccarello, Bernardo [U. PALERMO

2009-01-01

The conventional contour method determines one component of stress over the cross section of a part. The part is cut into two, the contour of the exposed surface is measured, and Bueckner's superposition principle is analytically applied to calculate stresses. In this paper, the contour method is extended to the measurement of multiple stress components by making multiple cuts with subsequent applications of superposition. The theory and limitations are described. The theory is experimentally tested on a 316L stainless steel disk with residual stresses induced by plastically indenting the central portion of the disk. The stress results are validated against independent measurements using neutron diffraction. The theory has implications beyond just multiple cuts. The contour method measurements and calculations for the first cut reveal how the residual stresses have changed throughout the part. Subsequent measurements of partially relaxed stresses by other techniques, such as laboratory x-rays, hole drilling, or neutron or synchrotron diffraction, can be superimposed back to the original state of the body.

A Meta-Path-Based Prediction Method for Human miRNA-Target Association

Directory of Open Access Journals (Sweden)

Jiawei Luo

2016-01-01

Full Text Available MicroRNAs (miRNAs are short noncoding RNAs that play important roles in regulating gene expressing, and the perturbed miRNAs are often associated with development and tumorigenesis as they have effects on their target mRNA. Predicting potential miRNA-target associations from multiple types of genomic data is a considerable problem in the bioinformatics research. However, most of the existing methods did not fully use the experimentally validated miRNA-mRNA interactions. Here, we developed RMLM and RMLMSe to predict the relationship between miRNAs and their targets. RMLM and RMLMSe are global approaches as they can reconstruct the missing associations for all the miRNA-target simultaneously and RMLMSe demonstrates that the integration of sequence information can improve the performance of RMLM. In RMLM, we use RM measure to evaluate different relatedness between miRNA and its target based on different meta-paths; logistic regression and MLE method are employed to estimate the weight of different meta-paths. In RMLMSe, sequence information is utilized to improve the performance of RMLM. Here, we carry on fivefold cross validation and pathway enrichment analysis to prove the performance of our methods. The fivefold experiments show that our methods have higher AUC scores compared with other methods and the integration of sequence information can improve the performance of miRNA-target association prediction.

Novel patch modelling method for efficient simulation and prediction uncertainty analysis of multi-scale groundwater flow and transport processes

Science.gov (United States)

Sreekanth, J.; Moore, Catherine

2018-04-01

The application of global sensitivity and uncertainty analysis techniques to groundwater models of deep sedimentary basins are typically challenged by large computational burdens combined with associated numerical stability issues. The highly parameterized approaches required for exploring the predictive uncertainty associated with the heterogeneous hydraulic characteristics of multiple aquifers and aquitards in these sedimentary basins exacerbate these issues. A novel Patch Modelling Methodology is proposed for improving the computational feasibility of stochastic modelling analysis of large-scale and complex groundwater models. The method incorporates a nested groundwater modelling framework that enables efficient simulation of groundwater flow and transport across multiple spatial and temporal scales. The method also allows different processes to be simulated within different model scales. Existing nested model methodologies are extended by employing 'joining predictions' for extrapolating prediction-salient information from one model scale to the next. This establishes a feedback mechanism supporting the transfer of information from child models to parent models as well as parent models to child models in a computationally efficient manner. This feedback mechanism is simple and flexible and ensures that while the salient small scale features influencing larger scale prediction are transferred back to the larger scale, this does not require the live coupling of models. This method allows the modelling of multiple groundwater flow and transport processes using separate groundwater models that are built for the appropriate spatial and temporal scales, within a stochastic framework, while also removing the computational burden associated with live model coupling. The utility of the method is demonstrated by application to an actual large scale aquifer injection scheme in Australia.

Multiple predictor smoothing methods for sensitivity analysis: Description of techniques

International Nuclear Information System (INIS)

Storlie, Curtis B.; Helton, Jon C.

2008-01-01

The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (i) locally weighted regression (LOESS), (ii) additive models, (iii) projection pursuit regression, and (iv) recursive partitioning regression. Then, in the second and concluding part of this presentation, the indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present

A simple method for HPLC retention time prediction: linear calibration using two reference substances.

Science.gov (United States)

Sun, Lei; Jin, Hong-Yu; Tian, Run-Tao; Wang, Ming-Juan; Liu, Li-Na; Ye, Liu-Ping; Zuo, Tian-Tian; Ma, Shuang-Cheng

2017-01-01

Analysis of related substances in pharmaceutical chemicals and multi-components in traditional Chinese medicines needs bulk of reference substances to identify the chromatographic peaks accurately. But the reference substances are costly. Thus, the relative retention (RR) method has been widely adopted in pharmacopoeias and literatures for characterizing HPLC behaviors of those reference substances unavailable. The problem is it is difficult to reproduce the RR on different columns due to the error between measured retention time (t R ) and predicted t R in some cases. Therefore, it is useful to develop an alternative and simple method for prediction of t R accurately. In the present study, based on the thermodynamic theory of HPLC, a method named linear calibration using two reference substances (LCTRS) was proposed. The method includes three steps, procedure of two points prediction, procedure of validation by multiple points regression and sequential matching. The t R of compounds on a HPLC column can be calculated by standard retention time and linear relationship. The method was validated in two medicines on 30 columns. It was demonstrated that, LCTRS method is simple, but more accurate and more robust on different HPLC columns than RR method. Hence quality standards using LCTRS method are easy to reproduce in different laboratories with lower cost of reference substances.

Non-animal methods to predict skin sensitization (II): an assessment of defined approaches *.

Science.gov (United States)

Kleinstreuer, Nicole C; Hoffmann, Sebastian; Alépée, Nathalie; Allen, David; Ashikaga, Takao; Casey, Warren; Clouet, Elodie; Cluzel, Magalie; Desprez, Bertrand; Gellatly, Nichola; Göbel, Carsten; Kern, Petra S; Klaric, Martina; Kühnl, Jochen; Martinozzi-Teissier, Silvia; Mewes, Karsten; Miyazawa, Masaaki; Strickland, Judy; van Vliet, Erwin; Zang, Qingda; Petersohn, Dirk

2018-05-01

Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals. Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients. The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple in vitro and in chemico skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain in silico predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization. Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools. Multiple non-animal testing strategies incorporating in vitro, in chemico, and in silico inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization.

Rainfall prediction with backpropagation method

Science.gov (United States)

Wahyuni, E. G.; Fauzan, L. M. F.; Abriyani, F.; Muchlis, N. F.; Ulfa, M.

2018-03-01

Rainfall is an important factor in many fields, such as aviation and agriculture. Although it has been assisted by technology but the accuracy can not reach 100% and there is still the possibility of error. Though current rainfall prediction information is needed in various fields, such as agriculture and aviation fields. In the field of agriculture, to obtain abundant and quality yields, farmers are very dependent on weather conditions, especially rainfall. Rainfall is one of the factors that affect the safety of aircraft. To overcome the problems above, then it’s required a system that can accurately predict rainfall. In predicting rainfall, artificial neural network modeling is applied in this research. The method used in modeling this artificial neural network is backpropagation method. Backpropagation methods can result in better performance in repetitive exercises. This means that the weight of the ANN interconnection can approach the weight it should be. Another advantage of this method is the ability in the learning process adaptively and multilayer owned on this method there is a process of weight changes so as to minimize error (fault tolerance). Therefore, this method can guarantee good system resilience and consistently work well. The network is designed using 4 input variables, namely air temperature, air humidity, wind speed, and sunshine duration and 3 output variables ie low rainfall, medium rainfall, and high rainfall. Based on the research that has been done, the network can be used properly, as evidenced by the results of the prediction of the system precipitation is the same as the results of manual calculations.

Predicting Falls in People with Multiple Sclerosis: Fall History Is as Accurate as More Complex Measures

Directory of Open Access Journals (Sweden)

Michelle H. Cameron

2013-01-01

Full Text Available Background. Many people with MS fall, but the best method for identifying those at increased fall risk is not known. Objective. To compare how accurately fall history, questionnaires, and physical tests predict future falls and injurious falls in people with MS. Methods. 52 people with MS were asked if they had fallen in the past 2 months and the past year. Subjects were also assessed with the Activities-specific Balance Confidence, Falls Efficacy Scale-International, and Multiple Sclerosis Walking Scale-12 questionnaires, the Expanded Disability Status Scale, Timed 25-Foot Walk, and computerized dynamic posturography and recorded their falls daily for the following 6 months with calendars. The ability of baseline assessments to predict future falls was compared using receiver operator curves and logistic regression. Results. All tests individually provided similar fall prediction (area under the curve (AUC 0.60–0.75. A fall in the past year was the best predictor of falls (AUC 0.75, sensitivity 0.89, specificity 0.56 or injurious falls (AUC 0.69, sensitivity 0.96, specificity 0.41 in the following 6 months. Conclusion. Simply asking people with MS if they have fallen in the past year predicts future falls and injurious falls as well as more complex, expensive, or time-consuming approaches.

Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

Directory of Open Access Journals (Sweden)

Marjan Čeh

2018-05-01

Full Text Available The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008–2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1 the non-linear nature of the prediction assignment task; (2 input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3 the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R2 values, sales ratios, mean average percentage error (MAPE, coefficient of dispersion (COD revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study.

Science.gov (United States)

Tacchella, Andrea; Romano, Silvia; Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

2017-01-01

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

Science.gov (United States)

Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

2017-12-12

Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity. In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

Study on validation method for femur finite element model under multiple loading conditions

Science.gov (United States)

Guan, Fengjiao; Zhang, Guanjun; Liu, Jie; Wang, Shujing; Luo, Xu

2018-03-01

Acquisition of accurate and reliable constitutive parameters related to bio-tissue materials was beneficial to improve biological fidelity of a Finite Element (FE) model and predict impact damages more effectively. In this paper, a femur FE model was established under multiple loading conditions with diverse impact positions. Then, based on sequential response surface method and genetic algorithms, the material parameters identification was transformed to a multi-response optimization problem. Finally, the simulation results successfully coincided with force-displacement curves obtained by numerous experiments. Thus, computational accuracy and efficiency of the entire inverse calculation process were enhanced. This method was able to effectively reduce the computation time in the inverse process of material parameters. Meanwhile, the material parameters obtained by the proposed method achieved higher accuracy.

IN-MACA-MCC: Integrated Multiple Attractor Cellular Automata with Modified Clonal Classifier for Human Protein Coding and Promoter Prediction

Directory of Open Access Journals (Sweden)

Kiran Sree Pokkuluri

2014-01-01

Full Text Available Protein coding and promoter region predictions are very important challenges of bioinformatics (Attwood and Teresa, 2000. The identification of these regions plays a crucial role in understanding the genes. Many novel computational and mathematical methods are introduced as well as existing methods that are getting refined for predicting both of the regions separately; still there is a scope for improvement. We propose a classifier that is built with MACA (multiple attractor cellular automata and MCC (modified clonal classifier to predict both regions with a single classifier. The proposed classifier is trained and tested with Fickett and Tung (1992 datasets for protein coding region prediction for DNA sequences of lengths 54, 108, and 162. This classifier is trained and tested with MMCRI datasets for protein coding region prediction for DNA sequences of lengths 252 and 354. The proposed classifier is trained and tested with promoter sequences from DBTSS (Yamashita et al., 2006 dataset and nonpromoters from EID (Saxonov et al., 2000 and UTRdb (Pesole et al., 2002 datasets. The proposed model can predict both regions with an average accuracy of 90.5% for promoter and 89.6% for protein coding region predictions. The specificity and sensitivity values of promoter and protein coding region predictions are 0.89 and 0.92, respectively.

A Class of Prediction-Correction Methods for Time-Varying Convex Optimization

Science.gov (United States)

Simonetto, Andrea; Mokhtari, Aryan; Koppel, Alec; Leus, Geert; Ribeiro, Alejandro

2016-09-01

This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of $1/h$, where $h$ is the length of the sampling interval. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions. The correction step adjusts for the distance between the current prediction and the optimizer at each time step, and consists either of one or multiple gradient steps or Newton steps, which respectively correspond to the gradient trajectory tracking (GTT) or Newton trajectory tracking (NTT) algorithms. Under suitable conditions, we establish that the asymptotic error incurred by both proposed methods behaves as $O(h^2)$, and in some cases as $O(h^4)$, which outperforms the state-of-the-art error bound of $O(h)$ for correction-only methods in the gradient-correction step. Moreover, when the characteristics of the objective function variation are not available, we propose approximate gradient and Newton tracking algorithms (AGT and ANT, respectively) that still attain these asymptotical error bounds. Numerical simulations demonstrate the practical utility of the proposed methods and that they improve upon existing techniques by several orders of magnitude.

A General Method for QTL Mapping in Multiple Related Populations Derived from Multiple Parents

Directory of Open Access Journals (Sweden)

Yan AO

2009-03-01

Full Text Available It's well known that incorporating some existing populations derived from multiple parents may improve QTL mapping and QTL-based breeding programs. However, no general maximum likelihood method has been available for this strategy. Based on the QTL mapping in multiple related populations derived from two parents, a maximum likelihood estimation method was proposed, which can incorporate several populations derived from three or more parents and also can be used to handle different mating designs. Taking a circle design as an example, we conducted simulation studies to study the effect of QTL heritability and sample size upon the proposed method. The results showed that under the same heritability, enhanced power of QTL detection and more precise and accurate estimation of parameters could be obtained when three F2 populations were jointly analyzed, compared with the joint analysis of any two F2 populations. Higher heritability, especially with larger sample sizes, would increase the ability of QTL detection and improve the estimation of parameters. Potential advantages of the method are as follows: firstly, the existing results of QTL mapping in single population can be compared and integrated with each other with the proposed method, therefore the ability of QTL detection and precision of QTL mapping can be improved. Secondly, owing to multiple alleles in multiple parents, the method can exploit gene resource more adequately, which will lay an important genetic groundwork for plant improvement.

A collapse pressure prediction model for horizontal shale gas wells with multiple weak planes

Directory of Open Access Journals (Sweden)

Ping Chen

2015-01-01

Full Text Available Since collapse of horizontal wellbore through long brittle shale interval is a major problem, the occurrence characteristics of weak planes were analyzed according to outcrop, core, and SEM and FMI data of shale rocks. A strength analysis method was developed for shale rocks with multiple weak planes based on weak-plane strength theory. An analysis was also conducted of the strength characteristics of shale rocks with uniform distribution of multiple weak planes. A collapse pressure prediction model for horizontal wells in shale formation with multiple weak planes was established, which takes into consideration the occurrence of each weak plane, wellbore stress condition, borehole azimuth, and in-situ stress azimuth. Finally, a case study of a horizontal shale gas well in southern Sichuan Basin was conducted. The results show that the intersection angle between the shale bedding plane and the structural fracture is generally large (nearly orthogonal; with the increase of weak plane number, the strength of rock mass declines sharply and is more heavily influenced by weak planes; when there are more than four weak planes, the rock strength tends to be isotropic and the whole strength of rock mass is greatly weakened, significantly increasing the risk of wellbore collapse. With the increase of weak plane number, the drilling fluid density (collapse pressure to keep borehole stability goes up gradually. For instance, the collapse pressure is 1.04 g/cm3 when there are no weak planes, and 1.55 g/cm3 when there is one weak plane, and 1.84 g/cm3 when there are two weak planes. The collapse pressure prediction model for horizontal wells proposed in this paper presented results in better agreement with those in actual situation. This model, more accurate and practical than traditional models, can effectively improve the accuracy of wellbore collapse pressure prediction of horizontal shale gas wells.

Predicting Multiple Functions of Sustainable Flood Retention Basins under Uncertainty via Multi-Instance Multi-Label Learning

Directory of Open Access Journals (Sweden)

Qinli Yang

2015-03-01

Full Text Available The ambiguity of diverse functions of sustainable flood retention basins (SFRBs may lead to conflict and risk in water resources planning and management. How can someone provide an intuitive yet efficient strategy to uncover and distinguish the multiple potential functions of SFRBs under uncertainty? In this study, by exploiting both input and output uncertainties of SFRBs, the authors developed a new data-driven framework to automatically predict the multiple functions of SFRBs by using multi-instance multi-label (MIML learning. A total of 372 sustainable flood retention basins, characterized by 40 variables associated with confidence levels, were surveyed in Scotland, UK. A Gaussian model with Monte Carlo sampling was used to capture the variability of variables (i.e., input uncertainty, and the MIML-support vector machine (SVM algorithm was subsequently applied to predict the potential functions of SFRBs that have not yet been assessed, allowing for one basin belonging to different types (i.e., output uncertainty. Experiments demonstrated that the proposed approach enables effective automatic prediction of the potential functions of SFRBs (e.g., accuracy >93%. The findings suggest that the functional uncertainty of SFRBs under investigation can be better assessed in a more comprehensive and cost-effective way, and the proposed data-driven approach provides a promising method of doing so for water resources management.

Prediction Method for the Complete Characteristic Curves of a Francis Pump-Turbine

Directory of Open Access Journals (Sweden)

Wei Huang

2018-02-01

Full Text Available Complete characteristic curves of a pump-turbine are essential for simulating the hydraulic transients and designing pumped storage power plants but are often unavailable in the preliminary design stage. To solve this issue, a prediction method for the complete characteristics of a Francis pump-turbine was proposed. First, based on Euler equations and the velocity triangles at the runners, a mathematical model describing the complete characteristics of a Francis pump-turbine was derived. According to multiple sets of measured complete characteristic curves, explicit expressions for the characteristic parameters of characteristic operating point sets (COPs, as functions of a specific speed and guide vane opening, were then developed to determine the undetermined coefficients in the mathematical model. Ultimately, by combining the mathematical model with the regression analysis of COPs, the complete characteristic curves for an arbitrary specific speed were predicted. Moreover, a case study shows that the predicted characteristic curves are in good agreement with the measured data. The results obtained by 1D numerical simulation of the hydraulic transient process using the predicted characteristics deviate little from the measured characteristics. This method is effective and sufficient for a priori simulations before obtaining the measured characteristics and provides important support for the preliminary design of pumped storage power plants.

Review of Monte Carlo methods for particle multiplicity evaluation

CERN Document Server

Armesto-Pérez, Nestor

2005-01-01

I present a brief review of the existing models for particle multiplicity evaluation in heavy ion collisions which are at our disposal in the form of Monte Carlo simulators. Models are classified according to the physical mechanisms with which they try to describe the different stages of a high-energy collision between heavy nuclei. A comparison of predictions, as available at the beginning of year 2000, for multiplicities in central AuAu collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and PbPb collisions at the CERN Large Hadron Collider (LHC) is provided.

Review of Monte Carlo methods for particle multiplicity evaluation

International Nuclear Information System (INIS)

Armesto, Nestor

2005-01-01

I present a brief review of the existing models for particle multiplicity evaluation in heavy ion collisions which are at our disposal in the form of Monte Carlo simulators. Models are classified according to the physical mechanisms with which they try to describe the different stages of a high-energy collision between heavy nuclei. A comparison of predictions, as available at the beginning of year 2000, for multiplicities in central AuAu collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and PbPb collisions at the CERN Large Hadron Collider (LHC) is provided

Seismic response analysis of structural system subjected to multiple support excitation

International Nuclear Information System (INIS)

Wu, R.W.; Hussain, F.A.; Liu, L.K.

1978-01-01

In the seismic analysis of a multiply supported structural system subjected to nonuniform excitations at each support point, the single response spectrum, the time history, and the multiple response spectrum are the three commonly employed methods. In the present paper the three methods are developed, evaluated, and the limitations and advantages of each method assessed. A numerical example has been carried out for a typical piping system. Considerably smaller responses have been predicted by the time history method than that by the single response spectrum method. This is mainly due to the fact that the phase and amplitude relations between the support excitations are faithfully retained in the time history method. The multiple response spectrum prediction has been observed to compare favourably with the time history method prediction. Based on the present evaluation, the multiple response spectrum method is the most efficient method for seismic response analysis of structural systems subjected to multiple support excitation. (Auth.)

HARMONIC ANALYSIS OF SVPWM INVERTER USING MULTIPLE-PULSES METHOD

Directory of Open Access Journals (Sweden)

Mehmet YUMURTACI

2009-01-01

Full Text Available Space Vector Modulation (SVM technique is a popular and an important PWM technique for three phases voltage source inverter in the control of Induction Motor. In this study harmonic analysis of Space Vector PWM (SVPWM is investigated using multiple-pulses method. Multiple-Pulses method calculates the Fourier coefficients of individual positive and negative pulses of the output PWM waveform and adds them together using the principle of superposition to calculate the Fourier coefficients of the all PWM output signal. Harmonic magnitudes can be calculated directly by this method without linearization, using look-up tables or Bessel functions. In this study, the results obtained in the application of SVPWM for values of variable parameters are compared with the results obtained with the multiple-pulses method.

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

Science.gov (United States)

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Multiple Linear Regression Modeling To Predict the Stability of Polymer-Drug Solid Dispersions: Comparison of the Effects of Polymers and Manufacturing Methods on Solid Dispersion Stability.

Science.gov (United States)

Fridgeirsdottir, Gudrun A; Harris, Robert J; Dryden, Ian L; Fischer, Peter M; Roberts, Clive J

2018-03-29

Solid dispersions can be a successful way to enhance the bioavailability of poorly soluble drugs. Here 60 solid dispersion formulations were produced using ten chemically diverse, neutral, poorly soluble drugs, three commonly used polymers, and two manufacturing techniques, spray-drying and melt extrusion. Each formulation underwent a six-month stability study at accelerated conditions, 40 °C and 75% relative humidity (RH). Significant differences in times to crystallization (onset of crystallization) were observed between both the different polymers and the two processing methods. Stability from zero days to over one year was observed. The extensive experimental data set obtained from this stability study was used to build multiple linear regression models to correlate physicochemical properties of the active pharmaceutical ingredients (API) with the stability data. The purpose of these models is to indicate which combination of processing method and polymer carrier is most likely to give a stable solid dispersion. Six quantitative mathematical multiple linear regression-based models were produced based on selection of the most influential independent physical and chemical parameters from a set of 33 possible factors, one model for each combination of polymer and processing method, with good predictability of stability. Three general rules are proposed from these models for the formulation development of suitably stable solid dispersions. Namely, increased stability is correlated with increased glass transition temperature ( T g ) of solid dispersions, as well as decreased number of H-bond donors and increased molecular flexibility (such as rotatable bonds and ring count) of the drug molecule.

Student nurse selection and predictability of academic success: The Multiple Mini Interview project.

Science.gov (United States)

Gale, Julia; Ooms, Ann; Grant, Robert; Paget, Kris; Marks-Maran, Di

2016-05-01

With recent reports of public enquiries into failure to care, universities are under pressure to ensure that candidates selected for undergraduate nursing programmes demonstrate academic potential as well as characteristics and values such as compassion, empathy and integrity. The Multiple Mini Interview (MMI) was used in one university as a way of ensuring that candidates had the appropriate numeracy and literacy skills as well as a range of communication, empathy, decision-making and problem-solving skills as well as ethical insights and integrity, initiative and team-work. To ascertain whether there is evidence of bias in MMIs (gender, age, nationality and location of secondary education) and to determine the extent to which the MMI is predictive of academic success in nursing. A longitudinal retrospective analysis of student demographics, MMI data and the assessment marks for years 1, 2 and 3. One university in southwest London. One cohort of students who commenced their programme in September 2011, including students in all four fields of nursing (adult, child, mental health and learning disability). Inferential statistics and a Bayesian Multilevel Model. MMI in conjunction with MMI numeracy test and MMI literacy test shows little or no bias in terms of ages, gender, nationality or location of secondary school education. Although MMI in conjunction with numeracy and literacy testing is predictive of academic success, it is only weakly predictive. The MMI used in conjunction with literacy and numeracy testing appears to be a successful technique for selecting candidates for nursing. However, other selection methods such as psychological profiling or testing of emotional intelligence may add to the extent to which selection methods are predictive of academic success on nursing. Copyright © 2016 Elsevier Ltd. All rights reserved.

A statistical method for predicting splice variants between two groups of samples using GeneChip® expression array data

Directory of Open Access Journals (Sweden)

Olson James M

2006-04-01

Full Text Available Abstract Background Alternative splicing of pre-messenger RNA results in RNA variants with combinations of selected exons. It is one of the essential biological functions and regulatory components in higher eukaryotic cells. Some of these variants are detectable with the Affymetrix GeneChip® that uses multiple oligonucleotide probes (i.e. probe set, since the target sequences for the multiple probes are adjacent within each gene. Hybridization intensity from a probe correlates with abundance of the corresponding transcript. Although the multiple-probe feature in the current GeneChip® was designed to assess expression values of individual genes, it also measures transcriptional abundance for a sub-region of a gene sequence. This additional capacity motivated us to develop a method to predict alternative splicing, taking advance of extensive repositories of GeneChip® gene expression array data. Results We developed a two-step approach to predict alternative splicing from GeneChip® data. First, we clustered the probes from a probe set into pseudo-exons based on similarity of probe intensities and physical adjacency. A pseudo-exon is defined as a sequence in the gene within which multiple probes have comparable probe intensity values. Second, for each pseudo-exon, we assessed the statistical significance of the difference in probe intensity between two groups of samples. Differentially expressed pseudo-exons are predicted to be alternatively spliced. We applied our method to empirical data generated from GeneChip® Hu6800 arrays, which include 7129 probe sets and twenty probes per probe set. The dataset consists of sixty-nine medulloblastoma (27 metastatic and 42 non-metastatic samples and four cerebellum samples as normal controls. We predicted that 577 genes would be alternatively spliced when we compared normal cerebellum samples to medulloblastomas, and predicted that thirteen genes would be alternatively spliced when we compared metastatic

Prediction of mechanical fatigue caused by multiple random excitations

NARCIS (Netherlands)

Bonte, M.H.A.; de Boer, Andries; Liebregts, R.

2004-01-01

A simulation method is presented for the fatigue analysis of automotive and other products that are subjected to multiple random excitations. The method is denoted as frequency domain stress-life fatigue analysis and was implemented in the automotive industry at DAF Trucks N.V. in Eindhoven, The

Multiple histogram method and static Monte Carlo sampling

NARCIS (Netherlands)

Inda, M.A.; Frenkel, D.

2004-01-01

We describe an approach to use multiple-histogram methods in combination with static, biased Monte Carlo simulations. To illustrate this, we computed the force-extension curve of an athermal polymer from multiple histograms constructed in a series of static Rosenbluth Monte Carlo simulations. From

Deep learning methods for protein torsion angle prediction.

Science.gov (United States)

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.

Science.gov (United States)

Zheng, Ce; Kurgan, Lukasz

2008-10-10

beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Q total, Q predicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Q total barrier and achieves Q total = 80.9%, MCC = 0.47, and Q predicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Experiments show that the proposed method constitutes an improvement over the competing prediction

Hybrid multiple criteria decision-making methods

DEFF Research Database (Denmark)

Zavadskas, Edmundas Kazimieras; Govindan, K.; Antucheviciene, Jurgita

2016-01-01

Formal decision-making methods can be used to help improve the overall sustainability of industries and organisations. Recently, there has been a great proliferation of works aggregating sustainability criteria by using diverse multiple criteria decision-making (MCDM) techniques. A number of revi...

A global calibration method for multiple vision sensors based on multiple targets

International Nuclear Information System (INIS)

Liu, Zhen; Zhang, Guangjun; Wei, Zhenzhong; Sun, Junhua

2011-01-01

The global calibration of multiple vision sensors (MVS) has been widely studied in the last two decades. In this paper, we present a global calibration method for MVS with non-overlapping fields of view (FOVs) using multiple targets (MT). MT is constructed by fixing several targets, called sub-targets, together. The mutual coordinate transformations between sub-targets need not be known. The main procedures of the proposed method are as follows: one vision sensor is selected from MVS to establish the global coordinate frame (GCF). MT is placed in front of the vision sensors for several (at least four) times. Using the constraint that the relative positions of all sub-targets are invariant, the transformation matrix from the coordinate frame of each vision sensor to GCF can be solved. Both synthetic and real experiments are carried out and good result is obtained. The proposed method has been applied to several real measurement systems and shown to be both flexible and accurate. It can serve as an attractive alternative to existing global calibration methods

Multiple Shooting and Time Domain Decomposition Methods

CERN Document Server

Geiger, Michael; Körkel, Stefan; Rannacher, Rolf

2015-01-01

This book offers a comprehensive collection of the most advanced numerical techniques for the efficient and effective solution of simulation and optimization problems governed by systems of time-dependent differential equations. The contributions present various approaches to time domain decomposition, focusing on multiple shooting and parareal algorithms.  The range of topics covers theoretical analysis of the methods, as well as their algorithmic formulation and guidelines for practical implementation. Selected examples show that the discussed approaches are mandatory for the solution of challenging practical problems. The practicability and efficiency of the presented methods is illustrated by several case studies from fluid dynamics, data compression, image processing and computational biology, giving rise to possible new research topics.  This volume, resulting from the workshop Multiple Shooting and Time Domain Decomposition Methods, held in Heidelberg in May 2013, will be of great interest to applied...

A link prediction method for heterogeneous networks based on BP neural network

Science.gov (United States)

Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

2018-04-01

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

Multiple-Factor Based Sparse Urban Travel Time Prediction

Directory of Open Access Journals (Sweden)

Xinyan Zhu

2018-02-01

Full Text Available The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.

A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction.

Science.gov (United States)

Qiu, Shibin; Lane, Terran

2009-01-01

The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

Directory of Open Access Journals (Sweden)

Kurgan Lukasz

2008-10-01

Full Text Available Abstract Background β-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM values serve as an input to the support vector machine (SVM predictor. Results We show that (1 all four predicted secondary structures are useful; (2 the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3 the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential β-turns, while the remaining four amino acids are useful to predict non-β-turns. Empirical evaluation using three nonredundant datasets shows favorable Qtotal, Qpredicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Qtotal barrier and achieves Qtotal = 80.9%, MCC = 0.47, and Qpredicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC competing methods, respectively. Conclusion Experiments show that the proposed method constitutes an

Prediction of orthostatic hypotension in multiple system atrophy and Parkinson disease

Science.gov (United States)

Sun, Zhanfang; Jia, Dandan; Shi, Yuting; Hou, Xuan; Yang, Xiaosu; Guo, Jifeng; Li, Nan; Wang, Junling; Sun, Qiying; Zhang, Hainan; Lei, Lifang; Shen, Lu; Yan, Xinxiang; Xia, Kun; Jiang, Hong; Tang, Beisha

2016-01-01

Orthostatic hypotension (OH) is common in multiple system atrophy (MSA) and Parkinson disease (PD), generally assessed through a lying-to-standing orthostatic test. However, standing blood pressure may not be available due to orthostatic intolerance or immobilization for such patients. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were successively measured in supine, sitting, and standing positions in patients with MSA and PD. Receiver operating characteristic analysis was used to evaluate diagnostic performance of the drops of sitting SBP or DBP. OH and severe OH were respectively regarded as “gold standard”. The drops of SBP in standing position were associated with increased disease severity for MSA and correlated with age for PD. In MSA group, drops in sitting SBP ≥ 14 mmHg or DBP ≥ 6 mmHg had highest validity for prediction of OH, and drops in sitting SBP ≥ 18 mmHg or DBP ≥ 8 mmHg for severe OH. In PD group, drops in sitting SBP ≥ 10 mmHg or DBP ≥ 6 mmHg had highest validity for prediction of OH. The lying-to-sitting orthostatic test is an alternative method for detection of OH in MSA and PD, especially when standing BP could not be validly measured due to various reasons. PMID:26867507

Optimization of breeding methods when introducing multiple ...

African Journals Online (AJOL)

Optimization of breeding methods when introducing multiple resistance genes from American to Chinese wheat. JN Qi, X Zhang, C Yin, H Li, F Lin. Abstract. Stripe rust is one of the most destructive diseases of wheat worldwide. Growing resistant cultivars with resistance genes is the most effective method to control this ...

MR Imaging in Monitoring and Predicting Treatment Response in Multiple Sclerosis.

Science.gov (United States)

Río, Jordi; Auger, Cristina; Rovira, Àlex

2017-05-01

MR imaging is the most sensitive tool for identifying lesions in patients with multiple sclerosis (MS). MR imaging has also acquired an essential role in the detection of complications arising from these treatments and in the assessment and prediction of efficacy. In the future, other radiological measures that have shown prognostic value may be incorporated within the models for predicting treatment response. This article examines the role of MR imaging as a prognostic tool in patients with MS and the recommendations that have been proposed in recent years to monitor patients who are treated with disease-modifying drugs. Copyright © 2017 Elsevier Inc. All rights reserved.

Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

KAUST Repository

Abdul Jameel, Abdul Gani; Naser, Nimal; Emwas, Abdul-Hamid M.; Dooley, Stephen; Sarathy, Mani

2016-01-01

An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN

A Multiple Model Prediction Algorithm for CNC Machine Wear PHM

Directory of Open Access Journals (Sweden)

Huimin Chen

2011-01-01

Full Text Available The 2010 PHM data challenge focuses on the remaining useful life (RUL estimation for cutters of a high speed CNC milling machine using measurements from dynamometer, accelerometer, and acoustic emission sensors. We present a multiple model approach for wear depth estimation of milling machine cutters using the provided data. The feature selection, initial wear estimation and multiple model fusion components of the proposed algorithm are explained in details and compared with several alternative methods using the training data. The final submission ranked #2 among professional and student participants and the method is applicable to other data driven PHM problems.

Location of brain lesions predicts conversion of clinically isolated syndromes to multiple sclerosis

DEFF Research Database (Denmark)

Giorgio, Antonio; Battaglini, Marco; Rocca, Maria Assunta

2013-01-01

OBJECTIVES: To assess in a large population of patients with clinically isolated syndrome (CIS) the relevance of brain lesion location and frequency in predicting 1-year conversion to multiple sclerosis (MS). METHODS: In this multicenter, retrospective study, clinical and MRI data at onset......: In CIS patients with hemispheric, multifocal, and brainstem/cerebellar onset, lesion probability map clusters were seen in clinically eloquent brain regions. Significant lesion clusters were not found in CIS patients with optic nerve and spinal cord onset. At 1 year, clinically definite MS developed...... in the converting group in projection, association, and commissural WM tracts, with larger clusters being in the corpus callosum, corona radiata, and cingulum. CONCLUSIONS: Higher frequency of lesion occurrence in clinically eloquent WM tracts can characterize CIS subjects with different types of onset...

A comparison of methods to predict historical daily streamflow time series in the southeastern United States

Science.gov (United States)

Farmer, William H.; Archfield, Stacey A.; Over, Thomas M.; Hay, Lauren E.; LaFontaine, Jacob H.; Kiang, Julie E.

2015-01-01

Effective and responsible management of water resources relies on a thorough understanding of the quantity and quality of available water. Streamgages cannot be installed at every location where streamflow information is needed. As part of its National Water Census, the U.S. Geological Survey is planning to provide streamflow predictions for ungaged locations. In order to predict streamflow at a useful spatial and temporal resolution throughout the Nation, efficient methods need to be selected. This report examines several methods used for streamflow prediction in ungaged basins to determine the best methods for regional and national implementation. A pilot area in the southeastern United States was selected to apply 19 different streamflow prediction methods and evaluate each method by a wide set of performance metrics. Through these comparisons, two methods emerged as the most generally accurate streamflow prediction methods: the nearest-neighbor implementations of nonlinear spatial interpolation using flow duration curves (NN-QPPQ) and standardizing logarithms of streamflow by monthly means and standard deviations (NN-SMS12L). It was nearly impossible to distinguish between these two methods in terms of performance. Furthermore, neither of these methods requires significantly more parameterization in order to be applied: NN-SMS12L requires 24 regional regressions—12 for monthly means and 12 for monthly standard deviations. NN-QPPQ, in the application described in this study, required 27 regressions of particular quantiles along the flow duration curve. Despite this finding, the results suggest that an optimal streamflow prediction method depends on the intended application. Some methods are stronger overall, while some methods may be better at predicting particular statistics. The methods of analysis presented here reflect a possible framework for continued analysis and comprehensive multiple comparisons of methods of prediction in ungaged basins (PUB

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method.

Science.gov (United States)

Tuta, Jure; Juric, Matjaz B

2018-03-24

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

Directory of Open Access Journals (Sweden)

Jure Tuta

2018-03-01

Full Text Available This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method, a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.. Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

Crack growth prediction method considering interaction between multiple cracks. Growth of surface cracks of dissimilar size under cyclic tensile and bending load

International Nuclear Information System (INIS)

Kamaya, Masayuki; Miyokawa, Eiichi; Kikuchi, Masanori

2011-01-01

When multiple cracks approach one another, the stress intensity factor is likely to change due to the interaction of the stress field. This causes change in growth rate and shape of cracks. In particular, when cracks are in parallel position to the loading direction, the shape of cracks becomes non-planar. In this study, the complex growth of interacting cracks is evaluated by using the S-Version finite element method, in which local detailed finite element mesh (local mesh) is superposed on coarse finite element model (global mesh) representing the global structure. In order to investigate the effect of interaction on the growth behavior, two parallel surface cracks are subjected to cyclic tensile or bending load. It is shown that the smaller crack is shielded by larger crack due to the interaction and stops growing when the difference in size of two cracks is significant. Based on simulations of various conditions, a procedure and criteria for evaluating crack growth for fitness-for-service assessment is proposed. According to the procedure, the interaction is not necessary to be considered in the crack growth prediction when the difference in size of two cracks exceeds the criterion. (author)

Novel hyperspectral prediction method and apparatus

Science.gov (United States)

Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf

2009-05-01

Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.

Combined adaptive multiple subtraction based on optimized event tracing and extended wiener filtering

Science.gov (United States)

Tan, Jun; Song, Peng; Li, Jinshan; Wang, Lei; Zhong, Mengxuan; Zhang, Xiaobo

2017-06-01

The surface-related multiple elimination (SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.

Modified-Fibonacci-Dual-Lucas method for earthquake prediction

Science.gov (United States)

Boucouvalas, A. C.; Gkasios, M.; Tselikas, N. T.; Drakatos, G.

2015-06-01

The FDL method makes use of Fibonacci, Dual and Lucas numbers and has shown considerable success in predicting earthquake events locally as well as globally. Predicting the location of the epicenter of an earthquake is one difficult challenge the other being the timing and magnitude. One technique for predicting the onset of earthquakes is the use of cycles, and the discovery of periodicity. Part of this category is the reported FDL method. The basis of the reported FDL method is the creation of FDL future dates based on the onset date of significant earthquakes. The assumption being that each occurred earthquake discontinuity can be thought of as a generating source of FDL time series The connection between past earthquakes and future earthquakes based on FDL numbers has also been reported with sample earthquakes since 1900. Using clustering methods it has been shown that significant earthquakes (conjunct Sun, Moon opposite Sun, Moon conjunct or opposite North or South Modes. In order to test improvement of the method we used all +8R earthquakes recorded since 1900, (86 earthquakes from USGS data). We have developed the FDL numbers for each of those seeds, and examined the earthquake hit rates (for a window of 3, i.e. +-1 day of target date) and for <6.5R. The successes are counted for each one of the 86 earthquake seeds and we compare the MFDL method with the FDL method. In every case we find improvement when the starting seed date is on the planetary trigger date prior to the earthquake. We observe no improvement only when a planetary trigger coincided with the earthquake date and in this case the FDL method coincides with the MFDL. Based on the MDFL method we present the prediction method capable of predicting global events or localized earthquakes and we will discuss the accuracy of the method in as far as the prediction and location parts of the method. We show example calendar style predictions for global events as well as for the Greek region using

ASPIC: a novel method to predict the exon-intron structure of a gene that is optimally compatible to a set of transcript sequences

Directory of Open Access Journals (Sweden)

Pesole Graziano

2005-10-01

Full Text Available Abstract Background: Currently available methods to predict splice sites are mainly based on the independent and progressive alignment of transcript data (mostly ESTs to the genomic sequence. Apart from often being computationally expensive, this approach is vulnerable to several problems – hence the need to develop novel strategies. Results: We propose a method, based on a novel multiple genome-EST alignment algorithm, for the detection of splice sites. To avoid limitations of splice sites prediction (mainly, over-predictions due to independent single EST alignments to the genomic sequence our approach performs a multiple alignment of transcript data to the genomic sequence based on the combined analysis of all available data. We recast the problem of predicting constitutive and alternative splicing as an optimization problem, where the optimal multiple transcript alignment minimizes the number of exons and hence of splice site observations. We have implemented a splice site predictor based on this algorithm in the software tool ASPIC (Alternative Splicing PredICtion. It is distinguished from other methods based on BLAST-like tools by the incorporation of entirely new ad hoc procedures for accurate and computationally efficient transcript alignment and adopts dynamic programming for the refinement of intron boundaries. ASPIC also provides the minimal set of non-mergeable transcript isoforms compatible with the detected splicing events. The ASPIC web resource is dynamically interconnected with the Ensembl and Unigene databases and also implements an upload facility. Conclusion: Extensive bench marking shows that ASPIC outperforms other existing methods in the detection of novel splicing isoforms and in the minimization of over-predictions. ASPIC also requires a lower computation time for processing a single gene and an EST cluster. The ASPIC web resource is available at http://aspic.algo.disco.unimib.it/aspic-devel/.

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response.

Science.gov (United States)

Kalincik, Tomas; Manouchehrinia, Ali; Sobisek, Lukas; Jokubaitis, Vilija; Spelman, Tim; Horakova, Dana; Havrdova, Eva; Trojano, Maria; Izquierdo, Guillermo; Lugaresi, Alessandra; Girard, Marc; Prat, Alexandre; Duquette, Pierre; Grammond, Pierre; Sola, Patrizia; Hupperts, Raymond; Grand'Maison, Francois; Pucci, Eugenio; Boz, Cavit; Alroughani, Raed; Van Pesch, Vincent; Lechner-Scott, Jeannette; Terzi, Murat; Bergamaschi, Roberto; Iuliano, Gerardo; Granella, Franco; Spitaleri, Daniele; Shaygannejad, Vahid; Oreja-Guevara, Celia; Slee, Mark; Ampapa, Radek; Verheul, Freek; McCombe, Pamela; Olascoaga, Javier; Amato, Maria Pia; Vucic, Steve; Hodgkinson, Suzanne; Ramo-Tello, Cristina; Flechter, Shlomo; Cristiano, Edgardo; Rozsa, Csilla; Moore, Fraser; Luis Sanchez-Menoyo, Jose; Laura Saladino, Maria; Barnett, Michael; Hillert, Jan; Butzkueven, Helmut

2017-09-01

Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of

Multiple-Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

Directory of Open Access Journals (Sweden)

Min Zhang

2018-01-01

Full Text Available Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. In this paper, a novel method called multiscale feature extraction (MFE and multiclass support vector machine (MSVM with particle parameter adaptive (PPA is proposed. MFE is used to preprocess the process signals, which decomposes the data into intrinsic mode function by empirical mode decomposition method, and instantaneous frequency of decomposed components was obtained by Hilbert transformation. Then, statistical features and principal component analysis are utilized to extract significant information from the features, to get effective data from multiple faults. MSVM method with PPA parameters optimization will classify the fault patterns. The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1 the proposed intelligent method (MFE_PPA_MSVM improves the classification recognition rate; (2 the accuracy will decline when the number of fault patterns increases; (3 prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. It verifies the method is feasible and efficient for fault diagnosis.

Predicting hearing thresholds and occupational hearing loss with multiple-frequency auditory steady-state responses.

Science.gov (United States)

Hsu, Ruey-Fen; Ho, Chi-Kung; Lu, Sheng-Nan; Chen, Shun-Sheng

2010-10-01

An objective investigation is needed to verify the existence and severity of hearing impairments resulting from work-related, noise-induced hearing loss in arbitration of medicolegal aspects. We investigated the accuracy of multiple-frequency auditory steady-state responses (Mf-ASSRs) between subjects with sensorineural hearing loss (SNHL) with and without occupational noise exposure. Cross-sectional study. Tertiary referral medical centre. Pure-tone audiometry and Mf-ASSRs were recorded in 88 subjects (34 patients had occupational noise-induced hearing loss [NIHL], 36 patients had SNHL without noise exposure, and 18 volunteers were normal controls). Inter- and intragroup comparisons were made. A predicting equation was derived using multiple linear regression analysis. ASSRs and pure-tone thresholds (PTTs) showed a strong correlation for all subjects (r = .77 ≈ .94). The relationship is demonstrated by the equationThe differences between the ASSR and PTT were significantly higher for the NIHL group than for the subjects with non-noise-induced SNHL (p tool for objectively evaluating hearing thresholds. Predictive value may be lower in subjects with occupational hearing loss. Regardless of carrier frequencies, the severity of hearing loss affects the steady-state response. Moreover, the ASSR may assist in detecting noise-induced injury of the auditory pathway. A multiple linear regression equation to accurately predict thresholds was shown that takes into consideration all effect factors.

Multiple Model Predictive Hybrid Feedforward Control of Fuel Cell Power Generation System

Directory of Open Access Journals (Sweden)

Long Wu

2018-02-01

Full Text Available Solid oxide fuel cell (SOFC is widely considered as an alternative solution among the family of the sustainable distributed generation. Its load flexibility enables it adjusting the power output to meet the requirements from power grid balance. Although promising, its control is challenging when faced with load changes, during which the output voltage is required to be maintained as constant and fuel utilization rate kept within a safe range. Moreover, it makes the control even more intractable because of the multivariable coupling and strong nonlinearity within the wide-range operating conditions. To this end, this paper developed a multiple model predictive control strategy for reliable SOFC operation. The resistance load is regarded as a measurable disturbance, which is an input to the model predictive control as feedforward compensation. The coupling is accommodated by the receding horizon optimization. The nonlinearity is mitigated by the multiple linear models, the weighted sum of which serves as the final control execution. The merits of the proposed control structure are demonstrated by the simulation results.

Research on neutron source multiplication method in nuclear critical safety

International Nuclear Information System (INIS)

Zhu Qingfu; Shi Yongqian; Hu Dingsheng

2005-01-01

The paper concerns in the neutron source multiplication method research in nuclear critical safety. Based on the neutron diffusion equation with external neutron source the effective sub-critical multiplication factor k s is deduced, and k s is different to the effective neutron multiplication factor k eff in the case of sub-critical system with external neutron source. The verification experiment on the sub-critical system indicates that the parameter measured with neutron source multiplication method is k s , and k s is related to the external neutron source position in sub-critical system and external neutron source spectrum. The relation between k s and k eff and the effect of them on nuclear critical safety is discussed. (author)

Optimization of Inventories for Multiple Companies by Fuzzy Control Method

OpenAIRE

Kawase, Koichi; Konishi, Masami; Imai, Jun

2008-01-01

In this research, Fuzzy control theory is applied to the inventory control of the supply chain between multiple companies. The proposed control method deals with the amountof inventories expressing supply chain between multiple companies. Referring past demand and tardiness, inventory amounts of raw materials are determined by Fuzzy inference. The method that an appropriate inventory control becomes possible optimizing fuzzy control gain by using SA method for Fuzzy control. The variation of ...

Unplanned Complex Suicide-A Consideration of Multiple Methods.

Science.gov (United States)

Ateriya, Navneet; Kanchan, Tanuj; Shekhawat, Raghvendra Singh; Setia, Puneet; Saraf, Ashish

2018-05-01

Detailed death investigations are mandatory to find out the exact cause and manner in non-natural deaths. In this reference, use of multiple methods in suicide poses a challenge for the investigators especially when the choice of methods to cause death is unplanned. There is an increased likelihood that doubts of homicide are raised in cases of unplanned complex suicides. A case of complex suicide is reported where the victim resorted to multiple methods to end his life, and what appeared to be an unplanned variant based on the death scene investigations. A meticulous crime scene examination, interviews of the victim's relatives and other witnesses, and a thorough autopsy are warranted to conclude on the cause and manner of death in all such cases. © 2017 American Academy of Forensic Sciences.

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

Science.gov (United States)

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

Science.gov (United States)

Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

2013-06-01

This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

DASPfind: new efficient method to predict drug–target interactions

KAUST Repository

Ba Alawi, Wail

2016-03-16

Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind

In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods.

Science.gov (United States)

Du, Hanwen; Cai, Yingchun; Yang, Hongbin; Zhang, Hongxiao; Xue, Yuhan; Liu, Guixia; Tang, Yun; Li, Weihua

2017-05-15

Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

A new method in predicting productivity of multi-stage fractured horizontal well in tight gas reservoirs

Directory of Open Access Journals (Sweden)

Yunsheng Wei

2016-10-01

Full Text Available The generally accomplished technique for horizontal wells in tight gas reservoirs is by multi-stage hydraulic fracturing, not to mention, the flow characteristics of a horizontal well with multiple transverse fractures are very intricate. Conventional methods, well as an evaluation unit, are difficult to accurately predict production capacity of each fracture and productivity differences between wells with a different number of fractures. Thus, a single fracture sets the minimum evaluation unit, matrix, fractures, and lateral wellbore model that are then combined integrally to approximate horizontal well with multiple transverse hydraulic fractures in tight gas reservoirs. This paper presents a new semi-analytical methodology for predicting the production capacity of a horizontal well with multiple transverse hydraulic fractures in tight gas reservoirs. Firstly, a mathematical flow model used as a medium, which is disturbed by finite conductivity vertical fractures and rectangular shaped boundaries, is established and explained by the Fourier integral transform. Then the idea of a single stage fracture analysis is incorporated to establish linear flow model within a single fracture with a variable rate. The Fredholm integral numerical solution is applicable for the fracture conductivity function. Finally, the pipe flow model along the lateral wellbore is adapted to couple multi-stages fracture mathematical models, and the equation group of predicting productivity of a multi-stage fractured horizontal well. The whole flow process from the matrix to bottom-hole and production interference between adjacent fractures is also established. Meanwhile, the corresponding iterative algorithm of the equations is given. In this case analysis, the productions of each well and fracture are calculated under the different bottom-hole flowing pressure, and this method also contributes to obtaining the distribution of pressure drop and production for every

Characterizing lentic freshwater fish assemblages using multiple sampling methods

Science.gov (United States)

Fischer, Jesse R.; Quist, Michael C.

2014-01-01

Characterizing fish assemblages in lentic ecosystems is difficult, and multiple sampling methods are almost always necessary to gain reliable estimates of indices such as species richness. However, most research focused on lentic fish sampling methodology has targeted recreationally important species, and little to no information is available regarding the influence of multiple methods and timing (i.e., temporal variation) on characterizing entire fish assemblages. Therefore, six lakes and impoundments (48–1,557 ha surface area) were sampled seasonally with seven gear types to evaluate the combined influence of sampling methods and timing on the number of species and individuals sampled. Probabilities of detection for species indicated strong selectivities and seasonal trends that provide guidance on optimal seasons to use gears when targeting multiple species. The evaluation of species richness and number of individuals sampled using multiple gear combinations demonstrated that appreciable benefits over relatively few gears (e.g., to four) used in optimal seasons were not present. Specifically, over 90 % of the species encountered with all gear types and season combinations (N = 19) from six lakes and reservoirs were sampled with nighttime boat electrofishing in the fall and benthic trawling, modified-fyke, and mini-fyke netting during the summer. Our results indicated that the characterization of lentic fish assemblages was highly influenced by the selection of sampling gears and seasons, but did not appear to be influenced by waterbody type (i.e., natural lake, impoundment). The standardization of data collected with multiple methods and seasons to account for bias is imperative to monitoring of lentic ecosystems and will provide researchers with increased reliability in their interpretations and decisions made using information on lentic fish assemblages.

Prediction methods environmental-effect reporting

International Nuclear Information System (INIS)

Jonker, R.J.; Koester, H.W.

1987-12-01

This report provides a survey of prediction methods which can be applied to the calculation of emissions in cuclear-reactor accidents, in the framework of environment-effect reports (dutch m.e.r.) or risk analyses. Also emissions during normal operation are important for m.e.r.. These can be derived from measured emissions of power plants being in operation. Data concerning the latter are reported. The report consists of an introduction into reactor technology, among which a description of some reactor types, the corresponding fuel cycle and dismantling scenarios - a discussion of risk-analyses for nuclear power plants and the physical processes which can play a role during accidents - a discussion of prediction methods to be employed and the expected developments in this area - some background information. (aughor). 145 refs.; 21 figs.; 20 tabs

Development of method for evaluating estimated inundation area by using river flood analysis based on multiple flood scenarios

Science.gov (United States)

Ono, T.; Takahashi, T.

2017-12-01

Non-structural mitigation measures such as flood hazard map based on estimated inundation area have been more important because heavy rains exceeding the design rainfall frequently occur in recent years. However, conventional method may lead to an underestimation of the area because assumed locations of dike breach in river flood analysis are limited to the cases exceeding the high-water level. The objective of this study is to consider the uncertainty of estimated inundation area with difference of the location of dike breach in river flood analysis. This study proposed multiple flood scenarios which can set automatically multiple locations of dike breach in river flood analysis. The major premise of adopting this method is not to be able to predict the location of dike breach correctly. The proposed method utilized interval of dike breach which is distance of dike breaches placed next to each other. That is, multiple locations of dike breach were set every interval of dike breach. The 2D shallow water equations was adopted as the governing equation of river flood analysis, and the leap-frog scheme with staggered grid was used. The river flood analysis was verified by applying for the 2015 Kinugawa river flooding, and the proposed multiple flood scenarios was applied for the Akutagawa river in Takatsuki city. As the result of computation in the Akutagawa river, a comparison with each computed maximum inundation depth of dike breaches placed next to each other proved that the proposed method enabled to prevent underestimation of estimated inundation area. Further, the analyses on spatial distribution of inundation class and maximum inundation depth in each of the measurement points also proved that the optimum interval of dike breach which can evaluate the maximum inundation area using the minimum assumed locations of dike breach. In brief, this study found the optimum interval of dike breach in the Akutagawa river, which enabled estimated maximum inundation area

Validating Whole-Airway CFD Predictions of DPI Aerosol Deposition at Multiple Flow Rates.

Science.gov (United States)

Longest, P Worth; Tian, Geng; Khajeh-Hosseini-Dalasm, Navvab; Hindle, Michael

2016-12-01

The objective of this study was to compare aerosol deposition predictions of a new whole-airway CFD model with available in vivo data for a dry powder inhaler (DPI) considered across multiple inhalation waveforms, which affect both the particle size distribution (PSD) and particle deposition. The Novolizer DPI with a budesonide formulation was selected based on the availability of 2D gamma scintigraphy data in humans for three different well-defined inhalation waveforms. Initial in vitro cascade impaction experiments were conducted at multiple constant (square-wave) particle sizing flow rates to characterize PSDs. The whole-airway CFD modeling approach implemented the experimentally determined PSDs at the point of aerosol formation in the inhaler. Complete characteristic airway geometries for an adult were evaluated through the lobar bronchi, followed by stochastic individual pathway (SIP) approximations through the tracheobronchial region and new acinar moving wall models of the alveolar region. It was determined that the PSD used for each inhalation waveform should be based on a constant particle sizing flow rate equal to the average of the inhalation waveform's peak inspiratory flow rate (PIFR) and mean flow rate [i.e., AVG(PIFR, Mean)]. Using this technique, agreement with the in vivo data was acceptable with <15% relative differences averaged across the three regions considered for all inhalation waveforms. Defining a peripheral to central deposition ratio (P/C) based on alveolar and tracheobronchial compartments, respectively, large flow-rate-dependent differences were observed, which were not evident in the original 2D in vivo data. The agreement between the CFD predictions and in vivo data was dependent on accurate initial estimates of the PSD, emphasizing the need for a combination in vitro-in silico approach. Furthermore, use of the AVG(PIFR, Mean) value was identified as a potentially useful method for characterizing a DPI aerosol at a constant flow rate.

An Intuitionistic Multiplicative ORESTE Method for Patients’ Prioritization of Hospitalization

Directory of Open Access Journals (Sweden)

Cheng Zhang

2018-04-01

Full Text Available The tension brought about by sickbeds is a common and intractable issue in public hospitals in China due to the large population. Assigning the order of hospitalization of patients is difficult because of complex patient information such as disease type, emergency degree, and severity. It is critical to rank the patients taking full account of various factors. However, most of the evaluation criteria for hospitalization are qualitative, and the classical ranking method cannot derive the detailed relations between patients based on these criteria. Motivated by this, a comprehensive multiple criteria decision making method named the intuitionistic multiplicative ORESTE (organísation, rangement et Synthèse dedonnées relarionnelles, in French was proposed to handle the problem. The subjective and objective weights of criteria were considered in the proposed method. To do so, first, considering the vagueness of human perceptions towards the alternatives, an intuitionistic multiplicative preference relation model is applied to represent the experts’ preferences over the pairwise alternatives with respect to the predetermined criteria. Then, a correlation coefficient-based weight determining method is developed to derive the objective weights of criteria. This method can overcome the biased results caused by highly-related criteria. Afterwards, we improved the general ranking method, ORESTE, by introducing a new score function which considers both the subjective and objective weights of criteria. An intuitionistic multiplicative ORESTE method was then developed and further highlighted by a case study concerning the patients’ prioritization.

The Research of Multiple Attenuation Based on Feedback Iteration and Independent Component Analysis

Science.gov (United States)

Xu, X.; Tong, S.; Wang, L.

2017-12-01

How to solve the problem of multiple suppression is a difficult problem in seismic data processing. The traditional technology for multiple attenuation is based on the principle of the minimum output energy of the seismic signal, this criterion is based on the second order statistics, and it can't achieve the multiple attenuation when the primaries and multiples are non-orthogonal. In order to solve the above problems, we combine the feedback iteration method based on the wave equation and the improved independent component analysis (ICA) based on high order statistics to suppress the multiple waves. We first use iterative feedback method to predict the free surface multiples of each order. Then, in order to predict multiples from real multiple in amplitude and phase, we design an expanded pseudo multi-channel matching filtering method to get a more accurate matching multiple result. Finally, we present the improved fast ICA algorithm which is based on the maximum non-Gauss criterion of output signal to the matching multiples and get better separation results of the primaries and the multiples. The advantage of our method is that we don't need any priori information to the prediction of the multiples, and can have a better separation result. The method has been applied to several synthetic data generated by finite-difference model technique and the Sigsbee2B model multiple data, the primaries and multiples are non-orthogonal in these models. The experiments show that after three to four iterations, we can get the perfect multiple results. Using our matching method and Fast ICA adaptive multiple subtraction, we can not only effectively preserve the effective wave energy in seismic records, but also can effectively suppress the free surface multiples, especially the multiples related to the middle and deep areas.

Learning Combinations of Multiple Feature Representations for Music Emotion Prediction

DEFF Research Database (Denmark)

Madsen, Jens; Jensen, Bjørn Sand; Larsen, Jan

2015-01-01

Music consists of several structures and patterns evolving through time which greatly influences the human decoding of higher-level cognitive aspects of music like the emotions expressed in music. For tasks, such as genre, tag and emotion recognition, these structures have often been identified...... and used as individual and non-temporal features and representations. In this work, we address the hypothesis whether using multiple temporal and non-temporal representations of different features is beneficial for modeling music structure with the aim to predict the emotions expressed in music. We test...

QSAR Study of Insecticides of Phthalamide Derivatives Using Multiple Linear Regression and Artificial Neural Network Methods

Directory of Open Access Journals (Sweden)

Adi Syahputra

2014-03-01

Full Text Available Quantitative structure activity relationship (QSAR for 21 insecticides of phthalamides containing hydrazone (PCH was studied using multiple linear regression (MLR, principle component regression (PCR and artificial neural network (ANN. Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84. Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl-5-chloro-N’-((5-methylthiophen-2-ylmethylene benzohydrazide, 2-(decalinecarbamoyl-5-chloro-N’-((thiophen-2-yl-methylene benzohydrazide and 2-(decaline carbamoyl-N’-(4-fluorobenzylidene-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.

Using multiple linear regression and physicochemical changes of amino acid mutations to predict antigenic variants of influenza A/H3N2 viruses.

Science.gov (United States)

Cui, Haibo; Wei, Xiaomei; Huang, Yu; Hu, Bin; Fang, Yaping; Wang, Jia

2014-01-01

Among human influenza viruses, strain A/H3N2 accounts for over a quarter of a million deaths annually. Antigenic variants of these viruses often render current vaccinations ineffective and lead to repeated infections. In this study, a computational model was developed to predict antigenic variants of the A/H3N2 strain. First, 18 critical antigenic amino acids in the hemagglutinin (HA) protein were recognized using a scoring method combining phi (ϕ) coefficient and information entropy. Next, a prediction model was developed by integrating multiple linear regression method with eight types of physicochemical changes in critical amino acid positions. When compared to other three known models, our prediction model achieved the best performance not only on the training dataset but also on the commonly-used testing dataset composed of 31878 antigenic relationships of the H3N2 influenza virus.

The trajectory prediction of spacecraft by grey method

International Nuclear Information System (INIS)

Wang, Qiyue; Wang, Zhongyu; Zhang, Zili; Wang, Yanqing; Zhou, Weihu

2016-01-01

The real-time and high-precision trajectory prediction of a moving object is a core technology in the field of aerospace engineering. The real-time monitoring and tracking technology are also significant guarantees of aerospace equipment. A dynamic trajectory prediction method called grey dynamic filter (GDF) which combines the dynamic measurement theory and grey system theory is proposed. GDF can use coordinates of the current period to extrapolate coordinates of the following period. At meantime, GDF can also keep the instantaneity of measured coordinates by the metabolism model. In this paper the optimal model length of GDF is firstly selected to improve the prediction accuracy. Then the simulation for uniformly accelerated motion and variably accelerated motion is conducted. The simulation results indicate that the mean composite position error of GDF prediction is one-fifth to that of Kalman filter (KF). By using a spacecraft landing experiment, the prediction accuracy of GDF is compared with the KF method and the primitive grey method (GM). The results show that the motion trajectory of spacecraft predicted by GDF is much closer to actual trajectory than the other two methods. The mean composite position error calculated by GDF is one-eighth to KF and one-fifth to GM respectively. (paper)

Method for measuring multiple scattering corrections between liquid scintillators

Energy Technology Data Exchange (ETDEWEB)

Verbeke, J.M., E-mail: verbeke2@llnl.gov; Glenn, A.M., E-mail: glenn22@llnl.gov; Keefer, G.J., E-mail: keefer1@llnl.gov; Wurtz, R.E., E-mail: wurtz1@llnl.gov

2016-07-21

A time-of-flight method is proposed to experimentally quantify the fractions of neutrons scattering between scintillators. An array of scintillators is characterized in terms of crosstalk with this method by measuring a californium source, for different neutron energy thresholds. The spectral information recorded by the scintillators can be used to estimate the fractions of neutrons multiple scattering. With the help of a correction to Feynman's point model theory to account for multiple scattering, these fractions can in turn improve the mass reconstruction of fissile materials under investigation.

Search Strategy of Detector Position For Neutron Source Multiplication Method by Using Detected-Neutron Multiplication Factor

International Nuclear Information System (INIS)

Endo, Tomohiro

2011-01-01

In this paper, an alternative definition of a neutron multiplication factor, detected-neutron multiplication factor kdet, is produced for the neutron source multiplication method..(NSM). By using kdet, a search strategy of appropriate detector position for NSM is also proposed. The NSM is one of the practical subcritical measurement techniques, i.e., the NSM does not require any special equipment other than a stationary external neutron source and an ordinary neutron detector. Additionally, the NSM method is based on steady-state analysis, so that this technique is very suitable for quasi real-time measurement. It is noted that the correction factors play important roles in order to accurately estimate subcriticality from the measured neutron count rates. The present paper aims to clarify how to correct the subcriticality measured by the NSM method, the physical meaning of the correction factors, and how to reduce the impact of correction factors by setting a neutron detector at an appropriate detector position

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model

Science.gov (United States)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO2 leaks and associated concentrations from geological CO2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems.

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model.

Science.gov (United States)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO 2 leaks and associated concentrations from geological CO 2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO 2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO 2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO 2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Methods and techniques for prediction of environmental impact

International Nuclear Information System (INIS)

1992-04-01

Environmental impact assessment (EIA) is the procedure that helps decision makers understand the environmental implications of their decisions. The prediction of environmental effects or impact is an extremely important part of the EIA procedure and improvements in existing capabilities are needed. Considerable attention is paid within environmental impact assessment and in handbooks on EIA to methods for identifying and evaluating environmental impacts. However, little attention is given to the issue distribution of information on impact prediction methods. The quantitative or qualitative methods for the prediction of environmental impacts appear to be the two basic approaches for incorporating environmental concerns into the decision-making process. Depending on the nature of the proposed activity and the environment likely to be affected, a combination of both quantitative and qualitative methods is used. Within environmental impact assessment, the accuracy of methods for the prediction of environmental impacts is of major importance while it provides for sound and well-balanced decision making. Pertinent and effective action to deal with the problems of environmental protection and the rational use of natural resources and sustainable development is only possible given objective methods and techniques for the prediction of environmental impact. Therefore, the Senior Advisers to ECE Governments on Environmental and Water Problems, decided to set up a task force, with the USSR as lead country, on methods and techniques for the prediction of environmental impacts in order to undertake a study to review and analyse existing methodological approaches and to elaborate recommendations to ECE Governments. The work of the task force was completed in 1990 and the resulting report, with all relevant background material, was approved by the Senior Advisers to ECE Governments on Environmental and Water Problems in 1991. The present report reflects the situation, state of

Multiple independent identification decisions: a method of calibrating eyewitness identifications.

Science.gov (United States)

Pryke, Sean; Lindsay, R C L; Dysart, Jennifer E; Dupuis, Paul

2004-02-01

Two experiments (N = 147 and N = 90) explored the use of multiple independent lineups to identify a target seen live. In Experiment 1, simultaneous face, body, and sequential voice lineups were used. In Experiment 2, sequential face, body, voice, and clothing lineups were used. Both studies demonstrated that multiple identifications (by the same witness) from independent lineups of different features are highly diagnostic of suspect guilt (G. L. Wells & R. C. L. Lindsay, 1980). The number of suspect and foil selections from multiple independent lineups provides a powerful method of calibrating the accuracy of eyewitness identification. Implications for use of current methods are discussed. ((c) 2004 APA, all rights reserved)

A linear multiple balance method for discrete ordinates neutron transport equations

International Nuclear Information System (INIS)

Park, Chang Je; Cho, Nam Zin

2000-01-01

A linear multiple balance method (LMB) is developed to provide more accurate and positive solutions for the discrete ordinates neutron transport equations. In this multiple balance approach, one mesh cell is divided into two subcells with quadratic approximation of angular flux distribution. Four multiple balance equations are used to relate center angular flux with average angular flux by Simpson's rule. From the analysis of spatial truncation error, the accuracy of the linear multiple balance scheme is ο(Δ 4 ) whereas that of diamond differencing is ο(Δ 2 ). To accelerate the linear multiple balance method, we also describe a simplified additive angular dependent rebalance factor scheme which combines a modified boundary projection acceleration scheme and the angular dependent rebalance factor acceleration schme. It is demonstrated, via fourier analysis of a simple model problem as well as numerical calculations, that the additive angular dependent rebalance factor acceleration scheme is unconditionally stable with spectral radius < 0.2069c (c being the scattering ration). The numerical results tested so far on slab-geometry discrete ordinates transport problems show that the solution method of linear multiple balance is effective and sufficiently efficient

Connecting clinical and actuarial prediction with rule-based methods

NARCIS (Netherlands)

Fokkema, M.; Smits, N.; Kelderman, H.; Penninx, B.W.J.H.

2015-01-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction

Insights from triangulation of two purchase choice elicitation methods to predict social decision making in healthcare.

Science.gov (United States)

Whitty, Jennifer A; Rundle-Thiele, Sharyn R; Scuffham, Paul A

2012-03-01

Discrete choice experiments (DCEs) and the Juster scale are accepted methods for the prediction of individual purchase probabilities. Nevertheless, these methods have seldom been applied to a social decision-making context. To gain an overview of social decisions for a decision-making population through data triangulation, these two methods were used to understand purchase probability in a social decision-making context. We report an exploratory social decision-making study of pharmaceutical subsidy in Australia. A DCE and selected Juster scale profiles were presented to current and past members of the Australian Pharmaceutical Benefits Advisory Committee and its Economic Subcommittee. Across 66 observations derived from 11 respondents for 6 different pharmaceutical profiles, there was a small overall median difference of 0.024 in the predicted probability of public subsidy (p = 0.003), with the Juster scale predicting the higher likelihood. While consistency was observed at the extremes of the probability scale, the funding probability differed over the mid-range of profiles. There was larger variability in the DCE than Juster predictions within each individual respondent, suggesting the DCE is better able to discriminate between profiles. However, large variation was observed between individuals in the Juster scale but not DCE predictions. It is important to use multiple methods to obtain a complete picture of the probability of purchase or public subsidy in a social decision-making context until further research can elaborate on our findings. This exploratory analysis supports the suggestion that the mixed logit model, which was used for the DCE analysis, may fail to adequately account for preference heterogeneity in some contexts.

Prediction Methods for Blood Glucose Concentration

DEFF Research Database (Denmark)

“Recent Results on Glucose–Insulin Predictions by Means of a State Observer for Time-Delay Systems” by Pasquale Palumbo et al. introduces a prediction model which in real time predicts the insulin concentration in blood which in turn is used in a control system. The method is tested in simulation...... EEG signals to predict upcoming hypoglycemic situations in real-time by employing artificial neural networks. The results of a 30-day long clinical study with the implanted device and the developed algorithm are presented. The chapter “Meta-Learning Based Blood Glucose Predictor for Diabetic......, but the insulin amount is chosen using factors that account for this expectation. The increasing availability of more accurate continuous blood glucose measurement (CGM) systems is attracting much interest to the possibilities of explicit prediction of future BG values. Against this background, in 2014 a two...

The multiple imputation method: a case study involving secondary data analysis.

Science.gov (United States)

Walani, Salimah R; Cleland, Charles M

2015-05-01

To illustrate with the example of a secondary data analysis study the use of the multiple imputation method to replace missing data. Most large public datasets have missing data, which need to be handled by researchers conducting secondary data analysis studies. Multiple imputation is a technique widely used to replace missing values while preserving the sample size and sampling variability of the data. The 2004 National Sample Survey of Registered Nurses. The authors created a model to impute missing values using the chained equation method. They used imputation diagnostics procedures and conducted regression analysis of imputed data to determine the differences between the log hourly wages of internationally educated and US-educated registered nurses. The authors used multiple imputation procedures to replace missing values in a large dataset with 29,059 observations. Five multiple imputed datasets were created. Imputation diagnostics using time series and density plots showed that imputation was successful. The authors also present an example of the use of multiple imputed datasets to conduct regression analysis to answer a substantive research question. Multiple imputation is a powerful technique for imputing missing values in large datasets while preserving the sample size and variance of the data. Even though the chained equation method involves complex statistical computations, recent innovations in software and computation have made it possible for researchers to conduct this technique on large datasets. The authors recommend nurse researchers use multiple imputation methods for handling missing data to improve the statistical power and external validity of their studies.

Artificial neural network intelligent method for prediction

Science.gov (United States)

Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

2017-09-01

Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

A feature point identification method for positron emission particle tracking with multiple tracers

Energy Technology Data Exchange (ETDEWEB)

Wiggins, Cody, E-mail: cwiggin2@vols.utk.edu [University of Tennessee-Knoxville, Department of Physics and Astronomy, 1408 Circle Drive, Knoxville, TN 37996 (United States); Santos, Roque [University of Tennessee-Knoxville, Department of Nuclear Engineering (United States); Escuela Politécnica Nacional, Departamento de Ciencias Nucleares (Ecuador); Ruggles, Arthur [University of Tennessee-Knoxville, Department of Nuclear Engineering (United States)

2017-01-21

A novel detection algorithm for Positron Emission Particle Tracking (PEPT) with multiple tracers based on optical feature point identification (FPI) methods is presented. This new method, the FPI method, is compared to a previous multiple PEPT method via analyses of experimental and simulated data. The FPI method outperforms the older method in cases of large particle numbers and fine time resolution. Simulated data show the FPI method to be capable of identifying 100 particles at 0.5 mm average spatial error. Detection error is seen to vary with the inverse square root of the number of lines of response (LORs) used for detection and increases as particle separation decreases. - Highlights: • A new approach to positron emission particle tracking is presented. • Using optical feature point identification analogs, multiple particle tracking is achieved. • Method is compared to previous multiple particle method. • Accuracy and applicability of method is explored.

Investigation of colistin sensitivity via three different methods in Acinetobacter baumannii isolates with multiple antibiotic resistance.

Science.gov (United States)

Sinirtaş, Melda; Akalin, Halis; Gedikoğlu, Suna

2009-09-01

In recent years there has been an increase in life-threatening infections caused by Acinetobacter baumannii with multiple antibiotic resistance, which has lead to the use of polymyxins, especially colistin, being reconsidered. The aim of this study was to investigate the colistin sensitivity of A. baumannii isolates with multiple antibiotic resistance via different methods, and to evaluate the disk diffusion method for colistin against multi-resistant Acinetobacter isolates, in comparison to the E-test and Phoenix system. The study was carried out on 100 strains of A. baumannii (colonization or infection) isolated from the microbiological samples of different patients followed in the clinics and intensive care units of Uludağ University Medical School between the years 2004 and 2005. Strains were identified and characterized for their antibiotic sensitivity by Phoenix system (Becton Dickinson, Sparks, MD, USA). In all studied A. baumannii strains, susceptibility to colistin was determined to be 100% with the disk diffusion, E-test, and broth microdilution methods. Results of the E-test and broth microdilution method, which are accepted as reference methods, were found to be 100% consistent with the results of the disk diffusion tests; no very major or major error was identified upon comparison of the tests. The sensitivity and the positive predictive value of the disk diffusion method were found to be 100%. Colistin resistance in A. baumannii was not detected in our region, and disk diffusion method results are in accordance with those of E-test and broth microdilution methods.

New prediction methods for collaborative filtering

Directory of Open Access Journals (Sweden)

Hasan BULUT

2016-05-01

Full Text Available Companies, in particular e-commerce companies, aims to increase customer satisfaction, hence in turn increase their profits, using recommender systems. Recommender Systems are widely used nowadays and they provide strategic advantages to the companies that use them. These systems consist of different stages. In the first stage, the similarities between the active user and other users are computed using the user-product ratings matrix. Then, the neighbors of the active user are found from these similarities. In prediction calculation stage, the similarities computed at the first stage are used to generate the weight vector of the closer neighbors. Neighbors affect the prediction value by the corresponding value of the weight vector. In this study, we developed two new methods for the prediction calculation stage which is the last stage of collaborative filtering. The performance of these methods are measured with evaluation metrics used in the literature and compared with other studies in this field.

A method for predicting monthly rainfall patterns

International Nuclear Information System (INIS)

Njau, E.C.

1987-11-01

A brief survey is made of previous methods that have been used to predict rainfall trends or drought spells in different parts of the earth. The basic methodologies or theoretical strategies used in these methods are compared with contents of a recent theory of Sun-Weather/Climate links (Njau, 1985a; 1985b; 1986; 1987a; 1987b; 1987c) which point towards the possibility of practical climatic predictions. It is shown that not only is the theoretical basis of each of these methodologies or strategies fully incorporated into the above-named theory, but also this theory may be used to develop a technique by which future monthly rainfall patterns can be predicted in further and finer details. We describe the latter technique and then illustrate its workability by means of predictions made on monthly rainfall patterns in some East African meteorological stations. (author). 43 refs, 11 figs, 2 tabs

A new wind power prediction method based on chaotic theory and Bernstein Neural Network

International Nuclear Information System (INIS)

Wang, Cong; Zhang, Hongli; Fan, Wenhui; Fan, Xiaochao

2016-01-01

The accuracy of wind power prediction is important for assessing the security and economy of the system operation when wind power connects to the grids. However, multiple factors cause a long delay and large errors in wind power prediction. Hence, efficient wind power forecasting approaches are still required for practical applications. In this paper, a new wind power forecasting method based on Chaos Theory and Bernstein Neural Network (BNN) is proposed. Firstly, the largest Lyapunov exponent as a judgment for wind power system's chaotic behavior is made. Secondly, Phase Space Reconstruction (PSR) is used to reconstruct the wind power series' phase space. Thirdly, the prediction model is constructed using the Bernstein polynomial and neural network. Finally, the weights and thresholds of the model are optimized by Primal Dual State Transition Algorithm (PDSTA). The practical hourly data of wind power generation in Xinjiang is used to test this forecaster. The proposed forecaster is compared with several current prominent research findings. Analytical results indicate that the forecasting error of PDSTA + BNN is 3.893% for 24 look-ahead hours, and has lower errors obtained compared with the other forecast methods discussed in this paper. The results of all cases studying confirm the validity of the new forecast method. - Highlights: • Lyapunov exponent is used to verify chaotic behavior of wind power series. • Phase Space Reconstruction is used to reconstruct chaotic wind power series. • A new Bernstein Neural Network to predict wind power series is proposed. • Primal dual state transition algorithm is chosen as the training strategy of BNN.

Predicting Metabolic Syndrome Using the Random Forest Method

Directory of Open Access Journals (Sweden)

Apilak Worachartcheewan

2015-01-01

Full Text Available Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III criteria. The Random Forest (RF method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females. RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.

Galerkin projection methods for solving multiple related linear systems

Energy Technology Data Exchange (ETDEWEB)

Chan, T.F.; Ng, M.; Wan, W.L.

1996-12-31

We consider using Galerkin projection methods for solving multiple related linear systems A{sup (i)}x{sup (i)} = b{sup (i)} for 1 {le} i {le} s, where A{sup (i)} and b{sup (i)} are different in general. We start with the special case where A{sup (i)} = A and A is symmetric positive definite. The method generates a Krylov subspace from a set of direction vectors obtained by solving one of the systems, called the seed system, by the CG method and then projects the residuals of other systems orthogonally onto the generated Krylov subspace to get the approximate solutions. The whole process is repeated with another unsolved system as a seed until all the systems are solved. We observe in practice a super-convergence behaviour of the CG process of the seed system when compared with the usual CG process. We also observe that only a small number of restarts is required to solve all the systems if the right-hand sides are close to each other. These two features together make the method particularly effective. In this talk, we give theoretical proof to justify these observations. Furthermore, we combine the advantages of this method and the block CG method and propose a block extension of this single seed method. The above procedure can actually be modified for solving multiple linear systems A{sup (i)}x{sup (i)} = b{sup (i)}, where A{sup (i)} are now different. We can also extend the previous analytical results to this more general case. Applications of this method to multiple related linear systems arising from image restoration and recursive least squares computations are considered as examples.

The fairness, predictive validity and acceptability of multiple mini interview in an internationally diverse student population--a mixed methods study.

Science.gov (United States)

Kelly, Maureen E; Dowell, Jon; Husbands, Adrian; Newell, John; O'Flynn, Siun; Kropmans, Thomas; Dunne, Fidelma P; Murphy, Andrew W

2014-12-21

International medical students, those attending medical school outside of their country of citizenship, account for a growing proportion of medical undergraduates worldwide. This study aimed to establish the fairness, predictive validity and acceptability of Multiple Mini Interview (MMI) in an internationally diverse student population. This was an explanatory sequential, mixed methods study. All students in First Year Medicine, National University of Ireland Galway 2012 were eligible to sit a previously validated 10 station MMI. Quantitative data comprised: demographics, selection tool scores and First Year Assessment scores. Qualitative data comprised separate focus groups with MMI Assessors, EU and Non-EU students. 109 students participated (45% of class). Of this 41.3% (n = 45) were Non-EU and 35.8% (n = 39) did not have English as first language. Age, gender and socioeconomic class did not impact on MMI scores. Non-EU students and those for whom English was not a first language achieved significantly lower scores on MMI than their EU and English speaking counterparts (difference in mean 11.9% and 12.2% respectively, PIELTS) (r = 0.5, PIELTS (r = 0.44; p = 0.006; n = 38) and EU school exit exam (r = 0.52; p<0.001; n = 56). MMI predicted EU student OSCE performance (r = 0.27; p = 0.03; n = 64). In the analysis of focus group data two overarching themes emerged: Authenticity and Cultural Awareness. MMI was considered a highly authentic assessment that offered a deeper understanding of the applicant than traditional tools, with an immediate relevance to clinical practice. Cultural specificity of some stations and English language proficiency were seen to disadvantage international students. Recommendations included cultural awareness training for MMI assessors, designing and piloting culturally neutral stations, lengthening station duration and providing high quality advance information to candidates. MMI is a welcome addition to assessment armamentarium for

A multiple regression method for genomewide association studies ...

Indian Academy of Sciences (India)

Bujun Mei

2018-06-07

Jun 7, 2018 ... Similar to the typical genomewide association tests using LD ... new approach performed validly when the multiple regression based on linkage method was employed. .... the model, two groups of scenarios were simulated.

Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

Directory of Open Access Journals (Sweden)

Tabitha A Graves

Full Text Available Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic and bear rubs (opportunistic. We used hierarchical abundance models (N-mixture models with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1 lead to the selection of the same variables as important and (2 provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3 yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight, and (4 improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed

An Approximate Method for Pitch-Damping Prediction

National Research Council Canada - National Science Library

Danberg, James

2003-01-01

...) method for predicting the pitch-damping coefficients has been employed. The CFD method provides important details necessary to derive the correlation functions that are unavailable from the current experimental database...

Epitope prediction methods

DEFF Research Database (Denmark)

Karosiene, Edita

Analysis. The chapter provides detailed explanations on how to use different methods for T cell epitope discovery research, explaining how input should be given as well as how to interpret the output. In the last chapter, I present the results of a bioinformatics analysis of epitopes from the yellow fever...... peptide-MHC interactions. Furthermore, using yellow fever virus epitopes, we demonstrated the power of the %Rank score when compared with the binding affinity score of MHC prediction methods, suggesting that this score should be considered to be used for selecting potential T cell epitopes. In summary...... immune responses. Therefore, it is of great importance to be able to identify peptides that bind to MHC molecules, in order to understand the nature of immune responses and discover T cell epitopes useful for designing new vaccines and immunotherapies. MHC molecules in humans, referred to as human...

Connecting clinical and actuarial prediction with rule-based methods.

Science.gov (United States)

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

Application of Soft Computing Techniques and Multiple Regression Models for CBR prediction of Soils

Directory of Open Access Journals (Sweden)

Fatimah Khaleel Ibrahim

2017-08-01

Full Text Available The techniques of soft computing technique such as Artificial Neutral Network (ANN have improved the predicting capability and have actually discovered application in Geotechnical engineering. The aim of this research is to utilize the soft computing technique and Multiple Regression Models (MLR for forecasting the California bearing ratio CBR( of soil from its index properties. The indicator of CBR for soil could be predicted from various soils characterizing parameters with the assist of MLR and ANN methods. The data base that collected from the laboratory by conducting tests on 86 soil samples that gathered from different projects in Basrah districts. Data gained from the experimental result were used in the regression models and soft computing techniques by using artificial neural network. The liquid limit, plastic index , modified compaction test and the CBR test have been determined. In this work, different ANN and MLR models were formulated with the different collection of inputs to be able to recognize their significance in the prediction of CBR. The strengths of the models that were developed been examined in terms of regression coefficient (R2, relative error (RE% and mean square error (MSE values. From the results of this paper, it absolutely was noticed that all the proposed ANN models perform better than that of MLR model. In a specific ANN model with all input parameters reveals better outcomes than other ANN models.

Symbolic interactionism as a theoretical perspective for multiple method research.

Science.gov (United States)

Benzies, K M; Allen, M N

2001-02-01

Qualitative and quantitative research rely on different epistemological assumptions about the nature of knowledge. However, the majority of nurse researchers who use multiple method designs do not address the problem of differing theoretical perspectives. Traditionally, symbolic interactionism has been viewed as one perspective underpinning qualitative research, but it is also the basis for quantitative studies. Rooted in social psychology, symbolic interactionism has a rich intellectual heritage that spans more than a century. Underlying symbolic interactionism is the major assumption that individuals act on the basis of the meaning that things have for them. The purpose of this paper is to present symbolic interactionism as a theoretical perspective for multiple method designs with the aim of expanding the dialogue about new methodologies. Symbolic interactionism can serve as a theoretical perspective for conceptually clear and soundly implemented multiple method research that will expand the understanding of human health behaviour.

FREEZING AND THAWING TIME PREDICTION METHODS OF FOODS II: NUMARICAL METHODS

Directory of Open Access Journals (Sweden)

Yahya TÜLEK

1999-03-01

Full Text Available Freezing is one of the excellent methods for the preservation of foods. If freezing and thawing processes and frozen storage method are carried out correctly, the original characteristics of the foods can remain almost unchanged over an extended periods of time. It is very important to determine the freezing and thawing time period of the foods, as they strongly influence the both quality of food material and process productivity and the economy. For developing a simple and effectively usable mathematical model, less amount of process parameters and physical properties should be enrolled in calculations. But it is a difficult to have all of these in one prediction method. For this reason, various freezing and thawing time prediction methods were proposed in literature and research studies have been going on.

Multiple genetic interaction experiments provide complementary information useful for gene function prediction.

Directory of Open Access Journals (Sweden)

Magali Michaut

Full Text Available Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.

Predicting the oral pharmacokinetic profiles of multiple-unit (pellet) dosage forms using a modeling and simulation approach coupled with biorelevant dissolution testing: case example diclofenac sodium.

Science.gov (United States)

Kambayashi, Atsushi; Blume, Henning; Dressman, Jennifer B

2014-07-01

The objective of this research was to characterize the dissolution profile of a poorly soluble drug, diclofenac, from a commercially available multiple-unit enteric coated dosage form, Diclo-Puren® capsules, and to develop a predictive model for its oral pharmacokinetic profile. The paddle method was used to obtain the dissolution profiles of this dosage form in biorelevant media, with the exposure to simulated gastric conditions being varied in order to simulate the gastric emptying behavior of pellets. A modified Noyes-Whitney theory was subsequently fitted to the dissolution data. A physiologically-based pharmacokinetic (PBPK) model for multiple-unit dosage forms was designed using STELLA® software and coupled with the biorelevant dissolution profiles in order to simulate the plasma concentration profiles of diclofenac from Diclo-Puren® capsule in both the fasted and fed state in humans. Gastric emptying kinetics relevant to multiple-units pellets were incorporated into the PBPK model by setting up a virtual patient population to account for physiological variations in emptying kinetics. Using in vitro biorelevant dissolution coupled with in silico PBPK modeling and simulation it was possible to predict the plasma profile of this multiple-unit formulation of diclofenac after oral administration in both the fasted and fed state. This approach might be useful to predict variability in the plasma profiles for other drugs housed in multiple-unit dosage forms. Copyright © 2014 Elsevier B.V. All rights reserved.

Investigation into Methods for Predicting Connection Temperatures

Directory of Open Access Journals (Sweden)

K. Anderson

2009-01-01

Full Text Available The mechanical response of connections in fire is largely based on material strength degradation and the interactions between the various components of the connection. In order to predict connection performance in fire, temperature profiles must initially be established in order to evaluate the material strength degradation over time. This paper examines two current methods for predicting connection temperatures: The percentage method, where connection temperatures are calculated as a percentage of the adjacent beam lower-flange, mid-span temperatures; and the lumped capacitance method, based on the lumped mass of the connection. Results from the percentage method do not correlate well with experimental results, whereas the lumped capacitance method shows much better agreement with average connection temperatures. A 3D finite element heat transfer model was also created in Abaqus, and showed good correlation with experimental results. 

Different protein-protein interface patterns predicted by different machine learning methods.

Science.gov (United States)

Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

2017-11-22

Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

Searching for intermediate-mass black holes in galaxies with low-luminosity AGN: a multiple-method approach

Science.gov (United States)

Koliopanos, F.; Ciambur, B.; Graham, A.; Webb, N.; Coriat, M.; Mutlu-Pakdil, B.; Davis, B.; Godet, O.; Barret, D.; Seigar, M.

2017-10-01

Intermediate Mass Black Holes (IMBHs) are predicted by a variety of models and are the likely seeds for super massive BHs (SMBHs). However, we have yet to establish their existence. One method, by which we can discover IMBHs, is by measuring the mass of an accreting BH, using X-ray and radio observations and drawing on the correlation between radio luminosity, X-ray luminosity and the BH mass, known as the fundamental plane of BH activity (FP-BH). Furthermore, the mass of BHs in the centers of galaxies, can be estimated using scaling relations between BH mass and galactic properties. We are initiating a campaign to search for IMBH candidates in dwarf galaxies with low-luminosity AGN, using - for the first time - three different scaling relations and the FP-BH, simultaneously. In this first stage of our campaign, we measure the mass of seven LLAGN, that have been previously suggested to host central IMBHs, investigate the consistency between the predictions of the BH scaling relations and the FP-BH, in the low mass regime and demonstrate that this multiple method approach provides a robust average mass prediction. In my talk, I will discuss our methodology, results and next steps of this campaign.

Solution of Constrained Optimal Control Problems Using Multiple Shooting and ESDIRK Methods

DEFF Research Database (Denmark)

Capolei, Andrea; Jørgensen, John Bagterp

2012-01-01

of this paper is the use of ESDIRK integration methods for solution of the initial value problems and the corresponding sensitivity equations arising in the multiple shooting algorithm. Compared to BDF-methods, ESDIRK-methods are advantageous in multiple shooting algorithms in which restarts and frequent...... algorithm. As we consider stiff systems, implicit solvers with sensitivity computation capabilities for initial value problems must be used in the multiple shooting algorithm. Traditionally, multi-step methods based on the BDF algorithm have been used for such problems. The main novel contribution...... discontinuities on each shooting interval are present. The ESDIRK methods are implemented using an inexact Newton method that reuses the factorization of the iteration matrix for the integration as well as the sensitivity computation. Numerical experiments are provided to demonstrate the algorithm....

Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

Energy Technology Data Exchange (ETDEWEB)

Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng; Pota, Hemanshu; Gadh, Rajit

2016-05-02

This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA) models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.

Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction.

Directory of Open Access Journals (Sweden)

Thomas H A Ederveen

Full Text Available Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35-52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4% and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity.

A Unified Spatiotemporal Modeling Approach for Predicting Concentrations of Multiple Air Pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Science.gov (United States)

Olives, Casey; Kim, Sun-Young; Sheppard, Lianne; Sampson, Paul D.; Szpiro, Adam A.; Oron, Assaf P.; Lindström, Johan; Vedal, Sverre; Kaufman, Joel D.

2014-01-01

Background: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time. Objectives: We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Methods: We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants’ homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations. Results: Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92. Conclusions: This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies. Citation: Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, Oron AP, Lindström J, Vedal S, Kaufman JD. 2015. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the Multi

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

International Nuclear Information System (INIS)

Liu, Guangming; Ouyang, Minggao; Lu, Languang; Li, Jianqiu; Hua, Jianfeng

2015-01-01

Highlights: • An energy prediction (EP) method is introduced for battery E RDE determination. • EP determines E RDE through coupled prediction of future states, parameters, and output. • The PAEP combines parameter adaptation and prediction to update model parameters. • The PAEP provides improved E RDE accuracy compared with DC and other EP methods. - Abstract: In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (E RDE ) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge (SOC). To enhance the E RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E RDE prediction. The correlation of SOC estimation and E RDE calculation is then discussed to illustrate the

Multiple Site-Directed and Saturation Mutagenesis by the Patch Cloning Method.

Science.gov (United States)

Taniguchi, Naohiro; Murakami, Hiroshi

2017-01-01

Constructing protein-coding genes with desired mutations is a basic step for protein engineering. Herein, we describe a multiple site-directed and saturation mutagenesis method, termed MUPAC. This method has been used to introduce multiple site-directed mutations in the green fluorescent protein gene and in the moloney murine leukemia virus reverse transcriptase gene. Moreover, this method was also successfully used to introduce randomized codons at five desired positions in the green fluorescent protein gene, and for simple DNA assembly for cloning.

Multiple-Features-Based Semisupervised Clustering DDoS Detection Method

Directory of Open Access Journals (Sweden)

Yonghao Gu

2017-01-01

Full Text Available DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.

Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours.

Science.gov (United States)

Sharifi, N; Ozgoli, S; Ramezani, A

2017-06-01

Mixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to 'design' an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. The method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. Although current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to suitable results with a perceptible reduction in medicine side effects. It is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth. Copyright © 2017 Elsevier B.V. All rights reserved.

Statistical Methods for Magnetic Resonance Image Analysis with Applications to Multiple Sclerosis

Science.gov (United States)

Pomann, Gina-Maria

image regression techniques have been shown to have modest performance for assessing the integrity of the blood-brain barrier based on imaging without contrast agents. These models have centered on the problem of cross-sectional classification in which patients are imaged at a single study visit and pre-contrast images are used to predict post-contrast imaging. In this paper, we extend these methods to incorporate historical imaging information, and we find the proposed model to exhibit improved performance. We further develop scan-stratified case-control sampling techniques that reduce the computational burden of local image regression models while respecting the low proportion of the brain that exhibits abnormal vascular permeability. In the third part of this thesis, we present methods to evaluate tissue damage in patients with MS. We propose a lag functional linear model to predict a functional response using multiple functional predictors observed at discrete grids with noise. Two procedures are proposed to estimate the regression parameter functions; 1) a semi-local smoothing approach using generalized cross-validation; and 2) a global smoothing approach using a restricted maximum likelihood framework. Numerical studies are presented to analyze predictive accuracy in many realistic scenarios. We find that the global smoothing approach results in higher predictive accuracy than the semi-local approach. The methods are employed to estimate a measure of tissue damage in patients with MS. In patients with MS, the myelin sheaths around the axons of the neurons in the brain and spinal cord are damaged. The model facilitates the use of commonly acquired imaging modalities to estimate a measure of tissue damage within lesions. The proposed model outperforms the cross-sectional models that do not account for temporal patterns of lesional development and repair.

Estimation of uncertainties in predictions of environmental transfer models: evaluation of methods and application to CHERPAC

International Nuclear Information System (INIS)

Koch, J.; Peterson, S-R.

1995-10-01

Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs

Estimation of uncertainties in predictions of environmental transfer models: evaluation of methods and application to CHERPAC

Energy Technology Data Exchange (ETDEWEB)

Koch, J. [Israel Atomic Energy Commission, Yavne (Israel). Soreq Nuclear Research Center; Peterson, S-R.

1995-10-01

Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs.

Multi-target QSPR modeling for simultaneous prediction of multiple gas-phase kinetic rate constants of diverse chemicals

Science.gov (United States)

Basant, Nikita; Gupta, Shikha

2018-03-01

The reactions of molecular ozone (O3), hydroxyl (•OH) and nitrate (NO3) radicals are among the major pathways of removal of volatile organic compounds (VOCs) in the atmospheric environment. The gas-phase kinetic rate constants (kO3, kOH, kNO3) are thus, important in assessing the ultimate fate and exposure risk of atmospheric VOCs. Experimental data for rate constants are not available for many emerging VOCs and the computational methods reported so far address a single target modeling only. In this study, we have developed a multi-target (mt) QSPR model for simultaneous prediction of multiple kinetic rate constants (kO3, kOH, kNO3) of diverse organic chemicals considering an experimental data set of VOCs for which values of all the three rate constants are available. The mt-QSPR model identified and used five descriptors related to the molecular size, degree of saturation and electron density in a molecule, which were mechanistically interpretable. These descriptors successfully predicted three rate constants simultaneously. The model yielded high correlations (R2 = 0.874-0.924) between the experimental and simultaneously predicted endpoint rate constant (kO3, kOH, kNO3) values in test arrays for all the three systems. The model also passed all the stringent statistical validation tests for external predictivity. The proposed multi-target QSPR model can be successfully used for predicting reactivity of new VOCs simultaneously for their exposure risk assessment.

Prediction of P53 mutants (multiple sites transcriptional activity based on structural (2D&3D properties.

Directory of Open Access Journals (Sweden)

R Geetha Ramani

Full Text Available Prediction of secondary site mutations that reinstate mutated p53 to normalcy has been the focus of intense research in the recent past owing to the fact that p53 mutants have been implicated in more than half of all human cancers and restoration of p53 causes tumor regression. However laboratory investigations are more often laborious and resource intensive but computational techniques could well surmount these drawbacks. In view of this, we formulated a novel approach utilizing computational techniques to predict the transcriptional activity of multiple site (one-site to five-site p53 mutants. The optimal MCC obtained by the proposed approach on prediction of one-site, two-site, three-site, four-site and five-site mutants were 0.775,0.341,0.784,0.916 and 0.655 respectively, the highest reported thus far in literature. We have also demonstrated that 2D and 3D features generate higher prediction accuracy of p53 activity and our findings revealed the optimal results for prediction of p53 status, reported till date. We believe detection of the secondary site mutations that suppress tumor growth may facilitate better understanding of the relationship between p53 structure and function and further knowledge on the molecular mechanisms and biological activity of p53, a targeted source for cancer therapy. We expect that our prediction methods and reported results may provide useful insights on p53 functional mechanisms and generate more avenues for utilizing computational techniques in biological data analysis.

A crack growth evaluation method for interacting multiple cracks

International Nuclear Information System (INIS)

Kamaya, Masayuki

2003-01-01

When stress corrosion cracking or corrosion fatigue occurs, multiple cracks are frequently initiated in the same area. According to section XI of the ASME Boiler and Pressure Vessel Code, multiple cracks are considered as a single combined crack in crack growth analysis, if the specified conditions are satisfied. In crack growth processes, however, no prescription for the interference between multiple cracks is given in this code. The JSME Post-Construction Code, issued in May 2000, prescribes the conditions of crack coalescence in the crack growth process. This study aimed to extend this prescription to more general cases. A simulation model was applied, to simulate the crack growth process, taking into account the interference between two cracks. This model made it possible to analyze multiple crack growth behaviors for many cases (e.g. different relative position and length) that could not be studied by experiment only. Based on these analyses, a new crack growth analysis method was suggested for taking into account the interference between multiple cracks. (author)

Upscaling permeability for three-dimensional fractured porous rocks with the multiple boundary method

Science.gov (United States)

Chen, Tao; Clauser, Christoph; Marquart, Gabriele; Willbrand, Karen; Hiller, Thomas

2018-02-01

Upscaling permeability of grid blocks is crucial for groundwater models. A novel upscaling method for three-dimensional fractured porous rocks is presented. The objective of the study was to compare this method with the commonly used Oda upscaling method and the volume averaging method. First, the multiple boundary method and its computational framework were defined for three-dimensional stochastic fracture networks. Then, the different upscaling methods were compared for a set of rotated fractures, for tortuous fractures, and for two discrete fracture networks. The results computed by the multiple boundary method are comparable with those of the other two methods and fit best the analytical solution for a set of rotated fractures. The errors in flow rate of the equivalent fracture model decrease when using the multiple boundary method. Furthermore, the errors of the equivalent fracture models increase from well-connected fracture networks to poorly connected ones. Finally, the diagonal components of the equivalent permeability tensors tend to follow a normal or log-normal distribution for the well-connected fracture network model with infinite fracture size. By contrast, they exhibit a power-law distribution for the poorly connected fracture network with multiple scale fractures. The study demonstrates the accuracy and the flexibility of the multiple boundary upscaling concept. This makes it attractive for being incorporated into any existing flow-based upscaling procedures, which helps in reducing the uncertainty of groundwater models.

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

Science.gov (United States)

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

Using the Direct Sampling Multiple-Point Geostatistical Method for Filling Gaps in Landsat 7 ETM+ SLC-off Imagery

KAUST Repository

Yin, Gaohong

2016-05-01

Since the failure of the Scan Line Corrector (SLC) instrument on Landsat 7, observable gaps occur in the acquired Landsat 7 imagery, impacting the spatial continuity of observed imagery. Due to the highly geometric and radiometric accuracy provided by Landsat 7, a number of approaches have been proposed to fill the gaps. However, all proposed approaches have evident constraints for universal application. The main issues in gap-filling are an inability to describe the continuity features such as meandering streams or roads, or maintaining the shape of small objects when filling gaps in heterogeneous areas. The aim of the study is to validate the feasibility of using the Direct Sampling multiple-point geostatistical method, which has been shown to reconstruct complicated geological structures satisfactorily, to fill Landsat 7 gaps. The Direct Sampling method uses a conditional stochastic resampling of known locations within a target image to fill gaps and can generate multiple reconstructions for one simulation case. The Direct Sampling method was examined across a range of land cover types including deserts, sparse rural areas, dense farmlands, urban areas, braided rivers and coastal areas to demonstrate its capacity to recover gaps accurately for various land cover types. The prediction accuracy of the Direct Sampling method was also compared with other gap-filling approaches, which have been previously demonstrated to offer satisfactory results, under both homogeneous area and heterogeneous area situations. Studies have shown that the Direct Sampling method provides sufficiently accurate prediction results for a variety of land cover types from homogeneous areas to heterogeneous land cover types. Likewise, it exhibits superior performances when used to fill gaps in heterogeneous land cover types without input image or with an input image that is temporally far from the target image in comparison with other gap-filling approaches.

Improvement of gas entrainment prediction method. Introduction of surface tension effect

International Nuclear Information System (INIS)

Ito, Kei; Sakai, Takaaki; Ohshima, Hiroyuki; Uchibori, Akihiro; Eguchi, Yuzuru; Monji, Hideaki; Xu, Yongze

2010-01-01

A gas entrainment (GE) prediction method has been developed to establish design criteria for the large-scale sodium-cooled fast reactor (JSFR) systems. The prototype of the GE prediction method was already confirmed to give reasonable gas core lengths by simple calculation procedures. However, for simplification, the surface tension effects were neglected. In this paper, the evaluation accuracy of gas core lengths is improved by introducing the surface tension effects into the prototype GE prediction method. First, the mechanical balance between gravitational, centrifugal, and surface tension forces is considered. Then, the shape of a gas core tip is approximated by a quadratic function. Finally, using the approximated gas core shape, the authors determine the gas core length satisfying the mechanical balance. This improved GE prediction method is validated by analyzing the gas core lengths observed in simple experiments. Results show that the analytical gas core lengths calculated by the improved GE prediction method become shorter in comparison to the prototype GE prediction method, and are in good agreement with the experimental data. In addition, the experimental data under different temperature and surfactant concentration conditions are reproduced by the improved GE prediction method. (author)

Comparison between Two Assessment Methods; Modified Essay Questions and Multiple Choice Questions

Directory of Open Access Journals (Sweden)

Assadi S.N.* MD

2015-09-01

Full Text Available Aims Using the best assessment methods is an important factor in educational development of health students. Modified essay questions and multiple choice questions are two prevalent methods of assessing the students. The aim of this study was to compare two methods of modified essay questions and multiple choice questions in occupational health engineering and work laws courses. Materials & Methods This semi-experimental study was performed during 2013 to 2014 on occupational health students of Mashhad University of Medical Sciences. The class of occupational health and work laws course in 2013 was considered as group A and the class of 2014 as group B. Each group had 50 students.The group A students were assessed by modified essay questions method and the group B by multiple choice questions method.Data were analyzed in SPSS 16 software by paired T test and odd’s ratio. Findings The mean grade of occupational health and work laws course was 18.68±0.91 in group A (modified essay questions and was 18.78±0.86 in group B (multiple choice questions which was not significantly different (t=-0.41; p=0.684. The mean grade of chemical chapter (p<0.001 in occupational health engineering and harmful work law (p<0.001 and other (p=0.015 chapters in work laws were significantly different between two groups. Conclusion Modified essay questions and multiple choice questions methods have nearly the same student assessing value for the occupational health engineering and work laws course.

Method and apparatus to predict the remaining service life of an operating system

Science.gov (United States)

Greitzer, Frank L.; Kangas, Lars J.; Terrones, Kristine M.; Maynard, Melody A.; Pawlowski, Ronald A. , Ferryman; Thomas A.; Skorpik, James R.; Wilson, Bary W.

2008-11-25

A method and computer-based apparatus for monitoring the degradation of, predicting the remaining service life of, and/or planning maintenance for, an operating system are disclosed. Diagnostic information on degradation of the operating system is obtained through measurement of one or more performance characteristics by one or more sensors onboard and/or proximate the operating system. Though not required, it is preferred that the sensor data are validated to improve the accuracy and reliability of the service life predictions. The condition or degree of degradation of the operating system is presented to a user by way of one or more calculated, numeric degradation figures of merit that are trended against one or more independent variables using one or more mathematical techniques. Furthermore, more than one trendline and uncertainty interval may be generated for a given degradation figure of merit/independent variable data set. The trendline(s) and uncertainty interval(s) are subsequently compared to one or more degradation figure of merit thresholds to predict the remaining service life of the operating system. The present invention enables multiple mathematical approaches in determining which trendline(s) to use to provide the best estimate of the remaining service life.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods.

Science.gov (United States)

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-01-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program "e-Bitter" is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-learning Methods

Science.gov (United States)

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-03-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

Least squares reverse time migration of controlled order multiples

Science.gov (United States)

Liu, Y.

2016-12-01

Imaging using the reverse time migration of multiples generates inherent crosstalk artifacts due to the interference among different order multiples. Traditionally, least-square fitting has been used to address this issue by seeking the best objective function to measure the amplitude differences between the predicted and observed data. We have developed an alternative objective function by decomposing multiples into different orders to minimize the difference between Born modeling predicted multiples and specific-order multiples from observational data in order to attenuate the crosstalk. This method is denoted as the least-squares reverse time migration of controlled order multiples (LSRTM-CM). Our numerical examples demonstrated that the LSRTM-CM can significantly improve image quality compared with reverse time migration of multiples and least-square reverse time migration of multiples. Acknowledgments This research was funded by the National Nature Science Foundation of China (Grant Nos. 41430321 and 41374138).

Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels.

Science.gov (United States)

Sumiyoshi, Chika; Harvey, Philip D; Takaki, Manabu; Okahisa, Yuko; Sato, Taku; Sora, Ichiro; Nuechterlein, Keith H; Subotnik, Kenneth L; Sumiyoshi, Tomiki

2015-09-01

Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1) to identify which outcome factors predict occupational functioning, quantified as work hours, and 2) to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB), the UCSD Performance-based Skills Assessment-Brief (UPSA-B), and the Social Functioning Scale Individuals' version modified for the MATRICS-PASS (Modified SFS for PASS), respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly) and a multiple logistic regression analyses (predicting dichotomized work status based on work hours). ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60-70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.

Method for Predicting Thermal Buckling in Rails

Science.gov (United States)

2018-01-01

A method is proposed herein for predicting the onset of thermal buckling in rails in such a way as to provide a means of avoiding this type of potentially devastating failure. The method consists of the development of a thermomechanical model of rail...

Decentralized Model Predictive Control for Cooperative Multiple Vehicles Subject to Communication Loss

Directory of Open Access Journals (Sweden)

Hojjat A. Izadi

2011-01-01

Full Text Available The decentralized model predictive control (DMPC of multiple cooperative vehicles with the possibility of communication loss/delay is investigated. The neighboring vehicles exchange their predicted trajectories at every sample time to maintain the cooperation objectives. In the event of a communication loss (packet dropout, the most recent available information, which is potentially delayed, is used. Then the communication loss problem changes to a cooperative problem when random large communication delays are present. Such large communication delays can lead to poor cooperation performance and unsafe behaviors such as collisions. A new DMPC approach is developed to improve the cooperation performance and achieve safety in the presence of the large communication delays. The proposed DMPC architecture estimates the tail of neighbor's trajectory which is not available due to the large communication delays for improving the performance. The concept of the tube MPC is also employed to provide the safety of the fleet against collisions, in the presence of large intervehicle communication delays. In this approach, a tube shaped trajectory set is assumed around the trajectory of the neighboring vehicles whose trajectory is delayed/lost. The radius of tube is a function of the communication delay and vehicle's maneuverability (in the absence of model uncertainty. The simulation of formation problem of multiple vehicles is employed to illustrate the effectiveness of the proposed approach.

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

NARCIS (Netherlands)

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

2009-01-01

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

An Advanced Method to Apply Multiple Rainfall Thresholds for Urban Flood Warnings

Directory of Open Access Journals (Sweden)

Jiun-Huei Jang

2015-11-01

Full Text Available Issuing warning information to the public when rainfall exceeds given thresholds is a simple and widely-used method to minimize flood risk; however, this method lacks sophistication when compared with hydrodynamic simulation. In this study, an advanced methodology is proposed to improve the warning effectiveness of the rainfall threshold method for urban areas through deterministic-stochastic modeling, without sacrificing simplicity and efficiency. With regards to flooding mechanisms, rainfall thresholds of different durations are divided into two groups accounting for flooding caused by drainage overload and disastrous runoff, which help in grading the warning level in terms of emergency and severity when the two are observed together. A flood warning is then classified into four levels distinguished by green, yellow, orange, and red lights in ascending order of priority that indicate the required measures, from standby, flood defense, evacuation to rescue, respectively. The proposed methodology is tested according to 22 historical events in the last 10 years for 252 urbanized townships in Taiwan. The results show satisfactory accuracy in predicting the occurrence and timing of flooding, with a logical warning time series for taking progressive measures. For systems with multiple rainfall thresholds already in place, the methodology can be used to ensure better application of rainfall thresholds in urban flood warnings.

Transcription-based prediction of response to IFNbeta using supervised computational methods.

Directory of Open Access Journals (Sweden)

Sergio E Baranzini

2005-01-01

Full Text Available Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s. Recombinant human interferon beta (rIFNbeta is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNbeta to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNbeta engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.

Mixed price and load forecasting of electricity markets by a new iterative prediction method

International Nuclear Information System (INIS)

Amjady, Nima; Daraeepour, Ali

2009-01-01

Load and price forecasting are the two key issues for the participants of current electricity markets. However, load and price of electricity markets have complex characteristics such as nonlinearity, non-stationarity and multiple seasonality, to name a few (usually, more volatility is seen in the behavior of electricity price signal). For these reasons, much research has been devoted to load and price forecast, especially in the recent years. However, previous research works in the area separately predict load and price signals. In this paper, a mixed model for load and price forecasting is presented, which can consider interactions of these two forecast processes. The mixed model is based on an iterative neural network based prediction technique. It is shown that the proposed model can present lower forecast errors for both load and price compared with the previous separate frameworks. Another advantage of the mixed model is that all required forecast features (from load or price) are predicted within the model without assuming known values for these features. So, the proposed model can better be adapted to real conditions of an electricity market. The forecast accuracy of the proposed mixed method is evaluated by means of real data from the New York and Spanish electricity markets. The method is also compared with some of the most recent load and price forecast techniques. (author)

Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks

Directory of Open Access Journals (Sweden)

Qifan Chen

2016-01-01

Full Text Available Predicting critical nodes of Opportunistic Sensor Network (OSN can help us not only to improve network performance but also to decrease the cost in network maintenance. However, existing ways of predicting critical nodes in static network are not suitable for OSN. In this paper, the conceptions of critical nodes, region contribution, and cut-vertex in multiregion OSN are defined. We propose an approach to predict critical node for OSN, which is based on multiple attribute decision making (MADM. It takes RC to present the dependence of regions on Ferry nodes. TOPSIS algorithm is employed to find out Ferry node with maximum comprehensive contribution, which is a critical node. The experimental results show that, in different scenarios, this approach can predict the critical nodes of OSN better.

Prediction of the number of 14 MeV neutron elastically scattered from large sample of aluminium using Monte Carlo simulation method

International Nuclear Information System (INIS)

Husin Wagiran; Wan Mohd Nasir Wan Kadir

1997-01-01

In neutron scattering processes, the effect of multiple scattering is to cause an effective increase in the measured cross-sections due to increase on the probability of neutron scattering interactions in the sample. Analysis of how the effective cross-section varies with thickness is very complicated due to complicated sample geometries and the variations of scattering cross-section with energy. Monte Carlo method is one of the possible method for treating the multiple scattering processes in the extended sample. In this method a lot of approximations have to be made and the accurate data of microscopic cross-sections are needed at various angles. In the present work, a Monte Carlo simulation programme suitable for a small computer was developed. The programme was capable to predict the number of neutrons scattered from various thickness of aluminium samples at all possible angles between 00 to 36011 with 100 increments. In order to make the the programme not too complicated and capable of being run on microcomputer with reasonable time, the calculations was done in two dimension coordinate system. The number of neutrons predicted from this model show in good agreement with previous experimental results

Permeability of the blood-brain barrier predicts conversion from optic neuritis to multiple sclerosis

DEFF Research Database (Denmark)

Cramer, Stig P; Modvig, Signe; Simonsen, Helle Juhl

2015-01-01

in the permeability of the blood-brain barrier in normal-appearing white matter of patients with multiple sclerosis and here, for the first time, we present a study on the capability of blood-brain barrier permeability in predicting conversion from optic neuritis to multiple sclerosis and a direct comparison...... with cerebrospinal fluid markers of inflammation, cellular trafficking and blood-brain barrier breakdown. To this end, we applied dynamic contrast-enhanced magnetic resonance imaging at 3 T to measure blood-brain barrier permeability in 39 patients with monosymptomatic optic neuritis, all referred for imaging...... fluid as well as levels of CXCL10 and MMP9 in the cerebrospinal fluid. These findings suggest that blood-brain barrier permeability, as measured by magnetic resonance imaging, may provide novel pathological information as a marker of neuroinflammation related to multiple sclerosis, to some extent...

Multiple nano elements of SCC--transition from phenomenology to predictive mechanistics

International Nuclear Information System (INIS)

Staehle, R.W.

2009-01-01

Full text of publication follows: Predicting the occurrence and rate of stress corrosion cracking in materials of construction is one of the most critical pathways for assuring the reliability of light water nuclear reactor plants. It is the general intention of operators of nuclear plants that they continue performing satisfactorily for times of 60 to 80 years at least. Such times are beyond existing experience, and there are no bases for choosing credible predictions. Present bases for predicting SCC rely on anecdotal experience for predicting what materials sustain SCC in specified environments and on phenomenological correlations using such parameters as K (stress intensity), 1/T (temperature), E(corr) (corrosion potential), pH, [x] a (concentration), other established quantities, and statistical correlations. While these phenomenological correlations have served the industry well in the past, they have also allowed grievous mistakes. Further, such correlations are flawed in their fundamental credibility. Predicting SCC in aqueous solutions means to predict its dependence upon the seven primary variables: potential, pH, species, alloy composition, alloy structure, stress and temperature. A serious prediction of SCC upon these seven primary variables can only be achieved by moving to fundamental nano elements. Unfortunately, useful predictability from the nano approach cannot be achieved quickly or easily; thus, it will continue to be necessary to rely on existing phenomenology. However, as the nano approach evolves, it can contribute increasingly to the quantitative capacity of the phenomenological approach. The nano approach will require quite different talents and thinking than are now applied to the prediction of SCC; while some of the boundary conditions of phenomenology must continue to be applied, elements of the nano approach will include accounting for at least, typically, the following multiple elements as they apply at the sites of initiation and at

Walking path-planning method for multiple radiation areas

International Nuclear Information System (INIS)

Liu, Yong-kuo; Li, Meng-kun; Peng, Min-jun; Xie, Chun-li; Yuan, Cheng-qian; Wang, Shuang-yu; Chao, Nan

2016-01-01

Highlights: • Radiation environment modeling method is designed. • Path-evaluating method and segmented path-planning method are proposed. • Path-planning simulation platform for radiation environment is built. • The method avoids to be misled by minimum dose path in single area. - Abstract: Based on minimum dose path-searching method, walking path-planning method for multiple radiation areas was designed to solve minimum dose path problem in single area and find minimum dose path in the whole space in this paper. Path-planning simulation platform was built using C# programming language and DirectX engine. The simulation platform was used in simulations dealing with virtual nuclear facilities. Simulation results indicated that the walking-path planning method is effective in providing safety for people walking in nuclear facilities.

Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

KAUST Repository

Abdul Jameel, Abdul Gani

2016-09-14

An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH–CH2 groups, naphthenic CH–CH2 groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using 1H NMR spectroscopy. Multiple linear regression (MLR) modeling was used to develop an empirical relationship between D/CN and the eight structural parameters. This was then used to predict the DCN of many hydrocarbon fuels. The developed model has a high correlation coefficient (R2 = 0.97) and was validated with experimentally measured DCN of twenty-two real fuel mixtures (e.g., gasolines and diesels) and fifty-nine blends of known composition, and the predicted values matched well with the experimental data.

Evaluation and comparison of mammalian subcellular localization prediction methods

Directory of Open Access Journals (Sweden)

Fink J Lynn

2006-12-01

Full Text Available Abstract Background Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER, peroxisome, and lysosome. The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE

Performance prediction method for a multi-stage Knudsen pump

Science.gov (United States)

Kugimoto, K.; Hirota, Y.; Kizaki, Y.; Yamaguchi, H.; Niimi, T.

2017-12-01

In this study, the novel method to predict the performance of a multi-stage Knudsen pump is proposed. The performance prediction method is carried out in two steps numerically with the assistance of a simple experimental result. In the first step, the performance of a single-stage Knudsen pump was measured experimentally under various pressure conditions, and the relationship of the mass flow rate was obtained with respect to the average pressure between the inlet and outlet of the pump and the pressure difference between them. In the second step, the performance of a multi-stage pump was analyzed by a one-dimensional model derived from the mass conservation law. The performances predicted by the 1D-model of 1-stage, 2-stage, 3-stage, and 4-stage pumps were validated by the experimental results for the corresponding number of stages. It was concluded that the proposed prediction method works properly.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

Science.gov (United States)

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites.

Directory of Open Access Journals (Sweden)

Jianjun He

Full Text Available It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches.

Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis

Directory of Open Access Journals (Sweden)

BUDIMAN

2012-01-01

Full Text Available Budiman, Arisoesilaningsih E. 2012. Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis. Biodiversitas 13: 18-22. The aims of this research was to determine the multiple regression models of vegetative and corm growth of Amorphophallus muelleri Blume in some age variations and habitat conditions of agroforestry in East Java. Descriptive exploratory research method was conducted by systematic random sampling at five agroforestries on four plantations in East Java: Saradan, Bojonegoro, Nganjuk and Blitar. In each agroforestry, we observed A. muelleri vegetative and corm growth on four growing age (1, 2, 3 and 4 years old respectively as well as environmental variables such as altitude, vegetation, climate and soil conditions. Data were analyzed using descriptive statistics to compare A. muelleri habitat in five agroforestries. Meanwhile, the influence and contribution of each environmental variable to the growth of A. muelleri vegetative and corm were determined using multiple regression analysis of SPSS 17.0. The multiple regression models of A. muelleri vegetative and corm growth were generated based on some characteristics of agroforestries and age showed high validity with R2 = 88-99%. Regression model showed that age, monthly temperatures, percentage of radiation and soil calcium (Ca content either simultaneously or partially determined the growth of A. muelleri vegetative and corm. Based on these models, the A. muelleri corm reached the optimal growth after four years of cultivation and they will be ready to be harvested. Additionally, the soil Ca content should reach 25.3 me.hg-1 as Sugihwaras agroforestry, with the maximal radiation of 60%.

Ensemble method for dengue prediction.

Science.gov (United States)

Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan

2018-01-01

In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Ensemble method for dengue prediction.

Directory of Open Access Journals (Sweden)

Anna L Buczak

Full Text Available In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico during four dengue seasons: 1 peak height (i.e., maximum weekly number of cases during a transmission season; 2 peak week (i.e., week in which the maximum weekly number of cases occurred; and 3 total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date.Our approach used ensemble models created by combining three disparate types of component models: 1 two-dimensional Method of Analogues models incorporating both dengue and climate data; 2 additive seasonal Holt-Winters models with and without wavelet smoothing; and 3 simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations.Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week.The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Factors predicting work outcome in Japanese patients with schizophrenia: role of multiple functioning levels

Directory of Open Access Journals (Sweden)

Chika Sumiyoshi

2015-09-01

Full Text Available Functional outcomes in individuals with schizophrenia suggest recovery of cognitive, everyday, and social functioning. Specifically improvement of work status is considered to be most important for their independent living and self-efficacy. The main purposes of the present study were 1 to identify which outcome factors predict occupational functioning, quantified as work hours, and 2 to provide cut-offs on the scales for those factors to attain better work status. Forty-five Japanese patients with schizophrenia and 111 healthy controls entered the study. Cognition, capacity for everyday activities, and social functioning were assessed by the Japanese versions of the MATRICS Cognitive Consensus Battery (MCCB, the UCSD Performance-based Skills Assessment-Brief (UPSA-B, and the Social Functioning Scale Individuals’ version modified for the MATRICS-PASS (Modified SFS for PASS, respectively. Potential factors for work outcome were estimated by multiple linear regression analyses (predicting work hours directly and a multiple logistic regression analyses (predicting dichotomized work status based on work hours. ROC curve analyses were performed to determine cut-off points for differentiating between the better- and poor work status. The results showed that a cognitive component, comprising visual/verbal learning and emotional management, and a social functioning component, comprising independent living and vocational functioning, were potential factors for predicting work hours/status. Cut-off points obtained in ROC analyses indicated that 60–70% achievements on the measures of those factors were expected to maintain the better work status. Our findings suggest that improvement on specific aspects of cognitive and social functioning are important for work outcome in patients with schizophrenia.

Development of motion image prediction method using principal component analysis

International Nuclear Information System (INIS)

Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

2012-01-01

Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

Predictive Value of Glasgow Coma Score and Full Outline of Unresponsiveness Score on the Outcome of Multiple Trauma Patients.

Science.gov (United States)

Baratloo, Alireza; Shokravi, Masumeh; Safari, Saeed; Aziz, Awat Kamal

2016-03-01

The Full Outline of Unresponsiveness (FOUR) score was developed to compensate for the limitations of Glasgow coma score (GCS) in recent years. This study aimed to assess the predictive value of GCS and FOUR score on the outcome of multiple trauma patients admitted to the emergency department. The present prospective cross-sectional study was conducted on multiple trauma patients admitted to the emergency department. GCS and FOUR scores were evaluated at the time of admission and at the sixth and twelfth hours after admission. Then the receiver operating characteristic (ROC) curve, sensitivity, specificity, as well as positive and negative predictive value of GCS and FOUR score were evaluated to predict patients' outcome. Patients' outcome was divided into discharge with and without a medical injury (motor deficit, coma or death). Finally, 89 patients were studied. Sensitivity and specificity of GCS in predicting adverse outcome (motor deficit, coma or death) were 84.2% and 88.6% at the time of admission, 89.5% and 95.4% at the sixth hour and 89.5% and 91.5% at the twelfth hour, respectively. These values for the FOUR score were 86.9% and 88.4% at the time of admission, 89.5% and 100% at the sixth hour and 89.5% and 94.4% at the twelfth hour, respectively. Findings of this study indicate that the predictive value of FOUR score and GCS on the outcome of multiple trauma patients admitted to the emergency department is similar.

DASPfind: new efficient method to predict drug–target interactions

KAUST Repository

Ba Alawi, Wail; Soufan, Othman; Essack, Magbubah; Kalnis, Panos; Bajic, Vladimir B.

2016-01-01

DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http://​www.​cbrc.​kaust.​edu.​sa/​daspfind.

A permutation-based multiple testing method for time-course microarray experiments

Directory of Open Access Journals (Sweden)

George Stephen L

2009-10-01

Full Text Available Abstract Background Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005 developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course and alternative (time-course hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation. Results In this paper, we propose a permutation-based multiple testing procedure based on the test statistic used by Storey et al. (2005. We also propose an efficient computation algorithm. Extensive simulations are conducted to investigate the performance of the permutation-based multiple testing procedure. The application of the proposed method is illustrated using the Caenorhabditis elegans dauer developmental data. Conclusion Our method is computationally efficient and applicable for identifying genes whose expression levels are time-dependent in a single biological group and for identifying the genes for which the time-profile depends on the group in a multi-group setting.

Multiple time-scale methods in particle simulations of plasmas

International Nuclear Information System (INIS)

Cohen, B.I.

1985-01-01

This paper surveys recent advances in the application of multiple time-scale methods to particle simulation of collective phenomena in plasmas. These methods dramatically improve the efficiency of simulating low-frequency kinetic behavior by allowing the use of a large timestep, while retaining accuracy. The numerical schemes surveyed provide selective damping of unwanted high-frequency waves and preserve numerical stability in a variety of physics models: electrostatic, magneto-inductive, Darwin and fully electromagnetic. The paper reviews hybrid simulation models, the implicitmoment-equation method, the direct implicit method, orbit averaging, and subcycling

An assessment on epitope prediction methods for protozoa genomes

Directory of Open Access Journals (Sweden)

Resende Daniela M

2012-11-01

Full Text Available Abstract Background Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve performance and a threshold dependent method that employs a confusion matrix; b integrating data from experimentally validated and in silico predicted epitopes; and c integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. Results A database-driven epitope prediction method was developed with built-in functions that were capable of: a removing experimental data redundancy; b parsing algorithms predictions and storage experimental validated and predict data; and c evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value

Fast Prediction Method for Steady-State Heat Convection

KAUST Repository

Wáng, Yì

2012-03-14

A reduced model by proper orthogonal decomposition (POD) and Galerkin projection methods for steady-state heat convection is established on a nonuniform grid. It was verified by thousands of examples that the results are in good agreement with the results obtained from the finite volume method. This model can also predict the cases where model parameters far exceed the sample scope. Moreover, the calculation time needed by the model is much shorter than that needed for the finite volume method. Thus, the nonuniform POD-Galerkin projection method exhibits high accuracy, good suitability, and fast computation. It has universal significance for accurate and fast prediction. Also, the methodology can be applied to more complex modeling in chemical engineering and technology, such as reaction and turbulence. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

Science.gov (United States)

Kountouris, Petros; Hirst, Jonathan D

2010-07-31

Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

Directory of Open Access Journals (Sweden)

Suqing Zheng

2018-03-01

Full Text Available In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc. combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

Directory of Open Access Journals (Sweden)

Fei Gao

2015-01-01

Full Text Available Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

Predicting high risk births with contraceptive prevalence and contraceptive method-mix in an ecologic analysis

Directory of Open Access Journals (Sweden)

Jamie Perin

2017-11-01

Full Text Available Abstract Background Increased contraceptive use has been associated with a decrease in high parity births, births that occur close together in time, and births to very young or to older women. These types of births are also associated with high risk of under-five mortality. Previous studies have looked at the change in the level of contraception use and the average change in these types of high-risk births. We aim to predict the distribution of births in a specific country when there is a change in the level and method of modern contraception. Methods We used data from full birth histories and modern contraceptive use from 207 nationally representative Demographic and Health Surveys covering 71 countries to describe the distribution of births in each survey based on birth order, preceding birth space, and mother’s age at birth. We estimated the ecologic associations between the prevalence and method-mix of modern contraceptives and the proportion of births in each category. Hierarchical modelling was applied to these aggregated cross sectional proportions, so that random effects were estimated for countries with multiple surveys. We use these results to predict the change in type of births associated with scaling up modern contraception in three different scenarios. Results We observed marked differences between regions, in the absolute rates of contraception, the types of contraceptives in use, and in the distribution of type of birth. Contraceptive method-mix was a significant determinant of proportion of high-risk births, especially for birth spacing, but also for mother’s age and parity. Increased use of modern contraceptives is especially predictive of reduced parity and more births with longer preceding space. However, increased contraception alone is not associated with fewer births to women younger than 18 years or a decrease in short-spaced births. Conclusions Both the level and the type of contraception are important factors in

Determination of 226Ra contamination depth in soil using the multiple photopeaks method

International Nuclear Information System (INIS)

Haddad, Kh.; Al-Masri, M.S.; Doubal, A.W.

2014-01-01

Radioactive contamination presents a diverse range of challenges in many industries. Determination of radioactive contamination depth plays a vital role in the assessment of contaminated sites, because it can be used to estimate the activity content. It is determined traditionally by measuring the activity distributions along the depth. This approach gives accurate results, but it is time consuming, lengthy and costly. The multiple photopeaks method was developed in this work for 226 Ra contamination depth determination in a NORM contaminated soil using in-situ gamma spectrometry. The developed method bases on linear correlation between the attenuation ratio of different gamma lines emitted by 214 Bi and the 226 Ra contamination depth. Although this method is approximate, but it is much simpler, faster and cheaper than the traditional one. This method can be applied for any case of multiple gamma emitter contaminant. -- Highlights: • The multiple photopeaks method was developed for 226 Ra contamination depth determination using in-situ gamma spectrometry. • The method bases on linear correlation between the attenuation ratio of 214 Bi gamma lines and 226 Ra contamination depth. • This method is simpler, faster and cheaper than the traditional one, it can be applied for any multiple gamma contaminant

Are Faculty Predictions or Item Taxonomies Useful for Estimating the Outcome of Multiple-Choice Examinations?

Science.gov (United States)

Kibble, Jonathan D.; Johnson, Teresa

2011-01-01

The purpose of this study was to evaluate whether multiple-choice item difficulty could be predicted either by a subjective judgment by the question author or by applying a learning taxonomy to the items. Eight physiology faculty members teaching an upper-level undergraduate human physiology course consented to participate in the study. The…

Predicting chaos in memristive oscillator via harmonic balance method.

Science.gov (United States)

Wang, Xin; Li, Chuandong; Huang, Tingwen; Duan, Shukai

2012-12-01

This paper studies the possible chaotic behaviors in a memristive oscillator with cubic nonlinearities via harmonic balance method which is also called the method of describing function. This method was proposed to detect chaos in classical Chua's circuit. We first transform the considered memristive oscillator system into Lur'e model and present the prediction of the existence of chaotic behaviors. To ensure the prediction result is correct, the distortion index is also measured. Numerical simulations are presented to show the effectiveness of theoretical results.

Trace element analysis of environmental samples by multiple prompt gamma-ray analysis method

International Nuclear Information System (INIS)

Oshima, Masumi; Matsuo, Motoyuki; Shozugawa, Katsumi

2011-01-01

The multiple γ-ray detection method has been proved to be a high-resolution and high-sensitivity method in application to nuclide quantification. The neutron prompt γ-ray analysis method is successfully extended by combining it with the γ-ray detection method, which is called Multiple prompt γ-ray analysis, MPGA. In this review we show the principle of this method and its characteristics. Several examples of its application to environmental samples, especially river sediments in the urban area and sea sediment samples are also described. (author)

Feedback-related brain activity predicts learning from feedback in multiple-choice testing.

Science.gov (United States)

Ernst, Benjamin; Steinhauser, Marco

2012-06-01

Different event-related potentials (ERPs) have been shown to correlate with learning from feedback in decision-making tasks and with learning in explicit memory tasks. In the present study, we investigated which ERPs predict learning from corrective feedback in a multiple-choice test, which combines elements from both paradigms. Participants worked through sets of multiple-choice items of a Swahili-German vocabulary task. Whereas the initial presentation of an item required the participants to guess the answer, corrective feedback could be used to learn the correct response. Initial analyses revealed that corrective feedback elicited components related to reinforcement learning (FRN), as well as to explicit memory processing (P300) and attention (early frontal positivity). However, only the P300 and early frontal positivity were positively correlated with successful learning from corrective feedback, whereas the FRN was even larger when learning failed. These results suggest that learning from corrective feedback crucially relies on explicit memory processing and attentional orienting to corrective feedback, rather than on reinforcement learning.

A Method for Driving Route Predictions Based on Hidden Markov Model

Directory of Open Access Journals (Sweden)

Ning Ye

2015-01-01

Full Text Available We present a driving route prediction method that is based on Hidden Markov Model (HMM. This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

A novel method for producing multiple ionization of noble gas

International Nuclear Information System (INIS)

Wang Li; Li Haiyang; Dai Dongxu; Bai Jiling; Lu Richang

1997-01-01

We introduce a novel method for producing multiple ionization of He, Ne, Ar, Kr and Xe. A nanosecond pulsed electron beam with large number density, which could be energy-controlled, was produced by incidence a focused 308 nm laser beam onto a stainless steel grid. On Time-of-Flight Mass Spectrometer, using this electron beam, we obtained multiple ionization of noble gas He, Ne, Ar and Xe. Time of fight mass spectra of these ions were given out. These ions were supposed to be produced by step by step ionization of the gas atoms by electron beam impact. This method may be used as a ideal soft ionizing point ion source in Time of Flight Mass Spectrometer

[Application of R-based multiple seasonal ARIMA model, in predicting the incidence of hand, foot and mouth disease in Shaanxi province].

Science.gov (United States)

Liu, F; Zhu, N; Qiu, L; Wang, J J; Wang, W H

2016-08-10

To apply the ' auto-regressive integrated moving average product seasonal model' in predicting the number of hand, foot and mouth disease in Shaanxi province. In Shaanxi province, the trend of hand, foot and mouth disease was analyzed and tested, under the use of R software, between January 2009 and June 2015. Multiple seasonal ARIMA model was then fitted under time series to predict the number of hand, foot and mouth disease in 2016 and 2017. Seasonal effect was seen in hand, foot and mouth disease in Shaanxi province. A multiple seasonal ARIMA (2,1,0)×(1,1,0)12 was established, with the equation as (1 -B)(1 -B12)Ln (Xt) =((1-1.000B)/(1-0.532B-0.363B(2))*(1-0.644B12-0.454B12(2)))*Epsilont. The mean of absolute error and the relative error were 531.535 and 0.114, respectively when compared to the simulated number of patients from Jun to Dec in 2015. RESULTS under the prediction of multiple seasonal ARIMA model showed that the numbers of patients in both 2016 and 2017 were similar to that of 2015 in Shaanxi province. Multiple seasonal ARIMA (2,1,0)×(1,1,0)12 model could be used to successfully predict the incidence of hand, foot and mouth disease in Shaanxi province.

Soft Computing Methods for Disulfide Connectivity Prediction.

Science.gov (United States)

Márquez-Chamorro, Alfonso E; Aguilar-Ruiz, Jesús S

2015-01-01

The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

Predictions for an invaded world: A strategy to predict the distribution of native and non-indigenous species at multiple scales

Science.gov (United States)

Reusser, D.A.; Lee, H.

2008-01-01

Habitat models can be used to predict the distributions of marine and estuarine non-indigenous species (NIS) over several spatial scales. At an estuary scale, our goal is to predict the estuaries most likely to be invaded, but at a habitat scale, the goal is to predict the specific locations within an estuary that are most vulnerable to invasion. As an initial step in evaluating several habitat models, model performance for a suite of benthic species with reasonably well-known distributions on the Pacific coast of the US needs to be compared. We discuss the utility of non-parametric multiplicative regression (NPMR) for predicting habitat- and estuary-scale distributions of native and NIS. NPMR incorporates interactions among variables, allows qualitative and categorical variables, and utilizes data on absence as well as presence. Preliminary results indicate that NPMR generally performs well at both spatial scales and that distributions of NIS are predicted as well as those of native species. For most species, latitude was the single best predictor, although similar model performance could be obtained at both spatial scales with combinations of other habitat variables. Errors of commission were more frequent at a habitat scale, with omission and commission errors approximately equal at an estuary scale. ?? 2008 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved.

Some problems of neutron source multiplication method for site measurement technology in nuclear critical safety

International Nuclear Information System (INIS)

Shi Yongqian; Zhu Qingfu; Hu Dingsheng; He Tao; Yao Shigui; Lin Shenghuo

2004-01-01

The paper gives experiment theory and experiment method of neutron source multiplication method for site measurement technology in the nuclear critical safety. The measured parameter by source multiplication method actually is a sub-critical with source neutron effective multiplication factor k s , but not the neutron effective multiplication factor k eff . The experiment research has been done on the uranium solution nuclear critical safety experiment assembly. The k s of different sub-criticality is measured by neutron source multiplication experiment method, and k eff of different sub-criticality, the reactivity coefficient of unit solution level, is first measured by period method, and then multiplied by difference of critical solution level and sub-critical solution level and obtained the reactivity of sub-critical solution level. The k eff finally can be extracted from reactivity formula. The effect on the nuclear critical safety and different between k eff and k s are discussed

INTEGRATED FUSION METHOD FOR MULTIPLE TEMPORAL-SPATIAL-SPECTRAL IMAGES

Directory of Open Access Journals (Sweden)

H. Shen

2012-08-01

Full Text Available Data fusion techniques have been widely researched and applied in remote sensing field. In this paper, an integrated fusion method for remotely sensed images is presented. Differently from the existed methods, the proposed method has the performance to integrate the complementary information in multiple temporal-spatial-spectral images. In order to represent and process the images in one unified framework, two general image observation models are firstly presented, and then the maximum a posteriori (MAP framework is used to set up the fusion model. The gradient descent method is employed to solve the fused image. The efficacy of the proposed method is validated using simulated images.

Comparison of four statistical and machine learning methods for crash severity prediction.

Science.gov (United States)

Iranitalab, Amirfarrokh; Khattak, Aemal

2017-11-01

Crash severity prediction models enable different agencies to predict the severity of a reported crash with unknown severity or the severity of crashes that may be expected to occur sometime in the future. This paper had three main objectives: comparison of the performance of four statistical and machine learning methods including Multinomial Logit (MNL), Nearest Neighbor Classification (NNC), Support Vector Machines (SVM) and Random Forests (RF), in predicting traffic crash severity; developing a crash costs-based approach for comparison of crash severity prediction methods; and investigating the effects of data clustering methods comprising K-means Clustering (KC) and Latent Class Clustering (LCC), on the performance of crash severity prediction models. The 2012-2015 reported crash data from Nebraska, United States was obtained and two-vehicle crashes were extracted as the analysis data. The dataset was split into training/estimation (2012-2014) and validation (2015) subsets. The four prediction methods were trained/estimated using the training/estimation dataset and the correct prediction rates for each crash severity level, overall correct prediction rate and a proposed crash costs-based accuracy measure were obtained for the validation dataset. The correct prediction rates and the proposed approach showed NNC had the best prediction performance in overall and in more severe crashes. RF and SVM had the next two sufficient performances and MNL was the weakest method. Data clustering did not affect the prediction results of SVM, but KC improved the prediction performance of MNL, NNC and RF, while LCC caused improvement in MNL and RF but weakened the performance of NNC. Overall correct prediction rate had almost the exact opposite results compared to the proposed approach, showing that neglecting the crash costs can lead to misjudgment in choosing the right prediction method. Copyright © 2017 Elsevier Ltd. All rights reserved.

Generic methods for aero-engine exhaust emission prediction

NARCIS (Netherlands)

Shakariyants, S.A.

2008-01-01

In the thesis, generic methods have been developed for aero-engine combustor performance, combustion chemistry, as well as airplane aerodynamics, airplane and engine performance. These methods specifically aim to support diverse emission prediction studies coupled with airplane and engine

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study [version 1; referees: 1 approved, 2 approved with reservations

Directory of Open Access Journals (Sweden)

Andrea Tacchella

2017-12-01

Full Text Available Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR phase, which proceeds to a secondary progressive (SP form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

Statistics of electron multiplication in multiplier phototube: iterative method

International Nuclear Information System (INIS)

Grau Malonda, A.; Ortiz Sanchez, J.F.

1985-01-01

An iterative method is applied to study the variation of dynode response in the multiplier phototube. Three different situations are considered that correspond to the following ways of electronic incidence on the first dynode: incidence of exactly one electron, incidence of exactly r electrons and incidence of an average anti-r electrons. The responses are given for a number of steps between 1 and 5, and for values of the multiplication factor of 2.1, 2.5, 3 and 5. We study also the variance, the skewness and the excess of jurtosis for different multiplication factors. (author)

Multiple centroid method to evaluate the adaptability of alfalfa genotypes

Directory of Open Access Journals (Sweden)

Moysés Nascimento

2015-02-01

Full Text Available This study aimed to evaluate the efficiency of multiple centroids to study the adaptability of alfalfa genotypes (Medicago sativa L.. In this method, the genotypes are compared with ideotypes defined by the bissegmented regression model, according to the researcher's interest. Thus, genotype classification is carried out as determined by the objective of the researcher and the proposed recommendation strategy. Despite the great potential of the method, it needs to be evaluated under the biological context (with real data. In this context, we used data on the evaluation of dry matter production of 92 alfalfa cultivars, with 20 cuttings, from an experiment in randomized blocks with two repetitions carried out from November 2004 to June 2006. The multiple centroid method proved efficient for classifying alfalfa genotypes. Moreover, it showed no unambiguous indications and provided that ideotypes were defined according to the researcher's interest, facilitating data interpretation.

Supplementary Material for: DASPfind: new efficient method to predict drug–target interactions

KAUST Repository

Ba Alawi, Wail

2016-01-01

Abstract Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery

Assessment of a method for the prediction of mandibular rotation.

Science.gov (United States)

Lee, R S; Daniel, F J; Swartz, M; Baumrind, S; Korn, E L

1987-05-01

A new method to predict mandibular rotation developed by Skieller and co-workers on a sample of 21 implant subjects with extreme growth patterns has been tested against an alternative sample of 25 implant patients with generally similar mean values, but with less extreme facial patterns. The method, which had been highly successful in retrospectively predicting changes in the sample of extreme subjects, was much less successful in predicting individual patterns of mandibular rotation in the new, less extreme sample. The observation of a large difference in the strength of the predictions for these two samples, even though their mean values were quite similar, should serve to increase our awareness of the complexity of the problem of predicting growth patterns in individual cases.

Use of multiple genetic markers in prediction of breeding values.

NARCIS (Netherlands)

Arendonk, van J.A.M.; Tier, B.; Kinghorn, B.P.

1994-01-01

Genotypes at a marker locus give information on transmission of genes from parents to offspring and that information can be used in predicting the individuals' additive genetic value at a linked quantitative trait locus (MQTL). In this paper a recursive method is presented to build the gametic

Dynamic reflexivity in action: an armchair walkthrough of a qualitatively driven mixed-method and multiple methods study of mindfulness training in schoolchildren.

Science.gov (United States)

Cheek, Julianne; Lipschitz, David L; Abrams, Elizabeth M; Vago, David R; Nakamura, Yoshio

2015-06-01

Dynamic reflexivity is central to enabling flexible and emergent qualitatively driven inductive mixed-method and multiple methods research designs. Yet too often, such reflexivity, and how it is used at various points of a study, is absent when we write our research reports. Instead, reports of mixed-method and multiple methods research focus on what was done rather than how it came to be done. This article seeks to redress this absence of emphasis on the reflexive thinking underpinning the way that mixed- and multiple methods, qualitatively driven research approaches are thought about and subsequently used throughout a project. Using Morse's notion of an armchair walkthrough, we excavate and explore the layers of decisions we made about how, and why, to use qualitatively driven mixed-method and multiple methods research in a study of mindfulness training (MT) in schoolchildren. © The Author(s) 2015.

GEKF, GUKF and GGPF based prediction of chaotic time-series with additive and multiplicative noises

International Nuclear Information System (INIS)

Wu Xuedong; Song Zhihuan

2008-01-01

On the assumption that random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables, this paper generalize the extended Kalman filtering (EKF), the unscented Kalman filtering (UKF) and the Gaussian particle filtering (GPF) to the case in which there is a positive probability that the observation in each time consists of noise alone and does not contain the chaotic signal (These generalized novel algorithms are referred to as GEKF, GUKF and GGPF correspondingly in this paper). Using weights and network output of neural networks to constitute state equation and observation equation for chaotic time-series prediction to obtain the linear system state transition equation with continuous update scheme in an online fashion, and the prediction results of chaotic time series represented by the predicted observation value, these proposed novel algorithms are applied to the prediction of Mackey–Glass time-series with additive and multiplicative noises. Simulation results prove that the GGPF provides a relatively better prediction performance in comparison with GEKF and GUKF. (general)

Failure analysis of high strength pipeline with single and multiple corrosions

International Nuclear Information System (INIS)

Chen, Yanfei; Zhang, Hong; Zhang, Juan; Li, Xin; Zhou, Jing

2015-01-01

Highlights: • We study failure of high strength pipelines with single corrosion. • We give regression equations for failure pressure prediction. • We propose assessment procedure for pipelines with multiple corrosions. - Abstract: Corrosion will compromise safety operation of oil and gas pipelines, accurate determination of failure pressure finds importance in residual strength assessment and corrosion allowance design of onshore and offshore pipelines. This paper investigates failure pressure of high strength pipeline with single and multiple corrosions using nonlinear finite element analysis. On the basis of developed regression equations for failure pressure prediction of high strength pipeline with single corrosion, the paper proposes an assessment procedure for predicting failure pressure of high strength pipeline with multiple corrosions. Furthermore, failure pressures predicted by proposed solutions are compared with experimental results and various assessment methods available in literature, where accuracy and versatility are demonstrated

Drug-Target Interactions: Prediction Methods and Applications.

Science.gov (United States)

Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

2018-01-01

Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Use of ultrasonic array method for positioning multiple partial discharge sources in transformer oil.

Science.gov (United States)

Xie, Qing; Tao, Junhan; Wang, Yongqiang; Geng, Jianghai; Cheng, Shuyi; Lü, Fangcheng

2014-08-01

Fast and accurate positioning of partial discharge (PD) sources in transformer oil is very important for the safe, stable operation of power systems because it allows timely elimination of insulation faults. There is usually more than one PD source once an insulation fault occurs in the transformer oil. This study, which has both theoretical and practical significance, proposes a method of identifying multiple PD sources in the transformer oil. The method combines the two-sided correlation transformation algorithm in the broadband signal focusing and the modified Gerschgorin disk estimator. The method of classification of multiple signals is used to determine the directions of arrival of signals from multiple PD sources. The ultrasonic array positioning method is based on the multi-platform direction finding and the global optimization searching. Both the 4 × 4 square planar ultrasonic sensor array and the ultrasonic array detection platform are built to test the method of identifying and positioning multiple PD sources. The obtained results verify the validity and the engineering practicability of this method.

Ensemble-based prediction of RNA secondary structures.

Science.gov (United States)

Aghaeepour, Nima; Hoos, Holger H

2013-04-24

Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between

Real-time prediction of respiratory motion based on local regression methods

International Nuclear Information System (INIS)

Ruan, D; Fessler, J A; Balter, J M

2007-01-01

Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using these techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on local regression. There are three major ingredients of this approach: (1) forming an augmented state space to capture system dynamics, (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion, (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumor motion and its observed location for ten patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths

Methods for early prediction of lactation flow in Holstein heifers

Directory of Open Access Journals (Sweden)

Vesna Gantner

2010-12-01

Full Text Available The aim of this research was to define methods for early prediction (based on I. milk control record of lactation flow in Holstein heifers as well as to choose optimal one in terms of prediction fit and application simplicity. Total of 304,569 daily yield records automatically recorded on a 1,136 first lactation Holstein cows, from March 2003 till August 2008., were included in analysis. According to the test date, calving date, the age at first calving, lactation stage when I. milk control occurred and to the average milk yield in first 25th, T1 (and 25th-45th, T2 lactation days, measuring monthcalving month-age-production-time-period subgroups were formed. The parameters of analysed nonlinear and linear methods were estimated for each defined subgroup. As models evaluation measures,adjusted coefficient of determination, and average and standard deviation of error were used. Considering obtained results, in terms of total variance explanation (R2 adj, the nonlinear Wood’s method showed superiority above the linear ones (Wilmink’s, Ali-Schaeffer’s and Guo-Swalve’s method in both time-period subgroups (T1 - 97.5 % of explained variability; T2 - 98.1 % of explained variability. Regarding the evaluation measures based on prediction error amount (eavg±eSD, the lowest average error of daily milk yield prediction (less than 0.005 kg/day, as well as of lactation milk yield prediction (less than 50 kg/lactation (T1 time-period subgroup and less than 30 kg/lactation (T2 time-period subgroup; were determined when Wood’s nonlinear prediction method were applied. Obtained results indicate that estimated Wood’s regression parameters could be used in routine work for early prediction of Holstein heifer’s lactation flow.

Regularization methods for ill-posed problems in multiple Hilbert scales

International Nuclear Information System (INIS)

Mazzieri, Gisela L; Spies, Ruben D

2012-01-01

Several convergence results in Hilbert scales under different source conditions are proved and orders of convergence and optimal orders of convergence are derived. Also, relations between those source conditions are proved. The concept of a multiple Hilbert scale on a product space is introduced, and regularization methods on these scales are defined, both for the case of a single observation and for the case of multiple observations. In the latter case, it is shown how vector-valued regularization functions in these multiple Hilbert scales can be used. In all cases, convergence is proved and orders and optimal orders of convergence are shown. Finally, some potential applications and open problems are discussed. (paper)

Predicting the cumulative effect of multiple disturbances on seagrass connectivity.

Science.gov (United States)

Grech, Alana; Hanert, Emmanuel; McKenzie, Len; Rasheed, Michael; Thomas, Christopher; Tol, Samantha; Wang, Mingzhu; Waycott, Michelle; Wolter, Jolan; Coles, Rob

2018-03-15

The rate of exchange, or connectivity, among populations effects their ability to recover after disturbance events. However, there is limited information on the extent to which populations are connected or how multiple disturbances affect connectivity, especially in coastal and marine ecosystems. We used network analysis and the outputs of a biophysical model to measure potential functional connectivity and predict the impact of multiple disturbances on seagrasses in the central Great Barrier Reef World Heritage Area (GBRWHA), Australia. The seagrass networks were densely connected, indicating that seagrasses are resilient to the random loss of meadows. Our analysis identified discrete meadows that are important sources of seagrass propagules and that serve as stepping stones connecting various different parts of the network. Several of these meadows were close to urban areas or ports and likely to be at risk from coastal development. Deep water meadows were highly connected to coastal meadows and may function as a refuge, but only for non-foundation species. We evaluated changes to the structure and functioning of the seagrass networks when one or more discrete meadows were removed due to multiple disturbance events. The scale of disturbance required to disconnect the seagrass networks into two or more components was on average >245 km, about half the length of the metapopulation. The densely connected seagrass meadows of the central GBRWHA are not limited by the supply of propagules; therefore, management should focus on improving environmental conditions that support natural seagrass recruitment and recovery processes. Our study provides a new framework for assessing the impact of global change on the connectivity and persistence of coastal and marine ecosystems. Without this knowledge, management actions, including coastal restoration, may prove unnecessary and be unsuccessful. © 2018 John Wiley & Sons Ltd.

The prediction of parental self-efficacy and hyper-anxiety symptoms based on the components of mindfulness in women with multiple sclerosis

Directory of Open Access Journals (Sweden)

Mohammad Mohammadi Pour

2017-08-01

Full Text Available Introduction: The present study aimed to predict parental self-efficacy and hyper-anxiety symptoms based on the components of mindfulness in women with multiple sclerosis (MS. Materials and Methods: The statistical population of this descriptive-correlational study included all women with MS in Mashhad during March-Jun 2016 who referred for treatment to clinics, neurologists and psychological centers. The statistical sample consisted of 105 women with MS who were selected using convenient sampling method. In order to collect data, Parental Self-Efficacy Questionnaire, Beck Anxiety Inventory (BIA and Mindfulness Questionnaire were used. Data were analyzed using multivariate regression method. Results: The results revealed that the components of mindfulness, judgment and non-reactivity can reduce anxiety significantly in women with MS. In addition, action with awareness, judgment and non-reactivity can increase parental self-efficacy (P

Preliminary Evidence that Self-Efficacy Predicts Physical Activity in Multiple Sclerosis

Science.gov (United States)

Motl, Robert W.; McAuley, Edward; Doerksen, Shawna; Hu, Liang; Morris, Katherine S.

2009-01-01

Individuals with multiple sclerosis (MS) are less physically active than nondiseased people. One method for increasing physical activity levels involves the identification of factors that correlate with physical activity and that are modifiable by a well designed intervention. This study examined two types of self-efficacy as cross-sectional and…

An ensemble method for predicting subnuclear localizations from primary protein structures.

Directory of Open Access Journals (Sweden)

Guo Sheng Han

Full Text Available BACKGROUND: Predicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods. METHODOLOGY/PRINCIPAL FINDINGS: A novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis. CONCLUSIONS: It can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method

Available Prediction Methods for Corrosion under Insulation (CUI: A Review

Directory of Open Access Journals (Sweden)

Burhani Nurul Rawaida Ain

2014-07-01

Full Text Available Corrosion under insulation (CUI is an increasingly important issue for the piping in industries especially petrochemical and chemical plants due to its unexpected catastrophic disaster. Therefore, attention towards the maintenance and prediction of CUI occurrence, particularly in the corrosion rates, has grown in recent years. In this study, a literature review in determining the corrosion rates by using various prediction models and method of the corrosion occurrence between the external surface piping and its insulation was carried out. The results, prediction models and methods available were presented for future research references. However, most of the prediction methods available are based on each local industrial data only which might be different based on the plant location, environment, temperature and many other factors which may contribute to the difference and reliability of the model developed. Thus, it is more reliable if those models or method supported by laboratory testing or simulation which includes the factors promoting CUI such as environment temperature, insulation types, operating temperatures, and other factors.

Predicting high risk births with contraceptive prevalence and contraceptive method-mix in an ecologic analysis.

Science.gov (United States)

Perin, Jamie; Amouzou, Agbessi; Walker, Neff

2017-11-07

Increased contraceptive use has been associated with a decrease in high parity births, births that occur close together in time, and births to very young or to older women. These types of births are also associated with high risk of under-five mortality. Previous studies have looked at the change in the level of contraception use and the average change in these types of high-risk births. We aim to predict the distribution of births in a specific country when there is a change in the level and method of modern contraception. We used data from full birth histories and modern contraceptive use from 207 nationally representative Demographic and Health Surveys covering 71 countries to describe the distribution of births in each survey based on birth order, preceding birth space, and mother's age at birth. We estimated the ecologic associations between the prevalence and method-mix of modern contraceptives and the proportion of births in each category. Hierarchical modelling was applied to these aggregated cross sectional proportions, so that random effects were estimated for countries with multiple surveys. We use these results to predict the change in type of births associated with scaling up modern contraception in three different scenarios. We observed marked differences between regions, in the absolute rates of contraception, the types of contraceptives in use, and in the distribution of type of birth. Contraceptive method-mix was a significant determinant of proportion of high-risk births, especially for birth spacing, but also for mother's age and parity. Increased use of modern contraceptives is especially predictive of reduced parity and more births with longer preceding space. However, increased contraception alone is not associated with fewer births to women younger than 18 years or a decrease in short-spaced births. Both the level and the type of contraception are important factors in determining the effects of family planning on changes in distribution of

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

Science.gov (United States)

Notaro, Marco; Schubach, Max; Robinson, Peter N; Valentini, Giorgio

2017-10-12

The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.

Short-term prediction method of wind speed series based on fractal interpolation

International Nuclear Information System (INIS)

Xiu, Chunbo; Wang, Tiantian; Tian, Meng; Li, Yanqing; Cheng, Yi

2014-01-01

Highlights: • An improved fractal interpolation prediction method is proposed. • The chaos optimization algorithm is used to obtain the iterated function system. • The fractal extrapolate interpolation prediction of wind speed series is performed. - Abstract: In order to improve the prediction performance of the wind speed series, the rescaled range analysis is used to analyze the fractal characteristics of the wind speed series. An improved fractal interpolation prediction method is proposed to predict the wind speed series whose Hurst exponents are close to 1. An optimization function which is composed of the interpolation error and the constraint items of the vertical scaling factors in the fractal interpolation iterated function system is designed. The chaos optimization algorithm is used to optimize the function to resolve the optimal vertical scaling factors. According to the self-similarity characteristic and the scale invariance, the fractal extrapolate interpolation prediction can be performed by extending the fractal characteristic from internal interval to external interval. Simulation results show that the fractal interpolation prediction method can get better prediction result than others for the wind speed series with the fractal characteristic, and the prediction performance of the proposed method can be improved further because the fractal characteristic of its iterated function system is similar to that of the predicted wind speed series

Calorimeter prediction based on multiple exponentials

International Nuclear Information System (INIS)

Smith, M.K.; Bracken, D.S.

2002-01-01

Calorimetry allows very precise measurements of nuclear material to be carried out, but it also requires relatively long measurement times to do so. The ability to accurately predict the equilibrium response of a calorimeter would significantly reduce the amount of time required for calorimetric assays. An algorithm has been developed that is effective at predicting the equilibrium response. This multi-exponential prediction algorithm is based on an iterative technique using commercial fitting routines that fit a constant plus a variable number of exponential terms to calorimeter data. Details of the implementation and the results of trials on a large number of calorimeter data sets will be presented

A cross docking pipeline for improving pose prediction and virtual screening performance

Science.gov (United States)

Kumar, Ashutosh; Zhang, Kam Y. J.

2018-01-01

Pose prediction and virtual screening performance of a molecular docking method depend on the choice of protein structures used for docking. Multiple structures for a target protein are often used to take into account the receptor flexibility and problems associated with a single receptor structure. However, the use of multiple receptor structures is computationally expensive when docking a large library of small molecules. Here, we propose a new cross-docking pipeline suitable to dock a large library of molecules while taking advantage of multiple target protein structures. Our method involves the selection of a suitable receptor for each ligand in a screening library utilizing ligand 3D shape similarity with crystallographic ligands. We have prospectively evaluated our method in D3R Grand Challenge 2 and demonstrated that our cross-docking pipeline can achieve similar or better performance than using either single or multiple-receptor structures. Moreover, our method displayed not only decent pose prediction performance but also better virtual screening performance over several other methods.

In silico toxicology: computational methods for the prediction of chemical toxicity

KAUST Repository

Raies, Arwa B.; Bajic, Vladimir B.

2016-01-01

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models.

In silico toxicology: computational methods for the prediction of chemical toxicity

KAUST Repository

Raies, Arwa B.

2016-01-06

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models.

Monitoring multiple components in vinegar fermentation using Raman spectroscopy.

Science.gov (United States)

Uysal, Reyhan Selin; Soykut, Esra Acar; Boyaci, Ismail Hakki; Topcu, Ali

2013-12-15

In this study, the utility of Raman spectroscopy (RS) with chemometric methods for quantification of multiple components in the fermentation process was investigated. Vinegar, the product of a two stage fermentation, was used as a model and glucose and fructose consumption, ethanol production and consumption and acetic acid production were followed using RS and the partial least squares (PLS) method. Calibration of the PLS method was performed using model solutions. The prediction capability of the method was then investigated with both model and real samples. HPLC was used as a reference method. The results from comparing RS-PLS and HPLC with each other showed good correlations were obtained between predicted and actual sample values for glucose (R(2)=0.973), fructose (R(2)=0.988), ethanol (R(2)=0.996) and acetic acid (R(2)=0.983). In conclusion, a combination of RS with chemometric methods can be applied to monitor multiple components of the fermentation process from start to finish with a single measurement in a short time. Copyright © 2013 Elsevier Ltd. All rights reserved.

Mechatronics technology in predictive maintenance method

Science.gov (United States)

Majid, Nurul Afiqah A.; Muthalif, Asan G. A.

2017-11-01

This paper presents recent mechatronics technology that can help to implement predictive maintenance by combining intelligent and predictive maintenance instrument. Vibration Fault Simulation System (VFSS) is an example of mechatronics system. The focus of this study is the prediction on the use of critical machines to detect vibration. Vibration measurement is often used as the key indicator of the state of the machine. This paper shows the choice of the appropriate strategy in the vibration of diagnostic process of the mechanical system, especially rotating machines, in recognition of the failure during the working process. In this paper, the vibration signature analysis is implemented to detect faults in rotary machining that includes imbalance, mechanical looseness, bent shaft, misalignment, missing blade bearing fault, balancing mass and critical speed. In order to perform vibration signature analysis for rotating machinery faults, studies have been made on how mechatronics technology is used as predictive maintenance methods. Vibration Faults Simulation Rig (VFSR) is designed to simulate and understand faults signatures. These techniques are based on the processing of vibrational data in frequency-domain. The LabVIEW-based spectrum analyzer software is developed to acquire and extract frequency contents of faults signals. This system is successfully tested based on the unique vibration fault signatures that always occur in a rotating machinery.

A hybrid measure-correlate-predict method for long-term wind condition assessment

International Nuclear Information System (INIS)

Zhang, Jie; Chowdhury, Souma; Messac, Achille; Hodge, Bri-Mathias

2014-01-01

Highlights: • A hybrid measure-correlate-predict (MCP) methodology with greater accuracy is developed. • Three sets of performance metrics are proposed to evaluate the hybrid MCP method. • Both wind speed and direction are considered in the hybrid MCP method. • The best combination of MCP algorithms is determined. • The developed hybrid MCP method is uniquely helpful for long-term wind resource assessment. - Abstract: This paper develops a hybrid measure-correlate-predict (MCP) strategy to assess long-term wind resource variations at a farm site. The hybrid MCP method uses recorded data from multiple reference stations to estimate long-term wind conditions at a target wind plant site with greater accuracy than is possible with data from a single reference station. The weight of each reference station in the hybrid strategy is determined by the (i) distance and (ii) elevation differences between the target farm site and each reference station. In this case, the wind data is divided into sectors according to the wind direction, and the MCP strategy is implemented for each wind direction sector separately. The applicability of the proposed hybrid strategy is investigated using five MCP methods: (i) the linear regression; (ii) the variance ratio; (iii) the Weibull scale; (iv) the artificial neural networks; and (v) the support vector regression. To implement the hybrid MCP methodology, we use hourly averaged wind data recorded at five stations in the state of Minnesota between 07-01-1996 and 06-30-2004. Three sets of performance metrics are used to evaluate the hybrid MCP method. The first set of metrics analyze the statistical performance, including the mean wind speed, wind speed variance, root mean square error, and mean absolute error. The second set of metrics evaluate the distribution of long-term wind speed; to this end, the Weibull distribution and the Multivariate and Multimodal Wind Distribution models are adopted. The third set of metrics analyze

Nonlinear Predictive Models for Multiple Mediation Analysis: With an Application to Explore Ethnic Disparities in Anxiety and Depression Among Cancer Survivors.

Science.gov (United States)

Yu, Qingzhao; Medeiros, Kaelen L; Wu, Xiaocheng; Jensen, Roxanne E

2018-04-02

Mediation analysis allows the examination of effects of a third variable (mediator/confounder) in the causal pathway between an exposure and an outcome. The general multiple mediation analysis method (MMA), proposed by Yu et al., improves traditional methods (e.g., estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. Previous studies find that compared with non-Hispanic cancer survivors, Hispanic survivors are more likely to endure anxiety and depression after cancer diagnoses. In this paper, we applied MMA on MY-Health study to identify mediators/confounders and quantify the indirect effect of each identified mediator/confounder in explaining ethnic disparities in anxiety and depression among cancer survivors who enrolled in the study. We considered a number of socio-demographic variables, tumor characteristics, and treatment factors as potential mediators/confounders and found that most of the ethnic differences in anxiety or depression between Hispanic and non-Hispanic white cancer survivors were explained by younger diagnosis age, lower education level, lower proportions of employment, less likely of being born in the USA, less insurance, and less social support among Hispanic patients.

Combining gene prediction methods to improve metagenomic gene annotation

Directory of Open Access Journals (Sweden)

Rosen Gail L

2011-01-01

Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

Seminal quality prediction using data mining methods.

Science.gov (United States)

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of

Force prediction in cold rolling mills by polynomial methods

Directory of Open Access Journals (Sweden)

Nicu ROMAN

2007-12-01

Full Text Available A method for steel and aluminium strip thickness control is provided including a new technique for predictive rolling force estimation method by statistic model based on polynomial techniques.

Do positive or negative stressful events predict the development of new brain lesions in people with Multiple Sclerosis?

Science.gov (United States)

Burns, Michelle Nicole; Nawacki, Ewa; Kwasny, Mary J.; Pelletier, Daniel; Mohr, David C.

2014-01-01

Background Stressful life events have long been suspected to contribute to multiple sclerosis (MS) disease activity. The few studies examining the relationship between stressful events and neuroimaging markers have been small and inconsistent. This study examined whether different types of stressful events and perceived stress could predict development of brain lesions. Methods This was a secondary analysis of 121 patients with MS followed for 48 weeks during a randomized controlled trial comparing Stress Management Therapy for MS to a waitlist control. Patients underwent MRI’s every 8 weeks. Monthly, patients completed an interview measure assessing stressful life events, and self-report measures of perceived stress, anxiety, and depressive symptoms, which were used to predict the presence of gadolinium enhancing (Gd+) and T2 lesions on MRI’s 29–62 days later. Participants classified stressful events as positive or negative. Negative events were considered “major” if they involved physical threat or threat to the patient’s family structure, and “moderate” otherwise. Results Positive stressful events predicted decreased risk for subsequent Gd+ lesions in the control group (OR=.53 for each additional positive stressful event, 95% CI=.30–.91) and less risk for new or enlarging T2 lesions regardless of group assignment (OR=.74, 95% CI=.55–.99). Across groups, major negative stressful events predicted Gd+ lesions (OR=1.77, 95% CI=1.18–2.64) and new or enlarging T2 lesions (OR=1.57, 95% CI=1.11–2.23), while moderate negative stressful events, perceived stress, anxiety, and depressive symptoms did not. Conclusions Major negative stressful events predict increased risk for Gd+ and T2 lesions, while positive stressful events predict decreased risk. PMID:23680407

Three-dimensional protein structure prediction: Methods and computational strategies.

Science.gov (United States)

Dorn, Márcio; E Silva, Mariel Barbachan; Buriol, Luciana S; Lamb, Luis C

2014-10-12

A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Kernel for Protein Secondary Structure Prediction

OpenAIRE

Guermeur , Yann; Lifchitz , Alain; Vert , Régis

2004-01-01

http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10338&mode=toc; International audience; Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is in...

Validity of a manual soft tissue profile prediction method following mandibular setback osteotomy.

Science.gov (United States)

Kolokitha, Olga-Elpis

2007-10-01

The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. To test the validity of the manual method the prediction tracings were compared to the actual post-operative tracings. The Dentofacial Planner software was used to develop the computerized post-surgical prediction tracings. Both manual and computerized prediction printouts were analyzed by using the cephalometric system PORDIOS. Statistical analysis was performed by means of t-test. Comparison between manual prediction tracings and the actual post-operative profile showed that the manual method results in more convex soft tissue profiles; the upper lip was found in a more prominent position, upper lip thickness was increased and, the mandible and lower lip were found in a less posterior position than that of the actual profiles. Comparison between computerized and manual prediction methods showed that in the manual method upper lip thickness was increased, the upper lip was found in a more anterior position and the lower anterior facial height was increased as compared to the computerized prediction method. Cephalometric simulation of post-operative soft tissue profile following orthodontic-surgical management of mandibular prognathism imposes certain limitations related to the methods implied. However, both manual and computerized prediction methods remain a useful tool for patient communication.

Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: a pattern classification approach.

Science.gov (United States)

Wu, Mon-Ju; Wu, Hanjing Emily; Mwangi, Benson; Sanches, Marsal; Selvaraj, Sudhakar; Zunta-Soares, Giovana B; Soares, Jair C

2015-03-01

Diagnosis of pediatric neuropsychiatric disorders such as unipolar depression is largely based on clinical judgment - without objective biomarkers to guide diagnostic process and subsequent therapeutic interventions. Neuroimaging studies have previously reported average group-level neuroanatomical differences between patients with pediatric unipolar depression and healthy controls. In the present study, we investigated the utility of multiple neuromorphometric indices in distinguishing pediatric unipolar depression patients from healthy controls at an individual subject level. We acquired structural T1-weighted scans from 25 pediatric unipolar depression patients and 26 demographically matched healthy controls. Multiple neuromorphometric indices such as cortical thickness, volume, and cortical folding patterns were obtained. A support vector machine pattern classification model was 'trained' to distinguish individual subjects with pediatric unipolar depression from healthy controls based on multiple neuromorphometric indices and model predictive validity (sensitivity and specificity) calculated. The model correctly identified 40 out of 51 subjects translating to 78.4% accuracy, 76.0% sensitivity and 80.8% specificity, chi-square p-value = 0.000049. Volumetric and cortical folding abnormalities in the right thalamus and right temporal pole respectively were most central in distinguishing individual patients with pediatric unipolar depression from healthy controls. These findings provide evidence that a support vector machine pattern classification model using multiple neuromorphometric indices may qualify as diagnostic marker for pediatric unipolar depression. In addition, our results identified the most relevant neuromorphometric features in distinguishing PUD patients from healthy controls. Copyright © 2015 Elsevier Ltd. All rights reserved.

River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method

Directory of Open Access Journals (Sweden)

H. Sanikhani

2016-02-01

Full Text Available Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The evaluation of the uncertainty associated to the forecast is seen as a fundamental information, not only to correctly assess the prediction, but also to compare forecasts from different methods and to evaluate actions and decisions conditionally on the expected values. Several probabilistic approaches have been proposed in the literature, including (1 methods that use resampling techniques to assess parameter and model uncertainty, such as the Metropolis algorithm or the Generalized Likelihood Uncertainty Estimation (GLUE methodology for an application to runoff prediction, (2 methods based on processing the forecast errors of past data to produce the probability distributions of future values and (3 methods that evaluate how the uncertainty propagates from the rainfall forecast to the river discharge prediction, as the Bayesian forecasting system. Materials and Methods: In this study, two different probabilistic methods are used for river flow prediction.Then the uncertainty related to the forecast is quantified. One approach is based on linear predictors and in the other, nearest neighbor was used. The nonlinear probabilistic ensemble can be used for nonlinear time series analysis using locally linear predictors, while NNPE utilize a method adapted for one step ahead nearest neighbor methods. In this regard, daily river discharge (twelve years of Dizaj and Mashin Stations on Baranduz-Chay basin in west Azerbijan and Zard-River basin in Khouzestan provinces were used, respectively. The first six years of data was applied for fitting the model. The next three years was used to calibration and the remained three yeas utilized for testing the models

A Prediction Method of Airport Noise Based on Hybrid Ensemble Learning

Directory of Open Access Journals (Sweden)

Tao XU

2014-05-01

Full Text Available Using monitoring history data to build and to train a prediction model for airport noise is a normal method in recent years. However, the single model built in different ways has various performances in the storage, efficiency and accuracy. In order to predict the noise accurately in some complex environment around airport, this paper presents a prediction method based on hybrid ensemble learning. The proposed method ensembles three algorithms: artificial neural network as an active learner, nearest neighbor as a passive leaner and nonlinear regression as a synthesized learner. The experimental results show that the three learners can meet forecast demands respectively in on- line, near-line and off-line. And the accuracy of prediction is improved by integrating these three learners’ results.

A method for the generation of random multiple Coulomb scattering angles

International Nuclear Information System (INIS)

Campbell, J.R.

1995-06-01

A method for the random generation of spatial angles drawn from non-Gaussian multiple Coulomb scattering distributions is presented. The method employs direct numerical inversion of cumulative probability distributions computed from the universal non-Gaussian angular distributions of Marion and Zimmerman. (author). 12 refs., 3 figs

Methods of fast, multiple-point in vivo T1 determination

International Nuclear Information System (INIS)

Zhang, Y.; Spigarelli, M.; Fencil, L.E.; Yeung, H.N.

1989-01-01

Two methods of rapid, multiple-point determination of T1 in vivo have been evaluated with a phantom consisting of vials of gel in different Mn + + concentrations. The first method was an inversion-recovery- on-the-fly technique, and the second method used a variable- tip-angle (α) progressive saturation with two sub- sequences of different repetition times. In the first method, 1/T1 was evaluated by an exponential fit. In the second method, 1/T1 was obtained iteratively with a linear fit and then readjusted together with α to a model equation until self-consistency was reached

VIKOR Method for Interval Neutrosophic Multiple Attribute Group Decision-Making

Directory of Open Access Journals (Sweden)

Yu-Han Huang

2017-11-01

Full Text Available In this paper, we will extend the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje method to multiple attribute group decision-making (MAGDM with interval neutrosophic numbers (INNs. Firstly, the basic concepts of INNs are briefly presented. The method first aggregates all individual decision-makers’ assessment information based on an interval neutrosophic weighted averaging (INWA operator, and then employs the extended classical VIKOR method to solve MAGDM problems with INNs. The validity and stability of this method are verified by example analysis and sensitivity analysis, and its superiority is illustrated by a comparison with the existing methods.

The energetic cost of walking: a comparison of predictive methods.

Directory of Open Access Journals (Sweden)

Patricia Ann Kramer

Full Text Available BACKGROUND: The energy that animals devote to locomotion has been of intense interest to biologists for decades and two basic methodologies have emerged to predict locomotor energy expenditure: those based on metabolic and those based on mechanical energy. Metabolic energy approaches share the perspective that prediction of locomotor energy expenditure should be based on statistically significant proxies of metabolic function, while mechanical energy approaches, which derive from many different perspectives, focus on quantifying the energy of movement. Some controversy exists as to which mechanical perspective is "best", but from first principles all mechanical methods should be equivalent if the inputs to the simulation are of similar quality. Our goals in this paper are 1 to establish the degree to which the various methods of calculating mechanical energy are correlated, and 2 to investigate to what degree the prediction methods explain the variation in energy expenditure. METHODOLOGY/PRINCIPAL FINDINGS: We use modern humans as the model organism in this experiment because their data are readily attainable, but the methodology is appropriate for use in other species. Volumetric oxygen consumption and kinematic and kinetic data were collected on 8 adults while walking at their self-selected slow, normal and fast velocities. Using hierarchical statistical modeling via ordinary least squares and maximum likelihood techniques, the predictive ability of several metabolic and mechanical approaches were assessed. We found that all approaches are correlated and that the mechanical approaches explain similar amounts of the variation in metabolic energy expenditure. Most methods predict the variation within an individual well, but are poor at accounting for variation between individuals. CONCLUSION: Our results indicate that the choice of predictive method is dependent on the question(s of interest and the data available for use as inputs. Although we

The energetic cost of walking: a comparison of predictive methods.

Science.gov (United States)

Kramer, Patricia Ann; Sylvester, Adam D

2011-01-01

The energy that animals devote to locomotion has been of intense interest to biologists for decades and two basic methodologies have emerged to predict locomotor energy expenditure: those based on metabolic and those based on mechanical energy. Metabolic energy approaches share the perspective that prediction of locomotor energy expenditure should be based on statistically significant proxies of metabolic function, while mechanical energy approaches, which derive from many different perspectives, focus on quantifying the energy of movement. Some controversy exists as to which mechanical perspective is "best", but from first principles all mechanical methods should be equivalent if the inputs to the simulation are of similar quality. Our goals in this paper are 1) to establish the degree to which the various methods of calculating mechanical energy are correlated, and 2) to investigate to what degree the prediction methods explain the variation in energy expenditure. We use modern humans as the model organism in this experiment because their data are readily attainable, but the methodology is appropriate for use in other species. Volumetric oxygen consumption and kinematic and kinetic data were collected on 8 adults while walking at their self-selected slow, normal and fast velocities. Using hierarchical statistical modeling via ordinary least squares and maximum likelihood techniques, the predictive ability of several metabolic and mechanical approaches were assessed. We found that all approaches are correlated and that the mechanical approaches explain similar amounts of the variation in metabolic energy expenditure. Most methods predict the variation within an individual well, but are poor at accounting for variation between individuals. Our results indicate that the choice of predictive method is dependent on the question(s) of interest and the data available for use as inputs. Although we used modern humans as our model organism, these results can be extended

A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks.

Science.gov (United States)

Peng, Wei; Lan, Wei; Zhong, Jiancheng; Wang, Jianxin; Pan, Yi

2017-07-15

MicroRNAs have been reported to have close relationship with diseases due to their deregulation of the expression of target mRNAs. Detecting disease-related microRNAs is helpful for disease therapies. With the development of high throughput experimental techniques, a large number of microRNAs have been sequenced. However, it is still a big challenge to identify which microRNAs are related to diseases. Recently, researchers are interesting in combining multiple-biological information to identify the associations between microRNAs and diseases. In this work, we have proposed a novel method to predict the microRNA-disease associations based on four biological properties. They are microRNA, disease, gene and environment factor. Compared with previous methods, our method makes predictions not only by using the prior knowledge of associations among microRNAs, disease, environment factors and genes, but also by using the internal relationship among these biological properties. We constructed four biological networks based on the similarity of microRNAs, diseases, environment factors and genes, respectively. Then random walking was implemented on the four networks unequally. In the walking course, the associations can be inferred from the neighbors in the same networks. Meanwhile the association information can be transferred from one network to another. The results of experiment showed that our method achieved better prediction performance than other existing state-of-the-art methods. Copyright © 2017 Elsevier Inc. All rights reserved.

RDL mutations predict multiple insecticide resistance in Anopheles sinensis in Guangxi, China.

Science.gov (United States)

Yang, Chan; Huang, Zushi; Li, Mei; Feng, Xiangyang; Qiu, Xinghui

2017-11-28

Anopheles sinensis is a major vector of malaria in China. The gamma-aminobutyric acid (GABA)-gated chloride channel, encoded by the RDL (Resistant to dieldrin) gene, is the important target for insecticides of widely varied structures. The use of various insecticides in agriculture and vector control has inevitably led to the development of insecticide resistance, which may reduce the control effectiveness. Therefore, it is important to investigate the presence and distribution frequency of the resistance related mutation(s) in An. sinensis RDL to predict resistance to both the withdrawn cyclodienes (e.g. dieldrin) and currently used insecticides, such as fipronil. Two hundred and forty adults of An. sinensis collected from nine locations across Guangxi Zhuang Autonomous Region were used. Two fragments of An. sinensis RDL (AsRDL) gene, covering the putative insecticide resistance related sites, were sequenced respectively. The haplotypes of each individual were reconstructed by the PHASE2.1 software, and confirmed by clone sequencing. The phylogenetic tree was built using maximum-likelihood and Bayesian inference methods. Genealogical relations among different haplotypes were also analysed using Network 5.0. The coding region of AsRDL gene was 1674 bp long, encoding a protein of 557 amino acids. AsRDL had 98.0% amino acid identity to that from Anopheles funestus, and shared common structural features of Cys-loop ligand-gated ion channels. Three resistance-related amino acid substitutions (A296S, V327I and T345S) were detected in all the nine populations of An. sinensis in Guangxi, with the 296S mutation being the most abundant (77-100%), followed by 345S (22-47%) and 327I (8-60%). 38 AsRDL haplotypes were identified from 240 individuals at frequencies ranging from 0.2 to 34.8%. Genealogical analysis suggested multiple origins of the 345S mutation in AsRDL. The near fixation of the 296S mutation and the occurrence of the 327I and 345S mutations in addition to 296S

Correction of measured multiplicity distributions by the simulated annealing method

International Nuclear Information System (INIS)

Hafidouni, M.

1993-01-01

Simulated annealing is a method used to solve combinatorial optimization problems. It is used here for the correction of the observed multiplicity distribution from S-Pb collisions at 200 GeV/c per nucleon. (author) 11 refs., 2 figs

A multiple-scale power series method for solving nonlinear ordinary differential equations

Directory of Open Access Journals (Sweden)

Chein-Shan Liu

2016-02-01

Full Text Available The power series solution is a cheap and effective method to solve nonlinear problems, like the Duffing-van der Pol oscillator, the Volterra population model and the nonlinear boundary value problems. A novel power series method by considering the multiple scales $R_k$ in the power term $(t/R_k^k$ is developed, which are derived explicitly to reduce the ill-conditioned behavior in the data interpolation. In the method a huge value times a tiny value is avoided, such that we can decrease the numerical instability and which is the main reason to cause the failure of the conventional power series method. The multiple scales derived from an integral can be used in the power series expansion, which provide very accurate numerical solutions of the problems considered in this paper.

Analysis and performance estimation of the Conjugate Gradient method on multiple GPUs

NARCIS (Netherlands)

Verschoor, M.; Jalba, A.C.

2012-01-01

The Conjugate Gradient (CG) method is a widely-used iterative method for solving linear systems described by a (sparse) matrix. The method requires a large amount of Sparse-Matrix Vector (SpMV) multiplications, vector reductions and other vector operations to be performed. We present a number of

What Predicts Use of Learning-Centered, Interactive Engagement Methods?

Science.gov (United States)

Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael

2014-01-01

What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using interactive…

A nonparametric multiple imputation approach for missing categorical data

Directory of Open Access Journals (Sweden)

Muhan Zhou

2017-06-01

Full Text Available Abstract Background Incomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness probabilities. Methods We propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each category. The donor set for imputation is formed by measuring distances between each missing value with other non-missing values. The distance function is calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model and the other fits a logistic regression for predicting missingness probabilities (the missingness model. A weighting scheme is used to accommodate contributions from two working models when generating the predictive score. A missing value is imputed by randomly selecting one of the non-missing values with the smallest distances. We conduct a simulation to evaluate the performance of the proposed method and compare it with several alternative methods. A real-data application is also presented. Results The simulation study suggests that the proposed method performs well when missingness probabilities are not extreme under some misspecifications of the working models. However, the calibration estimator, which is also based on two working models, can be highly unstable when missingness probabilities for some observations are extremely high. In this scenario, the proposed method produces more stable and better estimates. In addition, proper weights need to be chosen to balance the contributions from the two working models and achieve optimal results for the proposed method. Conclusions We conclude that the proposed multiple imputation method is a reasonable approach to dealing with missing categorical outcome data with

Multiple Contexts, Multiple Methods: A Study of Academic and Cultural Identity among Children of Immigrant Parents

Science.gov (United States)

Urdan, Tim; Munoz, Chantico

2012-01-01

Multiple methods were used to examine the academic motivation and cultural identity of a sample of college undergraduates. The children of immigrant parents (CIPs, n = 52) and the children of non-immigrant parents (non-CIPs, n = 42) completed surveys assessing core cultural identity, valuing of cultural accomplishments, academic self-concept,…

Deep Visual Attention Prediction

Science.gov (United States)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

Field evaluation of personal sampling methods for multiple bioaerosols.

Science.gov (United States)

Wang, Chi-Hsun; Chen, Bean T; Han, Bor-Cheng; Liu, Andrew Chi-Yeu; Hung, Po-Chen; Chen, Chih-Yong; Chao, Hsing Jasmine

2015-01-01

Ambient bioaerosols are ubiquitous in the daily environment and can affect health in various ways. However, few studies have been conducted to comprehensively evaluate personal bioaerosol exposure in occupational and indoor environments because of the complex composition of bioaerosols and the lack of standardized sampling/analysis methods. We conducted a study to determine the most efficient collection/analysis method for the personal exposure assessment of multiple bioaerosols. The sampling efficiencies of three filters and four samplers were compared. According to our results, polycarbonate (PC) filters had the highest relative efficiency, particularly for bacteria. Side-by-side sampling was conducted to evaluate the three filter samplers (with PC filters) and the NIOSH Personal Bioaerosol Cyclone Sampler. According to the results, the Button Aerosol Sampler and the IOM Inhalable Dust Sampler had the highest relative efficiencies for fungi and bacteria, followed by the NIOSH sampler. Personal sampling was performed in a pig farm to assess occupational bioaerosol exposure and to evaluate the sampling/analysis methods. The Button and IOM samplers yielded a similar performance for personal bioaerosol sampling at the pig farm. However, the Button sampler is more likely to be clogged at high airborne dust concentrations because of its higher flow rate (4 L/min). Therefore, the IOM sampler is a more appropriate choice for performing personal sampling in environments with high dust levels. In summary, the Button and IOM samplers with PC filters are efficient sampling/analysis methods for the personal exposure assessment of multiple bioaerosols.

A dynamic particle filter-support vector regression method for reliability prediction

International Nuclear Information System (INIS)

Wei, Zhao; Tao, Tao; ZhuoShu, Ding; Zio, Enrico

2013-01-01

Support vector regression (SVR) has been applied to time series prediction and some works have demonstrated the feasibility of its use to forecast system reliability. For accuracy of reliability forecasting, the selection of SVR's parameters is important. The existing research works on SVR's parameters selection divide the example dataset into training and test subsets, and tune the parameters on the training data. However, these fixed parameters can lead to poor prediction capabilities if the data of the test subset differ significantly from those of training. Differently, the novel method proposed in this paper uses particle filtering to estimate the SVR model parameters according to the whole measurement sequence up to the last observation instance. By treating the SVR training model as the observation equation of a particle filter, our method allows updating the SVR model parameters dynamically when a new observation comes. Because of the adaptability of the parameters to dynamic data pattern, the new PF–SVR method has superior prediction performance over that of standard SVR. Four application results show that PF–SVR is more robust than SVR to the decrease of the number of training data and the change of initial SVR parameter values. Also, even if there are trends in the test data different from those in the training data, the method can capture the changes, correct the SVR parameters and obtain good predictions. -- Highlights: •A dynamic PF–SVR method is proposed to predict the system reliability. •The method can adjust the SVR parameters according to the change of data. •The method is robust to the size of training data and initial parameter values. •Some cases based on both artificial and real data are studied. •PF–SVR shows superior prediction performance over standard SVR

Hybrid methods for airframe noise numerical prediction

Energy Technology Data Exchange (ETDEWEB)

Terracol, M.; Manoha, E.; Herrero, C.; Labourasse, E.; Redonnet, S. [ONERA, Department of CFD and Aeroacoustics, BP 72, Chatillon (France); Sagaut, P. [Laboratoire de Modelisation en Mecanique - UPMC/CNRS, Paris (France)

2005-07-01

This paper describes some significant steps made towards the numerical simulation of the noise radiated by the high-lift devices of a plane. Since the full numerical simulation of such configuration is still out of reach for present supercomputers, some hybrid strategies have been developed to reduce the overall cost of such simulations. The proposed strategy relies on the coupling of an unsteady nearfield CFD with an acoustic propagation solver based on the resolution of the Euler equations for midfield propagation in an inhomogeneous field, and the use of an integral solver for farfield acoustic predictions. In the first part of this paper, this CFD/CAA coupling strategy is presented. In particular, the numerical method used in the propagation solver is detailed, and two applications of this coupling method to the numerical prediction of the aerodynamic noise of an airfoil are presented. Then, a hybrid RANS/LES method is proposed in order to perform some unsteady simulations of complex noise sources. This method allows for significant reduction of the cost of such a simulation by considerably reducing the extent of the LES zone. This method is described and some results of the numerical simulation of the three-dimensional unsteady flow in the slat cove of a high-lift profile are presented. While these results remain very difficult to validate with experiments on similar configurations, they represent up to now the first 3D computations of this kind of flow. (orig.)

Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

DEFF Research Database (Denmark)

Jonsdottir, Svava Osk; Jorgensen, FS; Brunak, Søren

2005-01-01

about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability......MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information...... of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described....

The initial rise method extended to multiple trapping levels in thermoluminescent materials

Energy Technology Data Exchange (ETDEWEB)

Furetta, C. [CICATA-Legaria, Instituto Politecnico Nacional, 11500 Mexico D.F. (Mexico); Guzman, S. [Instituto de Ciencias Nucleares, Universidad Nacional Autonoma de Mexico, A.P. 70-543, 04510 Mexico D.F. (Mexico); Ruiz, B. [Instituto de Ciencias Nucleares, Universidad Nacional Autonoma de Mexico, A.P. 70-543, 04510 Mexico D.F. (Mexico); Departamento de Agricultura y Ganaderia, Universidad de Sonora, A.P. 305, 83190 Hermosillo, Sonora (Mexico); Cruz-Zaragoza, E., E-mail: ecruz@nucleares.unam.m [Instituto de Ciencias Nucleares, Universidad Nacional Autonoma de Mexico, A.P. 70-543, 04510 Mexico D.F. (Mexico)

2011-02-15

The well known Initial Rise Method (IR) is commonly used to determine the activation energy when only one glow peak is presented and analysed in the phosphor materials. However, when the glow peak is more complex, a wide peak and some holders appear in the structure. The application of the Initial Rise Method is not valid because multiple trapping levels are considered and then the thermoluminescent analysis becomes difficult to perform. This paper shows the case of a complex glow curve structure as an example and shows that the calculation is also possible using the IR method. The aim of the paper is to extend the well known Initial Rise Method (IR) to the case of multiple trapping levels. The IR method is applied to minerals extracted from Nopal cactus and Oregano spices because the thermoluminescent glow curve's shape suggests a trap distribution instead of a single trapping level.

The initial rise method extended to multiple trapping levels in thermoluminescent materials

International Nuclear Information System (INIS)

Furetta, C.; Guzman, S.; Ruiz, B.; Cruz-Zaragoza, E.

2011-01-01

The well known Initial Rise Method (IR) is commonly used to determine the activation energy when only one glow peak is presented and analysed in the phosphor materials. However, when the glow peak is more complex, a wide peak and some holders appear in the structure. The application of the Initial Rise Method is not valid because multiple trapping levels are considered and then the thermoluminescent analysis becomes difficult to perform. This paper shows the case of a complex glow curve structure as an example and shows that the calculation is also possible using the IR method. The aim of the paper is to extend the well known Initial Rise Method (IR) to the case of multiple trapping levels. The IR method is applied to minerals extracted from Nopal cactus and Oregano spices because the thermoluminescent glow curve's shape suggests a trap distribution instead of a single trapping level.

System for prediction and determination of the sub critic multiplication; Sistema para previsao e determinacao da multiplicacao subcritica

Energy Technology Data Exchange (ETDEWEB)

Martinez, Aquilino S.; Pereira, Valmir; Silva, Fernando C. da [Universidade Federal, Rio de Janeiro, RJ (Brazil). Inst. de Fisica

1997-12-01

It is presented a concept of a system which may be used to calculate and anticipate the subcritical multiplication of a PWR nuclear power plant. The system is divided into two different modules. The first module allows the theoretical prediction of the subcritical multiplication factor through the solution of the multigroup diffusion equation. The second module determines this factor based on the data acquired from the neutron detectors of a NPP external nuclear detection system. (author). 3 refs., 3 figs., 2 tabs.

A generic method for assignment of reliability scores applied to solvent accessibility predictions

Directory of Open Access Journals (Sweden)

Nielsen Morten

2009-07-01

Full Text Available Abstract Background Estimation of the reliability of specific real value predictions is nontrivial and the efficacy of this is often questionable. It is important to know if you can trust a given prediction and therefore the best methods associate a prediction with a reliability score or index. For discrete qualitative predictions, the reliability is conventionally estimated as the difference between output scores of selected classes. Such an approach is not feasible for methods that predict a biological feature as a single real value rather than a classification. As a solution to this challenge, we have implemented a method that predicts the relative surface accessibility of an amino acid and simultaneously predicts the reliability for each prediction, in the form of a Z-score. Results An ensemble of artificial neural networks has been trained on a set of experimentally solved protein structures to predict the relative exposure of the amino acids. The method assigns a reliability score to each surface accessibility prediction as an inherent part of the training process. This is in contrast to the most commonly used procedures where reliabilities are obtained by post-processing the output. Conclusion The performance of the neural networks was evaluated on a commonly used set of sequences known as the CB513 set. An overall Pearson's correlation coefficient of 0.72 was obtained, which is comparable to the performance of the currently best public available method, Real-SPINE. Both methods associate a reliability score with the individual predictions. However, our implementation of reliability scores in the form of a Z-score is shown to be the more informative measure for discriminating good predictions from bad ones in the entire range from completely buried to fully exposed amino acids. This is evident when comparing the Pearson's correlation coefficient for the upper 20% of predictions sorted according to reliability. For this subset, values of 0

Parametric modeling for damped sinusoids from multiple channels

DEFF Research Database (Denmark)

Zhou, Zhenhua; So, Hing Cheung; Christensen, Mads Græsbøll

2013-01-01

frequencies and damping factors are then computed with the multi-channel weighted linear prediction method. The estimated sinusoidal poles are then matched to each channel according to the extreme value theory of distribution of random fields. Simulations are performed to show the performance advantages......The problem of parametric modeling for noisy damped sinusoidal signals from multiple channels is addressed. Utilizing the shift invariance property of the signal subspace, the number of distinct sinusoidal poles in the multiple channels is first determined. With the estimated number, the distinct...... of the proposed multi-channel sinusoidal modeling methodology compared with existing methods....

Comparison of selected methods of prediction of wine exports and imports

Directory of Open Access Journals (Sweden)

Radka Šperková

2008-01-01

Full Text Available For prediction of future events, there exist a number of methods usable in managerial practice. Decision on which of them should be used in a particular situation depends not only on the amount and quality of input information, but also on a subjective managerial judgement. Paper performs a practical application and consequent comparison of results of two selected methods, which are statistical method and deductive method. Both methods were used for predicting wine exports and imports in (from the Czech Republic. Prediction was done in 2003 and it related to the economic years 2003/2004, 2004/2005, 2005/2006, and 2006/2007, within which it was compared with the real values of the given indicators.Within the deductive methods there were characterized the most important factors of external environment including the most important influence according to authors’ opinion, which was the integration of the Czech Republic into the EU from 1st May, 2004. On the contrary, the statistical method of time-series analysis did not regard the integration, which is comes out of its principle. Statistics only calculates based on data from the past, and cannot incorporate the influence of irregular future conditions, just as the EU integration. Because of this the prediction based on deductive method was more optimistic and more precise in terms of its difference from real development in the given field.

A Novel Grey Wave Method for Predicting Total Chinese Trade Volume

Directory of Open Access Journals (Sweden)

Kedong Yin

2017-12-01

Full Text Available The total trade volume of a country is an important way of appraising its international trade situation. A prediction based on trade volume will help enterprises arrange production efficiently and promote the sustainability of the international trade. Because the total Chinese trade volume fluctuates over time, this paper proposes a Grey wave forecasting model with a Hodrick–Prescott filter (HP filter to forecast it. This novel model first parses time series into long-term trend and short-term cycle. Second, the model uses a general GM (1,1 to predict the trend term and the Grey wave forecasting model to predict the cycle term. Empirical analysis shows that the improved Grey wave prediction method provides a much more accurate forecast than the basic Grey wave prediction method, achieving better prediction results than autoregressive moving average model (ARMA.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

Science.gov (United States)

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

A New Method for Simultaneous Measurement of the Integrated Reflectivity of Crystals at Multiple Orders of Reflection and Comparison with New Theoretical Calculations

International Nuclear Information System (INIS)

Lee, S.G.; Bak, J.G.; Jung, Y.S.; Bitter, M.; Hill, K.W.; Hoelzer, G.; Wehrhan, O.; Foerster, E.

2003-01-01

This paper describes a new method for the simultaneous measurement of the integrated reflectivity of a crystal for multiple orders of reflection at a predefined Bragg angle. The technique is demonstrated with a mica crystal for Bragg angles of 43 o , 47 o , and 50 o . The measured integrated reflectivity for Bragg reflections up to the 24th order is compared with new theoretical predictions, which are also presented in this paper

Combined visual and motor evoked potentials predict multiple sclerosis disability after 20 years.

Science.gov (United States)

Schlaeger, Regina; Schindler, Christian; Grize, Leticia; Dellas, Sophie; Radue, Ernst W; Kappos, Ludwig; Fuhr, Peter

2014-09-01

The development of predictors of multiple sclerosis (MS) disability is difficult due to the complex interplay of pathophysiological and adaptive processes. The purpose of this study was to investigate whether combined evoked potential (EP)-measures allow prediction of MS disability after 20 years. We examined 28 patients with clinically definite MS according to Poser's criteria with Expanded Disability Status Scale (EDSS) scores, combined visual and motor EPs at entry (T0), 6 (T1), 12 (T2) and 24 (T3) months, and a cranial magnetic resonance imaging (MRI) scan at T0 and T2. EDSS testing was repeated at year 14 (T4) and year 20 (T5). Spearman rank correlation was used. We performed a multivariable regression analysis to examine predictive relationships of the sum of z-transformed EP latencies (s-EPT0) and other baseline variables with EDSST5. We found that s-EPT0 correlated with EDSST5 (rho=0.72, pdisability in MS. © The Author(s) 2014.

Methods for predicting isochronous stress-strain curves

International Nuclear Information System (INIS)

Kiyoshige, Masanori; Shimizu, Shigeki; Satoh, Keisuke.

1976-01-01

Isochronous stress-strain curves show the relation between stress and total strain at a certain temperature with time as a parameter, and they are drawn up from the creep test results at various stress levels at a definite temperature. The concept regarding the isochronous stress-strain curves was proposed by McVetty in 1930s, and has been used for the design of aero-engines. Recently the high temperature characteristics of materials are shown as the isochronous stress-strain curves in the design guide for the nuclear energy equipments and structures used in high temperature creep region. It is prescribed that these curves are used as the criteria for determining design stress intensity or the data for analyzing the superposed effects of creep and fatigue. In case of the isochronous stress-strain curves used for the design of nuclear energy equipments with very long service life, it is impractical to determine the curves directly from the results of long time creep test, accordingly the method of predicting long time stress-strain curves from short time creep test results must be established. The method proposed by the authors, for which the creep constitution equations taking the first and second creep stages into account are used, and the method using Larson-Miller parameter were studied, and it was found that both methods were reliable for the prediction. (Kako, I.)

Analysis of the uranium price predicted to 24 months, implementing neural networks and the Monte Carlo method like predictive tools

International Nuclear Information System (INIS)

Esquivel E, J.; Ramirez S, J. R.; Palacios H, J. C.

2011-11-01

The present work shows predicted prices of the uranium, using a neural network. The importance of predicting financial indexes of an energy resource, in this case, allows establishing budgetary measures, as well as the costs of the resource to medium period. The uranium is part of the main energy generating fuels and as such, its price rebounds in the financial analyses, due to this is appealed to predictive methods to obtain an outline referent to the financial behaviour that will have in a certain time. In this study, two methodologies are used for the prediction of the uranium price: the Monte Carlo method and the neural networks. These methods allow predicting the indexes of monthly costs, for a two years period, starting from the second bimonthly of 2011. For the prediction the uranium costs are used, registered from the year 2005. (Author)

A neutron multiplicity analysis method for uranium samples with liquid scintillators

Energy Technology Data Exchange (ETDEWEB)

Zhou, Hao, E-mail: zhouhao_ciae@126.com [China Institute of Atomic Energy, P.O.BOX 275-8, Beijing 102413 (China); Lin, Hongtao [Xi' an Reasearch Institute of High-tech, Xi' an, Shaanxi 710025 (China); Liu, Guorong; Li, Jinghuai; Liang, Qinglei; Zhao, Yonggang [China Institute of Atomic Energy, P.O.BOX 275-8, Beijing 102413 (China)

2015-10-11

A new neutron multiplicity analysis method for uranium samples with liquid scintillators is introduced. An active well-type fast neutron multiplicity counter has been built, which consists of four BC501A liquid scintillators, a n/γdiscrimination module MPD-4, a multi-stop time to digital convertor MCS6A, and two Am–Li sources. A mathematical model is built to symbolize the detection processes of fission neutrons. Based on this model, equations in the form of R=F*P*Q*T could be achieved, where F indicates the induced fission rate by interrogation sources, P indicates the transfer matrix determined by multiplication process, Q indicates the transfer matrix determined by detection efficiency, T indicates the transfer matrix determined by signal recording process and crosstalk in the counter. Unknown parameters about the item are determined by the solutions of the equations. A {sup 252}Cf source and some low enriched uranium items have been measured. The feasibility of the method is proven by its application to the data analysis of the experiments.

The prediction of intelligence in preschool children using alternative models to regression.

Science.gov (United States)

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

CREME96 and Related Error Rate Prediction Methods

Science.gov (United States)

Adams, James H., Jr.

2012-01-01

Predicting the rate of occurrence of single event effects (SEEs) in space requires knowledge of the radiation environment and the response of electronic devices to that environment. Several analytical models have been developed over the past 36 years to predict SEE rates. The first error rate calculations were performed by Binder, Smith and Holman. Bradford and Pickel and Blandford, in their CRIER (Cosmic-Ray-Induced-Error-Rate) analysis code introduced the basic Rectangular ParallelePiped (RPP) method for error rate calculations. For the radiation environment at the part, both made use of the Cosmic Ray LET (Linear Energy Transfer) spectra calculated by Heinrich for various absorber Depths. A more detailed model for the space radiation environment within spacecraft was developed by Adams and co-workers. This model, together with a reformulation of the RPP method published by Pickel and Blandford, was used to create the CR ME (Cosmic Ray Effects on Micro-Electronics) code. About the same time Shapiro wrote the CRUP (Cosmic Ray Upset Program) based on the RPP method published by Bradford. It was the first code to specifically take into account charge collection from outside the depletion region due to deformation of the electric field caused by the incident cosmic ray. Other early rate prediction methods and codes include the Single Event Figure of Merit, NOVICE, the Space Radiation code and the effective flux method of Binder which is the basis of the SEFA (Scott Effective Flux Approximation) model. By the early 1990s it was becoming clear that CREME and the other early models needed Revision. This revision, CREME96, was completed and released as a WWW-based tool, one of the first of its kind. The revisions in CREME96 included improved environmental models and improved models for calculating single event effects. The need for a revision of CREME also stimulated the development of the CHIME (CRRES/SPACERAD Heavy Ion Model of the Environment) and MACREE (Modeling and

An Influence Function Method for Predicting Store Aerodynamic Characteristics during Weapon Separation,

Science.gov (United States)

1981-05-14

8217 AO-Ail 777 GRUMMAN AEROSPACE CORP BETHPAGE NY F/G 20/4 AN INFLUENCE FUNCTION METHOD FOR PREDICTING STORE AERODYNAMIC C--ETCCU) MAY 8 1 R MEYER, A...CENKO, S YARDS UNCLASSIFIED N ’.**~~N**n I EHEEKI j~j .25 Q~4 111110 111_L 5. AN INFLUENCE FUNCTION METHOD FOR PREDICTING STORE AERODYNAMIC...extended to their logical conclusion one is led quite naturally to consideration of an " Influence Function Method" for I predicting store aerodynamic

Statistics of electron multiplication in a multiplier phototube; Iterative method

International Nuclear Information System (INIS)

Ortiz, J. F.; Grau, A.

1985-01-01

In the present paper an iterative method is applied to study the variation of dynode response in the multiplier phototube. Three different situation are considered that correspond to the following ways of electronic incidence on the first dynode: incidence of exactly one electron, incidence of exactly r electrons and incidence of an average r electrons. The responses are given for a number of steps between 1 and 5, and for values of the multiplication factor of 2.1, 2.5, 3 and 5. We study also the variance, the skewness and the excess of jurtosis for different multiplication factors. (Author) 11 refs

The Initial Rise Method in the case of multiple trapping levels

International Nuclear Information System (INIS)

Furetta, C.; Guzman, S.; Cruz Z, E.

2009-10-01

The aim of the paper is to extent the well known Initial Rise Method (IR) to the case of multiple trapping levels. The IR method is applied to the minerals extracted from Nopal herb and Oregano spice because the thermoluminescent glow curves shape suggests a trap distribution instead of a single trapping level. (Author)

The Initial Rise Method in the case of multiple trapping levels

Energy Technology Data Exchange (ETDEWEB)

Furetta, C. [Centro de Investigacion en Ciencia Aplicada y Tecnologia Avanzada, IPN, Av. Legaria 694, Col. Irrigacion, 11500 Mexico D. F. (Mexico); Guzman, S.; Cruz Z, E. [Instituto de Ciencias Nucleares, UNAM, A. P. 70-543, 04510 Mexico D. F. (Mexico)

2009-10-15

The aim of the paper is to extent the well known Initial Rise Method (IR) to the case of multiple trapping levels. The IR method is applied to the minerals extracted from Nopal herb and Oregano spice because the thermoluminescent glow curves shape suggests a trap distribution instead of a single trapping level. (Author)

Novel Methods for Drug-Target Interaction Prediction using Graph Mining

KAUST Repository

Ba Alawi, Wail

2016-08-31

The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction

Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model

Science.gov (United States)

Graves, T.A.; Kendall, Katherine C.; Royle, J. Andrew; Stetz, J.B.; Macleod, A.C.

2011-01-01

Few studies link habitat to grizzly bear Ursus arctos abundance and these have not accounted for the variation in detection or spatial autocorrelation. We collected and genotyped bear hair in and around Glacier National Park in northwestern Montana during the summer of 2000. We developed a hierarchical Markov chain Monte Carlo model that extends the existing occupancy and count models by accounting for (1) spatially explicit variables that we hypothesized might influence abundance; (2) separate sub-models of detection probability for two distinct sampling methods (hair traps and rub trees) targeting different segments of the population; (3) covariates to explain variation in each sub-model of detection; (4) a conditional autoregressive term to account for spatial autocorrelation; (5) weights to identify most important variables. Road density and per cent mesic habitat best explained variation in female grizzly bear abundance; spatial autocorrelation was not supported. More female bears were predicted in places with lower road density and with more mesic habitat. Detection rates of females increased with rub tree sampling effort. Road density best explained variation in male grizzly bear abundance and spatial autocorrelation was supported. More male bears were predicted in areas of low road density. Detection rates of males increased with rub tree and hair trap sampling effort and decreased over the sampling period. We provide a new method to (1) incorporate multiple detection methods into hierarchical models of abundance; (2) determine whether spatial autocorrelation should be included in final models. Our results suggest that the influence of landscape variables is consistent between habitat selection and abundance in this system.

The initial rise method extended to multiple trapping levels in thermoluminescent materials.

Science.gov (United States)

Furetta, C; Guzmán, S; Ruiz, B; Cruz-Zaragoza, E

2011-02-01

The well known Initial Rise Method (IR) is commonly used to determine the activation energy when only one glow peak is presented and analysed in the phosphor materials. However, when the glow peak is more complex, a wide peak and some holders appear in the structure. The application of the Initial Rise Method is not valid because multiple trapping levels are considered and then the thermoluminescent analysis becomes difficult to perform. This paper shows the case of a complex glow curve structure as an example and shows that the calculation is also possible using the IR method. The aim of the paper is to extend the well known Initial Rise Method (IR) to the case of multiple trapping levels. The IR method is applied to minerals extracted from Nopal cactus and Oregano spices because the thermoluminescent glow curve's shape suggests a trap distribution instead of a single trapping level. Copyright © 2010 Elsevier Ltd. All rights reserved.

Prediction and Migration of Surface-related Resonant Multiples

KAUST Repository

Guo, Bowen; Schuster, Gerard T.; Huang, Yunsong

2015-01-01

Surface-related resonant multiples can be migrated to achieve better resolution than migrating primary reflections. We now derive the formula for migrating surface-related resonant multiples, and show its super-resolution characteristics. Moreover

Toxicity of ionic liquids: Database and prediction via quantitative structure–activity relationship method

International Nuclear Information System (INIS)

Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

2014-01-01

Highlights: • A comprehensive database on toxicity of ionic liquids (ILs) was established. • Relationship between structure and toxicity of IL has been analyzed qualitatively. • Two new QSAR models were developed for predicting toxicity of ILs to IPC-81. • Accuracy of proposed nonlinear SVM model is much higher than the linear MLR model. • The established models can be explored in designing novel green agents. - Abstract: A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure–Activity relationships (QSAR) model is conducted to predict the toxicities (EC 50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R 2 ) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R 2 and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs

Using Bayesian methods to predict climate impacts on groundwater availability and agricultural production in Punjab, India

Science.gov (United States)

Russo, T. A.; Devineni, N.; Lall, U.

2015-12-01

Lasting success of the Green Revolution in Punjab, India relies on continued availability of local water resources. Supplying primarily rice and wheat for the rest of India, Punjab supports crop irrigation with a canal system and groundwater, which is vastly over-exploited. The detailed data required to physically model future impacts on water supplies agricultural production is not readily available for this region, therefore we use Bayesian methods to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. Using measured values of historical precipitation, total canal water delivery, crop yield, and water table elevation, we present a method using a Markov chain Monte Carlo (MCMC) algorithm to solve for a distribution of values for each unknown parameter in a conceptual mass balance model. Due to heterogeneity across the state, and the resolution of input data, we estimate model parameters at the district-scale using spatial pooling. The resulting model is used to predict the impact of precipitation change scenarios on groundwater availability under multiple cropping options. Predicted groundwater declines vary across the state, suggesting that crop selection and water management strategies should be determined at a local scale. This computational method can be applied in data-scarce regions across the world, where water resource management is required to resolve competition between food security and available resources in a changing climate.

A method for uncertainty quantification in the life prediction of gas turbine components

Energy Technology Data Exchange (ETDEWEB)

Lodeby, K.; Isaksson, O.; Jaervstraat, N. [Volvo Aero Corporation, Trolhaettan (Sweden)

1998-12-31

A failure in an aircraft jet engine can have severe consequences which cannot be accepted and high requirements are therefore raised on engine reliability. Consequently, assessment of the reliability of life predictions used in design and maintenance are important. To assess the validity of the predicted life a method to quantify the contribution to the total uncertainty in the life prediction from different uncertainty sources is developed. The method is a structured approach for uncertainty quantification that uses a generic description of the life prediction process. It is based on an approximate error propagation theory combined with a unified treatment of random and systematic errors. The result is an approximate statistical distribution for the predicted life. The method is applied on life predictions for three different jet engine components. The total uncertainty became of reasonable order of magnitude and a good qualitative picture of the distribution of the uncertainty contribution from the different sources was obtained. The relative importance of the uncertainty sources differs between the three components. It is also highly dependent on the methods and assumptions used in the life prediction. Advantages and disadvantages of this method is discussed. (orig.) 11 refs.

Improving protein function prediction methods with integrated literature data

Directory of Open Access Journals (Sweden)

Gabow Aaron P

2008-04-01

Full Text Available Abstract Background Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity. Results We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder

Machine learning methods to predict child posttraumatic stress: a proof of concept study.

Science.gov (United States)

Saxe, Glenn N; Ma, Sisi; Ren, Jiwen; Aliferis, Constantin

2017-07-10

The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD. ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to

Improved prediction of meat and bone meal metabolizable energy content for ducks through in vitro methods.

Science.gov (United States)

Garcia, R A; Phillips, J G; Adeola, O

2012-08-01

Apparent metabolizable energy (AME) of meat and bone meal (MBM) for poultry is highly variable, but impractical to measure routinely. Previous efforts at developing an in vitro method for predicting AME have had limited success. The present study uses data from a previous publication on the AME of 12 MBM samples, determined using 288 White Pekin ducks, as well as composition data on these samples. Here, we investigate the hypothesis that 2 noncompositional attributes of MBM, particle size and protease resistance, will have utility in improving predictions of AME based on in vitro measurements. Using the same MBM samples as the previous study, 2 measurements of particle size were recorded and protease resistance was determined using a modified pepsin digestibility assay. Analysis of the results using a stepwise construction of multiple linear regression models revealed that the measurements of particle size were useful in building models for AME, but the measure of protease resistance was not. Relatively simple (4-term) and complex (7-term) models for both AME and nitrogen-corrected AME were constructed, with R-squared values ranging from 0.959 to 0.996. The rather minor analytical effort required to conduct the measurements involved is discussed. Although the generality of the results are limited by the number of samples involved and the species used, they suggest that AME for poultry can be accurately predicted through simple and inexpensive in vitro methods.

Genomic prediction based on data from three layer lines using non-linear regression models

NARCIS (Netherlands)

Huang, H.; Windig, J.J.; Vereijken, A.; Calus, M.P.L.

2014-01-01

Background - Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. Methods - In an attempt to alleviate